Ultrafine particle exposure and biomarkers of effect on small airways in children.

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 -1.55 to -1.70% per 1000#.cm-3] (decreases in Rrs indicating an increase in airway calibre), which was present for both atopic and non-atopic subjects. No effects on MBNW indices were observed. In conclusion, a modest detrimental effect of UFP on peripheral airway function among atopic subjects, as assessed by respiratory system reactance, was observed extending the main UPTECH study findings which reported a positive association with a biomarker for systemic inflammation, C-reactive protein (CRP). Further studies are warranted to explore the pathophysiological mechanisms underlying increased respiratory stiffness, and whether it persists through to adolescence and adulthood.

Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia.

BACKGROUND: Methods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO2) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model. METHODS: Satellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO2 and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO2 concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC). RESULTS: The NO2 LUR model predicted 84% of spatial variability in annual mean NO2 (RMSE: 1.2 ppb; cross-validated R2: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R2: 0.90). The annual average NO2 concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO2 cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO2 cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69. CONCLUSIONS: Our LUR models explained a high degree of spatial variability in annual mean NO2 and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO2 for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies.

Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5 Exposure Assessment in Australia.

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 µm, PM2.5). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM2.5, derived by relating satellite-observed aerosol optical depth to ground-level PM2.5 ("SAT-PM2.5"). We aimed to determine the validity of such satellite-based LUR models for PM2.5 in Australia. We used global SAT-PM2.5 estimates (∼10 km grid) and local land-use predictors to develop four LUR models for year-2015 (two satellite-based, two nonsatellite-based). We evaluated model performance at 51 independent monitoring sites not used for model development. An LUR model that included the SAT-PM2.5 predictor variable (and six others) explained the most spatial variability in PM2.5 (adjusted R2 = 0.63, RMSE (µg/m3 [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R2 = 0.52, RMSE: 1.15 [16%]). The evaluation R2 of the SAT-PM2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM2.5 exposure assessment in Australia.

Damp housing, gas stoves, and the burden of childhood asthma in Australia.

OBJECTIVE: To determine the proportion of the national childhood asthma burden associated with exposure to dampness and gas stoves in Australian homes. DESIGN: Comparative risk assessment modelling study. Setting, participants: Australian children aged 14 years or less, 2011. MAIN OUTCOME MEASURES: The population attributable fractions (PAFs) and number of disability-adjusted life years (DALYs) for childhood asthma associated with exposure to damp housing and gas stoves. RESULTS: 26.1% of Australian homes have dampness problems and 38.2% have natural gas as the main energy source for cooktop stoves. The PAF for childhood asthma attributable to damp housing was 7.9% (95% CI, 3.2-12.6%), causing 1760 disability-adjusted life years (DALYs; 95% CI, 416-3104 DALYs), or 42 DALYs/100 000 children. The PAF associated with gas stoves was 12.3% (95% CI, 8.9-15.8%), corresponding to 2756 DALYs (95% CI, 1271-4242), or 67 DALYs/100 000 children. If all homes with gas stoves were fitted with high efficiency range hoods to vent gas combustion products outdoors, the PAF and burden estimates were reduced to 3.4% (95% CI, 2.2-4.6%) and 761 DALYs (95% CI, 322-1199). CONCLUSIONS: Exposure to damp housing and gas stoves is common in Australia, and is associated with a considerable proportion of the childhood asthma burden. Strategies for reducing exposure to indoor dampness and gas combustion products should be communicated to parents of children with or at risk of asthma.

Childhood fish oil supplementation modifies associations between traffic related air pollution and allergic sensitisation.

