981 resultados para particle number size distribution


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An elevated particle number concentration (PNC) observed during nucleation events could play a significant contribution to the total particle load and therefore to the air pollution in the urban environments. Therefore, a field measurement study of PNC was commenced to investigate the temporal and spatial variations of PNC within the urban airshed of Brisbane, Australia. PNC was monitored at urban (QUT), roadside (WOO) and semi-urban (ROC) areas around the Brisbane region during 2009. During the morning traffic peak period, the highest relative fraction of PNC reached about 5% at QUT and WOO on weekdays. PNC peaks were observed around noon, which correlated with the highest solar radiation levels at all three stations, thus suggesting that high PNC levels were likely to be associated with new particle formation caused by photochemical reactions. Wind rose plots showed relatively higher PNC for the NE direction, which was associated with industrial pollution, accounting for 12%, 9% and 14% of overall PNC at QUT, WOO and ROC, respectively. Although there was no significant correlation between PNC at each station, the variation of PNC was well correlated among three stations during regional nucleation events. In addition, PNC at ROC was significantly influenced by upwind urban pollution during the nucleation burst events, with the average enrichment factor of 15.4. This study provides an insight into the influence of regional nucleation events on PNC in the Brisbane region and it the first study to quantify the effect of urban pollution on semi-urban PNC through the nucleation events. © 2012 Author(s).

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Alternative fuels and injection technologies are a necessary component of particulate emission reduction strategies for compression ignition engines. Consequently, this study undertakes a physicochemical characterization of diesel particulate matter (DPM) for engines equipped with alternative injection technologies (direct injection and common rail) and alternative fuels (ultra low sulfur diesel, a 20% biodiesel blend, and a synthetic diesel). Particle physical properties were addressed by measuring particle number size distributions, and particle chemical properties were addressed by measuring polycyclic aromatic hydrocarbons (PAHs) and reactive oxygen species (ROS). Particle volatility was determined by passing the polydisperse size distribution through a thermodenuder set to 300 °C. The results from this study, conducted over a four point test cycle, showed that both fuel type and injection technology have an impact on particle emissions, but injection technology was the more important factor. Significant particle number emission (54%–84%) reductions were achieved at half load operation (1% increase–43% decrease at full load) with the common rail injection system; however, the particles had a significantly higher PAH fraction (by a factor of 2 to 4) and ROS concentrations (by a factor of 6 to 16) both expressed on a test-cycle averaged basis. The results of this study have significant implications for the health effects of DPM emissions from both direct injection and common rail engines utilizing various alternative fuels.

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Quantifying spatial and/or temporal trends in environmental modelling data requires that measurements be taken at multiple sites. The number of sites and duration of measurement at each site must be balanced against costs of equipment and availability of trained staff. The split panel design comprises short measurement campaigns at multiple locations and continuous monitoring at reference sites [2]. Here we present a modelling approach for a spatio-temporal model of ultrafine particle number concentration (PNC) recorded according to a split panel design. The model describes the temporal trends and background levels at each site. The data were measured as part of the “Ultrafine Particles from Transport Emissions and Child Health” (UPTECH) project which aims to link air quality measurements, child health outcomes and a questionnaire on the child’s history and demographics. The UPTECH project involves measuring aerosol and particle counts and local meteorology at each of 25 primary schools for two weeks and at three long term monitoring stations, and health outcomes for a cohort of students at each school [3].

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There is significant toxicological evidence of the effects of ultrafine particles (<100nm) on human health (WHO 2005). Studies show that the number concentration of particles has been associated with adverse human health effects (Englert 2004). This work is part of a major study called ‘Ultrafine Particles form Traffic Emissions and Children’s Health’ (UPTECH), which seeks to determine the effect of the exposure to traffic related ultrafine particles on children’s health in schools (http://www.ilaqh.qut.edu.au/Misc/UPT ECH%20Home.htm). Quantification of spatial variation of particle number concentration (PNC) in a microscale environment and identification of the main affecting parameters and their contribution levels are the main aims of this analysis.

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The Clay Minerals Society Source Clay kaolinites, Georgia KGa-1 and KGa-2, have been subjected to particle size determinations by 1) conventional sedimentation methods, 2) electron microscopy and image analysis, and 3) laser scattering using improved algorithms for the interaction of light with small particles. Particle shape, size distribution, and crystallinity vary considerably for each kaolinite. Replicate analyses of separated size fractions showed that in the <2 µm range, the sedimentation/centrifugation method of Tanner and Jackson (1947) is reproducible for different kaolinite types and that the calculated size ranges are in reasonable agreement with the size bins estimated from laser scattering. Particle sizes determined by laser scattering must be calculated using Mie theory when the dominant particle size is less than ∼5 µm. Based on this study of two well-known and structurally different kaolinites, laser scattering, with improved data reduction algorithms that include Mie theory, should be considered an internally consistent and rapid technique for clay particle sizing.

