Investigation into chemistry of new particle formation and growth in subtropical urban environment

The role of different chemical compounds, particularly organics, involved in the new particle formation (NPF) and its consequent growth are not fully understood. Therefore, this study was conducted to investigate the chemistry of aerosol particles during NPF events in an urban subtropical environment. Aerosol chemical composition was measured along with particle number size distribution (PNSD) and several other air quality parameters at five sites across an urban subtropical environment. An Aerodyne compact Time-of-Flight Aerosol Mass Spectrometer (c-TOF-AMS) and a TSI Scanning Mobility Particle Sizer (SMPS) measured aerosol chemical composition and PNSD, respectively. Five NPF events, with growth rates in the range 3.3-4.6 nm, were detected at two sites. The NPF events happened on relatively warmer days with lower humidity and higher solar radiation. Temporal percent fractions of nitrate, sulphate, ammonium and organics were modelled using the Generalised Additive Model (GAM), with a basis of penalised spline. Percent fractions of organics increased after the NPF events, while the mass fraction of ammonium and sulphate decreased. This uncovered the important role of organics in the growth of newly formed particles. Three organic markers, factors f43, f44 and f57, were calculated and the f44 vs f43 trends were compared between nucleation and non-nucleation days. f44 vs f43 followed a different pattern on nucleation days compared to non-nucleation days, whereby f43 decreased for vehicle emission generated particles, while both f44 and f43 decreased for NPF generated particles. It was found for the first time that vehicle generated and newly formed particles cluster in different locations on f44 vs f43 plot and this finding can be used as a tool for source apportionment of measured particles.

Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2–6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.

Influences of biodiesel chemical compositions and physical properties on engine exhaust particle emissions

This thesis presents a comprehensive study on the influences of biodiesel chemical composition and physical properties on diesel engine exhaust particle emissions. It examines biodiesels from several feedstocks having wide variations in their chemical composition (carbon chain length, unsaturation and oxygen content) and physical properties (density, viscosity, surface tension, boiling point etc.), and evaluates their influence on exhaust particle emissions. The outcome of this thesis is significant since it reveals the importance of regulating biodiesels chemical composition in order to ensure lowest possible emissions with better overall performance.

Shortest path and vehicle trajectory aided map-matching for low frequency GPS data

Map-matching algorithms that utilise road segment connectivity along with other data (i.e.position, speed and heading) in the process of map-matching are normally suitable for high frequency (1 Hz or higher) positioning data from GPS. While applying such map-matching algorithms to low frequency data (such as data from a fleet of private cars, buses or light duty vehicles or smartphones), the performance of these algorithms reduces to in the region of 70% in terms of correct link identification, especially in urban and sub-urban road networks. This level of performance may be insufficient for some real-time Intelligent Transport System (ITS) applications and services such as estimating link travel time and speed from low frequency GPS data. Therefore, this paper develops a new weight-based shortest path and vehicle trajectory aided map-matching (stMM) algorithm that enhances the map-matching of low frequency positioning data on a road map. The well-known A* search algorithm is employed to derive the shortest path between two points while taking into account both link connectivity and turn restrictions at junctions. In the developed stMM algorithm, two additional weights related to the shortest path and vehicle trajectory are considered: one shortest path-based weight is related to the distance along the shortest path and the distance along the vehicle trajectory, while the other is associated with the heading difference of the vehicle trajectory. The developed stMM algorithm is tested using a series of real-world datasets of varying frequencies (i.e. 1 s, 5 s, 30 s, 60 s sampling intervals). A high-accuracy integrated navigation system (a high-grade inertial navigation system and a carrier-phase GPS receiver) is used to measure the accuracy of the developed algorithm. The results suggest that the algorithm identifies 98.9% of the links correctly for every 30 s GPS data. Omitting the information from the shortest path and vehicle trajectory, the accuracy of the algorithm reduces to about 73% in terms of correct link identification. The algorithm can process on average 50 positioning fixes per second making it suitable for real-time ITS applications and services.

Influence of the structural characteristic of pyrolysis products on thermal stability of styrene-butadiene rubber composites reinforced by different particle sized kaolinites

A series of rubber composites were prepared by blending styrene-butadiene rubber (SBR) latex and the different particle sized kaolinites. The thermal stabilities of the rubber composites were characterized using thermogravimetry, digital photography, scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and Raman spectroscopy. Kaolinite SBR composites showed much greater thermal stability when compared with that of the pure SBR. With the increase of kaolinite particle size, the pyrolysis products became much looser; the char layer and crystalline carbon content gradually decreased in the pyrolysis residues. The pyrolysis residues of the SBR composites filled with the different particle sized kaolinites showed some remarkable changes in structural characteristics. The increase of kaolinite particle size was not beneficial to form the compact and stable crystalline carbon in the pyrolysis process, and resulted in a negative influence in improving the thermal stability of kaolinite/SBR composites.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.