BACKGROUND: Studies of potential adverse effects of traffic related air pollution (TRAP) on allergic disease have had mixed findings. Nutritional studies to examine whether fish oil supplementation may protect against development of allergic disease through their anti-inflammatory actions have also had mixed findings. Extremely few studies to date have considered whether air pollution and dietary factors such as fish oil intake may interact, which was the rationale for this study. METHODS: We conducted a secondary analysis of the Childhood Asthma Prevention Study (CAPS) birth cohort, where children were randomised to fish oil supplementation or placebo from early life to age 5 years. We examined interactions between supplementation and TRAP (using weighted road density at place of residence as our measure of traffic related air pollution exposure) with allergic disease and lung function outcomes at age 5 and 8 years. RESULTS: Outcome information was available on approximately 400 children (~ 70% of the original birth cohort). Statistically significant interactions between fish oil supplementation and TRAP were seen for house dust mite (HDM), inhalant and all-allergen skin prick tests (SPTs) and for HDM-specific interleukin-5 response at age 5. Adjusting for relevant confounders, relative risks (RRs) for positive HDM SPT were RR 1.74 (95% CI 1.22-2.48) per 100 m local road or 33.3 m of motorway within 50 m of the home for those randomised to the control group and 1.03 (0.76-1.41) for those randomised to receive the fish oil supplement. The risk differential was highest in an analysis restricted to those who did not change address between ages 5 and 8 years. In this sub-group, supplementation also protected against the effect of traffic exposure on pre-bronchodilator FEV1/FVC ratio. CONCLUSIONS: Results suggest that fish oil supplementation may protect against pro-allergic sensitisation effects of TRAP exposure. Strengths of this analysis are that supplementation was randomised and independent of TRAP exposure, however, findings need to be confirmed in a larger experimental study with the interaction investigated as a primary hypothesis, potentially also exploring epigenetic mechanisms. More generally, studies of adverse health effects of air pollution may benefit from considering potential effect modification by diet and other factors. TRIAL REGISTRATION: Australia New Zealand Clinical Trial Registry. www.anzctr.org.au Registration: ACTRN12605000042640 , Date: 26th July 2005. Retrospectively registered, trial commenced prior to registry availability.

Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research.

Exposure to traffic related nitrogen dioxide (NO2) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO2. The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.

Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers.

Including satellite observations of nitrogen dioxide (NO2) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO2 samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO2 estimates from existing national satellite-based LUR models (developed with 68 regulatory monitors). The samplers spanned roadside, urban near traffic (≤100 m to a major road), and urban background (>100 m to a major road) locations. We evaluated model performance using R2 (predicted NO2 regressed on independent measurements of NO2), mean-square-error R2 (MSE-R2), RMSE, and bias. Our models captured up to 69% of spatial variability in NO2 at urban near-traffic and urban background locations, and up to 58% of variability at all validation sites, including roadside locations. The absolute agreement of measurements and predictions (measured by MSE-R2) was similar to their correlation (measured by R2). Few previous studies have performed independent evaluations of national satellite-based LUR models, and there is little information on the performance of models developed with a small number of NO2 monitors. We have demonstrated that such models are a valid approach for estimating NO2 exposures in Australian cities.

Neighbourhood walkability, road density and socio-economic status in Sydney, Australia.

BACKGROUND: Planning and transport agencies play a vital role in influencing the design of townscapes, travel modes and travel behaviors, which in turn impact on the walkability of neighbourhoods and residents' physical activity opportunities. Optimising neighbourhood walkability is desirable in built environments, however, the population health benefits of walkability may be offset by increased exposure to traffic related air pollution. This paper describes the spatial distribution of neighbourhood walkability and weighted road density, a marker for traffic related air pollution, in Sydney, Australia. As exposure to air pollution is related to socio-economic status in some cities, this paper also examines the spatial distribution of weighted road density and walkability by socio-economic status (SES). METHODS: We calculated walkability, weighted road density (as a measure of traffic related air pollution) and SES, using predefined and validated measures, for 5858 Sydney neighbourhoods, representing 3.6 million population. We overlaid tertiles of walkability and weighted road density to define "sweet-spots" (high walkability-low weighted road density), and "sour- spots" (low walkability-high weighted road density) neighbourhoods. We also examined the distribution of walkability and weighted road density by SES quintiles. RESULTS: Walkability and weighted road density showed a clear east-west gradient across the region. Our study found that only 4 % of Sydney's population lived in sweet-spot" neighbourhoods with high walkability and low weighted road density (desirable), and these tended to be located closer to the city centre. A greater proportion of neighbourhoods had health limiting attributes of high weighted road density or low walkability (about 20 % each), and over 5 % of the population lived in "sour-spot" neighbourhoods with low walkability and high weighted road density (least desirable). These neighbourhoods were more distant from the city centre and scattered more widely. There were no linear trends between walkability/weighted road density and neighbourhood SES. CONCLUSIONS: Our walkability and weighted road density maps and associated analyses by SES can help identify neighbourhoods with inequalities in health-promoting or health-limiting environments. Planning agencies should seek out opportunities for increased neighbourhood walkability through improved urban development and transport planning, which simultaneously minimizes exposure to traffic related air pollution.

