Influence of ventilation and filtration on indoor particle concentrations in urban office buildings

This study aimed to quantify the efficiency of deep bag and electrostatic filters, and assess the influence of ventilation systems using these filters on indoor fine (<2.5 µm) and ultrafine particle concentrations in commercial office buildings. Measurements and modelling were conducted for different indoor and outdoor particle source scenarios at three office buildings in Brisbane, Australia. Overall, the in-situ efficiency, measured for particles in size ranges 6 to 3000 nm, of the deep bag filters ranged from 26.3 to 46.9% for the three buildings, while the in-situ efficiency of the electrostatic filter in one building was 60.2%. The highest PN and PM2.5 concentrations in one of the office buildings (up to 131% and 31% higher than the other two buildings, respectively) were due to the proximity of the building’s HVAC air intakes to a nearby bus-only roadway, as well as its higher outdoor ventilation rate. The lowest PN and PM2.5 concentrations (up to 57% and 24% lower than the other two buildings, respectively) were measured in a building that utilised both outdoor and mixing air filters in its HVAC system. Indoor PN concentrations were strongly influenced by outdoor levels and were significantly higher during rush-hours (up to 41%) and nucleation events (up to 57%), compared to working-hours, for all three buildings. This is the first time that the influence of new particle formation on indoor particle concentrations has been identified and quantified. A dynamic model for indoor PN concentration, which performed adequately in this study also revealed that using mixing/outdoor air filters can significantly reduce indoor particle concentration in buildings where indoor air was strongly influenced by outdoor particle levels. This work provides a scientific basis for the selection and location of appropriate filters and outdoor air intakes, during the design of new, or upgrade of existing, building HVAC systems. The results also serve to provide a better understanding of indoor particle dynamics and behaviours under different ventilation and particle source scenarios, and highlight effective methods to reduce exposure to particles in commercial office buildings.

The linking of plate tectonics and evolutionary divergence

It is exciting to be living at a time when the big questions in biology can be investigated using modern genetics and computing [1]. Bauzà-Ribot et al.[2] take on one of the fundamental drivers of biodiversity, the effect of continental drift in the formation of the world’s biota 3 and 4, employing next-generation sequencing of whole mitochondrial genomes and modern Bayesian relaxed molecular clock analysis. Bauzà-Ribot et al.[2] conclude that vicariance via plate tectonics best explains the genetic divergence between subterranean metacrangonyctid amphipods currently found on islands separated by the Atlantic Ocean. This finding is a big deal in biogeography, and science generally [3], as many other presumed biotic tectonic divergences have been explained as probably due to more recent transoceanic dispersal events [4]. However, molecular clocks can be problematic 5 and 6 and we have identified three issues with the analyses of Bauzà-Ribot et al.[2] that cast serious doubt on their results and conclusions. When we reanalyzed their mitochondrial data and attempted to account for problems with calibration 5 and 6, modeling rates across branches 5 and 7 and substitution saturation [5], we inferred a much younger date for their key node. This implies either a later trans-Atlantic dispersal of these crustaceans, or more likely a series of later invasions of freshwaters from a common marine ancestor, but either way probably not ancient tectonic plate movements.

Biodiesel with controlled physicochemical properties, a means to further reduce diesel engine particle emissions

The issue of particle emissions from diesel engines is still a matter of concern due its deleterious effects both on human health and environment(Ristovski et al., 2012). Recently, International Agency for Research on Cancer (IARC) inclusion of diesel engine exhaust particles as carcinogenic to human health added a new margin on it. Apart from the use of after treatment technology, biodiesel is also considered as potential way to reduce particle emission alongside with other emissions(Xue, Grift, & Hansen, 2011). Global biodiesel production is still reasonably small compared to its counterpart fossil diesel, but even this small amount comes from a wide variety of feed stocks. Contrary to fossil diesel, the important physicochemical properties of biodiesel vary among different feed stocks(Hoekman, Broch, Robbins, Ceniceros, & Natarajan, 2012).

Metrological performances of a diffusion charger particle counter for personal monitoring

Airborne particles have been shown to be associated with a wide range of adverse health effects, which has led to a recent increase in medical research to gain better insight into their health effects. However, accurate evaluation of the exposure-dose-response relationship is highly dependent on the ability to track actual exposure levels of people to airborne particles. This is quite a complex task, particularly in relation to submicrometer and ultrafine particles, which can vary quite significantly in terms of particle surface area and number concentrations. Therefore, suitable monitors that can be worn for measuring personal exposure to these particles are needed. This paper presents an evaluation of the metrological performance of six diffusion charger sensors, NanoTracer (Philips Aerasense) monitors, when measuring particle number and surface area concentrations, as well as particle number distribution mean when compared to reference instruments. Tests in the laboratory (by generating monodisperse and polydisperse aerosols) and in the field (using natural ambient particles) were designed to evaluate the response of these devices under both steady-state and dynamics conditions. Results showed that the NanoTracers performed well when measuring steady state aerosols, however they strongly underestimated actual concentrations during dynamic response testing. The field experiments also showed that, when the majority of the particles were smaller than 20 nm, which occurs during particle formation events in the atmosphere, the NanoTracer underestimated number concentration quite significantly. Even though the NanoTracer can be used for personal monitoring of exposure to ultrafine particles, it also has limitations which need to be considered in order to provide meaningful results.