Melanoma cell colony expansion parameters revealed by approximate Bayesian computation

In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of understanding how to estimate model parameters using experimental in vitro image-based data. To address this issue, a new approximate Bayesian computation (ABC) algorithm is proposed to estimate key parameters governing the expansion of melanoma cell (MM127) colonies, including cell diffusivity, D, cell proliferation rate, λ, and cell-to-cell adhesion, q, in two experimental scenarios, namely with and without a chemical treatment to suppress cell proliferation. Even when little prior biological knowledge about the parameters is assumed, all parameters are precisely inferred with a small posterior coefficient of variation, approximately 2–12%. The ABC analyses reveal that the posterior distributions of D and q depend on the experimental elapsed time, whereas the posterior distribution of λ does not. The posterior mean values of D and q are in the ranges 226–268 µm2h−1, 311–351 µm2h−1 and 0.23–0.39, 0.32–0.61 for the experimental periods of 0–24 h and 24–48 h, respectively. Furthermore, we found that the posterior distribution of q also depends on the initial cell density, whereas the posterior distributions of D and λ do not. The ABC approach also enables information from the two experiments to be combined, resulting in greater precision for all estimates of D and λ.

Size-resolved particle distribution and gaseous concentrations by real-world road tunnel measurement

Measurements of aerosol particle number size distributions (15-700 nm), CO and NOx were performed in a bus tunnel, Australia. Daily mean particle size distributions of mixed diesel/CNG (Compressed Natural Gas) buses traffic flow were determined in 4 consecutive measurement days. EFs (Emission Factors) of Particle size distribution of diesel buses and CNG buses were obtained by MLR (Multiple Linear Regression) methods, particle distributions of diesel buses and CNG buses were observed as single accumulation mode and nuclei-mode separately. Particle size distributions of mixed traffic flow were decomposed by two log-normal fitting curves for each 30 minutes interval mean scans, all the mix fleet PSD emission can be well fitted by the summation of two log-normal distribution curves, and these were composed of nuclei mode curve and accumulation curve, which were affirmed as the CNG buses and diesel buses PN emission curves respectively. Finally, particle size distributions of diesel buses and CNG buses were quantified by statistical whisker-box charts. For log-normal particle size distribution of diesel buses, accumulation mode diameters were 74.5～87.5nm, geometric standard deviations were 1.89～1.98. As to log-normal particle size distribution of CNG buses, nuclei-mode diameters were 21～24 nm, geometric standard deviations were 1.27～1.31.

Ultrafine particle emission of waste incinerators and comparison to the exposure of urban citizens

On the basis of the growing interest on the impact of airborne particles on human exposure as well as the strong debate in Western countries on the emissions of waste incinerators, this work reviewed existing literature to: (i) show the emission factors of ultrafine particles (particles with a diameter less than 100 nm) of waste incinerators, and; (ii) assess the contribution of waste incinerators in terms of ultrafine particles to exposure and dose of people living in the surrounding areas of the plants in order to estimate eventual risks. The review identified only a limited number of studies measuring ultrafine particle emissions, and in general they report low particle number concentrations at the stack (the median value was equal to 5.5×103 part cm-3), in most cases higher than the outdoor background value. The lowest emissions were achieved by utilization of the bag-house filter which has an overall number-based filtration efficiency higher than 99%. Referring to reference case, the corresponding emission factor is equal to 9.1×1012 part min-1, that is lower than one single high-duty vehicle. Since the higher particle number concentrations found in the most contributing microenvironments to the exposure (indoor home, transportation, urban outdoor), the contribution of the waste incinerators to the daily dose can be considered as negligible.

