Sam Clifford

School children's personal exposure to ultrafine particles in the urban environment.

There has been considerable scientific interest in personal exposure to ultrafine particles (UFP). In this study, the inhaled particle surface area doses and dose relative intensities in the tracheobronchial and alveolar regions of lungs were calculated using measured 24-h UFP time series of school children personal exposures. Bayesian hierarchical modeling was used to determine mean doses and dose intensities for the various microenvironments. Analysis of measured personal exposures for 137 participating children from 25 schools in the Brisbane Metropolitan Area showed similar trends for all participating children. Bayesian regression modeling was performed to calculate the daily proportion of childrens total doses in different microenvironments. The proportion of total daily alveolar doses for home, school, commuting, and other were 55.3%, 35.3%, 4.5%, and 5.0%, respectively, with the home microenvironment contributing a majority of childrens total daily dose. Childrens mean indoor dose was never higher than the outdoors at any of the schools, indicating there were no persistent indoor particle sources in the classrooms during the measurements. Outdoor activities, eating/cooking at home, and commuting were the three activities with the highest dose intensities. Childrens exposure during school hours was more strongly influenced by urban background particles than traffic near the school.

Children's well-being at schools: Impact of climatic conditions and air pollution

Human civilization is currently facing two particular challenges: population growth with a strong trend towards urbanization and climate change. The latter is now no longer seriously questioned. The primary concern is to limit anthropogenic climate change and to adapt our societies to its effects. Schools are a key part of the structure of our societies. If future generations are to take control of the manifold global problems, we have to offer our children the best possible infrastructure for their education: not only in terms of the didactic concepts, but also with regard to the climatic conditions in the school environment. Between the ages of 6 and 19, children spend up to 8h a day in classrooms. The conditions are, however, often inacceptable and regardless of the geographic situation, all the current studies report similar problems: classrooms being too small for the high number of school children, poor ventilation concepts, considerable outdoor air pollution and strong sources of indoor air pollution. There have been discussions about a beneficial and healthy air quality in classrooms for many years now and in recent years extensive studies have been carried out worldwide. The problems have been clearly outlined on a scientific level and there are prudent and feasible concepts to improve the situation. The growing number of publications also highlights the importance of this subject. High carbon dioxide concentrations in classrooms, which indicate poor ventilation conditions, and the increasing particle matter in urban outdoor air have, in particular, been identified as primary causes of poor indoor air quality in schools. Despite this, the conditions in most schools continue to be in need of improvement. There are many reasons for this. In some cases, the local administrative bodies do not have the budgets required to address such concerns, in other cases regulations and laws stand in contradiction to the demands for better indoor air quality, and sometimes the problems are simply ignored. This review summarizes the current results and knowledge gained from the scientific literature on air quality in classrooms. Possible scenarios for the future are discussed and guideline values proposed which can serve to help authorities, government organizations and commissions improve the situation on a global level.

Spatial variation of particle number concentration in school microscale environments and its impact on exposure assessment.

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.

Ultrafine Particles from Traffic Emissions and Children's Health (UPTECH) in Brisbane, Queensland (Australia): study design and implementation.

Ultrafine particles are particles that are less than 0.1 micrometres (µm) in diameter. Due to their very small size they can penetrate deep into the lungs, and potentially cause more damage than larger particles. The Ultrafine Particles from Traffic Emissions and Children’s Health (UPTECH) study is the first Australian epidemiological study to assess the health effects of ultrafine particles on children’s health in general and peripheral airways in particular. The study is being conducted in Brisbane, Australia. Continuous indoor and outdoor air pollution monitoring was conducted within each of the twenty five participating school campuses to measure particulate matter, including in the ultrafine size range, and gases. Respiratory health effects were evaluated by conducting the following tests on participating children at each school: spirometry, forced oscillation technique (FOT) and multiple breath nitrogen washout test (MBNW) (to assess airway function), fraction of exhaled nitric oxide (FeNO, to assess airway inflammation), blood cotinine levels (to assess exposure to second-hand tobacco smoke), and serum C-reactive protein (CRP) levels (to measure systemic inflammation). A pilot study was conducted prior to commencing the main study to assess the feasibility and reliably of measurement of some of the clinical tests that have been proposed for the main study. Air pollutant exposure measurements were not included in the pilot study.

Atmospheric Visibility and PM10 as Indicators of New Particle Formation in an Urban Environment.

It is well-known that new particle formation (NPF) in the atmosphere is inhibited by pre-existing particles in the air that act as condensation sinks to decrease the concentration and, thus, the supersaturation of precursor gases. In this study, we investigate the effects of two parameters-atmospheric visibility, expressed as the particle backscatter coefficient (BSP), and PM10 particulate mass concentration-on the occurrences of NPF events in an urban environment where the majority of precursor gases originate from motor vehicle and industrial sources. This is the first attempt to derive direct relationships between these two parameters and the occurrence of NPF. NPF events were identified from data obtained with a neutral cluster and air ion spectrometer over 245 days within a calendar year. Bayesian logistic regression was used to determine the probability of observing NPF as functions of BSP and PM10. We show that the BSP at 08 h on a given day is a reliable indicator of an NPF event later that day. The posterior median probability of observing an NPF event was greater than 0.5 (95%) when the BSP at 08 h was less than 6.8 Mm(-1).

Children's personal exposure to air pollution in rural villages in Bhutan.

