The efficacy and potential risks of controlling sprouting in Finnish birches (Betula spp.) with the fungal decomposer Chondrostereum purpureum

Sprouting of fast-growing broad-leaved trees causes problems in young coniferous stands, under power transmission lines and along roads and railways. Public opinion and the Finnish Forest Certification System oppose the use of chemical herbicides to control sprouting, which means that most areas with problems rely on mechanical cutting. However, cutting is a poor control method for many broad-leaved species because the removal of leaders can stimulate the sprouting of side branches and cut stumps quickly re-sprout. In order to be effective, cutting must be carried out frequently but each cut increases the costs, making this control method increasingly difficult and expensive once begun. As such, alternative methods for sprout control that are both effective and environmentally sound represent a continuing challenge to managers and research biologists. Using biological control agents to prevent sprouting has been given serious consideration recently. Dutch and Canadian researchers have demonstrated the potential of the white-rot fungus Chondrostereum purpureum (Pers. ex Fr.) Pouzar as a control agent of stump sprouting in many hardwoods. These findings have focused the attention of the Finnish forestry community on the utilization of C. purpureum for biocontrol purposes. Primarily, this study sought determines the efficacy of native C. purpureum as an inhibitor of birch stump sprouting in Finland and to clarify its mode of action. Additionally, genotypic variation in Finnish C. purpureum was examined and the environmental risks posed by a biocontrol program using this fungus were assessed. Experimental results of the study demonstrated that C. purpureum clearly affects the sprouting of birch: both the frequency of living stumps and the number of living sprouts per stump were effectively reduced by the treatment. However, the treatment had no effect on the maximum height of new sprouts. There were clear differences among fungal isolates in preventing sprouting and those that possessed high oxidative activities as measured in the laboratory inhibited sprouting most efficiently in the field. The most effective treatment time during the growing season was in early and mid summer (May July). Genetic diversity in Nordic and Baltic populations of C. purpureum was found to be high at the regional scale but locally homogeneous. This natural distribution of diversity means that using local genotypes in biocontrol programs would effectively prevent the introduction of novel genes or genotypes. While a biocontrol program using local strains of C. purpureum would be environmentally neutral, pruned birches that are close to the treatment site would have a high susceptibility to infect by the fungus during the early spring.

Dingoes at the doorstep: Preliminary data on the ecology of dingoes in urban areas

Wild carnivores are becoming increasing common in urban areas. In Australia, dingoes exist, in most large cities and towns within their extended range. However, little empirical data is available to inform dingo management or address potential dingo–human conflicts during urban planning. From GPS tracking data, the nine dingoes, predominately juvenile and female, we tracked lived within 700 m of residential homes at all times and frequently crossed roads, visited backyards and traversed built-up areas. Home range sizes ranged between 0.37 km2 and 100.32 km2. Dingoes were mostly nocturnal, averaging 591 m/h between dusk and dawn. Juvenile and adult dingoes spent up to 19% and 72% of their time in urban habitats. Fresh scats from most areas surveyed tested positive to a variety of common zoonoses. These data suggest dingoes are capable of exploiting peri-urban areas and might contribute to human health and safety risks, the significance of which remains unknown.

A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.

A review of project evaluation methodologies to address net impacts and risks of toll road projects to the community

Project evaluation is a process of measuring costs, benefits, risks and uncertainties for the purpose of decision-making by estimating and assessing impacts of the project to the community. The effects of impacts of toll roads are similar but different from the general non-tolled roads. Project evaluation methodologies are extensively studied and applied to various transport infrastructure projects. However, there is no definitive methodology to evaluate toll roads. This review discusses the impacts of toll roads then reviews the limitations of existing project evaluation methodologies when evaluating toll road impacts. The review identified gaps of knowledge of toll evaluations. First, the treatment of toll in project evaluation, particularly in Cost-Benefit Analysis requires further study to explore the appropriate methodology. Secondly, the project evaluation methodology needs to place strong emphasis on empirically based risk and uncertainty assessment. Addressing the limitations of the existing project evaluation methodologies leads to improvements of the methodology in practical level as well as fills the gap of knowledge of project evaluation for toll roads with respect to net impacts to the community.

A review of project evaluation methodologies for toll road projects

Project evaluation is a process of measuring costs, benefits, risks and uncertainties for the purpose of decision-making by estimating and assessing impacts of the project to the community. The effects of impacts of toll roads are similar but different from the general non-tolled roads. Project evaluation methodologies are extensively studied and applied to various transport infrastructure projects. However, there is no definitive methodology to evaluate toll roads. This review discusses the impacts of toll roads then reviews the limitations of existing project evaluation methodologies when evaluating toll road impacts. The review identified gaps of knowledge of toll evaluations. First, the treatment of toll in project evaluation, particularly in Cost-Benefit Analysis requires further study to explore the appropriate methodology. Secondly, the project evaluation methodology needs to place strong emphasis on empirically based risk and uncertainty assessment. Addressing the limitations of the existing project evaluation methodologies leads to improvements of the methodology in practical level as well as fills the gap of knowledge of project evaluation for toll roads with respect to net impacts to the community.

Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.

Taxonomy of factors which influence heavy metal build-up on urban road surfaces

Heavy metals build-up on urban road surfaces is a complex process and influenced by a diverse range of factors. Although numerous research studies have been conducted in the area of heavy metals build-up, limited research has been undertaken to rank these factors in terms of their influence on the build-up process. This results in limitations in the identification of the most critical factor/s for accurately estimating heavy metal loads and for designing effective stormwater treatment measures. The research study undertook an in-depth analysis of the factors which influence heavy metals build-up based on data generated from a number of different geographical locations around the world. Traffic volume was found to be the highest ranked factor in terms of influencing heavy metals build-up while land use was ranked the second. Proximity to arterial roads, antecedent dry days and road surface roughness has a relatively lower ranking. Furthermore, the study outcomes advances the conceptual understanding of heavy metals build-up based on the finding that with increasing traffic volume, total heavy metal build-up load increases while the variability decreases. The outcomes from this research study are expected to contribute to more accurate estimation of heavy metals build-up loads leading to more effective stormwater treatment design.