Air pollution dispersion models for human exposure predictions in London

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London

Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.

Investigating the impact of London's ultra low emission zone on children's health: children's health in London and Luton (CHILL) protocol for a prospective parallel cohort study

BACKGROUND

Air pollution harms health across the life course. Children are at particular risk of adverse effects during development, which may impact on health in later life. Interventions that improve air quality are urgently needed both to improve public health now, and prevent longer-term increased vulnerability to chronic disease. Low Emission Zones are a public health policy intervention aimed at reducing traffic-derived contributions to urban air pollution, but evidence that they deliver health benefits is lacking. We describe a natural experiment study (CHILL: Children's Health in London and Luton) to evaluate the impacts of the introduction of London's Ultra Low Emission Zone (ULEZ) on children's health.

METHODS

CHILL is a prospective two-arm parallel longitudinal cohort study recruiting children at age 6-9 years from primary schools in Central London (the focus of the first phase of the ULEZ) and Luton (a comparator site), with the primary outcome being the impact of changes in annual air pollutant exposures (nitrogen oxides [NOx], nitrogen dioxide [NO2], particulate matter with a diameter of less than 2.5micrograms [PM2.5], and less than 10 micrograms [PM10]) across the two sites on lung function growth, measured as post-bronchodilator forced expiratory volume in one second (FEV1) over five years. Secondary outcomes include physical activity, cognitive development, mental health, quality of life, health inequalities, and a range of respiratory and health economic data.

DISCUSSION

CHILL's prospective parallel cohort design will enable robust conclusions to be drawn on the effectiveness of the ULEZ at improving air quality and delivering improvements in children's respiratory health. With increasing proportions of the world's population now living in large urban areas exceeding World Health Organisation air pollution limit guidelines, our study findings will have important implications for the design and implementation of Low Emission and Clean Air Zones in the UK, and worldwide.

CLINICALTRIALS

GOV: NCT04695093 (05/01/2021).

Quantifying the change of brake wear particulate matter emissions through powertrain electrification in passenger vehicles

With vehicle fleets transitioning from internal combustion engines (ICE) to electric powertrains, we have used friction brake power simulations, for different vehicle classes and driving styles, to predict the impact of regenerative braking systems (RBS) on brake wear particulate matter emissions (PM10 and PM2.5). Under the same powertrain, subcompact (SC) vehicles were predicted to require between 38 and 68% less friction brake power than heavier sports utility vehicles (L-SUV). However, despite electric and hybrid vehicles being heavier than ICE vehicles, the results show that RBS would reduce brake wear by between 64 and 95%. The study highlights the effect of aggressive braking on the amount of friction brake power required, with electric powertrains more likely to require friction braking to perform short, but aggressive braking compared with longer, slower braking events. Brake wear reductions varied under different driving conditions, as the level of mitigation depends on the complex interaction of several variables, including: vehicle speed, deceleration rate, regenerative braking technology and vehicle mass. Urban brake wear emission factors for electric powertrains ranged from 3.9 to 5.5 mg PM10/km and 1.5-2.1 mg PM2.5/km, providing an average reduction in PM emission factors of 68%. Rural and motorway driving conditions had lower brake wear emission factors, with plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV) emitting negligible PM10 and PM2.5 brake wear. Although electric powertrain uptake, vehicle mileage driven and driving styles are dependent upon national policies and strategies, by 2035, we project that total UK brake wear PM emissions would reduce by up to 39% compared with 2020 levels. This analysis supports the transition towards electric and hybrid vehicle fleets to reduce brake wear emissions, however increases in tyre wear, road wear, and resuspension due to increased vehicle mass may offset these benefits.

Can the UK meet the World Health Organization PM2.5 interim target of 10 μg m-3 by 2030?

The recent United Kingdom (UK) Environment Act consultation had the intention of setting two targets for PM2.5 (particles with an aerodynamic diameter less than 2.5 μm), one related to meeting an annual average concentration and the second to reducing population exposure. As part of the consultation, predictions of PM2.5 concentrations in 2030 were made by combining European Union (EU) and UK government's emissions forecasts, with the Climate Change Committee's (CCC) Net Zero vehicle forecasts, and in London with the addition of local policies based on the London Environment Strategy (LES). Predictions in 2018 showed 6.4% of the UK's area and 82.6% of London's area had PM2.5 concentrations above the World Health Organization (WHO) interim target of 10 μg m-3, but by 2030, over 99% of the UK's area was predicted to be below it. However, kerbside concentrations in London and other major cities were still at risk of exceeding 10 μg m-3. With local action on PM2.5 in London, population weighted concentrations showed full compliance with the WHO interim target of 10 μg m-3 in 2030. However, predicting future PM2.5 concentrations and interpreting the results will always be difficult and uncertain for many reasons, such as imperfect models and the difficulty in estimating future emissions. To help understand the sensitivity of the model's PM2.5 predictions in 2030, current uncertainty was quantified using PM2.5 measurements and showed large areas in the UK that were still at risk of exceeding the WHO interim target despite the model predictions being below 10 μg m-3. Our results do however point to the benefits that policy at EU, UK and city level can have on achieving the WHO interim target of 10 μg m-3. These results were submitted to the UK Environment Act consultation. Nevertheless, the issues addressed here could be applicable to other European cities.

Associations between air pollution and mental health service use in dementia: a retrospective cohort study

BACKGROUND

Little is known about the role of air pollution in how people with dementia use mental health services.

OBJECTIVE

We examined longitudinal associations between air pollution exposure and mental health service use in people with dementia.

METHODS

In 5024 people aged 65 years or older with dementia in South London, high resolution estimates of nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) levels in ambient air were linked to residential addresses. Associations between air pollution and Community Mental Health Team (CMHT) events (recorded over 9 years) were examined using negative binomial regression models. Cognitive function was measured using the Mini Mental State Examination (MMSE) and health and social functioning was measured using the Health of the Nation Outcomes Scale (HoNOS65+). Associations between air pollution and both MMSE and HoNOS65+ scores were assessed using linear regression models.

FINDINGS

In the first year of follow-up, increased exposure to all air pollutants was associated with an increase in the use of CMHTs in a dose-response manner. These associations were strongest when we compared the highest air pollution quartile (quartile 4: Q4) with the lowest quartile (Q1) (eg, NO2: adjusted incidence rate ratio (aIRR) 1.27, 95% CI 1.11 to 1.45, p<0.001). Dose-response patterns between PM2.5 and CMHT events remained at 5 and 9 years. Associations were strongest for patients with vascular dementia. NO2 levels were linked with poor functional status, but not cognitive function.

CONCLUSIONS

Residential air pollution exposure is associated with increased CMHT usage among people with dementia.

CLINICAL IMPLICATIONS

Efforts to reduce pollutant exposures in urban settings might reduce the use of mental health services in people with dementia, freeing up resources in already considerably stretched psychiatric services.

Does It Measure Up? A Comparison of Pollution Exposure Assessment Techniques Applied across Hospitals in England

Weighted averages of air pollution measurements from monitoring stations are commonly assigned as air pollution exposures to specific locations. However, monitoring networks are spatially sparse and fail to adequately capture the spatial variability. This may introduce bias and exposure misclassification. Advanced methods of exposure assessment are rarely practicable in estimating daily concentrations over large geographical areas. We propose an accessible method using temporally adjusted land use regression models (daily LUR). We applied this to produce daily concentration estimates for nitrogen dioxide, ozone, and particulate matter in a healthcare setting across England and compared them against geographically extrapolated measurements (inverse distance weighting) from air pollution monitors. The daily LUR estimates outperformed IDW. The precision gains varied across air pollutants, suggesting that, for nitrogen dioxide and particulate matter, the health effects may be underestimated. The results emphasised the importance of spatial heterogeneity in investigating the societal impacts of air pollution, illustrating improvements achievable at a lower computational cost.

Associations between air pollutants and blood pressure in an ethnically diverse cohort of adolescents in London, England

Longitudinal evidence on the association between air pollution and blood pressure (BP) in adolescence is scarce. We explored this association in an ethnically diverse cohort of schoolchildren. Sex-stratified, linear random-effects modelling was used to examine how modelled residential exposure to annual average nitrogen dioxide (NO2), particulate matter (PM2.5, PM10) and ozone (O3), measures in μg/m3, associated with blood pressure. Estimates were based on 3,284 adolescents; 80% from ethnic minority groups, recruited from 51 schools, and followed up from 11-13 to 14-16 years old. Ethnic minorities were exposed to higher modelled annual average concentrations of pollution at residential postcode level than their White UK peers. A two-pollutant model (NO2 & PM2.5), adjusted for ethnicity, age, anthropometry, and pubertal status, highlighted associations with systolic, but not diastolic BP. A μg/m3 increase in NO2 was associated with a 0.30 mmHg (95% CI 0.18 to 0.40) decrease in systolic BP for girls and 0.19 mmHg (95% CI 0.07 to 0.31) decrease in systolic BP for boys. In contrast, a 1 μg/m3 increase in PM2.5 was associated with 1.34 mmHg (95% CI 0.85 to 1.82) increase in systolic BP for girls and 0.57 mmHg (95% CI 0.04 to 1.03) increase in systolic BP for boys. Associations did not vary by ethnicity, body size or socio-economic advantage. Associations were robust to adjustments for noise levels and lung function at 11-13 years. In summary, higher ambient levels of NO2 were associated with lower and PM2.5 with higher systolic BP across adolescence, with stronger associations for girls.

Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants

For the monitoring of urban air pollution, smart sensors are often seen as a welcome addition to fixed-site monitoring (FSM) networks. Due to price and simple installation, increases in spatial representation are thought to be achieved by large numbers of these sensors, however, a number of sensor errors have been identified. Based on a high-resolution modelling system, up to 400 pseudo smart sensors were perturbated with the aim of simulating common sensor errors and added to the existing FSM network in Hong Kong, resulting in 1200 pseudo networks for PM_2.5 and 1040 pseudo networks for NO₂. For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM_2.5, improvements (up to 16%) to the high baseline representativeness (PWAR = 0.74) were achievable only by the addition of high-quality sensors and favourable environmental conditions. The baseline FSM network represents NO₂ less well (PWAR = 0.52), as local emissions in the study domain resulted in high spatial pollution variation. Due to higher levels of pollution (population-weighted average 37.3 ppb) in comparison to sensor error ranges, smart sensors of a wider quality range were able to improve network representativeness (up to 42%). Marginal representativeness increases were found to exponentially decrease with existing sensor number. The quality and maintenance of added sensors had a stronger effect on overall network representativeness than the number of sensors added. Often, a small number of added sensors of a higher quality class led to larger improvements than hundreds of lower-class sensors. Whereas smart sensor performance and maintenance are important prerequisites particularly for developed cities where pollutant concentration is low and there is an existing FSM network, our study shows that for places with high pollutant variability and concentration such as encountered in some developing countries, smart sensors will provide benefits for understanding population exposure.

Towards healthy school neighbourhoods: A baseline analysis in Greater London

Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m (5-10 min walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO₂ and PM_2.5), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The neighbourhood of the most deprived cluster of schools had a median of 2.5 ha greenspace, 29.0 µg/m³ of NO₂, 19.3 µg/m³ of PM_2.5, 20 fast food retailers, and five child pedestrian crashes over a three-year period. The neighbourhood of the least deprived cluster of schools had a median of 21.8 ha greenspace, 15.6 µg/m³ of NO₂, 15.1 µg/m³ of PM_2.5, 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.

A Review of Road Traffic-Derived Non-Exhaust Particles: Emissions, Physicochemical Characteristics, Health Risks, and Mitigation Measures

Implementation of regulatory standards has reduced exhaust emissions of particulate matter from road traffic substantially in the developed world. However, nonexhaust particle emissions arising from the wear of brakes, tires, and the road surface, together with the resuspension of road dust, are unregulated and exceed exhaust emissions in many jurisdictions. While knowledge of the sources of nonexhaust particles is fairly good, source-specific measurements of airborne concentrations are few, and studies of the toxicology and epidemiology do not give a clear picture of the health risk posed. This paper reviews the current state of knowledge, with a strong focus on health-related research, highlighting areas where further research is an essential prerequisite for developing focused policy responses to nonexhaust particles.

Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)

This paper provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of the agents and parameters, as well as provide the codes alongside the descriptions to avoid possible ambiguity. The model's main parameters are thoroughly tested through sensitivity experiments and are calibrated with the city's air pollution monitoring networks. This paper also provides the instructions to the model, possible artefacts, and the configurations to submit the model on the HPC cluster.•An ODD protocol is used to document the agent-based model TRAPSim.•Sensitivity experiments and calibration are explained.•The step-by-step codes and annotations are attached in the protocol and HPC sections.

Association between air pollution exposure and mental health service use among individuals with first presentations of psychotic and mood disorders: retrospective cohort study

BACKGROUND

Growing evidence suggests that air pollution exposure may adversely affect the brain and increase risk for psychiatric disorders such as schizophrenia and depression. However, little is known about the potential role of air pollution in severity and relapse following illness onset.

AIMS

To examine the longitudinal association between residential air pollution exposure and mental health service use (an indicator of illness severity and relapse) among individuals with first presentations of psychotic and mood disorders.

METHOD

We identified individuals aged ≥15 years who had first contact with the South London and Maudsley NHS Foundation Trust for psychotic and mood disorders in 2008-2012 (n = 13 887). High-resolution (20 × 20 m) estimates of nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM2.5 and PM10) levels in ambient air were linked to residential addresses. In-patient days and community mental health service (CMHS) events were recorded over 1-year and 7-year follow-up periods.

RESULTS

Following covariate adjustment, interquartile range increases in NO2, NOx and PM2.5 were associated with 18% (95% CI 5-34%), 18% (95% CI 5-34%) and 11% (95% CI 3-19%) increased risk for in-patient days after 1 year. Similarly, interquartile range increases in NO2, NOx, PM2.5 and PM10 were associated with 32% (95% CI 25-38%), 31% (95% CI 24-37%), 7% (95% CI 4-11%) and 9% (95% CI 5-14%) increased risk for CMHS events after 1 year. Associations persisted after 7 years.

CONCLUSIONS

Residential air pollution exposure is associated with increased mental health service use among people recently diagnosed with psychotic and mood disorders. Assuming causality, interventions to reduce air pollution exposure could improve mental health prognoses and reduce healthcare costs.

Air pollution and trajectories of adolescent conduct problems: the roles of ethnicity and racism; evidence from the DASH longitudinal study

PURPOSE

No known UK empirical research has investigated prospective associations between ambient air pollutants and conduct problems in adolescence. Ethnic minority children are disproportionately exposed to structural factors that could moderate any observed relationships. This prospective study examined whether exposure to PM2.5 and NO2 concentrations is associated with conduct problems in adolescence, and whether racism or ethnicity moderate such associations.

METHODS

Longitudinal associations between annual mean estimated PM2.5 and NO2 concentrations at the residential address and trajectories of conduct problems, and the potential influence of racism and ethnicity were examined school-based sample of 4775 participants (2002-2003 to 2005-2006) in London, using growth curve models.

RESULTS

Overall, in the fully adjusted model, exposure to lower concentrations of PM2.5 and NO2 was associated with a decrease in conduct problems during adolescence, while exposure to higher concentrations was associated with a flattened trajectory of conduct symptoms. Racism amplified the effect of PM2.5 (β = 0.05 (95% CI 0.01 to 0.10, p < 0.01)) on adolescent trajectories of conduct problems over time. At higher concentrations of PM2.5, there was a divergence of trajectories of adolescent conduct problems between ethnic minority groups, with White British and Black Caribbean adolescents experiencing an increase in conduct problems over time.

CONCLUSION

These findings suggest that the intersections between air pollution, ethnicity, and racism are important influences on the development of conduct problems in adolescence.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

PURPOSE OF REVIEW

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.

RECENT FINDINGS

Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

A proposed population-health based metric for evaluating representativeness of air quality monitoring in cities: Using Hong Kong as a demonstration

City air quality monitoring (AQM) network are typically sparsely distributed due to high operation costs. It is of the question of how well it can reflect public health risks to air pollution given the diversity and heterogeneity in pollution, and spatial variations in population density. Combing high-resolution air quality model, spatial population distribution and health risk factors, we proposed a population-health based metric for AQM representativeness. This metric was demonstrated in Hong Kong using hourly modelling data of PM10, PM2.5, NO2 and O3 in 2019 with grid cells of 45m * 48m. Individual and total hospital admission risks (%AR) of these pollutants were calculated for each cell, and compared with those calculated at 16 monitoring sites using the similarity frequency (SF) method. AQM Representativeness was evaluated by SF and a population-health based network representation index (PHNI), which is population-weighted SF over the study-domain. The representativeness varies substantially among sites as well as between population- and area-based evaluation methods, reflecting heterogeneity in pollution and population. The current AQM network reflects population health risks well for PM10 (PHNI = 0.87) and PM2.5 (PHNI = 0.82), but is less able to represent risks for NO2 (PHNI = 0.59) and O3 (PHNI = 0.78). Strong seasonal variability in PHNI was found for PM, increasing by >11% during autumn and winter compared to summer due to regional transport. NO2 is better represented in urban than rural, reflecting the heterogeneity of urban traffic pollution. Combined health risk (%ARtotal) is well represented by the current AQM network (PHNI = 1), which is more homogenous due to the dominance and anti-correlation of NO2 and O3 related %AR. The proposed PHNI metric is useful to compare the health risk representativeness of AQM for individual and multiple pollutants and can be used to compare the effectiveness of AQM across cities.