Exposure assessment studies conducted in developing countries have been based on fixed-site monitoring to date. This is a major deficiency, leading to errors in estimating the actual exposures, which are a function of time spent and pollutant concentrations in different microenvironments. This study quantified school childrens daily personal exposure to ultrafine particles (UFP) using real-time monitoring, as well as volatile organic compounds (VOCs) and NO2 using passive sampling in rural Bhutan in order to determine the factors driving the exposures. An activity diary was used to track childrens time activity patterns, and difference in mean exposure levels across sex and indoor/outdoor were investigated with ANOVA. 82 children, attending three primary schools participated in this study; S1 and S2 during the wet season and S3 during the dry season. Mean daily UFP exposure (cm(-3)) was 1.08×10(4) for children attending S1, 9.81×10(3) for S2, and 4.19×10(4) for S3. The mean daily NO2 exposure (µg m(-3)) was 4.27 for S1, 3.33 for S2 and 5.38 for S3 children. Likewise, children attending S3 also experienced higher daily exposure to a majority of the VOCs than those attending S1 and S2. Time-series of UFP personal exposures provided detailed information on identifying sources of these particles and quantifying their contributions to the total daily exposures for each microenvironment. The highest UFP exposure resulted from cooking/eating, contributing to 64% of the daily exposure, due to firewood combustion in houses using traditional mud cookstoves. The lowest UFP exposures were during the hours that children spent outdoors at school. The outcomes of this study highlight the significant contributions of lifestyle and socio-economic factors in personal exposures and have applications in environmental risk assessment and household air pollution mitigation in Bhutan.

Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling

From remote sensing of the environment, to brain scans in medicine, the growth in the use of image data has motivated a parallel increase in statistical techniques for analysing these images. A particular area of growth has been in Bayesian models and corresponding computational methods. Bayesian approaches have been proposed to address the gamut of supervised and unsupervised inferential aims in image analysis. In this article we provide a general review of these approaches, with a focus on unsupervised analysis of 2-D images. Four exemplar methods that canvas the broad aims of image modelling and analysis are described. An exposition of these approaches is provided by applying them to an environmental case study involving the use of satellite data to assess water quality in the Great Barrier Reef, Australia. The techniques considered in detail are hidden Markov random fields (MRF), Gaussian MRF, Poisson/gamma random fields, and Voronoi tessellations. We also consider a variety of enabling computational algorithms, including MCMC, variational Bayes and integrated nested Laplace approximations. We compare the different aims and inferential capabilities of the models and discuss the advantages and drawbacks of the corresponding computational algorithms.

Application of multi-metric approach to characterization of particle emissions from nanotechnology and non-nanotechnology processes

We assessed particle emission, using a three-tiered assessment process, to identify points of particle emission, temporal and spatial size and number concentration and to validate engineering controls in relation to selected nanotechnology processes and during the operation of laser printers.

Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees

Data aggregation is a necessity when working with big data. Data reduction steps without loss of information are a scientific and computational challenge but are critical to enable effective data processing and information delineation in data-rich studies. We investigated the effect of four spatial aggregation schemes on Landsat imagery on prediction accuracy of green photosynthetic vegetation (PV) based on fractional cover (FCover). To reduce data volume we created an evenly spaced grid, overlaid that on the PV band and delineated the arithmetic mean of PV fractions contained within each grid cell. The aggregated fractions and the corresponding geographic grid cell coordinates were then used for boosted regression tree prediction models. Model goodness of fit was evaluated by the Root Mean Squared Error (RMSE). Two spatial resolutions (3000 m and 6000 m) offer good prediction accuracy whereas others show either too much unexplained variability model prediction results or the aggregation resolution smoothed out local PV in heterogeneous land. We further demonstrate the suitability of our aggregation scheme, offering an increased processing time without losing significant topographic information. These findings support the feasibility of using geographic coordinates in the prediction of PV and yield satisfying accuracy in our study area.

Joint-level energetics differentiate isoinertial from speed-power resistance training—a Bayesian analysis

Background There is convincing evidence for the benefits of resistance training on vertical jump improvements, but little evidence to guide optimal training prescription. The inability to detect small between modality effects may partially reflect the use of ANOVA statistics. This study represents the results of a sub-study from a larger project investigating the effects of two resistance training methods on load carriage running energetics. Bayesian statistics were used to compare the effectiveness of isoinertial resistance against speed-power training to change countermovement jump (CMJ) and squat jump (SJ) height, and joint energetics. Methods Active adults were randomly allocated to either a six-week isoinertial (n = 16; calf raises, leg press, and lunge), or a speed-power training program (n = 14; countermovement jumps, hopping, with hip flexor training to target pre-swing running energetics). Primary outcome variables included jump height and joint power. Bayesian mixed modelling and Functional Data Analysis were used, where significance was determined by a non-zero crossing of the 95% Bayesian Credible Interval (CrI). Results The gain in CMJ height after isoinertial training was 1.95 cm (95% CrI [0.85–3.04] cm) greater than the gain after speed-power training, but the gain in SJ height was similar between groups. In the CMJ, isoinertial training produced a larger increase in power absorption at the hip by a mean 0.018% (equivalent to 35 W) (95% CrI [0.007–0.03]), knee by 0.014% (equivalent to 27 W) (95% CrI [0.006–0.02]) and foot by 0.011% (equivalent to 21 W) (95% CrI [0.005–0.02]) compared to speed-power training. Discussion Short-term isoinertial training improved CMJ height more than speed-power training. The principle adaptive difference between training modalities was at the level of hip, knee and foot power absorption.