Influence of filtration on I/O particle concentration ratios at urban office buildings

Epidemiological research has consistently shown an association between fine and ultrafine particle concentrations, and increases in both respiratory and cardiovascular morbidity and mortality. These particles, often found in vehicle emissions outside buildings, can penetrate inside via their envelopes and mechanically ventilated systems. Indoor activities such as printing, cooking and cleaning, as well as the movement of building occupants are also an additional source of these particles. In this context, the filtration systems of mechanically ventilated buildings can reduce indoor particle concentrations. Several studies have quantified the efficiency of dry-media and electrostatic filters, but they mainly focused on the particle size range > 300 nm. Some others studied ultrafine particles but their investigations were conducted in laboratories. At this point, there is still only limited information on in situ filter efficiency and an incomplete understanding of filtration influence on I/O ratios of particle concentrations. To help address these gaps in knowledge and provide new information for the selection of appropriate filter types in office building HVAC systems, we aimed to: (1) measure particle concentrations at up and down stream flows of filter devices, as well as outdoor and indoor office buildings; (2) quantify efficiency of different filter types at different buildings; and (3) assess the impact of these filters on I/O ratios at different indoor and outdoor source operation scenarios

Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors

The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34–97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data

Associations of neighbourhood environmental attributes and socio-economic status with health-related quality of life in urban mid-aged and older adults : Mediation by physical activity and sedentary behaviour

This study examined the associations of objectively assessed physical features of the neighbourhood environment with physical and mental aspects of health-related quality of life (HRQoL) as measured by the SF-36, and the roles of physical activity and sedentary behaviour in these associations. We used data from a national sample of Australian mid-aged and older adults living in urban areas (N = 4141). Environmental attributes were computed for 1-km-radius areas surrounding participants' residential addresses. Neighbourhood socio-economic status (SES) and average annual concentrations of PM2.5 were the only attributes related to HRQoL in the expected direction in the total- and direct-effect regression models. All other environmental attributes were related to HRQoL via physical activity behaviours and leisure-time sitting. The associations of most environmental features with HRQoL mediated by physical activity and sedentary behaviours were inconsistent, positive through some pathways and negative through others. This study suggests that neighbourhood SES may in part benefit HRQoL by helping promote an active lifestyle. Neighbourhood attributes defining walkability may benefit HRQoL by providing opportunities for walking and resistance training and, through these, by helping reduce leisure-time sitting. However, the same attributes also may limit opportunities for household activities and gardening and negatively impact on HRQoL through these pathways

Microbial contents of vacuum cleaner bag dust and emitted bioaerosols and their implications for human exposure indoors

Vacuum cleaners can release large concentrations of particles, both in their exhaust air and from resuspension of settled dust. However, the size, variability, and microbial diversity of these emissions are unknown, despite evidence to suggest they may contribute to allergic responses and infection transmission indoors. This study aimed to evaluate bioaerosol emission from various vacuum cleaners. We sampled the air in an experimental flow tunnel where vacuum cleaners were run, and their airborne emissions were sampled with closed-face cassettes. Dust samples were also collected from the dust bag. Total bacteria, total archaea, Penicillium/Aspergillus, and total Clostridium cluster 1 were quantified with specific quantitative PCR protocols, and emission rates were calculated. Clostridium botulinum and antibiotic resistance genes were detected in each sample using endpoint PCR. Bacterial diversity was also analyzed using denaturing gradient gel electrophoresis (DGGE), image analysis, and band sequencing. We demonstrated that emission of bacteria and molds (Penicillium/Aspergillus) can reach values as high as 1E5 cell equivalents/min and that those emissions are not related to each other. The bag dust bacterial and mold content was also consistent across the vacuums we assessed, reaching up to 1E7 bacterial or mold cell equivalents/g. Antibiotic resistance genes were detected in several samples. No archaea or C. botulinum was detected in any air samples. Diversity analyses showed that most bacteria are from human sources, in keeping with other recent results. These results highlight the potential capability of vacuum cleaners to disseminate appreciable quantities of molds and human-associated bacteria indoors and their role as a source of exposure to bioaerosols