Road Verge Vegetation and the Capture of Particulate Matter Air Pollution

Urban air quality is considered a major issue in cities worldwide, with particulate matter (PM) recognised as one of the most harmful pollutants regarding human health. The use of plants to act as air filters and immobilise PM has been identified as a potential method to improve the air quality in these areas. The majority of the work has focused on trees, with the application of shrub and herbaceous species largely overlooked. Two contrasting leaf morphologies from a shrub and herbaceous plant species were sampled at four locations across Southampton (UK), from varying traffic conditions. Samples were analysed for the mass of PM captured, particle size, and elemental composition. These analyses were used to characterise the different sites and the plants’ effectiveness at immobilisation of PM. Captured PM mass was shown to be directly related to traffic density, with greater traffic density leading to higher levels of captured PM. PM origins were attributed to emissions from vehicles and the resuspension of particles by vehicle movement. The bulk of the PM mass was shown to originate from natural, crustal sources including large proportions of Al, Si, and/or Ca. Increases in elements from anthropogenic enhancement (such as Fe and Zn) were related to high traffic density. Particle size analysis identified that, despite the use of standard leaf-washing protocols with a final 2.5 µm filter, PM was dominated by fine particles (<2.5 µm physical diameter), with particles >10 µm rare. Bramble leaves were calculated to have a species-specific deposition velocity 0.51 cm s−1 greater than ivy, with deposition velocities calculated at 1.8 and 1.3 cm s−1 for ivy and 2.3 and 1.8 cm s−1 for bramble at Redbridge Road and Brinton’s Road, respectively. These values can allow for the more accurate modelling and estimation of the PM removal abilities of these plants.

What is new in the exposome?

The exposome concept refers to the totality of exposures from a variety of external and internal sources including chemical agents, biological agents, or radiation, from conception onward, over a complete lifetime. It encompasses also "psychosocial components" including the impact of social relations and socio-economic position on health. In this review we provide examples of recent contributions from exposome research, where we believe their application will be of the greatest value for moving forward. So far, environmental epidemiology has mainly focused on hard outcomes, such as mortality, disease exacerbation and hospitalizations. However, there are many subtle outcomes that can be related to environmental exposures, and investigations can be facilitated by an improved understanding of internal biomarkers of exposure and response, through the application of omic technologies. Second, though we have a wealth of studies on environmental pollutants, the assessment of causality is often difficult because of confounding, reverse causation and other uncertainties. Biomarkers and omic technologies may allow better causal attribution, for example using instrumental variables in triangulation, as we discuss here. Even more complex is the understanding of how social relationships (in particular socio-economic differences) influence health and imprint on the fundamental biology of the individual. The identification of molecular changes that are intermediate between social determinants and disease status is a way to fill the gap. Another field in which biomarkers and omics are relevant is the study of mixtures. Epidemiology often deals with complex mixtures (e.g. ambient air pollution, food, smoking) without fully disentangling the compositional complexity of the mixture, or with rudimentary approaches to reflect the overall effect of multiple exposures or components. From the point of view of disease mechanisms, most models hypothesize that several stages need to be transitioned through health to the induction of disease, but very little is known about the characteristics and temporal sequence of such stages. Exposome models reinforce the idea of a biography-to-biology transition, in that everyone's disease is the product of the individual history of exposures, superimposed on their underlying genetic susceptibilities. Finally, exposome research is facilitated by technological developments that complement traditional epidemiological study designs. We describe in depth one such new tools, adductomics. In general, the development of high-resolution and high-throughput technologies interrogating multiple -omics (such as epigenomics, transcriptomics, proteomics, adductomics and metabolomics) yields an unprecedented perspective into the impact of the environment in its widest sense on disease. The world of the exposome is rapidly evolving, though a huge gap still needs to be filled between the original expectations and the concrete achievements. Perhaps the most urgent need is for the establishment of a new generation of cohort studies with appropriately specified biosample collection, improved questionnaire data (including social variables), and the deployment of novel technologies that allow better characterization of individual environmental exposures, ranging from personal monitoring to satellite based observations.

Airway dendritic cell maturation in children exposed to air pollution

Urban particulate matter (PM) enhances airway dendritic cell (DC) maturation in vitro. However, to date, there are no data on the association between exposure to urban PM and DC maturation in vivo. We sought to determine whether exposure of school-age children (8 to 14 y) to PM was associated with expression of CD86, a marker of maturation of airway conventional DCs (cDC). Healthy London school children underwent spirometry and sputum induction. Flow cytometry was used to identify CD86 and CCR7 expression on cDC subsets (CD1c+ cDC2 and CD141+ cDC1). Tertiles of mean annual exposure to PM ≤ 10 microns (PM10) at the school address were determined using the London Air Quality Toolkit model. Tertiles of exposure from the 409 children from 19 schools recruited were; lower (23.1 to 25.6 μg/m3, n = 138), middle (25.6 to 26.8 μg/m3, n = 126), and upper (26.8 to 31.0 μg/m3, n = 145). DC expression was assessed in 164/370 (44%) children who completed sputum induction. The proportion (%) of cDC expressing CD86 in the lower exposure tertile (n = 47) was lower compared with the upper exposure tertile (n = 49); (52% (44 to 70%) vs 66% (51 to 82%), p<0.05). There was a higher percentage of cDC1 cells in the lower tertile of exposure (6.63% (2.48 to 11.64) vs. 2.63% (0.72 to 7.18), p<0.05). Additionally; children in the lower exposure tertile had increased FEV1 compared with children in the upper tertile; (median z-score 0.15 (-0.59 to 0.75) vs. -0.21 (-0.86 to 0.48), p<0.05. Our data reveal that children attending schools in the highest areas of PM exposure in London exhibit increased numbers of "mature" airway cDCs, as evidenced by their expression of the surface marker CD86. This data is supportive of previous in vitro data demonstrating an alteration in the maturation of airway cDCs in response to exposure to pollutants.

An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London

A comprehensive modelling approach has been developed to predict population exposure to the ambient air PM_2.5 concentrations in different microenvironments in London. The modelling approach integrates air pollution dispersion and exposure assessment, including treatment of the locations and time activity of the population in three microenvironments, namely, residential, work and transport, based on national demographic information. The approach also includes differences between urban centre and suburban areas of London by taking account of the population movements and the infiltration of PM_2.5 from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM_2.5 at street scale for the year 2008, including both regional and urban contributions. Model analysis of the exposure in the three microenvironments shows that most of the total exposure, 85%, occurred at home and work microenvironments and 15% in the transport microenvironment. However, the annual population weighted mean (PWM) concentrations of PM_2.5 for London in transport microenvironments were almost twice as high (corresponding to 13-20 μg/m³) as those for home and work environments (7-12 μg/m³). Analysis has shown that the PWM PM_2.5 concentrations in central London were almost 20% higher than in the surrounding suburban areas. Moreover, the population exposure in the central London per unit area was almost three times higher than that in suburban regions. The exposure resulting from all activities, including outdoor to indoor infiltration, was about 20% higher, when compared with the corresponding value obtained assuming inside home exposure for all times. The exposure assessment methodology used in this study predicted approximately over one quarter (-28%) lower population exposure, compared with using simply outdoor concentrations at residential locations. An important implication of this study is that for estimating population exposure, one needs to consider the population movements, and the infiltration of pollution from outdoors to indoors.

High resolution vehicular PM10 emissions over megacity Delhi: Relative contributions of exhaust and non-exhaust sources

Exposure to particulate matter (PM) from traffic can cause adverse health risks. Recent studies project an increase in non-exhaust emissions in the future despite a reduction in exhaust emissions. While there is a lot of research on exhaust emissions, the challenges remain to quantify non-exhaust emissions, especially in developing countries. In this work, an approach has been developed, and on-road vehicular non-exhaust PM emissions are estimated due to brake wear, tyre wear, road wear and resuspension, at very high resolution (100 m²) over an Indian megacity Delhi. Further, the relative contribution of non-exhaust emissions to the total vehicular emission was also calculated. The total PM10 emissions in megacity Delhi were 31.5 Gg/year, which is mainly dominated by the non-exhaust sources. The non-exhaust emissions were found to be six times (86%) of the exhaust emission (14%). The highest contribution to the total vehicular PM emission comes from the cars (34%) followed by buses (23%) and heavy commercial vehicles (HCVs, 17%), which is dominated by resuspension of dust. Cars and buses contribute less to exhaust emissions and more to non-exhaust emissions. Majors roads are the largest contributors to the total emissions in Delhi. The emissions from HCVs, diesel cars along with the other diesel vehicles result in diesel vehicles contributing more than the petrol vehicles to both exhaust and non-exhaust emissions. As India target to reduce PM pollution under the national clean air program, the current study will be useful to plan a suitable intervention to mitigate air pollution and associated health impacts.

