Assessing the representativeness of a groundwater quality monitoring network

The monitoring sites comprising a state of the environment (SOE) network must be carefully selected to ensure that they will be representative of the broader resource. Hierarchical cluster analysis (HCA) is a data-driven technique that can potentially be employed to assess the representativeness of a SOE monitoring network. The objective of this paper is to explore the use of HCA as an approach for assessing the representativeness of the New Zealand National Groundwater Monitoring Programme (NGMP), which is comprised of 110 monitoring sites across the country.

Relating vehicle generated pollutants to urban stormwater quality

Rapid urbanisation and resulting continuous increase in traffic has been recognised as key factors in the contribution of increased pollutant loads to urban stormwater and in turn to receiving waters. Urbanisation primarily increases anthropogenic activities and the percentage of impervious surfaces in urban areas. These processes are collectively responsible for urban stormwater pollution. In this regard, urban traffic and land use related activities have been recognised as the primary pollutant sources. This is primarily due to the generation of a range of key pollutants such as solids, heavy metals and PAHs. Appropriate treatment system design is the most viable approach to mitigate stormwater pollution. However, limited understanding of the pollutant process and transport pathways constrains effective treatment design. This highlights necessity for the detailed understanding of traffic and other land use related pollutants processes and pathways in relation to urban stormwater pollution. This study has created new knowledge in relation to pollutant processes and transport pathways encompassing atmospheric pollutants, atmospheric deposition and build-up on ground surfaces of traffic generated key pollutants. The research study was primarily based on in-depth experimental investigations. This thesis describes the extensive knowledge created relating to the processes of atmospheric pollutant build-up, atmospheric deposition and road surface build-up and establishing their relationships as a chain of processes. The analysis of atmospheric deposition revealed that both traffic and land use related sources contribute total suspended particulate matter (TSP) to the atmosphere. Traffic sources become dominant during weekdays whereas land use related sources become dominant during weekends due to the reduction in traffic sources. The analysis further concluded that atmospheric TSP, polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) concentrations are highly influenced by total average daily heavy duty traffic, traffic congestion and the fraction of commercial and industrial land uses. A set of mathematical equation were developed to predict TSP, PAHs and HMs concentrations in the atmosphere based on the influential traffic and land use related parameters. Dry deposition samples were collected for different antecedent dry days and wet deposition samples were collected immediately after rainfall events. The dry deposition was found to increase with the antecedent dry days and consisted of relatively coarser particles (greater than 1.4 ìm) when compared to wet deposition. The wet deposition showed a strong affinity to rainfall depth, but was not related to the antecedent dry period. It was also found that smaller size particles (less than 1.4 ìm) travel much longer distances from the source and deposit mainly with the wet deposition. Pollutants in wet deposition are less sensitive to the source characteristics compared to dry deposition. Atmospheric deposition of HMs is not directly influenced by land use but rather by proximity to high emission sources such as highways. Therefore, it is important to consider atmospheric deposition as a key pollutant source to urban stormwater in the vicinity of these types of sources. Build-up was analysed for five different particle size fractions, namely, <1 ìm, 1-75 ìm, 75-150 ìm, 150-300 ìm and >300 ìm for solids, PAHs and HMs. The outcomes of the study indicated that PAHs and HMs in the <75 ìm size fraction are generated mainly by traffic related activities whereas the > 150 ìm size fraction is generated by both traffic and land use related sources. Atmospheric deposition is an important source for HMs build-up on roads, whereas the contribution of PAHs from atmospheric sources is limited. A comprehensive approach was developed to predict traffic and other land use related pollutants in urban stormwater based on traffic and other land use characteristics. This approach primarily included the development of a set of mathematical equations to predict traffic generated pollutants by linking traffic and land use characteristics to stormwater quality through mathematical modelling. The outcomes of this research will contribute to the design of appropriate treatment systems to safeguard urban receiving water quality for future traffic growth scenarios. The „real world. application of knowledge generated was demonstrated through mathematical modelling of solids in urban stormwater, accounting for the variability in traffic and land use characteristics.