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It has not yet been established whether the spatial variation of particle number concentration (PNC) within a microscale environment can have an effect on exposure estimation results. In general, the degree of spatial variation within microscale environments remains unclear, since previous studies have only focused on spatial variation within macroscale environments. The aims of this study were to determine the spatial variation of PNC within microscale school environments, in order to assess the importance of the number of monitoring sites on exposure estimation. Furthermore, this paper aims to identify which parameters have the largest influence on spatial variation, as well as the relationship between those parameters and spatial variation. Air quality measurements were conducted for two consecutive weeks at each of the 25 schools across Brisbane, Australia. PNC was measured at three sites within the grounds of each school, along with the measurement of meteorological and several other air quality parameters. Traffic density was recorded for the busiest road adjacent to the school. Spatial variation at each school was quantified using coefficient of variation (CV). The portion of CV associated with instrument uncertainty was found to be 0.3 and therefore, CV was corrected so that only non-instrument uncertainty was analysed in the data. The median corrected CV (CVc) ranged from 0 to 0.35 across the schools, with 12 schools found to exhibit spatial variation. The study determined the number of required monitoring sites at schools with spatial variability and tested the deviation in exposure estimation arising from using only a single site. Nine schools required two measurement sites and three schools required three sites. Overall, the deviation in exposure estimation from using only one monitoring site was as much as one order of magnitude. The study also tested the association of spatial variation with wind speed/direction and traffic density, using partial correlation coefficients to identify sources of variation and non-parametric function estimation to quantify the level of variability. Traffic density and road to school wind direction were found to have a positive effect on CVc, and therefore, also on spatial variation. Wind speed was found to have a decreasing effect on spatial variation when it exceeded a threshold of 1.5 (m/s), while it had no effect below this threshold. Traffic density had a positive effect on spatial variation and its effect increased until it reached a density of 70 vehicles per five minutes, at which point its effect plateaued and did not increase further as a result of increasing traffic density.

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Airborne particulate pollutant is considered to be one of the major harmful emissions produced by vehicle engines as it has been directly linked to serious health problems. Passengers spend long times at bus stations and may be exposed to high concentrations of pollution. Particle pollution at two bus stations in Brisbane, Australia were monitored. The two bus stations consisted of markedly different site geography and surroundings with one situated in a street canyon and the other elevated above ground level. The same flow of traffic operated through both stations. Real time measurements of ultrafine particle concentration, size distribution and meteorological conditions were carried out on the platform continuously over several days. The results showed that the particle number concentrations were significantly different at the two stations, suggesting that the layout of site geometry and surroundings was a dominant determining factor through the injection of fresh air into the station platforms and the rates of dilution.

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Particle number concentrations vary significantly with environment and, in this study, we attempt to assess the significance of these differences. Towards this aim, we reviewed 85 papers that have reported particle number concentrations levels at 126 sites covering different environments. We grouped the results into eight categories according to measurement location including: road tunnel, on-road, road-side, street canyon, urban, urban background, rural, and clean background. From these reports, the overall median number concentration for each of the eight site categories was calculated. The eight location categories may be classified into four distinct groups. The mean median particle number locations for these four types were found to be statistically different from each other. Rural and clean background sites had the lowest concentrations of about 3x103 cm-3. Urban and urban background sites showed concentrations that were three times higher (9x103 cm-3). The mean concentration for the street canyon, roadside and on-road measurement sites was 4.6x104 cm-3, while the highest concentrations were observed in the road tunnels (8.6x104 cm-3). This variation is important when assessing human exposure-response for which there is very little data available, making it difficult to develop health guidelines, a basis for national regulations. Our analyses shows that the current levels in environments affected by vehicle emissions are 3 to 28 times higher than in the natural environments. At present, there is no threshold level in response to exposure to ultrafine particles. Therefore, future control and management strategies should target a decrease of these particles in urban environments by more than one order of magnitude to bring them down to the natural background. At present there is a long way to go to achieve this.

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Particle number concentrations vary significantly with environment and, in this study, we attempt to assess the significance of these differences. Towards this aim, we reviewed 85 papers that have reported particle number concentrations levels at 126 sites covering different environments. We grouped the results into eight categories according to measurement location including: road tunnel, on-road, road-side, street canyon, urban, urban background, rural, and clean background. Median values were calculated for each category. This review was restricted to papers that presented concentrations numerically. The majority of the reports were based on either CPC or SMPS measurements, with a limited number of papers reporting results from both instruments at the same site. Hence there were several overlaps between the number of CPC and SMPS measuring sites. Most of the studies reported multiple measurements at a given study site, while some studies included results from more than one site. From these reports, the overall median value for each location category was calculated...

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An attractive microstructural possibility for enhancing the ductility of high-strength nanocrystals is to develop a bimodal grain-size distribution, in which the fine grains provide strength, and the coarser grains enable strain hardening. Annealing of nanocrystalline Ni over a range of temperatures and times led to microstructures with varying volume fractions of coarse grains and a change in texture. Tensile tests revealed a drastic reduction in ductility with increasing volume fraction of coarse grains. The reduction in ductility may be related to the segregation of sulphur to grain boundaries.

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We demonstrate here that mesoporous tin dioxide (abbreviated M-SnO2) with a broad pore size distribution can be a prospective anode in lithium-ion batteries. M-SnO2 with pore size ranging between 2 and 7.5 nm was synthesized using a hydrothermal procedure involving two different surfactants of slightly different sizes, and characterized. The irreversible capacity loss that occurs during the first discharge and charge cycle is 890 mAh g(-1), which is smaller than the 1,010-mAh g(-1) loss recorded for mesoporous SnO2 (abbreviated S-SnO2) synthesized using a single surfactant. After 50 cycles, the discharge capacity of M-SnO2 (504 mAh g(-1)) is higher than that of S-SnO2 (401 mAh g(-1)) and solid nanoparticles of SnO2 (abbreviated nano-SnO2 < 4 mAh g(-1)) and nano-SnO2. Transmission electron microscopy revealed higher disorder in the pore arrangement in M-SnO2. This, in turn imparts lower stiffness to M-SnO2 (elastic modulus, E (R) a parts per thousand aEuro parts per thousand 14.5 GPa) vis-a-vis S-SnO2 (E (R) a parts per thousand aEuro parts per thousand 20.5 GPa), as obtained using the nanoindentation technique. Thus, the superior battery performance of M-SnO2 is attributed to its intrinsic material mechanical property. The fluidity of the internal microstructure of M-SnO2 resulted in a lower degree of aggregation of Sn particles compared to S-SnO2 and nano-SnO2 structural stabilization and long-term cyclability.