302 resultados para Wind exposure


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Concentrations of ultrafine (<0.1µm) particles (UFPs) and PM2.5 (<2.5µm) were measured whilst commuting along a similar route by train, bus, ferry and automobile in Sydney, Australia. One trip on each transport mode was undertaken during both morning and evening peak hours throughout a working week, for a total of 40 trips. Analyses comprised one-way ANOVA to compare overall (i.e. all trips combined) geometric mean concentrations of both particle fractions measured across transport modes, and assessment of both the correlation between wind speed and individual trip means of UFPs and PM2.5, and the correlation between the two particle fractions. Overall geometric mean concentrations of UFPs and PM2.5 ranged from 2.8 (train) to 8.4 (bus) × 104 particles cm-3 and 22.6 (automobile) to 29.6 (bus) µg m-3, respectively, and a statistically significant difference (p <0.001) between modes was found for both particle fractions. Individual trip geometric mean concentrations were between 9.7 × 103 (train) and 2.2 × 105 (bus) particles cm-3 and 9.5 (train) to 78.7 (train) µg m-3. Estimated commuter exposures were variable, and the highest return trip mean PM2.5 exposure occurred in the ferry mode, whilst the highest UFP exposure occurred during bus trips. The correlation between fractions was generally poor, and in keeping with the duality of particle mass and number emissions in vehicle-dominated urban areas. Wind speed was negatively correlated with, and a generally poor determinant of, UFP and PM2.5 concentrations, suggesting a more significant role for other factors in determining commuter exposure.

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Commuting in various transport modes represents an activity likely to incur significant exposure to traffic emissions. This study investigated the determinants and characteristics of exposure to ultrafine (< 100 nm) particles (UFPs) in four transport modes in Sydney, with a specific focus on exposure in automobiles, which remain the transport mode of choice for approximately 70% of Sydney commuters. UFP concentrations were measured using a portable condensation particle counter (CPC) inside five automobiles commuting on above ground and tunnel roadways, and in buses, ferries and trains. Determinant factors investigated included wind speed, cabin ventilation (automobiles only) and traffic volume. The results showed that concentrations varied significantly as a consequence of transport mode, vehicle type and ventilation characteristics. The effects of wind speed were minimal relative to those of traffic volume (especially heavy diesel vehicles) and cabin ventilation, with the latter proving to be a strong determinant of UFP ingress into automobiles. The effect of ~70 minutes of commuting on total daily exposure was estimated using a range of UFP concentrations reported for several microenvironments. A hypothetical Sydney resident commuting by automobile and spending 8.5 minutes of their day in the M5 East tunnel could incur anywhere from a lower limit of 3-11% to an upper limit of 37-69% of daily UFP exposure during a return commute, depending on the concentrations they encountered in other microenvironments, the type of vehicle they used and the ventilation setting selected. However, commute-time exposures at either extreme of the values presented are unlikely to occur in practice. The range of exposures estimated for other transport modes were comparable to those of automobiles, and in the case of buses, higher than automobiles.

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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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Measurements of particle concentrations and distributions in terms of number, surface area, and mass were performed simultaneously at eight sampling points within a symmetric street canyon of an Italian city. The aim was to obtain a useful benchmark for validation of wind tunnel experiments and numerical schemes: to this purpose, the influence of wind directions and speeds was considered. Particle number concentrations (PNCs) were higher on the leeward side than the windward side of the street canyon due to the wind vortex effect. Different vertical PNC profiles were observed between the two canyon sides depending on the wind direction and speed at roof level. A decrease in particle concentrations was observed with increasing rooftop wind speed, except for the coarse fraction indicating a possible particle resuspension due to the traffic and wind motion. This study confirms that particle concentration fields in urban street canyons are strongly influenced by traffic emissions and meteorological parameters, especially wind direction and speed.

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Farmers' exposure to pesticides is high in developing countries. As a result many farmers suffer from ill-health, both short and long term. Deaths are not uncommon. This paper addresses this issue. Field survey data from Sri Lanka are used to estimate farmers' expenditure on defensive behavior (DE) and to determine factors that influence DE. The avertive behavior approach is used to estimate costs. Tobit regression analysis is used to determine factors that influence DE. Field survey data show that farmers' expenditures on DE are low. This is inversely related to high incidence of ill health among farmers using pesticides.

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Traffic emissions are an important contributor to ambient air pollution, especially in large cities featuring extensive and high density traffic networks. Bus fleets represent a significant part of inner city traffic causing an increase in exposure to general public, passengers and drivers along bus routes and at bus stations. Limited information is available on quantification of the levels, and governing parameters affecting the air pollution exposure at bus stations. The presented study investigated the bus emissions-dominated ambient air in a large, inner city bus station, with a specific focus on submicrometer particles. The study’s objectives were (i) quantification of the concentration levels; (ii) characterisation of the spatio-temporal variation; (iii) identification of the parameters governing the emissions levels at the bus station and (iv) assessment of the relationship between particle concentrations measured at the street level (background) and within the bus station. The results show that up to 90% of the emissions at the station are ultrafine particles (smaller than 100 nm), with the concentration levels up to 10 times the value of urban ambient air background (annual) and up to 4 times the local ambient air background. The governing parameters affecting particle concentration at the station were bus flow rate and meteorological conditions (wind velocity). Particle concentration followed a diurnal trend, with an increase in the morning and evening, associated with traffic rush hours. Passengers’ exposure could be significant compared to the average outdoor and indoor exposure levels.

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As part of a large study investigating indoor air in residential houses in Brisbane, Australia, the purpose of this work was to quantify indoor exposure to submicrometer particles and PM2.5 for the inhabitants of 14 houses. Particle concentrations were measured simultaneously for more than 48 hours in the kitchens of all the houses by using a condensation particle counter (CPC) and a photometer (DustTrak). The occupants of the houses were asked to fill in a diary, noting the time and duration of any activity occurring throughout the house during measurement, as well as their presence or absence from home. From the time series concentration data and the information about indoor activities, exposure to the inhabitants of the houses was calculated for the entire time they spent at home as well as during indoor activities resulting in particle generation. The results show that the highest median concentration level occurred during cooking periods for both particle number concentration (47.5´103 particles cm-3) and PM2.5 concentration (13.4 mg m-3). The highest residential exposure period was the sleeping period for both particle number exposure (31%) and PM2.5 exposure (45.6%). The percentage of the average residential particle exposure level in total 24h particle exposure level was approximating 70% for both particle number and PM2.5 exposure.