Scheduling techniques to optimise rail operations

In Australia, railway systems play a vital role in transporting the sugarcane crop from farms to mills. The sugarcane transport system is very complex and uses daily schedules, consisting of a set of locomotives runs, to satisfy the requirements of the mill and harvesters. The total cost of sugarcane transport operations is very high; over 35% of the total cost of sugarcane production in Australia is incurred in cane transport. Efficient schedules for sugarcane transport can reduce the cost and limit the negative effects that this system can have on the raw sugar production system. There are several benefits to formulating the train scheduling problem as a blocking parallel-machine job shop scheduling (BPMJSS) problem, namely to prevent two trains passing in one section at the same time; to keep the train activities (operations) in sequence during each run (trip) by applying precedence constraints; to pass the trains on one section in the correct order (priorities of passing trains) by applying disjunctive constraints; and, to ease passing trains by solving rail conflicts by applying blocking constraints and Parallel Machine Scheduling. Therefore, the sugarcane rail operations are formulated as BPMJSS problem. A mixed integer programming and constraint programming approaches are used to describe the BPMJSS problem. The model is solved by the integration of constraint programming, mixed integer programming and search techniques. The optimality performance is tested by Optimization Programming Language (OPL) and CPLEX software on small and large size instances based on specific criteria. A real life problem is used to verify and validate the approach. Constructive heuristics and new metaheuristics including simulated annealing and tabu search are proposed to solve this complex and NP-hard scheduling problem and produce a more efficient scheduling system. Innovative hybrid and hyper metaheuristic techniques are developed and coded using C# language to improve the solutions quality and CPU time. Hybrid techniques depend on integrating heuristic and metaheuristic techniques consecutively, while hyper techniques are the complete integration between different metaheuristic techniques, heuristic techniques, or both.

Qualitative and quantitative analysis of six sigma in service organizations

The concept of Six Sigma was initiated in the 1980s by Motorola. Since then it has been implemented in several manufacturing and service organizations. Till now Six Sigma implementation is mostly limited to healthcare and financial services in private sector. Its implementation is now gradually picking up in services such as call center, education, construction and related engineering etc. in private as well as public sector. Through a literature review, a questionnaire survey, and multiple case study approach the paper develops a conceptual framework to facilitate widening the scope of Six Sigma implementation in service organizations. Using grounded theory methodology, this study develops theory for Six Sigma implementation in service organizations. The study involves a questionnaire survey and case studies to understand and build a conceptual framework. The survey was conducted in service organizations in Singapore and exploratory in nature. The case studies involved three service organizations which implemented Six Sigma. The objective is to explore and understand the issues highlighted by the survey and the literature. The findings confirm the inclusion of critical success factors, critical-to-quality characteristics, and set of tools and techniques as observed from the literature. In case of key performance indicator, there are different interpretations about it in literature and also by industry practitioners. Some literature explain key performance indicator as performance metrics whereas some feel it as key process input or output variables, which is similar to interpretations by practitioners of Six Sigma. The response of not relevant and unknown to us as reasons for not implementing Six Sigma shows the need for understanding specific requirements of service organizations. Though much theoretical description is available about Six Sigma, but there has been limited rigorous academic research on it. This gap is far more pronounced about Six Sigma implementation in service organizations, where the theory is not mature enough. Identifying this need, the study contributes by going through theory building exercise and developing a conceptual framework to understand the issues involving its implementation in service organizations.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Impact of roof surface runoff on urban water quality

The pollutant impacts of urban stormwater runoff on receiving waters are well documented in research literature. However, it is road surfaces that are commonly identified as the significant pollutant source. This paper presents the outcomes of an extensive program of research into the role of roof surfaces in urban water quality with particular focus on solids, nutrients and organic carbon. The outcomes confirmed that roof surfaces play an important role in influencing the pollutant characteristics of urban stormwater runoff. Pollutant build-up and wash-off characteristics for roads and roof surfaces were found to be appreciably different. The pollutant wash-off characteristics exhibited by roof surfaces show that it influences the first flush phenomenon more significantly than road surfaces. In most urban catchments, as roof surfaces constitutes a higher fraction of impervious area compared to road surfaces, it is important that the pollutant generation role of roof surfaces is specifically taken into consideration in stormwater quality mitigation strategies.