Remote measurement of diesel locomotive emission factors and particle size distributions

A technique for analysing exhaust emission plumes from unmodified locomotives under real world conditions is described and applied to the task of characterizing plumes from railway trains servicing an Australian shipping port. The method utilizes the simultaneous measurement, downwind of the railway line, of the following pollutants; particle number, PM2.5 mass fraction, SO2, NOx and CO2, with the last of these being used as an indicator of fuel combustion. Emission factors are then derived, in terms of number of particles and mass of pollutant emitted per unit mass of fuel consumed. Particle number size distributions are also presented. The practical advantages of the method are discussed including the capacity to routinely collect emission factor data for passing trains and to thereby build up a comprehensive real world database for a wide range of pollutants. Samples from 56 train movements were collected, analyzed and presented. The quantitative results for emission factors are: EF(N)=(1.7±1)×1016 kg-1, EF(PM2.5)= (1.1±0.5) g·kg-1, EF(NOx)= (28±14) g·kg-1, and EF(SO2 )= (1.4±0.4) g·kg-1. The findings are compared with comparable previously published work. Statistically significant (p<α, α=0.05) correlations within the group of locomotives sampled were found between the emission factors for particle number and both SO2 and NOx.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Role of particle size and composition in metal adsorption by solids deposited on urban road surfaces

Despite common knowledge that the metal content adsorbed by fine particles is relatively higher compared to coarser particles, the reasons for this phenomenon has gained little research attention. The research study discussed in the paper investigated the variations in metal content for different particle sizes of solids associated with pollutant build-up on urban road surfaces. Data analysis confirmed that parameters favourable for metal adsorption to solids such as specific surface area, organic carbon content, effective cation exchange capacity and clay forming minerals content decrease with the increase in particle size. Furthermore, the mineralogical composition of solids was found to be the governing factor influencing the specific surface area and effective cation exchange capacity. There is high quartz content in particles >150µm compared to particles <150µm. As particle size reduces below 150µm, the clay forming minerals content increases, providing favourable physical and chemical properties that influence adsorption.

Understanding mild acid pretreatment of sugarcane bagasse through particle scale modeling

Sugarcane bagasse is an abundant and sustainable resource, generated as a by-product of sugarcane milling. The cellulosic material within bagasse can be broken down into glucose molecules and fermented to produce ethanol, making it a promising feedstock for biofuel production. Mild acid pretreatment hydrolyses the hemicellulosic component of biomass, thus allowing enzymes greater access to the cellulosic substrate during saccharification. A particle-scale mathematical model describing the mild acid pretreatment of sugarcane bagasse has been developed, using a volume averaged framework. Discrete population-balance equations are used to characterise the polymer degradation kinetics, and diffusive effects account for mass transport within the cell wall of the bagasse. As the fibrous material hydrolyses over time, variations in the porosity of the cell wall and the downstream effects on the reaction kinetics are accounted for using conservation of volume arguments. Non-dimensionalization of the model equations reduces the number of parameters in the system to a set of four dimensionless ratios that compare the timescales of different reaction and diffusion events. Theoretical yield curves are compared to macroscopic experimental observations from the literature and inferences are made as to constraints on these “unknown” parameters. These results enable connections to be made between experimental data and the underlying thermodynamics of acid pretreatment. Consequently, the results suggest that data-fitting techniques used to obtain kinetic parameters should be carefully applied, with prudent consideration given to the chemical and physiological processes being modeled.

Spatio-temporal modelling of ultrafine particle number concentration

This thesis developed semi-parametric regression models for estimating the spatio-temporal distribution of outdoor airborne ultrafine particle number concentration (PNC). The models developed incorporate multivariate penalised splines and random walks and autoregressive errors in order to estimate non-linear functions of space, time and other covariates. The models were applied to data from the "Ultrafine Particles from Traffic Emissions and Child" project in Brisbane, Australia, and to longitudinal measurements of air quality in Helsinki, Finland. The spline and random walk aspects of the models reveal how the daily trend in PNC changes over the year in Helsinki and the similarities and differences in the daily and weekly trends across multiple primary schools in Brisbane. Midday peaks in PNC in Brisbane locations are attributed to new particle formation events at the Port of Brisbane and Brisbane Airport.