The Health Effects of 72 Hours of Simulated Wind Turbine Infrasound: A Double-Blind Randomized Crossover Study in Noise-Sensitive, Healthy Adults.

BACKGROUND: Large electricity-generating wind turbines emit both audible sound and inaudible infrasound at very low frequencies that are outside of the normal human range of hearing. Sufferers of wind turbine syndrome (WTS) have attributed their ill-health and particularly their sleep disturbance to the signature pattern of infrasound. Critics have argued that these symptoms are psychological in origin and are attributable to nocebo effects. OBJECTIVES: We aimed to test the effects of 72 h of infrasound (1.6-20 Hz at a sound level of ∼90 dB pk re 20µPa, simulating a wind turbine infrasound signature) exposure on human physiology, particularly sleep. METHODS: We conducted a randomized double-blind triple-arm crossover laboratory-based study of 72 h exposure with a >10-d washout conducted in a noise-insulated sleep laboratory in the style of a studio apartment. The exposures were infrasound (∼90 dB pk), sham infrasound (same speakers not generating infrasound), and traffic noise exposure [active control; at a sound pressure level of 40-50 dB LAeq,night and 70 dB LAFmax transient maxima, night (2200 to 0700 hours)]. The following physiological and psychological measures and systems were tested for their sensitivity to infrasound: wake after sleep onset (WASO; primary outcome) and other measures of sleep physiology, wake electroencephalography, WTS symptoms, cardiovascular physiology, and neurobehavioral performance. RESULTS: We randomized 37 noise-sensitive but otherwise healthy adults (18-72 years of age; 51% female) into the study before a COVID19-related public health order forced the study to close. WASO was not affected by infrasound compared with sham infrasound (-1.36 min; 95% CI: -6.60, 3.88, p=0.60) but was worsened by the active control traffic exposure compared with sham by 6.07 min (95% CI: 0.75, 11.39, p=0.02). Infrasound did not worsen any subjective or objective measures used. DISCUSSION: Our findings did not support the idea that infrasound causes WTS. High level, but inaudible, infrasound did not appear to perturb any physiological or psychological measure tested in these study participants. https://doi.org/10.1289/EHP10757.

Redistribution of traffic related air pollution associated with a new road tunnel.

The aim of this study was to assess the effect of a new road tunnel on the concentration and distribution of traffic-related air pollution (TRAP), specifically nitrogen dioxide (NO(2)) and particulate matter (PM), and to determine its relationship to change in traffic flow. We used continuously recorded data from four monitoring stations at nonroadside locations within the study area and three regional monitors outside the area. The four monitors in the study area were in background locations where smaller pollutant changes were expected compared with changes near the bypassed main road. We also deployed passive samplers to assess finer spatial variability in NO(2) including application of a land use regression model (LUR). The study was conducted from 2006 to 2008. Analysis of the continuously recorded data showed that the tunnel intervention did not lead to consistent reductions in NO(2) or PM over the wider study area. However, there were significant decreases in NO(2), NO(x), and PM(10) in the eastern section of the study area. Analysis of passive sampler data indicated that the greatest reductions in NO(2) concentrations occurred within 100 m of the bypassed main road. The LUR model also demonstrated that changes in NO(2) were most marked adjacent to the bypassed main road. These findings support the use of methods that highlight fine spatial variability in TRAP and demonstrate the utility of traffic interventions in reducing air pollution exposures for populations living close to main roads.