In-vehicle nitrogen dioxide concentrations in road tunnels

There is a lack of knowledge regarding in-vehicle concentrations of nitrogen dioxide (NO) during transit through road tunnels in urban environments. Furthermore, previous studies have tended to involve a single vehicle and the range of in-vehicle NO concentrations that vehicle occupants may be exposed to is not well defined. This study describes simultaneous measurements of in-vehicle and outside-vehicle NO concentrations on a route through Sydney, Australia that included several major tunnels, minor tunnels and busy surface roads. Tests were conducted on nine passenger vehicles to assess how vehicle characteristics and ventilation settings affected in-vehicle NO concentrations and the in-vehicle-to-outside vehicle (I/O) concentration ratio. NO was measured directly using a cavity attenuated phase shift (CAPS) technique that gave a high temporal and spatial resolution. In the major tunnels, transit-average in-vehicle NO concentrations were lower than outside-vehicle concentrations for all vehicles with cabin air recirculation either on or off. However, markedly lower I/O ratios were obtained with recirculation on (0.08–0.36), suggesting that vehicle occupants can significantly lower their exposure to NO in tunnels by switching recirculation on. The highest mean I/O ratios for NO were measured in older vehicles (0.35–0.36), which is attributed to older vehicles having higher air exchange rates. The results from this study can be used to inform the design and operation of future road tunnels and modelling of personal exposure to NO

The neighbourhood environment and profiles of the metabolic syndrome

Background: There is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components. Methods: We used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO2 and PM2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles. Results: LCA yielded three latent classes, one including only participants without MetS ("Lower probability of MetS components" profile). The other two classes/profiles, consisting of participants with and without MetS, were "Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure" and "Higher probability of MetS components". Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO2 were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM2.5 was associated with unhealthier metabolic profiles with MetS. Conclusions: This study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components

Associations of the neighbourhood built and natural environment with cardiometabolic health indicators : A cross-sectional analysis of environmental moderators and behavioural mediators

Background: Most studies examining the effects of neighbourhood urban design on cardiometabolic health focused solely on the built or natural environment. Also, they did not consider the roles of neighbourhood socioeconomic status (SES) and ambient air pollution in the observed associations, and the extent to which these associations were mediated by physical activity and sedentary behaviours. Methods: We used data from the AusDiab3 study (N = 4141), a national cohort study of Australian adults to address the above-mentioned knowledge gaps. Spatial data were used to compute indices of neighbourhood walkability (population density, intersection density, non-commercial land use mix, commercial land use), natural environment (parkland and blue spaces) and air pollution (annual average concentrations of nitrogen dioxide (NO2) and fine particulate matter <2.5 μm in diameter (PM2.5)). Census indices were used to define neighbourhood SES. Clinical assessments collected data on adiposity, blood pressure, blood glucose and blood lipids. Generalised additive mixed models were used to estimate associations. Results: Neighbourhood walkability showed indirect beneficial associations with most indicators of cardiometabolic health via resistance training, walking and sitting for different purposes; indirect detrimental associations with the same indicators via vigorous gardening; and direct detrimental associations with blood pressure. The neighbourhood natural environment had beneficial indirect associations with most cardiometabolic health indicators via resistance training and leisure-time sitting, and beneficial direct associations with adiposity and blood lipids. Neighbourhood SES and air pollution moderated only a few associations of the neighbourhood environment with physical activity, blood lipids and blood pressure. Conclusions: Within a low-density and low-pollution context, denser, walkable neighbourhoods with good access to nature may benefit residents’ cardiometabolic health by facilitating the adoption of an active lifestyle. Possible disadvantages of living in denser neighbourhoods for older populations are having limited opportunities for gardening, higher levels of noise and less healthy dietary patterns associated with eating out