Impact of particle formation on atmospheric ions and particle number concentrations in an urban environment

A measurement campaign was conducted from 3 to 19 December 2012 at an urban site of Brisbane, Australia. Size distribution of ions and particle number concentrations were measured to investigate the influence of particle formation and biomass burning on atmospheric ion and particle concentrations. Overall ion and particle number concentrations during the measurement period were found to be (-1.2 x 103 cm-3 | +1.6 x 103 cm-3) and 4.4 x 103, respectively. The results of correlation analysis between concentrations of ions and nitrogen oxides indicated that positive and negative ions originated from similar sources, and that vehicle exhaust emissions had a more significant influence on intermediate/large ions, while cluster ions rapidly attached to larger particles once emitted into the atmosphere. Diurnal variations in ion concentration suggested the enrichment of intermediate and large ions on new particle formation event days, indicating that they were involved in the particle formation processes. Elevated total ions, particularly larger ions, and particle number concentrations were found during biomass burning episodes. This could be due to the attachment of cluster ions onto accumulation mode particles or production of charged particles from biomass burning, which were in turn transported to the measurement site. The results of this work enhance scientific understanding of the sources of atmospheric ions in an urban environment, as well as their interactions with particles during particle formation processes.

Particle doses in the pulmonary lobes of electronic and conventional cigarette users

The main aim of the present study was to estimate size segregated doses from e-cigarette aerosols as a function of the airway generation number in lung lobes.. After a 2-second puff, 7.7×1010 particles (DTot) with a surface area of 3.6×103 mm2 (STot), and 3.3×1010 particles with a surface area of 4.2×103 mm2 were deposited in the respiratory system for the electronic and conventional cigarettes, respectively. Alveolar and tracheobronchial deposited doses were compared to the ones received by non-smoking individuals in Western countries, showing a similar order of magnitude. Total regional doses (DR), in head and lobar tracheobronchial and alveolar regions, ranged from 2.7×109 to 1.3×1010 particles and 1.1×109 to 5.3×1010 particles, for the electronic and conventional cigarettes, respectively. DR in the right-upper lung lobe was about twice that found in left-upper lobe and 20% greater in right-lower lobe than the left-lower lobe.

Inhaled particle surface area dose: Stationary vs personal monitoring

The aim of this paper is to determine the suitability of solely stationary measurements for exposure assessment and management applications. For this purpose, quantified inhaled particle surface area (IPSA) doses using both stationary and personal particle exposure monitors were evaluated and compared.

Evaluation of a meteorological and three-dimensional chemical transport model for gaseous and particle concentrations in an urban airshed

The Air Pollution Model and Chemical Transport Model (TAPM-CTM) framework has been tested and applied originally in Sydney to quantify particle and gaseous concentration (Cope et al, 2014). However, the model performance had not been tested in the south-eastern Queensland region (SEQR), Australia.

Influence of kaolinite particle size on cross-link density, microstructure and mechanical properties of latex blending styrene butadiene rubber composites

Cross-link density, microstructure and mechanical properties of styrene butadiene rubber (SBR) composites filled with different particle sized kaolinites are investigated. With the increase of kaolinite particle size, the cross-link density of the filled SBR composites, the dispersibility and orientation degree of kaolinite particles gradually decrease. Some big cracks in filled rubber composites are distributed along the fringe of kaolinite aggregates, and the absorbance of all the absorption bands of kaolinites gradually increase with the increase of kaolinite particle size. All mechanical property indexes of kaolinite filled SBR composites decrease due to the decrease of cross-linking and reduction of interface interaction between filler and rubber matrix.

The trajectory of minor stroke recovery for men and their female spousal caregivers: Literature review

Title The trajectory of minor stroke recovery for men and their female spousal caregivers: literature review Aim This paper is a report of a narrative review to examine the current state of knowledge regarding the impact of minor stroke on male patients and their female spousal caregivers’ recovery trajectory and quality of life. Background Minor stroke survivors are often discharged early in the recovery process. The perception of the healthcare community that these patients and their female spousal caregivers will experience an uneventful recovery may lead to inadequate preparation for the postdischarge period. Methods A range of databases was searched to identify papers addressing ‘minor stroke’, ‘transitions’, ‘quality of life’, ‘chronic disease’, ‘caregivers’ and ‘spouse caregivers’, including AARP Ageline, AMED, CINAHL, Evidence Based Medicine Reviews, MEDLINE and PsychInfo. Papers published in English from 1990 to December 2006 were included. Thirty-four papers were in the final data set. Results Minor stroke survivors and their female spousal caregivers may experience major challenges in adaptations postdischarge. The trajectory of minor stroke recovery may necessitate a re-evaluation of life plans, rethinking of priorities and integration of resulting disabilities into current and emerging life situations for both stroke survivors and their female spousal caregivers. In many cases these adaptations are compounded by transitions associated with the normal ageing process. Conclusion While there is extensive literature on stroke recovery and the role of caregivers in general, there is little available describing the recovery of minor stroke survivors in relation to the normal ageing process. Further research is needed examining recovery from a transitional perspective, to support nurses and other health professionals discharge planning.