A Systems-level approach to building sustainable assessment cultures : moderation, quality task design and dependability of judgement

This paper considers the conditions that are necessary at system and local levels for teacher assessment to be valid, reliable and rigorous. With sustainable assessment cultures as a goal, the paper examines how education systems can support local level efforts for quality learning and dependable teacher assessment. This is achieved through discussion of relevant research and consideration of a case study involving an evaluation of a cross-sectoral approach to promoting confidence in school-based assessment in Queensland, Australia. Building on the reported case study, essential characteristics for developing sustainable assessment cultures are presented, including: leadership in learning; alignment of curriculum, pedagogy and assessment; the design of quality assessment tasks and accompanying standards, and evidence-based judgement and moderation. Taken together, these elements constitute a new framework for building assessment capabilities and promoting quality assurance.

Patients undergoing subacute rehabilitation have accurate expectations of their health-related quality of life at discharge

Background Expectations held by patients and health professionals may affect treatment choices and participation (by both patients and health professionals) in therapeutic interventions in contemporary patient-centered healthcare environments. If patients in rehabilitation settings overestimate their discharge health-related quality of life, they may become despondent as their progress falls short of their expectations. On the other hand, underestimating their discharge health-related quality of life may lead to a lack of motivation to participate in therapies if they do not perceive likely benefit. There is a scarcity of empirical evidence evaluating whether patients' expectations of future health states are accurate. The purpose of this study is to evaluate the accuracy with which older patients admitted for subacute in-hospital rehabilitation can anticipate their discharge health-related quality of life. Methods A prospective longitudinal cohort investigation of agreement between patients' anticipated discharge health-related quality of life (as reported on the EQ-5D instrument at admission to a rehabilitation unit) and their actual self-reported health-related quality of life at the time of discharge from this unit was undertaken. The mini-mental state examination was used as an indicator of patients' cognitive ability. Results Overall, 232(85%) patients had all assessment data completed and were included in analysis. Kappa scores ranged from 0.42-0.68 across the five EQ-5D domains and two patient cognition groups. The percentage of exact correct matches within each domain ranged from 69% to 85% across domains and cognition groups. Overall 40% of participants in each cognition group correctly anticipated all of their self-reported discharge EQ-5D domain responses. Conclusions Patients admitted for subacute in-hospital rehabilitation were able to anticipate the discharge health-related quality of life on the EQ-5D instrument with a moderate level of accuracy. This finding adds to the foundational empirical work supporting joint treatment decision making and patient-centered models of care during rehabilitation following acute illness or injury. Accurate patient expectations of the impact of treatment (or disease progression) on future health-related related quality of life is likely to allow patients and health professionals to successfully target interventions to priority areas where meaningful gains can be achieved.

Impact of prescribed burning on urban air quality : a case study

For fuel management and/or ecological reasons prescribed burnings are conducted each year across Australia. Smoke from prescribed burnings could be the major source of air pollution in urban environment during the period of intensive prescribed burning. To investigate the impact of prescribed burning on air quality and the characteristics of prescribed burning particles, field measurements were conducted during the end period of a prescribed burning event in September 2011, Brisbane, Australia.

Understanding the airport passenger landside retail experience

The landside environment of an airport terminal is an important area for both passengers and the airport as it is the first area passengers enter and experience, influencing passengers’ overall airport experiences. This paper focuses on landside passenger experiences and factors which influence the quality of these experiences. Data collection occurred through video recorded observations of 40 passengers’ airport experiences at two Australian international departure terminals. The Observer software was used to code and analyse data. Indicative results show that passengers spend over half of their landside dwell time undertaking processing activities. The results highlight the important influencing role passengers’ companions have over the proportion of landside dwell time passengers spend undertaking discretionary activities. The findings provide an understanding of passenger landside experiences and how they can be improved. The significance of these findings lies in their potential application to landside airport terminal design with specific examples outlined. This new knowledge will assist in improving passenger airport experiences through informing future airport planning and design of landside spaces and retail environments.

Quality based frame selection for video face recognition

Quality based frame selection is a crucial task in video face recognition, to both improve the recognition rate and to reduce the computational cost. In this paper we present a framework that uses a variety of cues (face symmetry, sharpness, contrast, closeness of mouth, brightness and openness of the eye) to select the highest quality facial images available in a video sequence for recognition. Normalized feature scores are fused using a neural network and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face recognition system. Experiments on the Honda/UCSD database shows that the proposed method selects the best quality face images in the video sequence, resulting in improved recognition performance.