Associations Between Ambient Particulate Air Pollution and Cognitive Function in Indonesian Children Living in Forest Fire-Prone Provinces.

Smoke from forest fires can reach hazardous levels for extended periods of time. We aimed to determine if there is an association between particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) and living in a forest fire-prone province and cognitive function. We used data from the Indonesian Family and Life Survey. Cognitive function was assessed by the Ravens Colored Progressive Matrices (RCPM). We used regression models to estimate associations between PM2.5 and living in a forest fire-prone province and cognitive function. In multivariable models, we found very small positive relationships between PM2.5 levels and RCPM scores (PM2.5 level at year of survey: ß = 0.1%; 95% confidence interval (CI) [0.01, 0.19%]). There were no differences in RCPM scores for children living in forest fire-prone provinces compared with children living in non-forest fire-prone provinces (mean difference = -1.16%, 95% CI [-2.53, 0.21]). RCPM scores were lower for children who had lived in a forest fire-prone province all their lives compared with children who lived in a non-forest fire-prone province all their life (ß = -1.50%; 95% CI [-2.94, -0.07]). Living in a forest fire-prone province for a prolonged period of time negatively affected cognitive scores after adjusting for individual factors.

Effects of tap water processing on the concentration of disinfection by-products.

AIM: This study examined the effects on disinfection by-product (DBP) concentrations of common household methods for processing drinking water. METHODS: We investigated the effects of refrigerator storage, jug filtering, boiling in an electric kettle, and supply from an instant boiling water unit, with or without filtering, on four species of trihalomethanes (THMs) and nine species of haloacetic acids (HAAs) in water ready for consumption in Sydney, Australia. Water samples were processed in such a way as to simulate real life conditions for drinking filtered water or hot water drinks prepared from tap water drawn from public water supply systems. RESULTS: There was a large reduction in total THMs in kettle-boiled water, instant boiled water, jug-filtered water and instant boiled-filtered water (reductions of 85.8, 93.5, 92.6 and 87.8% of their concentration in tap water respectively). Refrigerator storage did not appear to have a consequential effect on THMs or HAAs. Jug-filtering and instant boiling and filtering resulted in large decreases (77-94%) in all species of HAAs in tap water. CONCLUSION: This study suggests that different methods of processing tap water can change DBP concentration to an extent that would have a meaningful impact on exposure assessment in epidemiological studies.

Policy Implications for Protecting Health from the Hazards of Fire Smoke. A Panel Discussion Report from the Workshop Landscape Fire Smoke: Protecting Health in an Era of Escalating Fire Risk.

Globally, and nationally in Australia, bushfires are expected to increase in frequency and intensity due to climate change. To date, protection of human health from fire smoke has largely relied on individual-level actions. Recent bushfires experienced during the Australian summer of 2019-2020 occurred over a prolonged period and encompassed far larger geographical areas than previously experienced, resulting in extreme levels of smoke for extended periods of time. This particular bushfire season resulted in highly challenging conditions, where many people were unable to protect themselves from smoke exposures. The Centre for Air pollution, energy and health Research (CAR), an Australian research centre, hosted a two-day symposium, Landscape Fire Smoke: Protecting health in an era of escalating fire risk, on 8 and 9 October 2020. One component of the symposium was a dedicated panel discussion where invited experts were asked to examine alternative policy settings for protecting health from fire smoke hazards with specific reference to interventions to minimise exposure, protection of outdoor workers, and current systems for communicating health risk. This paper documents the proceedings of the expert panel and participant discussion held during the workshop.

Associations between long-term exposure to ambient air pollution and Parkinson's disease prevalence: A cross-sectional study.