Impacts of air pollution and noise on risk of preterm birth and stillbirth in London

BACKGROUND

Evidence for associations between ambient air pollution and preterm birth and stillbirth is inconsistent. Road traffic produces both air pollutants and noise, but few studies have examined these co-exposures together and none to date with all-cause or cause-specific stillbirths.

OBJECTIVES

To analyse the relationship between long-term exposure to air pollution and noise at address level during pregnancy and risk of preterm birth and stillbirth.

METHODS

The study population comprised 581,774 live and still births in the Greater London area, 2006-2010. Outcomes were preterm birth (<37 completed weeks gestation), all-cause stillbirth and cause-specific stillbirth. Exposures during pregnancy to particulate matter with diameter <2.5 μm (PM2.5) and <10 μm (PM10), ozone (O3), primary traffic air pollutants (nitrogen dioxide, nitrogen oxides, PM2.5 from traffic exhaust and traffic non-exhaust), and road traffic noise were estimated based on maternal address at birth.

RESULTS

An interquartile range increase in O3 exposure was associated with elevated risk of preterm birth (OR 1.15 95% CI: 1.11, 1.18, for both Trimester 1 and 2), all-cause stillbirth (Trimester 1 OR 1.17 95% CI: 1.07, 1.27; Trimester 2 OR 1.20 95% CI: 1.09, 1.32) and asphyxia-related stillbirth (Trimester 1 OR 1.22 95% CI: 1.01, 1.49). Odds ratios with the other air pollutant exposures examined were null or <1, except for primary traffic non-exhaust related PM2.5, which was associated with 3% increased odds of preterm birth (Trimester 1) and 7% increased odds stillbirth (Trimester 1 and 2) when adjusted for O3. Elevated risk of preterm birth was associated with increasing road traffic noise, but only after adjustment for certain air pollutant exposures.

DISCUSSION

Our findings suggest that exposure to higher levels of O3 and primary traffic non-exhaust related PM2.5 during pregnancy may increase risk of preterm birth and stillbirth; and a possible relationship between long-term traffic-related noise and risk of preterm birth. These findings extend and strengthen the evidence base for important public health impacts of ambient ozone, particulate matter and noise in early life.

Particulate matter accumulation capacity of plants in Hanoi, Vietnam

Population growth, urbanization, environmental conditions and rapid development have caused particulate matter (PM) levels to rise above all national and international health standards during the last two decades in many South-East Asian countries. These PM levels needs to be reduced urgently as they increase the risk of cardiovascular and respiratory health problems for millions of people. Plants have shown to efficiently reduce PM in the air by accumulation on their leaves. In order to investigate which plant species accumulate most PM, we screened 49 common plant species for their PM accumulation capacity in one of the tropical cities with the highest PM concentrations of the world, Hanoi (Vietnam). Using this subset of plants, we tested if certain leaf characteristics (leaf hydrophilicity, stomatal densities and the specific leaf area) can predict the PM accumulation efficiency of plant species. Our results show that the PM accumulation capacity varies substantially among species and that Muntingia calabura accumulated most PM in our subset of plants. We observed that plants with hydrophilic leaves, a low specific leaf area and a high abaxial stomatal density accumulated significantly more PM. Plants with these characteristics should be preferred by urban architects to reduce PM levels in tropical environments.

Association of Air Pollution Exposure With Psychotic Experiences During Adolescence

IMPORTANCE

Urbanicity is a well-established risk factor for clinical (eg, schizophrenia) and subclinical (eg, hearing voices and paranoia) expressions of psychosis. To our knowledge, no studies have examined the association of air pollution with adolescent psychotic experiences, despite air pollution being a major environmental problem in cities.

OBJECTIVES

To examine the association between exposure to air pollution and adolescent psychotic experiences and test whether exposure mediates the association between urban residency and adolescent psychotic experiences.

DESIGN, SETTING, AND PARTICIPANTS

The Environmental-Risk Longitudinal Twin Study is a population-based cohort study of 2232 children born during the period from January 1, 1994, through December 4, 1995, in England and Wales and followed up from birth through 18 years of age. The cohort represents the geographic and socioeconomic composition of UK households. Of the original cohort, 2066 (92.6%) participated in assessments at 18 years of age, of whom 2063 (99.9%) provided data on psychotic experiences. Generation of the pollution data was completed on October 4, 2017, and data were analyzed from May 4 to November 21, 2018.

EXPOSURES

High-resolution annualized estimates of exposure to 4 air pollutants-nitrogen dioxide (NO2), nitrogen oxides (NOx), and particulate matter with aerodynamic diameters of less than 2.5 (PM2.5) and less than 10 μm (PM10)-were modeled for 2012 and linked to the home addresses of the sample plus 2 commonly visited locations when the participants were 18 years old.

MAIN OUTCOMES AND MEASURES

At 18 years of age, participants were privately interviewed regarding adolescent psychotic experiences. Urbanicity was estimated using 2011 census data.

RESULTS

Among the 2063 participants who provided data on psychotic experiences, sex was evenly distributed (52.5% female). Six hundred twenty-three participants (30.2%) had at least 1 psychotic experience from 12 to 18 years of age. Psychotic experiences were significantly more common among adolescents with the highest (top quartile) level of annual exposure to NO2 (odds ratio [OR], 1.71; 95% CI, 1.28-2.28), NOx (OR, 1.72; 95% CI, 1.30-2.29), and PM2.5 (OR, 1.45; 95% CI, 1.11-1.90). Together NO2 and NOx statistically explained 60% of the association between urbanicity and adolescent psychotic experiences. No evidence of confounding by family socioeconomic status, family psychiatric history, maternal psychosis, childhood psychotic symptoms, adolescent smoking and substance dependence, or neighborhood socioeconomic status, crime, and social conditions occurred.

CONCLUSIONS AND RELEVANCE

In this study, air pollution exposure-particularly NO2 and NOx-was associated with increased odds of adolescent psychotic experiences, which partly explained the association between urban residency and adolescent psychotic experiences. Biological (eg, neuroinflammation) and psychosocial (eg, stress) mechanisms are plausible.

An ultra-spatially resolved method to quali-quantitative monitor particulate matter in urban environment

Monitoring the amount and composition of airborne particulate matter (PM) in the urban environment is a crucial aspect to guarantee citizen health. To focus the action of stakeholders in limiting air pollution, fast and highly spatially resolved methods for monitoring PM are required. Recently, the trees' capability in capturing PM inspired the development of several methods intended to use trees as biomonitors; this results in the potential of having an ultra-spatially resolved network of low-cost PM monitoring stations throughout cities, without the needing of on-site stations. Within this context, we propose a fast and reliable method to qualitatively and quantitatively characterize the PM present in urban air based on the analysis of tree leaves by scanning electron microscopy combined with X-ray spectroscopy (SEM/EDX). We have tested our method in the Real Bosco di Capodimonte urban park (Naples, Italy), by collecting leaves from Quercus ilex trees along transects parallel to the main wind directions. The coarse (PM_10-2.5) and fine (PM_2.5) amounts obtained per unit leaf area have been validated by weighting the PM washed from leaves belonging to the same sample sets. PM size distribution and elemental composition match appropriately with the known pollution sources in the sample sites (i.e., traffic and marine aerosol). The proposed methodology will then allow the use of the urban forest as an ultra-spatially resolved PM monitoring network, also supporting the work of urban green planners and stakeholders.

Impact of London's low emission zone on air quality and children's respiratory health: a sequential annual cross-sectional study

BACKGROUND

Low emission zones (LEZ) are an increasingly common, but unevaluated, intervention aimed at improving urban air quality and public health. We investigated the impact of London's LEZ on air quality and children's respiratory health.

METHODS

We did a sequential annual cross-sectional study of 2164 children aged 8-9 years attending primary schools between 2009-10 and 2013-14 in central London, UK, following the introduction of London's LEZ in February, 2008. We examined the association between modelled pollutant exposures of nitrogen oxides (including nitrogen dioxide [NO₂]) and particulate matter with a diameter of less than 2·5 μm (PM_2·5) and less than 10 μm (PM₁₀) and lung function: postbronchodilator forced expiratory volume in 1 s (FEV₁, primary outcome), forced vital capacity (FVC), and respiratory or allergic symptoms. We assigned annual exposures by each child's home and school address, as well as spatially resolved estimates for the 3 h (0600-0900 h), 24 h, and 7 days before each child's assessment, to isolate long-term from short-term effects.