Simulation of Au particle interaction on graphene sheets

The interaction of Au particles with few layer graphene is of interest for the formation of the next generation of sensing devices(1). In this paper we investigate the coupling of single gold nanoparticles to a graphene sheet, and multiple gold nanoparticles with a graphene sheet using COMSOL Multiphysics. By using these simulations we are able to determine the electric field strength and associated hot-spots for various gold nanoparticle-graphene systems. The Au nanoparticles were modelled as 8 nm diameter spheres on 1.5 nm thick (5 layers) graphene, with properties of graphene obtained from the refractive index data of Weber(2) and the Au refractive index data from Palik(3). The field was incident along the plane of the sheet with polarisation tested for both s and p. The study showed strong localised interaction between the Au and graphene with limited spread; however the double particle case where the graphene sheet separated two Au nanoparticles showed distinct interaction between the particles and graphene. An offset was introduced (up to 4 nm) resulting in much reduced coupling between the opposed particles as the distance apart increased. Findings currently suggest that the graphene layer has limited interaction with incident fields with a single particle present whilst reducing the coupling region to a very fine area when opposing particles are involved. It is hoped that the results of this research will provide insight into graphene-plasmon interactions and spur the development of the next generation of sensing devices.

Numerical simulations of particle deposition in metal foam heat exchangers

Australia is a high-potential country for geothermal power with reserves currently estimated in the tens of millions of petajoules, enough to power the nation for at least 1000 years at current usage. However, these resources are mainly located in isolated arid regions where water is scarce. Therefore, wet cooling systems for geothermal plants in Australia are the least attractive solution and thus air-cooled heat exchangers are preferred. In order to increase the efficiency of such heat exchangers, metal foams have been used. One issue raised by this solution is the fouling caused by dust deposition. In this case, the heat transfer characteristics of the metal foam heat exchanger can dramatically deteriorate. Exploring the particle deposition property in the metal foam exchanger becomes crucial. This paper is a numerical investigation aimed to address this issue. Two dimensional (2D) numerical simulations of a standard one-row tube bundle wrapped with metal foam in cross-flow are performed and highlight preferential particle deposition areas.

Numerical simulations of particle deposition in metal foam heat exchangers

Australia is a high potential country for geothermal power with reserves currently estimated in the tens of millions of petajoules, enough to power the nation for at least 1000 years at current usage.However, these resources are mainly located in isolated arid regions where water is scarce. Therefore, wet cooling systems for geothermal plants in Australia are the least attractive solution and thus air-cooled heat exchangers are preferred. In order to increase the efficiency of such heat exchangers, metal foams have been used. One issue raised by this solution is the fouling caused by dust deposition. In this case, the heat transfer characteristics of the metal foam heat exchanger can dramatically deteriorate. Exploring the particle deposition property in the metal foam exchanger becomes crucial. This paper is a numerical investigation aimed to address this issue. Two-dimensional(2D numerical simulations of a standard one-row tube bundle wrapped with metal foam in cross-flow are performed and highlight preferential particle deposition areas.

On Chinese Modal Particle A (啊) : A Pragmatic and Semantic Study

Chinese modal particles feature prominently in Chinese people’s daily use of the language, but their pragmatic and semantic functions are elusive as commonly recognised by Chinese linguists and teachers of Chinese as a foreign language. This book originates from an extensive and intensive empirical study of the Chinese modal particle a (啊), one of the most frequently used modal particles in Mandarin Chinese. In order to capture all the uses and the underlying meanings of the particle, the author transcribed the first 20 episodes, about 20 hours in length, of the popular Chinese TV drama series Kewang ‘Expectations’, which yielded a corpus data of more than 142’000 Chinese characters with a total of 1829 instances of the particle all used in meaningful communicative situations. Within its context of use, every single occurrence of the particle was analysed in terms of its pragmatic and semantic contributions to the hosting utterance. Upon this basis the core meanings were identified which were seen as constituting the modal nature of the particle.

Representations for evolutionary design modelling

This research looked at using the metaphor of biological evolution as a way of solving architectural design problems. Drawing from fields such as language grammars, algorithms and cellular biology, this thesis looked at ways of encoding design information for processing. The aim of this work is to help in the building of software that support the architectural design process and allow designers to examine more variations.

A novel hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration with regard to DGs

This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for distribution feeder reconfiguration (DFR) considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. The approach is tested on a real distribution feeder. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving DFR problem.