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The small size and large surface area of ultrafine particles (UFP) enhance their ability to deposit in the lung periphery and their reactivity. The Ultrafine Particles from Traffic Emissions and Children's Health (UPTECH) cross-sectional study was conducted in 8–11-year-old schoolchildren attending 25 primary (elementary) schools, randomly selected from the Brisbane Metropolitan Area, Queensland, Australia. Main study findings outlined indirect evidence of distal airway deposition (raised C reactive protein) but as yet, there is no direct evidence in the literature of effects of UFP exposure on peripheral airway function. We present further UPTECH study data from two sensitive peripheral airway function tests, Oscillometry and Multiple Breath Nitrogen Washout (MBNW), performed in 577 and 627 children (88% and 96% of UPTECH study cohort) respectively: mean(SD) age 10.1(0.9) years, 46% male, with 50% atopy and 14% current asthma. Bayesian generalised linear mixed effects regression models were used to estimate the effect of UFP particle number count (PNC) exposure on key oscillometry (airway resistance, (Rrs), and reactance, (Xrs)) and MBNW (lung clearance index, (LCI) and functional residual capacity, (FRC)) indices. We adjusted for age, sex, and height, and potential confounders including socio-economic disadvantage, PM2.5 and NO2 exposure. All models contained an interaction term between UFP PNC exposure and atopy, allowing estimation of the effect of exposure on non-atopic and atopic students. Increasing UFP PNC was associated with greater lung stiffness as evidenced by a decrease in Xrs [mean (95% credible interval) −1.63 (−3.36 to −0.05)%] per 1000#.cm−3]. It was also associated with greater lung stiffness (decrease in Xrs) in atopic subjects across all models [mean change ranging from −2.06 to −2.40% per 1000#.cm−3]. A paradoxical positive effect was observed for Rrs across all models [mean change ranging from −1.5

Air pollution exposure is associated with a range of adverse health effects, including cardiovascular and respiratory diseases. Particle-bound ROS has been recognised as one of the prevailing parameters to indicate the toxic potential of airborne particulate matter (PM). The temporal variability of particle-bound ROS is a very important metric crucial for the improvement of public health and risk assessment policies. To our knowledge this is the first study aiming to investigate diurnal ROS profiles in both the particle and gas phase and associate them with diurnal variations of important pollutants. For that purpose, we have successfully applied a new instrument to continuously monitor diurnal ROS profiles at two locations in Hong Kong: a busy roadside and an urban background. Data was collected over both the working week and during weekends. We have observed a high correlation between particle-bound ROS and lower concentrations of black carbon (BC) at the roadside during the working week. These associations were less significant over the weekend and at all times with ozone. Our results suggest that most of the particle-bound ROS from both the particle and the gas phase arises from fresh emission sources directly from the traffic. A very interesting observation came out as a result of this study where measured ROS concentration was decreasing with the rise of ozone in conjunction with particle number, suggesting potential role of ROS in particle growth and aging. © 2019

To estimate the oxidative potential (OP) of particulate matter (PM), two commonly used cell-free, molecular probes were applied: dithiothreitol (DTT) and dichloro-dihydro-fluorescein diacetate (DCFH-DA), and their performance was compared with 9,10-bis (phenylethynyl) anthracene-nitroxide (BPEAnit). To the best of our knowledge, this is the first study in which the performance of the DTT and DCFH has been compared with the BPEAnit probe. The average concentrations of PM, organic carbon (OC) and elemental carbon (EC) for fine (PM2.5) and coarse (PM10) particles were determined. The results were 44.8 ± 13.7, 9.8 ± 5.1 and 9.3 ± 4.8 µg·m−3 for PM2.5 and 75.5 ± 25.1, 16.3 ± 8.7 and 11.8 ± 5.3 µg·m−3 for PM10, respectively, for PM, OC and EC. The water-soluble organic carbon (WSOC) fraction accounted for 42 ± 14% and 28 ± 9% of organic carbon in PM2.5 and PM10, respectively. The average volume normalized OP values for the three assays depended on both the sampling periods and the PM fractions. The OPBPEAnit had its peak at 2 p.m.; in the afternoon, it was three times higher compared to the morning and late afternoon values. The DCFH and BPEAnit results were correlated (r = 0.64), while there was no good agreement between the BPEAnit and the DTT (r = 0.14). The total organic content of PM does not necessarily represent oxidative capacity and it shows varying correlation with the OP. With respect to the two PM fractions studied, the OP was mostly associated with smaller particles.

In environmental monitoring, the ability to obtain high-quality data across space and time is often limited by the cost of purchasing, deploying and maintaining a large collection of equipment, and the employment of personnel to perform these tasks. An ideal design for a monitoring campaign would be dense enough in time to capture short-range variation at each site, long enough in time to examine trends at each site and across all sites, and dense enough in space to allow modelling of the relationship between the means at each of the sites. This paper outlines a methodology for semiparametric spatiotemporal modelling of data that is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data are hourly averaged particle number concentration (PNC) and were collected as part of the International Laboratory for Air Quality and Health's Ultrafine Particles from Traffic Emissions and Children's Health (UPTECH) project. The panel design comprises two weeks of continuous measurements taken at each of a number of government primary schools in the Brisbane Metropolitan Area, with each school visited sequentially. The school data are augmented by data from long-term monitoring stations at three locations in Brisbane, Australia. The temporal part of the model explains daily and weekly cycles in PNC at the schools. The temporal variation is modelled hierarchically with a penalised random walk term common to all sites and a similar term accounting for the remaining temporal trend at each site. The modelling of temporal trends requires an acknowledgement that the observations are correlated rather than independent. At each school and long-term monitoring site, peaks in PNC can be attributed to the morning and afternoon rush hour traffic and new particle formation events. The spatial component of the model describes the school-to-school variation in mean PNC at each school and with