FINDINGS

The percentage of children living at addresses exceeding the EU limit value for annual NO₂ (40 μg/m³) fell from 99% (444/450) in 2009 to 34% (150/441) in 2013. Over this period, we identified a reduction in NO₂ at both roadside (median -1·35 μg/m³ per year; 95% CI -2·09 to -0·61; p=0·0004) and background locations (-0·97; -1·56 to -0·38; p=0·0013), but not for PM₁₀. The effect on PM_2·5 was equivocal. We found no association between postbronchodilator FEV₁ and annual residential pollutant attributions. By contrast, FVC was inversely correlated with annual NO₂ (-0·0023 L/μg per m³; -0·0044 to -0·0002; p=0·033) and PM₁₀ (-0·0090 L/μg per m³; -0·0175 to -0·0005; p=0·038).

INTERPRETATION

Within London's LEZ, a smaller lung volume in children was associated with higher annual air pollutant exposures. We found no evidence of a reduction in the proportion of children with small lungs over this period, despite small improvements in air quality in highly polluted urban areas during the implementation of London's LEZ. Interventions that deliver larger reductions in emissions might yield improvements in children's health.

FUNDING

National Institute for Health Research Biomedical Research Centre at Guy's and St Thomas' National Health Service (NHS) Foundation Trust and King's College London, NHS Hackney, Lee Him donation, and Felicity Wilde Charitable Trust.

Are noise and air pollution related to the incidence of dementia? A cohort study in London, England

OBJECTIVE

To investigate whether the incidence of dementia is related to residential levels of air and noise pollution in London.

DESIGN

Retrospective cohort study using primary care data.

SETTING

75 Greater London practices.

PARTICIPANTS

130 978 adults aged 50-79 years registered with their general practices on 1 January 2005, with no recorded history of dementia or care home residence.

PRIMARY AND SECONDARY OUTCOME MEASURES

A first recorded diagnosis of dementia and, where specified, subgroups of Alzheimer's disease and vascular dementia during 2005-2013. The average annual concentrations during 2004 of nitrogen dioxide (NO2), particulate matter with a median aerodynamic diameter ≤2.5 µm (PM2.5) and ozone (O3) were estimated at 20×20 m resolution from dispersion models. Traffic intensity, distance from major road and night-time noise levels (Lnight) were estimated at the postcode level. All exposure measures were linked anonymously to clinical data via residential postcode. HRs from Cox models were adjusted for age, sex, ethnicity, smoking and body mass index, with further adjustments explored for area deprivation and comorbidity.

RESULTS

2181 subjects (1.7%) received an incident diagnosis of dementia (39% mentioning Alzheimer's disease, 29% vascular dementia). There was a positive exposure response relationship between dementia and all measures of air pollution except O3, which was not readily explained by further adjustment. Adults living in areas with the highest fifth of NO2 concentration (>41.5 µg/m3) versus the lowest fifth (<31.9 µg/m3) were at a higher risk of dementia (HR=1.40, 95% CI 1.12 to 1.74). Increases in dementia risk were also observed with PM2.5, PM2.5 specifically from primary traffic sources only and Lnight, but only NO2 and PM2.5 remained statistically significant in multipollutant models. Associations were more consistent for Alzheimer's disease than vascular dementia.

CONCLUSIONS

We have found evidence of a positive association between residential levels of air pollution across London and being diagnosed with dementia, which is unexplained by known confounding factors.

Full-chain health impact assessment of traffic-related air pollution and childhood asthma

BACKGROUND

Asthma is the most common chronic disease in children. Traffic-related air pollution (TRAP) may be an important exposure contributing to its development. In the UK, Bradford is a deprived city suffering from childhood asthma rates higher than national and regional averages and TRAP is of particular concern to the local communities.

AIMS

We estimated the burden of childhood asthma attributable to air pollution and specifically TRAP in Bradford. Air pollution exposures were estimated using a newly developed full-chain exposure assessment model and an existing land-use regression model (LUR).

METHODS

We estimated childhood population exposure to NO_x and, by conversion, NO₂ at the smallest census area level using a newly developed full-chain model knitting together distinct traffic (SATURN), vehicle emission (COPERT) and atmospheric dispersion (ADMS-Urban) models. We compared these estimates with measurements and estimates from ESCAPE's LUR model. Using the UK incidence rate for childhood asthma, meta-analytical exposure-response functions, and estimates from the two exposure models, we estimated annual number of asthma cases attributable to NO₂ and NO_x in Bradford, and annual number of asthma cases specifically attributable to traffic.

RESULTS

The annual average census tract levels of NO₂ and NO_x estimated using the full-chain model were 15.41 and 25.68 μg/m³, respectively. On average, 2.75 μg/m³ NO₂ and 4.59 μg/m³ NO_x were specifically contributed by traffic, without minor roads and cold starts. The annual average census tract levels of NO₂ and NO_x estimated using the LUR model were 21.93 and 35.60 μg/m³, respectively. The results indicated that up to 687 (or 38% of all) annual childhood asthma cases in Bradford may be attributable to air pollution. Up to 109 cases (6%) and 219 cases (12%) may be specifically attributable to TRAP, with and without minor roads and cold starts, respectively.

CONCLUSIONS

This is the first study undertaking full-chain health impact assessment of TRAP and childhood asthma in a disadvantaged population with public concern about TRAP. It further adds to scarce literature exploring the impact of different exposure assessments. In conservative estimates, air pollution and TRAP are estimated to cause a large, but largely preventable, childhood asthma burden. Future progress with childhood asthma requires a move beyond the prevalent disease control-based approach toward asthma prevention.

Local- and regional-scale air pollution modelling (PM10) and exposure assessment for pregnancy trimesters, infancy, and childhood to age 15 years: Avon Longitudinal Study of Parents And Children (ALSPAC)

We established air pollution modelling to study particle (PM₁₀) exposures during pregnancy and infancy (1990-1993) through childhood and adolescence up to age ~15 years (1991-2008) for the Avon Longitudinal Study of Parents And Children (ALSPAC) birth cohort. For pregnancy trimesters and infancy (birth to 6 months; 7 to 12 months) we used local (ADMS-Urban) and regional/long-range (NAME-III) air pollution models, with a model constant for local, non-anthropogenic sources. For longer exposure periods (annually and the average of birth to age ~8 and to age ~15 years to coincide with relevant follow-up clinics) we assessed spatial contrasts in local sources of PM₁₀ with a yearly-varying concentration for all background sources. We modelled PM₁₀ (μg/m³) for 36,986 address locations over 19 years and then accounted for changes in address in calculating exposures for different periods: trimesters/infancy (n = 11,929); each year of life to age ~15 (n = 10,383). Intra-subject exposure contrasts were largest between pregnancy trimesters (5^th to 95^th centile: 24.4-37.3 μg/m³) and mostly related to temporal variability in regional/long-range PM₁₀. PM₁₀ exposures fell on average by 11.6 μg/m³ from first year of life (mean concentration = 31.2 μg/m³) to age ~15 (mean = 19.6 μg/m³), and 5.4 μg/m³ between follow-up clinics (age ~8 to age ~15). Spatial contrasts in 8-year average PM₁₀ exposures (5^th to 95^th centile) were relatively low: 25.4-30.0 μg/m³ to age ~8 years and 20.7-23.9 μg/m³ from age ~8 to age ~15 years. The contribution of local sources to total PM₁₀ was 18.5%-19.5% during pregnancy and infancy, and 14.4%-17.0% for periods leading up to follow-up clinics. Main roads within the study area contributed on average ~3.0% to total PM₁₀ exposures in all periods; 9.5% of address locations were within 50 m of a main road. Exposure estimates will be used in a number of planned epidemiological studies.

Methods for Assessing Long-Term Exposures to Outdoor Air Pollutants

Purpose of Review

Epidemiological studies of health effects of long-term exposure to outdoor air pollution rely on different exposure assessment methods. This review discusses widely used methods with a special focus on new developments.

Recent Findings

New data and study designs have been applied, including satellite measurements of fine particles and nitrogen dioxide (NO₂). The methods to apply satellite data for epidemiological studies are improving rapidly and have already contributed significantly to national-, continental- and global-scale models. Spatiotemporal models have been developed allowing more detailed temporal resolution compared to spatial models. The development of hybrid models combining dispersion models, satellite observations, land use and surface monitoring has improved models substantially. Mobile monitoring designs to develop models for long-term UFP exposure have been conducted.

