A reference station-based GNSS computing mode to support unified precise point positioning and real-time kinematic services

Currently, the GNSS computing modes are of two classes: network-based data processing and user receiver-based processing. A GNSS reference receiver station essentially contributes raw measurement data in either the RINEX file format or as real-time data streams in the RTCM format. Very little computation is carried out by the reference station. The existing network-based processing modes, regardless of whether they are executed in real-time or post-processed modes, are centralised or sequential. This paper describes a distributed GNSS computing framework that incorporates three GNSS modes: reference station-based, user receiver-based and network-based data processing. Raw data streams from each GNSS reference receiver station are processed in a distributed manner, i.e., either at the station itself or at a hosting data server/processor, to generate station-based solutions, or reference receiver-specific parameters. These may include precise receiver clock, zenith tropospheric delay, differential code biases, ambiguity parameters, ionospheric delays, as well as line-of-sight information such as azimuth and elevation angles. Covariance information for estimated parameters may also be optionally provided. In such a mode the nearby precise point positioning (PPP) or real-time kinematic (RTK) users can directly use the corrections from all or some of the stations for real-time precise positioning via a data server. At the user receiver, PPP and RTK techniques are unified under the same observation models, and the distinction is how the user receiver software deals with corrections from the reference station solutions and the ambiguity estimation in the observation equations. Numerical tests demonstrate good convergence behaviour for differential code bias and ambiguity estimates derived individually with single reference stations. With station-based solutions from three reference stations within distances of 22–103 km the user receiver positioning results, with various schemes, show an accuracy improvement of the proposed station-augmented PPP and ambiguity-fixed PPP solutions with respect to the standard float PPP solutions without station augmentation and ambiguity resolutions. Overall, the proposed reference station-based GNSS computing mode can support PPP and RTK positioning services as a simpler alternative to the existing network-based RTK or regionally augmented PPP systems.

Air pollution, temperature and pediatric influenza in Brisbane, Australia

Previous studies have demonstrated the importance of weather variables in influencing the incidence of influenza. However, the role of air pollution is often ignored in identifying the environmental drivers of influenza. This research aims to examine the impacts of air pollutants and temperature on the incidence of pediatric influenza in Brisbane, Australia. Lab-confirmed daily data on influenza counts among children aged 0-14years in Brisbane from 2001 January 1st to 2008 December 31st were retrieved from Queensland Health. Daily data on maximum and minimum temperatures for the same period were supplied by the Australian Bureau of Meteorology. Winter was chosen as the main study season due to it having the highest pediatric influenza incidence. Four Poisson log-linear regression models, with daily pediatric seasonal influenza counts as the outcome, were used to examine the impacts of air pollutants (i.e., ozone (O3), particulate matter≤10μm (PM10) and nitrogen dioxide (NO2)) and temperature (using a moving average of ten days for these variables) on pediatric influenza. The results show that mean temperature (Relative risk (RR): 0.86; 95% Confidence Interval (CI): 0.82-0.89) was negatively associated with pediatric seasonal influenza in Brisbane, and high concentrations of O3 (RR: 1.28; 95% CI: 1.25-1.31) and PM10 (RR: 1.11; 95% CI: 1.10-1.13) were associated with more pediatric influenza cases. There was a significant interaction effect (RR: 0.94; 95% CI: 0.93-0.95) between PM10 and mean temperature on pediatric influenza. Adding the interaction term between mean temperature and PM10 substantially improved the model fit. This study provides evidence that PM10 needs to be taken into account when evaluating the temperature-influenza relationship. O3 was also an important predictor, independent of temperature.

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

InsulLiving house post-occupancy energy performance report

This research was commissioned by Metecno Pty Ltd, trading as Bondor®. The InsulLiving house was designed and constructed by Bondor®. The house instrumentation (electricity circuits, indoor environment, weather station) was provided by Bondor and supplied and installed by independent contractors. This report contains analysis of data collected from the InsulLiving house at Burpengary during 1 year of occupancy by a family of four for the period 1 April 2012 – 31 March 2013. The data shows a daily average electricity consumption 48% less than the regional average. The analysis confirms that the 9 star house performed thermally slightly better than the simulated performance. The home was 'near zero energy', with its modest 2.1kW solar power system meeting all of the needs for space heating and cooling, lighting and most water heating.

Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?

BACKGROUND Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia. METHODOLOGY We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns. RESULTS Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63-47.43%) and 23.20% (95% CrI: 16.10-32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71%; 95% CrI: 0.48-0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5%; 95% CrI: 1.39-3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level. CONCLUSIONS Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.

Transforming morning to afternoon using linear regression techniques

Visual localization in outdoor environments is often hampered by the natural variation in appearance caused by such things as weather phenomena, diurnal fluctuations in lighting, and seasonal changes. Such changes are global across an environment and, in the case of global light changes and seasonal variation, the change in appearance occurs in a regular, cyclic manner. Visual localization could be greatly improved if it were possible to predict the appearance of a particular location at a particular time, based on the appearance of the location in the past and knowledge of the nature of appearance change over time. In this paper, we investigate whether global appearance changes in an environment can be learned sufficiently to improve visual localization performance. We use time of day as a test case, and generate transformations between morning and afternoon using sample images from a training set. We demonstrate the learned transformation can be generalized from training data and show the resulting visual localization on a test set is improved relative to raw image comparison. The improvement in localization remains when the area is revisited several weeks later.

Urban Food Security, Urban Resilience and Climate Change

The growing importance of logistics in increasingly globalised production and consumption systems strengthens the case for explicit consideration of the climate risks that may impact on the operation of ports in the future, as well as the formulation of adaptation responses that act to enhance their resilience. Within a logistics chain, seaports are functional nodes of significant strategic importance, and are considered as critical gateways linking local and national supply chains to global markets. However, they are more likely to be exposed to vagaries of climate-related extreme events due to their coastal locations. As such, they need to be adaptive and respond to the projected impacts of climate change, in particular extreme weather events. These impacts are especially important in the logistics context as they could result in varying degrees of business interruption; including business closure in the worst case scenario. Since trans-shipment of freight for both the import and export of goods and raw materials has a significant impact on Australia’s sustained economic growth it was considered important to undertake a study of port functional assets, to assess their vulnerability to climate change, to model the potential impacts of climate-related extreme events, and to highlight possible adaptation responses.

Animal stories : flash flood 2011

Flash flood disasters happen suddenly. The Toowoomba Lockyer Valley flash flood in January 2011 was not forecast by the Bureau of Meteorology until after it had occurred. Domestic and wild animals gave the first warning of the disaster in the days leading up to the event and large animals gave warnings on the morning of the disaster. Twenty-three people, including 5 children in the disaster zone died. More than 500 people were listed as missing. Some of those who died, perished because they stayed in the disaster zone to look after their animals while other members of their family escaped to safety. Some people who were in danger refused to be rescued because they could not take their pets with them. During a year spent recording accounts of the survivors of the disaster, animals were often mentioned by survivors. Despite the obvious perils, people risked their lives to save their animals; people saw animals try to save each other; animals rescued people; people rescued animals; animals survived where people died; animals were used to find human victims in the weeks after the disaster; and animals died. The stories of the flood present challenges for pet owners, farmers, counter disaster planners, weather forecasters and emergency responders in preparing for disasters, responding to them and recovering after them.

Experimental evaluation of an autonomous surface vehicle for water quality and greenhouse gas emission monitoring

This paper describes the experimental evaluation of a novel Autonomous Surface Vehicle capable of navigating complex inland water reservoirs and measuring a range of water quality properties and greenhouse gas emissions. The 16 ft long solar powered catamaran is capable of collecting water column profiles whilst in motion. It is also directly integrated with a reservoir scale floating sensor network to allow remote mission uploads, data download and adaptive sampling strategies. This paper describes the onboard vehicle navigation and control algorithms as well as obstacle avoidance strategies. Experimental results are shown demonstrating its ability to maintain track and avoid obstacles on a variety of large-scale missions and under differing weather conditions, as well as its ability to continuously collect various water quality parameters complimenting traditional manual monitoring campaigns.