Evaluation of the luminous environment in open-plan offices with skylights

High Dynamic Range (HDR) imaging was used to collect luminance information at workstations in 2 open-plan office buildings in Queensland, Australia: one lit by skylights, vertical windows and electric light, and another by skylights and electric light. This paper compares illuminance and luminance data collected in these offices with occupant feedback to evaluate these open-plan environments based on available and emerging metrics for visual comfort and glare. This study highlights issues of daylighting quality and measurement specific to open plan spaces. The results demonstrate that overhead glare is a serious threat to user acceptance of skylights, and that electric and daylight integration and controls have a major impact on the perception of daylighting quality. With regards to measurement of visual comfort it was found that the Daylight Glare Probability (DGP) gave poor agreement with occupant reports of discomfort glare in open-plan spaces with skylights, and the CIE Glare Index (CGI) gave the best agreement. Horizontal and vertical illuminances gave no indication of visual comfort in these spaces.

Personal exposure to ultrafine particles at schools microenvironment

This work was motivated by the limited knowledge on personal exposure to ultrafine (UF) particles, especially for children (Mejía et al. 2011). Most research efforts in the past have investigated particle mass concentration and only a limited number of studies have been conducted to quantify other particle metrics, such as particle number, in the classrooms and school microenvironment in general (Diapouli et al. 2008; Guo et al. 2008; Weichenthal et al. 2008; Mullen et al. 2011).

Preliminary results on the chemical composition of outdoor airborne particles at urban schools and possible implications for the air quality in classrooms

Vehicle emissions are a significant source of fine particles (Dp < 2.5 µm) in an urban environment. These fine particles have been shown to have detrimental health effects, with children thought to be more susceptible. Vehicle emissions are mainly carbonaceous in nature, and carbonaceous aerosols can be defined as either elemental carbon (EC) or organic carbon (OC). EC is a soot-like material emitted from primary sources while OC fraction is a complex mixture of hundreds of organic compounds from either primary or secondary sources (Cao et al., 2006). Therefore the ratio of OC/EC can aid in the identification of source. The purpose of this paper is to use the concentration of OC and EC in fine particles to determine the levels of vehicle emissions in schools. It is expected that this will improve the understanding of the potential exposure of children in a school environment to vehicle emissions.

Retinal and choroidal thickness in myopic anisometropia

PURPOSE: To examine the foveal retinal thickness (RT) and subfoveal choroidal thickness (ChT) between the fellow eyes of myopic anisometropes. METHODS: Twenty-two young (mean age 23 ± 5 years), healthy myopic anisometropes (≥ 1 D spherical equivalent [SEq] anisometropia) without amblyopia or strabismus were recruited. Spectral domain optical coherence tomography (SD-OCT) was used to capture images of the retina and choroid. Customised software was used to register, align and average multiple foveal OCT B-Scan images from each subject in order to enhance image quality. Two independent masked observers then manually determined the RT and ChT at the centre of the fovea from each SD-OCT image, which were then averaged. Axial length was measured using optical low coherence biometry during relaxed accommodation. RESULTS: The mean absolute SEq anisometropia was 1.74 ± 0.95 D and the mean interocular difference in axial length was 0.58 ± 0.41 mm. There was a strong correlation between SEq anisometropia and the interocular difference in axial length (r = 0.90, p < 0.001). Measures of RT and ChT were highly correlated between the two observers (r = 0.99 and 0.97 respectively) and in close agreement (mean inter-observer difference: RT 1.3 ± 2.2 µm, ChT 1.5 ± 13.7 µm). There was no significant difference in RT between the more (218 ± 18 µm) and less myopic eyes (215 ± 18 µm) (p > 0.05). However, the mean subfoveal ChT was significantly thinner in the more myopic eye (252 ± 46 µm) compared to the fellow, less myopic eye (286 ± 58 µm) (p < 0.001). There was a moderate correlation between the interocular difference in ChT and the interocular difference in axial length (r = -0.50, p < 0.01). CONCLUSIONS: Foveal RT was similar between the fellow eyes of myopic anisometropes; however, the subfoveal choroid was significantly thinner in the more myopic (longer) eye of our anisometropic cohort. The interocular difference in ChT correlated with the magnitude of axial anisometropia.