BACKGROUND: Epidemiological studies have reported contradictory results regarding the effects of ambient air pollution on Parkinson's disease (PD). This study investigated the associations between long-term exposure to particulate matter <2.5 µm in diameter (PM2.5) and nitrogen dioxide (NO2) and PD among participants in the 45 and Up Study, which comprised adults older than 45 years living in New South Wales, Australia. METHODS: We conducted a cross-sectional analysis of long-term exposure to PM2.5 and NO2 concentrations and prevalence of PD using data from around 240,000 cohort members from the 45 and Up Study, NSW. Annual average concentrations of NO2 and PM2.5 were estimated at the participants' residential address using satellite-based land use regression models. Logistic regression was used to quantify the associations between these pollutants and ever physician-diagnosed PD, after adjusting for a range of individual- and area-level covariates. RESULTS: Among the 236,390 participants with complete data, 1,428 (0.6%) reported physician-diagnosed PD. Annual mean PM2.5 and NO2 concentrations for the cohort were 5.8 and 11.9 µg m-3, respectively, and were positively, but not statistically significantly associated with PD. The odds ratio for a 1 µg m-3 increase in PM2.5 was 1.01 (95% confidence interval (CI): 0.98-1.04). The adjusted odds ratio for a 5 µg m-3 increase in NO2 was 1.03 (95% CI: 0.98-1.08). In subgroup analyses, larger associations for NO2 were observed among past smokers (OR 1.11 (95% CI: 1.02-1.20) per 5 µg m-3 increase). CONCLUSIONS: Overall, we found limited evidence of associations between long-term exposure to NO2 or PM2.5 and PD. The associations observed among past smokers require further corroboration.

All-cause mortality and long-term exposure to low level air pollution in the '45 and up study' cohort, Sydney, Australia, 2006-2015.

BACKGROUND: Epidemiological studies show that long-term exposure to ambient air pollution reduces life expectancy. Most studies have been in environments with relatively high concentrations such as North America, Europe and Asia. Associations at the lower end of the concentration-response function are not well defined. OBJECTIVES: We assessed associations between all-cause mortality and exposure to annual average particulate matter <2.5 µm (PM2.5) and nitrogen dioxide (NO2) in Sydney, Australia, where concentrations are relatively low. METHODS: The '45 and Up Study' comprises a prospective longitudinal cohort from the state of New South Wales, Australia with 266,969 participants linked to death registry data. We analyzed data for the participants who resided in Sydney at baseline questionnaire (n = 75,268). Exposures to long-term pollution were estimated using annual averages from a chemical transport model (PM2.5), and a satellite-based land-use regression model (NO2). Socio-demographic information was extracted from the baseline questionnaire. Cox proportional hazard models were applied to estimate associations, while adjusting for covariates. RESULTS: In our cohort mean annual PM2.5 was 4.5 µg/m3 and mean NO2 was 17.8 µg/m3. The mortality rate was 4.4% over the 7 years of follow up. Models that adjusted for individual-level and area-level risk factors resulted in a detrimental non statistically significant hazard ratio (HR) of 1.05 (95% CI: 0.98-1.12) per 1 µg/m3 increase in PM2.5, and 1.03 (95% CI: 0.98-1.07) per 5 µg/m3 increase in NO2. CONCLUSIONS: We found evidence that low-level air pollution exposure was associated with increased risk of mortality in this cohort of adults aged 45 years and over, even at the relatively low concentrations seen in Sydney. However, a clear determination of the association with mortality is difficult because the results were sensitive to some covariates. Our findings are supportive of emerging evidence that exposure to low levels of air pollution reduces life expectancy.

A Comparison of the Health Effects of Ambient Particulate Matter Air Pollution from Five Emission Sources.