GNSS ambiguity resolution with controllable failure rate for long baseline network RTK

Many large-scale GNSS CORS networks have been deployed around the world to support various commercial and scientific applications. To make use of these networks for real-time kinematic positioning services, one of the major challenges is the ambiguity resolution (AR) over long inter-station baselines in the presence of considerable atmosphere biases. Usually, the widelane ambiguities are fixed first, followed by the procedure of determination of the narrowlane ambiguity integers based on the ionosphere-free model in which the widelane integers are introduced as known quantities. This paper seeks to improve the AR performance over long baseline through efficient procedures for improved float solutions and ambiguity fixing. The contribution is threefold: (1) instead of using the ionosphere-free measurements, the absolute and/or relative ionospheric constraints are introduced in the ionosphere-constrained model to enhance the model strength, thus resulting in the better float solutions; (2) the realistic widelane ambiguity precision is estimated by capturing the multipath effects due to the observation complexity, leading to improvement of reliability of widelane AR; (3) for the narrowlane AR, the partial AR for a subset of ambiguities selected according to the successively increased elevation is applied. For fixing the scalar ambiguity, an error probability controllable rounding method is proposed. The established ionosphere-constrained model can be efficiently solved based on the sequential Kalman filter. It can be either reduced to some special models simply by adjusting the variances of ionospheric constraints, or extended with more parameters and constraints. The presented methodology is tested over seven baselines of around 100 km from USA CORS network. The results show that the new widelane AR scheme can obtain the 99.4 % successful fixing rate with 0.6 % failure rate; while the new rounding method of narrowlane AR can obtain the fix rate of 89 % with failure rate of 0.8 %. In summary, the AR reliability can be efficiently improved with rigorous controllable probability of incorrectly fixed ambiguities.

Climate change and children’s health : a call for research on what works to protect children

Climate change is affecting and will increasingly influence human health and wellbeing. Children are particularly vulnerable to the impact of climate change. An extensive literature review regarding the impact of climate change on children’s health was conducted in April 2012 by searching electronic databases PubMed, Scopus, ProQuest, ScienceDirect, and Web of Science, as well as relevant websites, such as IPCC and WHO. Climate change affects children’s health through increased air pollution, more weather-related disasters, more frequent and intense heat waves, decreased water quality and quantity, food shortage and greater exposure to toxicants. As a result, children experience greater risk of mental disorders, malnutrition, infectious diseases, allergic diseases and respiratory diseases. Mitigation measures like reducing carbon pollution emissions, and adaptation measures such as early warning systems and post-disaster counseling are strongly needed. Future health research directions should focus on: (1) identifying whether climate change impacts on children will be modified by gender, age and socioeconomic status; (2) refining outcome measures of children’s vulnerability to climate change; (3) projecting children’s disease burden under climate change scenarios; (4) exploring children’s disease burden related to climate change in low-income countries, and ; (5) identifying the most cost-effective mitigation and adaptation actions from a children’s health perspective.

Climate change and financial regulation : challenges for the financial sector post the Global Financial Crisis

Regulation has played a significant role in shaping the financial services sector in Australia over the past few decades. Regulatory changes have included the establishment of the Australian Prudential Regulation Authority (APRA), floating the Australian dollar, allowing foreign financial institutions to operate domestically, the introduction of the superannuation guarantee charge, and the removal of interest rate controls. As the economy emerges from the worst financial crisis since the great depression, a new force of change that is recognised as one of the most significant sources of risk and opportunity facing the business community in the foreseeable future is that of climate change. Climate change is expected to be a significant change agent in the financial services sector as extreme weather patterns, sea level rises, and atmospheric changes impact on asset values (both investment and lending), project finance, and risk products. The financial services industry will be particularly affected by these developments, both as a provider of financial products (capital, credit, investment, advice, and insurance), and also through its powerful influence on the economy in terms of capital allocation. In addition, industry constituents will be heavily impacted by government regulation in this area (reporting, emissions trading and environmental policies), with respect to their own business practices and also those of their clients. This study reports the results of interviews conducted with senior members of the finance sector working in the sustainability area to gauge their perceptions of the challenges facing the sector with respect to climate change. Our results confirm that that regulatory intervention will be critical to climate change response gaining traction and momentum. In particular, regulatory certainty will promote engagement, particularly in relation to the Carbon Pollution Reduction Scheme (CPRS), with other developments needed in terms of information disclosure, performance and remuneration, and incentive programs. Accordingly, the significant potential risks and opportunities that climate change presents to the sector, and the broader economy, will in part be managed/realised only if a swift and significant regulatory response is achieved.

