A Method of Preparing Future Hourly Weather Data for the Study of Global Warming Impact on the Built Environment

The dynamic interaction between building systems and external climate is extremely complex, involving a large number of difficult-to-predict variables. In order to study the impact of global warming on the built environment, the use of building simulation techniques together with forecast weather data are often necessary. Since all building simulation programs require hourly meteorological input data for their thermal comfort and energy evaluation, the provision of suitable weather data becomes critical. Based on a review of the existing weather data generation models, this paper presents an effective method to generate approximate future hourly weather data suitable for the study of the impact of global warming. Depending on the level of information available for the prediction of future weather condition, it is shown that either the method of retaining to current level, constant offset method or diurnal modelling method may be used to generate the future hourly variation of an individual weather parameter. An example of the application of this method to the different global warming scenarios in Australia is presented. Since there is no reliable projection of possible change in air humidity, solar radiation or wind characters, as a first approximation, these parameters have been assumed to remain at the current level. A sensitivity test of their impact on the building energy performance shows that there is generally a good linear relationship between building cooling load and the changes of weather variables of solar radiation, relative humidity or wind speed.

Modelling bus lost time : an additional parameter influencing bus dwell time and station platform capacity at a BRT station platform

The common approach to estimate bus dwell time at a BRT station is to apply the traditional dwell time methodology derived for suburban bus stops. In spite of being sensitive to boarding and alighting passenger numbers and to some extent towards fare collection media, these traditional dwell time models do not account for the platform crowding. Moreover, they fall short in accounting for the effects of passenger/s walking along a relatively longer BRT platform. Using the experience from Brisbane busway (BRT) stations, a new variable, Bus Lost Time (LT), is introduced in traditional dwell time model. The bus lost time variable captures the impact of passenger walking and platform crowding on bus dwell time. These are two characteristics which differentiate a BRT station from a bus stop. This paper reports the development of a methodology to estimate bus lost time experienced by buses at a BRT platform. Results were compared with the Transit Capacity and Quality of Servce Manual (TCQSM) approach of dwell time and station capacity estimation. When the bus lost time was used in dwell time calculations it was found that the BRT station platform capacity reduced by 10.1%.

Vertical vegetation design decisions and their impact on energy consumption in subtropical cities

Vertical vegetation is vegetation growing on, or adjacent to, the unused sunlit exterior surfaces of buildings in cities. Vertical vegetation can improve the energy efficiency of the building on which it is installed mainly by insulating, shading and transpiring moisture from foliage and substrate. Several design parameters may affect the extent of the vertical vegetation's improvement of energy performance. Examples are choice of vegetation, growing medium geometry, north/south aspect and others. The purpose of this study is to quantitatively map out the contribution of several parameters to energy savings in a subtropical setting. The method is thermal simulation based on EnergyPlus configured to reflect the special characteristics of vertical vegetation. Thermal simulation results show that yearly cooling energy savings can reach 25% with realistic design choices in subtropical environments. Heating energy savings are negligible. The most important parameter is the aspect of walls covered by vegetation. Vertical vegetation covering walls facing north (south for the northern hemisphere) will result in the highest energy savings. In making plant selections, the most significant parameter is Leaf Area Index (LAI). Plants with larger LAI, preferably LAI>4, contribute to greater savings whereas vertical vegetation with LAI<2 can actually consume energy. The choice of growing media and its thickness influence both heating and cooling energy consumption. Change of growing medium thickness from 6cm to 8cm causes dramatic increase in energy savings from 2% to 18%. For cooling, it is best to use a growing material with high water retention, due to the importance of evapotranspiration for cooling. Similarly, for increased savings in cooling energy, sufficient irrigation is required. Insufficient irrigation results in the vertical vegetation requiring more energy to cool the building. To conclude, the choice of design parameters for vertical vegetation is crucial in making sure that it contributes to energy savings rather than energy consumption. Optimal design decisions can create a dramatic sustainability enhancement for the built environment in subtropical climates.