This article briefly reviews evidence of health effects associated with exposure to particulate matter (PM) air pollution from five common outdoor emission sources: traffic, coal-fired power stations, diesel exhaust, domestic wood combustion heaters, and crustal dust. The principal purpose of this review is to compare the evidence of health effects associated with these different sources with a view to answering the question: Is exposure to PM from some emission sources associated with worse health outcomes than exposure to PM from other sources? Answering this question will help inform development of air pollution regulations and environmental policy that maximises health benefits. Understanding the health effects of exposure to components of PM and source-specific PM are active fields of investigation. However, the different methods that have been used in epidemiological studies, along with the differences in populations, emission sources, and ambient air pollution mixtures between studies, make the comparison of results between studies problematic. While there is some evidence that PM from traffic and coal-fired power station emissions may elicit greater health effects compared to PM from other sources, overall the evidence to date does not indicate a clear ‘hierarchy’ of harmfulness for PM from different emission sources. Further investigations of the health effects of source-specific PM with more advanced approaches to exposure modeling, measurement, and statistics, are required before changing the current public health protection approach of minimising exposure to total PM mass.

Long-term exposure to low concentrations of air pollutants and hospitalisation for respiratory diseases: A prospective cohort study in Australia.

BACKGROUND: Short- and long-term spatiotemporal variation in exposure to air pollution is associated with respiratory morbidity in areas with moderate-to-high level of air pollution, but very few studies have examined whether these associations also exist in areas with low level exposure. OBJECTIVES: We assessed the association between spatial variation in long-term exposure to PM2.5 and NO2 and hospitalisation for all respiratory diseases, asthma, chronic obstructive pulmonary disease (COPD), and pneumonia, in older adults residing in Sydney, Australia, a city with low-level concentrations. METHODS: We recorded data on hospitalisations for 100,084 participants, who were aged >45 years at entry in 2006-2009 until June 2014. Annual NO2 and PM2.5 concentrations were estimated for the participants' residential addresses and Cox proportional hazards regression was used to model the association between exposure to air pollutants and first episode of hospitalisation, controlling for personal and area level covariates. We further investigated the shape of the exposure-response association and potential effect modification by age, sex, education level, smoking status, and BMI. RESULTS: NO2 and PM2.5 annual mean exposure estimates were 17.5 µg·m-3 and 4.5 µg·m-3 respectively. NO2 and PM2.5 was positively, although not significantly, associated with asthma. The adjusted hazard ratio for a 1 µg·m-3 increase in PM2.5 was 1.08, 95% confidence interval 0.89-1.30. The adjusted hazard ratio for a 5 µg·m-3 increase in NO2 was 1.03, 95% confidence interval 0.88-1.19. We found no positive statistically significant associations with hospitalisation for all respiratory diseases, and pneumonia while negative associations were observed with COPD. CONCLUSIONS: We found weak positive associations of exposure to air pollution with hospitalisation for asthma while there was no evidence of an association for all respiratory diseases.

Weighted road density and allergic disease in children at high risk of developing asthma.

BACKGROUND: Evidence for an association between traffic-related air pollution and allergic disease is inconsistent, possibly because the adverse effects may be limited to susceptible subgroups and these have not been identified. This study examined children in the Childhood Asthma Prevention Study (CAPS), potentially susceptible to air pollution effects because of a family history of asthma. METHODS: We examined cross-sectional associations at age eight years between road density within 75 m and 50 m of home address weighted by road type (traffic density), as a proxy for traffic-related air pollution, on the following allergic and respiratory outcomes: skin prick tests (SPTs), total and specific serum IgE, pre- and post-bronchodilator lung function, airway hyperresponsiveness, exhaled NO, and reported asthma and rhinitis. RESULTS: Weighted road density was positively associated with allergic sensitisation and allergic rhinitis. Adjusted relative risk (RR) for house dust mite (HDM) positive SPT was 1.25 (95% CI: 1.06-1.48), for detectable house dust mite-specific IgE was 1.19 (95% CI: 1.01-1.41) and for allergic rhinitis was 1.30 (95% CI: 1.03-1.63) per 100 m local road or 33.3 m motorway within 50 m of home. Associations were also seen with small decrements of peak and mid-expiratory flows and increased risk of asthma, current wheeze and rhinitis in atopic children. CONCLUSION: Associations between road density and allergic disease were found in a potentially susceptible subgroup of children at high risk of developing atopy and asthma.