Summary

Methods to assess long-term exposure to outdoor air pollution have improved significantly over the past decade. Application of satellite data and mobile monitoring designs is promising new methods.

Impact of London's road traffic air and noise pollution on birth weight: retrospective population based cohort study

Objective To investigate the relation between exposure to both air and noise pollution from road traffic and birth weight outcomes.Design Retrospective population based cohort study.Setting Greater London and surrounding counties up to the M25 motorway (2317 km²), UK, from 2006 to 2010.Participants 540 365 singleton term live births.Main outcome measures Term low birth weight (LBW), small for gestational age (SGA) at term, and term birth weight.Results Average air pollutant exposures across pregnancy were 41 μg/m³ nitrogen dioxide (NO₂), 73 μg/m³ nitrogen oxides (NO_x), 14 μg/m³ particulate matter with aerodynamic diameter <2.5 μm (PM_2.5), 23 μg/m³ particulate matter with aerodynamic diameter <10 μm (PM₁₀), and 32 μg/m³ ozone (O₃). Average daytime (L_Aeq,16hr) and night-time (L_night) road traffic A-weighted noise levels were 58 dB and 53 dB respectively. Interquartile range increases in NO₂, NO_x, PM_2.5, PM₁₀, and source specific PM_2.5 from traffic exhaust (PM_{2.5 traffic exhaust}) and traffic non-exhaust (brake or tyre wear and resuspension) (PM_{2.5 traffic non-exhaust}) were associated with 2% to 6% increased odds of term LBW, and 1% to 3% increased odds of term SGA. Air pollutant associations were robust to adjustment for road traffic noise. Trends of decreasing birth weight across increasing road traffic noise categories were observed, but were strongly attenuated when adjusted for primary traffic related air pollutants. Only PM_{2.5 traffic exhaust} and PM_2.5 were consistently associated with increased risk of term LBW after adjustment for each of the other air pollutants. It was estimated that 3% of term LBW cases in London are directly attributable to residential exposure to PM_2.5>13.8 μg/m³during pregnancy.Conclusions The findings suggest that air pollution from road traffic in London is adversely affecting fetal growth. The results suggest little evidence for an independent exposure-response effect of traffic related noise on birth weight outcomes.

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina

To quantify the on-road PM_2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM_2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM_2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM_2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM_2.5 where the hybrid approach estimated 2.5 times more primary on-road PM_2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic-related emissions.

Global Associations between Air Pollutants and Chronic Obstructive Pulmonary Disease Hospitalizations. A Systematic Review

RATIONALE

Exacerbations are key events in chronic obstructive pulmonary disease (COPD), affecting lung function decline and quality of life. The effect of exposure to different air pollutants on COPD exacerbations is not clear.

OBJECTIVES

To carry out a systematic review, examining associations between air pollutants and hospital admissions for COPD exacerbations.

METHODS

MEDLINE, Embase, BIOSIS, Science Citation Index, and the Air Pollution Epidemiology Database were searched for publications published between 1980 and September 2015. Inclusion criteria were focused on studies presenting solely a COPD outcome defined by hospital admissions and a measure of gaseous air pollutants and particle fractions. The association between each pollutant and COPD admissions was investigated in metaanalyses using random effects models. Analyses were stratified by geographical clusters for investigation of the consistency of the evidence worldwide.

MEASUREMENTS AND MAIN RESULTS

Forty-six studies were included, and results for all the pollutants under investigation showed marginal positive associations; however, the number of included studies was small, the studies had high heterogeneity, and there was evidence of small-study bias. Geographical clustering of the effects of pollution on COPD hospital admissions was evident and reduced heterogeneity significantly.

CONCLUSIONS

The most consistent association was between a 1-mg/m³ increase in carbon monoxide level and COPD-related admissions (odds ratio, 1.02; 95% confidence interval, 1.01-1.03). The heterogeneity was moderate, and there was a consistent positive association in both Europe and North America, although levels were clearly below World Health Organization guideline values. There is mixed evidence on the effects of environmental pollution on COPD exacerbations. Limitations of previous studies included the low spatiotemporal resolution of pollutants, inadequate control for confounding factors, and the use of aggregated health data that ignored personal characteristics. The need for more targeted exposure estimates in a large number of geographical locations is evident.

Exposure estimation errors to nitrogen oxides on a population scale due to daytime activity away from home

Accurate estimation of exposure to air pollution is necessary for assessing the impact of air pollution on the public health. Most environmental epidemiology studies assign the home address exposure to the study subjects. Here, we quantify the exposure estimation error at the population scale due to assigning it solely at the residence place. A cohort of most schoolchildren in Israel (~950,000), age 6-18, and a representative cohort of Israeli adults (~380,000), age 24-65, were used. For each subject the home and the work or school addresses were geocoded. Together, these two microenvironments account for the locations at which people are present during most of the weekdays. For each subject, we estimated ambient nitrogen oxide concentrations at the home and work or school addresses using two air quality models: a stationary land use regression model and a dynamic dispersion-like model. On average, accounting for the subjects' work or school address as well as for the daily pollutant variation reduced the estimation error of exposure to ambient NO_x/NO₂ by 5-10ppb, since daytime concentrations at work/school and at home can differ significantly. These results were consistent regardless which air quality model as used and even for subjects that work or study close to their home. Yet, due to their usually short commute, assigning schoolchildren exposure solely at their residential place seems to be a reasonable estimation. In contrast, since adults commute for longer distances, assigning exposure of adults only at the residential place has a lower correlation with the daily weighted exposure, resulting in larger exposure estimation errors. We show that exposure misclassification can result from not accounting for the subjects' time-location trajectories through the spatiotemporally varying pollutant concentrations field.

Associations of night-time road traffic noise with carotid intima-media thickness and blood pressure: The Whitehall II and SABRE study cohorts

BACKGROUND

Road traffic noise has been linked to increased risk of stroke, for which hypertension and carotid intima-media thickness (cIMT) are risk factors. A link between traffic noise and hypertension has been established, but there are few studies on blood pressure and no studies on cIMT.

OBJECTIVES

To examine cross-sectional associations for long-term exposure to night-time noise with cIMT, systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension.

METHODS

The study population consisted of 2592 adults from the Whitehall II and SABRE cohort studies living within Greater London who had cIMT, SBP and DBP measured. Exposure to night-time road traffic noise (A-weighted dB, referred to as dBA) was estimated at each participant's residential postcode centroid.

RESULTS

Mean night-time road noise levels were 52dBA (SD=4). In the pooled analysis adjusted for cohort, sex, age, ethnicity, marital status, smoking, area-level deprivation and NOx there was a 9.1μm (95% CI: -7.1, 25.2) increase in cIMT in association with 10dBA increase in night-time noise. Analyses by noise categories of 55-60dBA (16.2μm, 95% CI: -8.7, 41.2), and >60dBA (21.2μm, 95% CI: -2.5, 44.9) vs. <55dBA were also positive but non-significant, expect among those not using antihypertensive medication and exposed to >60dBA vs. <55dBA (32.6μm, 95% CI: 6.2, 59.0). Associations for SBP, DPB and hypertension were close to null.

CONCLUSIONS

After adjustments, including for air pollution, the association between night-time road traffic noise and cIMT was only observed among non-medication users but associations with blood pressure and hypertension were largely null.

Effect of Exhaust- and Nonexhaust-Related Components of Particulate Matter on Long-Term Survival After Stroke

BACKGROUND AND PURPOSE

Outdoor air pollution represents a potentially modifiable risk factor for stroke. We examined the link between ambient pollution and mortality up to 5 years poststroke, especially for pollutants associated with vehicle exhaust.

METHODS

Data from the South London Stroke Register, a population-based register covering an urban, multiethnic population, were used. Hazard ratios (HR) for a 1 interquartile range increase in particulate matter <2.5 µm diameter (PM_2.5) and PM <10 µm (PM₁₀) were estimated poststroke using Cox regression, overall and broken down into exhaust and nonexhaust components. Analysis was stratified for ischemic and hemorrhagic strokes and was further broken down by Oxford Community Stroke Project classification.

RESULTS

The hazard of death associated with PM_2.5 up to 5 years after stroke was significantly elevated (P=0.006) for all strokes (HR=1.28; 95% confidence interval [CI], 1.08-1.53) and ischemic strokes (HR, 1.32; 95% CI, 1.08-1.62). Within ischemic subtypes, PM_2.5 pollution increased mortality risk for total anterior circulation infarcts by 2-fold (HR, 2.01; 95% CI, 1.17-3.48; P=0.012) and by 78% for lacunar infarcts (HR, 1.78; 95% CI, 1.18-2.66; P=0.006). PM₁₀ pollution was associated with 45% increased mortality risk for lacunar infarct strokes (HR, 1.45; 95% CI, 1.06-2.00; P=0.022). Separating PM_2.5 and PM₁₀ into exhaust and nonexhaust components did not show increased mortality.