Considering the economic value of natural design elements at city scale

With increasing signs of climate change and the influence of national and international carbon-related laws and agreements, governments all over the world are grappling with how to rapidly transition to low-carbon living. This includes adapting to the impacts of climate change that are very likely to be experienced due to current emission levels (including extreme weather and sea level changes), and mitigating against further growth in greenhouse gas emissions that are likely to result in further impacts. Internationally, the concept of ‘Biophilic Urbanism’, a term coined by Professors Tim Beatley and Peter Newman to refer to the use of natural elements as design features in urban landscapes, is emerging as a key component in addressing such climate change challenges in rapidly growing urban contexts. However, the economics of incorporating such options is not well understood and requires further attention to underpin a mainstreaming of biophilic urbanism. Indeed, there appears to be an ad hoc, reactionary approach to creating economic arguments for or against the design, installation or maintenance of natural elements such as green walls, green roofs, streetscapes, and parklands. With this issue in mind, this paper will overview research as part of an industry collaborative research project that considers the potential for using a number of environmental economic valuation techniques that have evolved over the last several decades in agricultural and resource economics, to systematically value the economic value of biophilic elements in the urban context. Considering existing literature on environmental economic valuation techniques, the paper highlights opportunities for creating a standardised language for valuing biophilic elements. The conclusions have implications for expanding the field of environmental economic value to support the economic evaluations and planning of the greater use of natural elements in cities. Insights are also noted for the more mature fields of agricultural and resource economics.

Navigating the future of roads – considering potential impacts of environmental and social trends on road infrastructure

Providing mobility corridors for communities, enabling freight networks to transport goods and services, and a pathway for emergency services and disaster relief operations, roads are a vital component of our societal system. In the coming decades, a number of modern issues will face road agencies as a result of climate change, resource scarcity and energy related challenges that will have implications for society. To date, these issues have been discussed on a case by case basis, leading to a fragmented approach by state and federal agencies in considering the future of roads – with potentially significant cost and risk implications. Within this context, this paper summarises part of a research project undertaken within the ‘Greening the Built Environment’ program of the Sustainable Built Environment National Research Centre (SBEnrc, Australia), which identified key factors or ‘trends’ affecting the future of roads and key strategies to ensure that road agencies can continue to deliver road infrastructure that meets societal needs in an environmentally appropriate manner. The research was conducted over two years, including a review of academic and state agency literature, four stakeholder workshops in Western Australia and Queensland, and industry consultation. The project was supported financially and through peer review and contribution, by Main Roads Western Australia, QLD Department of Transport and Main Roads, Parsons Brinckerhoff, John Holland Group, and the Australian Green Infrastructure Council (AGIC). The project highlighted several potential trends that are expected to affect road agencies in the future, including predicted resource and materials shortages, increases in energy and natural resources prices, increased costs related to greenhouse gas emissions, changing use and expectations of roads, and changes in the frequency and intensity of weather events. Exploring the implications of these potential futures, the study then developed a number of strategies in order to prepare transport agencies for the associated risks that such trends may present. An unintended outcome of the project was the development of a process for enquiring into future scenarios, which will be explored further in Stage 2 of the project (2013-2014). The study concluded that regardless of the type and scale of response by the agency, strategies must be holistic in approach, and remain dynamic and flexible.

A preliminary analysis of convective windstorm environments across Australia

This manuscript documents a preliminary analysis of convective windstorm environments across Australia. It combines radiosonde, reanalysis and severe weather observations to achieve this objective. Severe weather observations across Australia are revealed to have significant issues with stationarity, even when only the past thirty years are considered. Radiosonde and reanalysis observations are shown to agree relatively well for several cities in Australia. In addition, significantly different environments are documented to generate severe wind and tornado events in a sub-tropical environment such as Brisbane compared with a more mid-latitude-like environment such as Perth. The potential to extend this analysis for the remainder of Australia is also briefly discussed.