The impact of flood and post-flood cleaning on airborne microbiological and particle contamination in residential houses

In January 2011, Brisbane, Australia, experienced a major river flooding event. We aimed to investigate its effects on air quality and assess the role of prompt cleaning activities in reducing the airborne exposure risk. A comprehensive, multi-parameter indoor and outdoor measurement campaign was conducted in 41 residential houses, 2 and 6 months after the flood. The median indoor air concentrations of supermicrometer particle number (PN), PM10, fungi and bacteria 2 months after the flood were comparable to those previously measured in Brisbane. These were 2.88 p cm-3, 15 µg m-3, 804 cfu m-3 and 177 cfu m-3 for flood-affected houses (AFH), and 2.74 p cm-3, 15 µg m-3, 547 cfu m-3 and 167 cfu m-3 for non-affected houses (NFH), respectively. The I/O (indoor/outdoor) ratios of these pollutants were 1.08, 1.38, 0.74 and 1.76 for AFH and 1.03, 1.32, 0.83 and 2.17 for NFH, respectively. The average of total elements (together with transition metals) in indoor dust was 2296 ± 1328 µg m-2 for AFH and 1454 ± 678 µg m-2 for NFH, respectively. In general, the differences between AFH and NFH were not statistically significant, implying the absence of a measureable effect on air quality from the flood. We postulate that this was due to the very swift and effective cleaning of the flooded houses by 60,000 volunteers. Among the various cleaning methods, the use of both detergent and bleach was the most efficient at controlling indoor bacteria. All cleaning methods were equally effective for indoor fungi. This study provides quantitative evidence of the significant impact of immediate post-flood cleaning on mitigating the effects of flooding on indoor bioaerosol contamination and other pollutants.

Modelling the impact of traffic incidents on travel time reliability

Traffic incidents are recognised as one of the key sources of non-recurrent congestion that often leads to reduction in travel time reliability (TTR), a key metric of roadway performance. A method is proposed here to quantify the impacts of traffic incidents on TTR on freeways. The method uses historical data to establish recurrent speed profiles and identifies non-recurrent congestion based on their negative impacts on speeds. The locations and times of incidents are used to identify incidents among non-recurrent congestion events. Buffer time is employed to measure TTR. Extra buffer time is defined as the extra delay caused by traffic incidents. This reliability measure indicates how much extra travel time is required by travellers to arrive at their destination on time with 95% certainty in the case of an incident, over and above the travel time that would have been required under recurrent conditions. An extra buffer time index (EBTI) is defined as the ratio of extra buffer time to recurrent travel time, with zero being the best case (no delay). A Tobit model is used to identify and quantify factors that affect EBTI using a selected freeway segment in the Southeast Queensland, Australia network. Both fixed and random parameter Tobit specifications are tested. The estimation results reveal that models with random parameters offer a superior statistical fit for all types of incidents, suggesting the presence of unobserved heterogeneity across segments. What factors influence EBTI depends on the type of incident. In addition, changes in TTR as a result of traffic incidents are related to the characteristics of the incidents (multiple vehicles involved, incident duration, major incidents, etc.) and traffic characteristics.

Characterisation of the impact of open biomass burning on urban air quality in Brisbane, Australia

Open biomass burning from wildfires and the prescribed burning of forests and farmland is a frequent occurrence in South-East Queensland (SEQ), Australia. This work reports on data collected from 10-30 September 2011, which covers the days before (10-14 September), during (15-20 September) and after (21-30 September) a period of biomass burning in SEQ. The aim of this project was to comprehensively quantify the impact of the biomass burning on air quality in Brisbane, the capital city of Queensland. A multi-parameter field measurement campaign was conducted and ambient air quality data from 13 monitoring stations across SEQ were analysed. During the burning period, the average concentrations of all measured pollutants increased (from 20% to 430%) compared to the non-burning period (both before and after burning), except for total xylenes. The average concentration of O3, NO2, SO2, benzene, formaldehyde, PM10, PM2.5 and visibility-reducing particles reached their highest levels for the year, which were up to 10 times higher than annual average levels, while PM10, PM2.5 and SO2 concentrations exceeded the WHO 24-hour guidelines and O3 concentration exceeded the WHO maximum 8-hour average threshold during the burning period. Overall spatial variations showed that all measured pollutants, with the exception of O3, were closer to spatial homogeneity during the burning compared to the non-burning period. In addition to the above, elevated concentrations of three biomass burning organic tracers (levoglucosan, mannosan and galactosan), together with the amount of non-refractory organic particles (PM1) and the average value of f60 (attributed to levoglucosan), reinforce that elevated pollutant concentration levels were due to emissions from open biomass burning events, 70% of which were prescribed burning events. This study, which is the first and most comprehensive of its kind in Australia, provides quantitative evidence of the significant impact of open biomass burning events, especially prescribed burning, on urban air quality. The current results provide a solid platform for more detailed health and modelling investigations in the future.