CONCLUSIONS

Exposure to certain outdoor PM pollution, particularly PM_2.5, increased mortality risk poststroke up to 5 years after the initial stroke.

Air pollution, ethnicity and telomere length in east London schoolchildren: An observational study

BACKGROUND

Short telomeres are associated with chronic disease and early mortality. Recent studies in adults suggest an association between telomere length and exposure to particulate matter, and that ethnicity may modify the relationship. However associations in children are unknown.

OBJECTIVES

We examined associations between air pollution and telomere length in an ethnically diverse group of children exposed to high levels of traffic derived pollutants, particularly diesel exhaust, and to environmental tobacco smoke.

METHODS

Oral DNA from 333 children (8-9years) participating in a study on air quality and respiratory health in 23 inner city London schools was analysed for relative telomere length using monochrome multiplex qPCR. Annual, weekly and daily exposures to nitrogen oxides and particulate matter were obtained from urban dispersion models (2008-10) and tobacco smoke by urinary cotinine. Ethnicity was assessed by self-report and continental ancestry by analysis of 28 random genomic markers. We used linear mixed effects models to examine associations with telomere length.

RESULTS

Telomere length increased with increasing annual exposure to NO_x (model coefficient 0.003, [0.001, 0.005], p<0.001), NO₂ (0.009 [0.004, 0.015], p<0.001), PM_2.5 (0.041, [0.020, 0.063], p<0.001) and PM₁₀ (0.096, [0.044, 0.149], p<0.001). There was no association with environmental tobacco smoke. Telomere length was increased in children reporting black ethnicity (22% [95% CI 10%, 36%], p<0.001) CONCLUSIONS: Pollution exposure is associated with longer telomeres in children and genetic ancestry is an important determinant of telomere length. Further studies should investigate both short and long-term associations between pollutant exposure and telomeres in childhood and assess underlying mechanisms.

Associations between exhaust and non-exhaust particulate matter and stroke incidence by stroke subtype in South London

BACKGROUND

Airborne particulate matter (PM) consists of particles from diverse sources, including vehicle exhausts. Associations between short-term PM changes and stroke incidence have been shown. Cumulative exposures over several months, or years, are less well studied; few studies examined ischaemic subtypes or PM source.

AIMS

This study combines a high resolution urban air quality model with a population-based stroke register to explore associations between long-term exposure to PM and stroke incidence.

METHOD

Data from the South London Stroke Register from 2005-2012 were included. Poisson regression explored association between stroke incidence and long-term (averaged across the study period) exposure to PM2.5(PM<2.5μm diameter) and PM10(PM<10μm), nitric oxide, nitrogen dioxide, nitrogen oxides and ozone, at the output area level (average population=309). Estimates were standardised for age and sex and adjusted for socio-economic deprivation. Models were stratified for ischaemic and haemorrhagic strokes and further broken down by Oxford Community Stroke Project classification and Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification.

RESULTS

1800 strokes were recorded (incidence=42.6/100,000 person-years). No associations were observed between PM and overall ischaemic or haemorrhagic incidence. For an interquartile range increase in PM2.5, there was a 23% increase in incidence (Incidence rate ratio=1.23 (95%CI: 1.03-1.44)) of total anterior circulation infarcts (TACI) and 20% increase for PM2.5 from exhausts (1.20(1.01-1.41)). There were similar associations with PM10, overall (1.21(1.01-1.44)) and from exhausts (1.20(1.01-1.41)). TACI incidence was not associated with non-exhaust sources. There were no associations with other stroke subtypes or pollutants.

CONCLUSION

Outdoor air pollution, particularly that arising from vehicle exhausts, may increase risk of TACI but not other stroke subtypes.

Linking e-health records, patient-reported symptoms and environmental exposure data to characterise and model COPD exacerbations: protocol for the COPE study

INTRODUCTION

Relationships between exacerbations of chronic obstructive pulmonary disease (COPD) and environmental factors such as temperature, humidity and air pollution are not well characterised, due in part to oversimplification in the assignment of exposure estimates to individuals and populations. New developments in miniature environmental sensors mean that patients can now carry a personal air quality monitor for long periods of time as they go about their daily lives. This creates the potential for capturing a direct link between individual activities, environmental exposures and the health of patients with COPD. Direct associations then have the potential to be scaled up to population levels and tested using advanced human exposure models linked to electronic health records.

METHODS AND ANALYSIS

This study has 5 stages: (1) development and deployment of personal air monitors; (2) recruitment and monitoring of a cohort of 160 patients with COPD for up to 6 months with recruitment of participants through the Clinical Practice Research Datalink (CPRD); (3) statistical associations between personal exposure with COPD-related health outcomes; (4) validation of a time-activity exposure model and (5) development of a COPD prediction model for London.

ETHICS AND DISSEMINATION

The Research Ethics Committee for Camden and Islington has provided ethical approval for the conduct of the study. Approval has also been granted by National Health Service (NHS) Research and Development and the Independent Scientific Advisory Committee. The results of the study will be disseminated through appropriate conference presentations and peer-reviewed journals.

Traffic pollution and the incidence of cardiorespiratory outcomes in an adult cohort in London

OBJECTIVES

The epidemiological evidence for adverse health effects of long-term exposure to air and noise pollution from traffic is not coherent. Further, the relative roles of background versus near traffic pollution concentrations in this process are unclear. We investigated relationships between modelled concentrations of air and noise pollution from traffic and incident cardiorespiratory disease in London.

METHODS

Among 211 016 adults aged 40-79 years registered in 75 Greater London practices between 2005 and 2011, the first diagnosis for a range of cardiovascular and respiratory outcomes were identified from primary care and hospital records. Annual baseline concentrations for nitrogen oxide (NO_x), particulate matter with a median aerodynamic diameter <2.5 μm (PM_2.5) attributable to exhaust and non-exhaust sources, traffic intensity and noise were estimated at 20 m² resolution from dispersion models, linked to clinical data via residential postcode. HRs were adjusted for confounders including smoking and area deprivation.

RESULTS

The largest observed associations were between traffic-related air pollution and heart failure (HR=1.10 for 20 μg/m³ change in NO_x, 95% CI 1.01 to 1.21). However, no other outcomes were consistently associated with any of the pollution indicators, including noise. The greater variations in modelled air pollution from traffic between practices, versus within, hampered meaningful fine spatial scale analyses.

CONCLUSIONS

The associations observed with heart failure may suggest exacerbatory effects rather than underlying chronic disease. However, the overall failure to observe wider associations with traffic pollution may reflect that exposure estimates based on residence inadequately represent the relevant pattern of personal exposure, and future studies must address this issue.

Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies

Road traffic gives rise to noise and air pollution exposures, both of which are associated with adverse health effects especially for cardiovascular disease, but mechanisms may differ. Understanding the variability in correlations between these pollutants is essential to understand better their separate and joint effects on human health. We explored associations between modelled noise and air pollutants using different spatial units and area characteristics in London in 2003-2010. We modelled annual average exposures to road traffic noise (LAeq,24h, Lden, LAeq,16h, Lnight) for ~190,000 postcode centroids in London using the UK Calculation of Road Traffic Noise (CRTN) method. We used a dispersion model (KCLurban) to model nitrogen dioxide, nitrogen oxide, ozone, total and the traffic-only component of particulate matter ≤2.5μm and ≤10μm. We analysed noise and air pollution correlations at the postcode level (~50 people), postcodes stratified by London Boroughs (~240,000 people), neighbourhoods (Lower layer Super Output Areas) (~1600 people), 1km grid squares, air pollution tertiles, 50m, 100m and 200m in distance from major roads and by deprivation tertiles. Across all London postcodes, we observed overall moderate correlations between modelled noise and air pollution that were stable over time (Spearman's rho range: |0.34-0.55|). Correlations, however, varied considerably depending on the spatial unit: largest ranges were seen in neighbourhoods and 1km grid squares (both Spearman's rho range: |0.01-0.87|) and was less for Boroughs (Spearman's rho range: |0.21-0.78|). There was little difference in correlations between exposure tertiles, distance from road or deprivation tertiles. Associations between noise and air pollution at the relevant geographical unit of analysis need to be carefully considered in any epidemiological analysis, in particular in complex urban areas. Low correlations near roads, however, suggest that independent effects of road noise and traffic-related air pollution can be reliably determined within London.

