Assessment of volatile organic compounds in urban schools

There is an increased concern about airborne particles not only because of their environmental effects, but also due to their potential adverse health effects on humans, especially children. Despite the growing evidence of airborne particles having an impact on children’s health, there have been limited studies investigating the long term health effects as well as the chemical composition of ambient air which further helps in determining their toxicity. Therefore, a systematic study on the chemical composition of air in school environment has been carried out in Brisbane, which is known as “Ultrafine Particles from Traffic Emissions on Children’s Health” (UPTECH). This study is also a part of the larger project focusing on analysis of the chemical composition of ambient air, as well as source apportionment and the quantification of ambient concentrations of organic pollutants in the vicinity of schools. However, this particular paper presents some of the results on concentration of different Volatile Organic Compounds in both indoor and outdoor location from different schools. The database consisted of 750 samples (500 outdoor and 250 indoor) collected for VOCs at 25 different schools. The sampling and analysis were conducted following the standard methods. A total of 90 individual VOCs were identified from the schools studied. Compounds such as toluene, acetic acid, nonanal, benzaldehyde, 2- ethyl 1- hexanol, limonene were the most common in indoors whereas isopentane, toluene, hexane, heptane were dominant in outdoors. The indoor/ outdoor ratio of average sum of VOCs were found to be more than one in most of the schools indicating that there might be additional indoor sources along with the outdoor air in those schools. However, further expansion of the study in relation to source apportionment, correlating with traffic and meteorological data is in progress.

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

Indoor air quality and energy management through real–time sensing in commercial buildings

Rapid growth in the global population requires expansion of building stock, which in turn calls for increased energy demand. This demand varies in time and also between different buildings, yet, conventional methods are only able to provide mean energy levels per zone and are unable to capture this inhomogeneity, which is important to conserve energy. An additional challenge is that some of the attempts to conserve energy, through for example lowering of ventilation rates, have been shown to exacerbate another problem, which is unacceptable indoor air quality (IAQ). The rise of sensing technology over the past decade has shown potential to address both these issues simultaneously by providing high–resolution tempo–spatial data to systematically analyse the energy demand and its consumption as well as the impacts of measures taken to control energy consumption on IAQ. However, challenges remain in the development of affordable services for data analysis, deployment of large–scale real–time sensing network and responding through Building Energy Management Systems. This article presents the fundamental drivers behind the rise of sensing technology for the management of energy and IAQ in urban built environments, highlights major challenges for their large–scale deployment and identifies the research gaps that should be closed by future investigations.

Understanding urban rail travel for improved patronage forecasting

Improved forecasting of urban rail patronage is essential for effective policy development and efficient planning for new rail infrastructure. Past modelling and forecasting of urban rail patronage has been based on legacy modelling approaches and often conducted at the general level of public transport demand, rather than being specific to urban rail. This project canvassed current Australian practice and international best practice to develop and estimate time series and cross-sectional models of rail patronage for Australian mainland state capital cities. This involved the implementation of a large online survey of rail riders and non-riders for each of the state capital cities, thereby resulting in a comprehensive database of respondent socio-economic profiles, travel experience, attitudes to rail and other modes of travel, together with stated preference responses to a wide range of urban travel scenarios. Estimation of the models provided a demonstration of their ability to provide information on the major influences on the urban rail travel decision. Rail fares, congestion and rail service supply all have a strong influence on rail patronage, while a number of less significant factors such as fuel price and access to a motor vehicle are also influential. Of note, too, is the relative homogeneity of rail user profiles across the state capitals. Rail users tended to have higher incomes and education levels. They are also younger and more likely to be in full-time employment than non-rail users. The project analysis reported here represents only a small proportion of what could be accomplished utilising the survey database. More comprehensive investigation was beyond the scope of the project and has been left for future work.