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Children are particularly susceptible to air pollution and schools are examples of urban microenvironments that can account for a large portion of children's exposure to airborne particles. Thus this paper aimed to determine the sources of primary airborne particles that children are exposed to at school by analyzing selected organic molecular markers at 11 urban schools in Brisbane, Australia. Positive matrix factorization analysis identified four sources at the schools: vehicle emissions, biomass burning, meat cooking and plant wax emissions accounting for 45%, 29%, 16% and 7%, of the organic carbon respectively. Biomass burning peaked in winter due to prescribed burning of bushland around Brisbane. Overall, the results indicated that both local (traffic) and regional (biomass burning) sources of primary organic aerosols influence the levels of ambient particles that children are exposed at the schools. These results have implications for potential control strategies for mitigating exposure at schools. [Copyright &y& Elsevier]

Abstract: Long-term exposure to vehicle emissions has been associated with detrimental health effects. Children are amongst the most susceptible group and schools represent an environment where they can experience significant exposure to vehicle emissions. However, there are limited studies on children's exposure to vehicle emissions in schools. The aim of this study was to quantify the concentration of organic aerosol (OA) and in particular, vehicle emissions that children are exposed to during school hours. Therefore an Aerodyne compact time-of-flight aerosol mass spectrometer (TOF-AMS) was deployed at five urban schools in Brisbane, Australia. TOF-AMS enabled the chemical composition of the non-refractory (NR-PM1) to be analysed with a high temporal resolution to assess the concentration of vehicle emissions and other OA components during school hours. The organic fraction at each school comprised the majority of NR-PM1. Primary emissions were found to dominate the OA at only one school which had an O:C ratio of 0.17, due to fuel powered gardening equipment used near the TOF-AMS. A significant source of the OA at two of the schools was aged vehicle emissions from nearby highways. More oxidised OA was observed at the remaining two schools, which also recorded strong biomass burning influences. In general, the diurnal cycle of the total OA concentration varied between schools and was found to be at a minimum during school hours. The major organic component that school children were exposed to during school hours was secondary OA at all schools. Peak exposure of school children to vehicle emissions occurred during school drop-off and pick-up times. Unless a school is located near major roads, children are exposed predominately to regional secondary OA as opposed to local emissions during school hours in urban environments. [Copyright &y& Elsevier]

Open biomass burning from wildfires and the prescribed burning of forests and farmland is a frequent occurrence in South-East Queensland (SEQ), Australia. This work reports on data collected from 10 to 30 September 2011, which covers the days before (10–14 September), during (15–20 September) and after (21–30 September) a period of biomass burning in SEQ. The aim of this project was to comprehensively quantify the impact of the biomass burning on air quality in Brisbane, the capital city of Queensland. A multi-parameter field measurement campaign was conducted and ambient air quality data from 13 monitoring stations across SEQ were analysed. During the burning period, the average concentrations of all measured pollutants increased (from 20% to 430%) compared to the non-burning period (both before and after burning), except for total xylenes. The average concentration of O 3 , NO 2 , SO 2 , benzene, formaldehyde, PM 10 , PM 2.5 and visibility-reducing particles reached their highest levels for the year, which were up to 10 times higher than annual average levels, while PM 10 , PM 2.5 and SO 2 concentrations exceeded the WHO 24-hour guidelines and O 3 concentration exceeded the WHO maximum 8-hour average threshold during the burning period. Overall spatial variations showed that all measured pollutants, with the exception of O 3 , were closer to spatial homogeneity during the burning compared to the non-burning period. In addition to the above, elevated concentrations of three biomass burning organic tracers (levoglucosan, mannosan and galactosan), together with the amount of non-refractory organic particles (PM 1 ) and the average value of f60 (attributed to levoglucosan), reinforce that elevated pollutant concentration levels were due to emissions from open biomass burning events, 70% of which were prescribed burning events. This study, which is the first and most comprehensive of its kind in Australia, provides quantitative evidence of the significant impact of open biomass burning events, especially prescribed burning, on urban air quality. The current results provide a solid platform for more detailed health and modelling investigations in the future. [ABSTRACT FROM AUTHOR]

Ambient ultrafine particle number concentrations (PNC) have inhomogeneous spatio-temporal distributions and depend on a number of different urban factors, including background conditions and distant sources. This paper quantitatively compares exposure to ambient ultrafine particles at urban schools in two cities in developed countries, with high insolation climatic conditions, namely Brisbane (Australia) and Barcelona (Spain). The analysis used comprehensive indoor and outdoor air quality measurements at 25 schools in Brisbane and 39 schools in Barcelona. PNC modes were analysed with respect to ambient temperature, land use and urban characteristics, combined with the measured elemental carbon concentrations, NO x (Brisbane) and NO 2 (Barcelona). The trends and modes of the quantified weekday average daily cycles of ambient PNC exhibited significant differences between the two cities. PNC increases were observed during traffic rush hours in both cases. However, the mid-day peak was dominant in Brisbane schools and had the highest contribution to total PNC for both indoors and outdoors. In Barcelona, the contribution from traffic was highest for ambient PNC, while the mid-day peak had a slightly higher contribution for indoor concentrations. Analysis of the relationships between PNC and land use characteristics in Barcelona schools showed a moderate correlation with the percentage of road network area and an anti-correlation with the percentage of green area. No statistically significant correlations were found for Brisbane. Overall, despite many similarities between the two cities, school-based exposure patterns were different. The main source of ambient PNC at schools was shown to be traffic in Barcelona and mid-day new particle formation in Brisbane. The mid-day PNC peak in Brisbane could have been driven by the combined effect of background and meteorological conditions, as well as other local/distant sources. The results have implications for urban development, especially in terms of air quality mitigation and management at schools. [ABSTRACT FROM AUTHOR]

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 noninstrument uncertainty was analyzed 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 1 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 nonparametric 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. [ABSTRACT FROM AUTHOR]