Long-term exposure to traffic pollution and hospital admissions in London

Evidence on the effects of long-term exposure to traffic pollution on health is inconsistent. In Greater London we examined associations between traffic pollution and emergency hospital admissions for cardio-respiratory diseases by applying linear and piecewise linear Poisson regression models in a small-area analysis. For both models the results for children and adults were close to unity. In the elderly, linear models found negative associations whereas piecewise models found non-linear associations characterized by positive risks in the lowest and negative risks in the highest exposure category. An increased risk was observed among those living in areas with the highest socioeconomic deprivation. Estimates were not affected by adjustment for traffic noise. The lack of convincing positive linear associations between primary traffic pollution and hospital admissions agrees with a number of other reports, but may reflect residual confounding. The relatively greater vulnerability of the most deprived populations has important implications for public health.

Is long-term exposure to traffic pollution associated with mortality? A small-area study in London

Long-term exposure to primary traffic pollutants may be harmful for health but few studies have investigated effects on mortality. We examined associations for six primary traffic pollutants with all-cause and cause-specific mortality in 2003-2010 at small-area level using linear and piecewise linear Poisson regression models. In linear models most pollutants showed negative or null association with all-cause, cardiovascular or respiratory mortality. In the piecewise models we observed positive associations in the lowest exposure range (e.g. relative risk (RR) for all-cause mortality 1.07 (95% credible interval (CI) = 1.00-1.15) per 0.15 μg/m(3) increase in exhaust related primary particulate matter ≤2.5 μm (PM2.5)) whereas associations in the highest exposure range were negative (corresponding RR 0.93, 95% CI: 0.91-0.96). Overall, there was only weak evidence of positive associations with mortality. That we found the strongest positive associations in the lowest exposure group may reflect residual confounding by unmeasured confounders that varies by exposure group.

Long-Term Exposure to Primary Traffic Pollutants and Lung Function in Children: Cross-Sectional Study and Meta-Analysis

Background

There is widespread concern about the possible health effects of traffic-related air pollution. Nitrogen dioxide (NO₂) is a convenient marker of primary pollution. We investigated the associations between lung function and current residential exposure to a range of air pollutants (particularly NO₂, NO, NOx and particulate matter) in London children. Moreover, we placed the results for NO₂ in context with a meta-analysis of published estimates of the association.

Methods and Findings

Associations between primary traffic pollutants and lung function were investigated in 4884 children aged 9–10 years who participated in the Child Heart and Health Study in England (CHASE). A systematic literature search identified 13 studies eligible for inclusion in a meta-analysis. We combined results from the meta-analysis with the distribution of the values of FEV₁ in CHASE to estimate the prevalence of children with abnormal lung function (FEV₁<80% of predicted value) expected under different scenarios of NO₂ exposure. In CHASE, there were non-significant inverse associations between all pollutants except ozone and both FEV1 and FVC. In the meta-analysis, a 10 μg/m³ increase in NO₂ was associated with an 8 ml lower FEV₁ (95% CI: -14 to -1 ml; p: 0.016). The observed effect was not modified by a reported asthma diagnosis. On the basis of these results, a 10 μg/m³ increase in NO₂ level would translate into a 7% (95% CI: 4% to 12%) increase of the prevalence of children with abnormal lung function.

Conclusions

Exposure to traffic pollution may cause a small overall reduction in lung function and increase the prevalence of children with clinically relevant declines in lung function.

Effects of Air Pollution and the Introduction of the London Low Emission Zone on the Prevalence of Respiratory and Allergic Symptoms in Schoolchildren in East London: A Sequential Cross-Sectional Study

The adverse effects of traffic-related air pollution on children’s respiratory health have been widely reported, but few studies have evaluated the impact of traffic-control policies designed to reduce urban air pollution. We assessed associations between traffic-related air pollutants and respiratory/allergic symptoms amongst 8–9 year-old schoolchildren living within the London Low Emission Zone (LEZ). Information on respiratory/allergic symptoms was obtained using a parent-completed questionnaire and linked to modelled annual air pollutant concentrations based on the residential address of each child, using a multivariable mixed effects logistic regression analysis. Exposure to traffic-related air pollutants was associated with current rhinitis: NOx (OR 1.01, 95% CI 1.00–1.02), NO₂ (1.03, 1.00–1.06), PM₁₀ (1.16, 1.04–1.28) and PM_2.5 (1.38, 1.08–1.78), all per μg/m³ of pollutant, but not with other respiratory/allergic symptoms. The LEZ did not reduce ambient air pollution levels, or affect the prevalence of respiratory/allergic symptoms over the period studied. These data confirm the previous association between traffic-related air pollutant exposures and symptoms of current rhinitis. Importantly, the London LEZ has not significantly improved air quality within the city, or the respiratory health of the resident population in its first three years of operation. This highlights the need for more robust measures to reduce traffic emissions.

Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London

Aims

Road traffic noise has been associated with hypertension but evidence for the long-term effects on hospital admissions and mortality is limited. We examined the effects of long-term exposure to road traffic noise on hospital admissions and mortality in the general population.

Methods and results

The study population consisted of 8.6 million inhabitants of London, one of Europe's largest cities. We assessed small-area-level associations of day- (7:00–22:59) and nighttime (23:00–06:59) road traffic noise with cardiovascular hospital admissions and all-cause and cardiovascular mortality in all adults (≥25 years) and elderly (≥75 years) through Poisson regression models. We adjusted models for age, sex, area-level socioeconomic deprivation, ethnicity, smoking, air pollution, and neighbourhood spatial structure. Median daytime exposure to road traffic noise was 55.6 dB. Daytime road traffic noise increased the risk of hospital admission for stroke with relative risk (RR) 1.05 [95% confidence interval (CI): 1.02–1.09] in adults, and 1.09 (95% CI: 1.04–1.14) in the elderly in areas >60 vs. <55 dB. Nighttime noise was associated with stroke admissions only among the elderly. Daytime noise was significantly associated with all-cause mortality in adults [RR 1.04 (95% CI: 1.00–1.07) in areas >60 vs. <55 dB]. Positive but non-significant associations were seen with mortality for cardiovascular and ischaemic heart disease, and stroke. Results were similar for the elderly.

Conclusions

Long-term exposure to road traffic noise was associated with small increased risks of all-cause mortality and cardiovascular mortality and morbidity in the general population, particularly for stroke in the elderly.

Traffic-related air pollution in relation to cognitive function in older adults

Supplemental Digital Content is available in the text.

Background:

Few epidemiologic studies have investigated associations of air pollution with cognition in older adults, and none has specifically compared associations across particle sources. We investigated whether exposure to particulate air pollution, characterized by size and source, was associated with cognitive function and decline in cognitive function.

Methods:

We included participants of the Whitehall II cohort who were residents of greater London and who attended the medical examination in study wave 2007–2009 (n = 2867). Annual average concentrations of particulate matter (PM) (PM₁₀ and PM_2.5 from all sources and from traffic exhaust) were modeled at resolution of 20 × 20 m for 2003–2009. We investigated the relationship between exposure to particles and a cognitive battery composed of tests of reasoning, memory, and phonemic and semantic fluency. We also investigated exposure in relation to decline in these tests over 5 years.

Results:

Mean age of participants was 66 (standard deviation = 6) years. All particle metrics were associated with lower scores in reasoning and memory measured in the 2007–2009 wave but not with lower verbal fluency. Higher PM_2.5 of 1.1 μg/m³ (lag 4) was associated with a 0.03 (95% confidence interval = −0.06 to 0.002) 5-year decline in standardized memory score and a 0.04 (−0.07 to −0.01) decline when restricted to participants remaining in London between study waves.

Conclusions:

This study provides support for an association between particulate air pollution and some measures of cognitive function, as well as decline over time in cognition; however, it does not support the hypothesis that traffic-related particles are more strongly associated with cognitive function than particles from all sources.

A comparison of exposure metrics for traffic-related air pollutants: application to epidemiology studies in Detroit, Michigan

Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into “low”, “medium” or “high” traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments.

Associations between inflammatory and immune response genes and adverse respiratory outcomes following exposure to outdoor air pollution: a HuGE systematic review

Variants of inflammatory and immune response genes have been associated with adverse respiratory outcomes following exposure to air pollution. However, the genes involved and their associations are not well characterized, and there has been no systematic review. Thus, we conducted a review following the guidelines of the Human Genome Epidemiology Network. Six observational studies and 2 intervention studies with 14,903 participants were included (2001-2010). Six studies showed at least 1 significant gene-pollutant interaction. Meta-analysis was not possible due to variations in genes, pollutants, exposure estimates, and reported outcomes. The most commonly studied genes were tumor necrosis factor α (TNFA) (n = 6) and toll-like receptor 4 (TLR4) (n = 3). TNFA -308G>A modified the action of ozone and nitrogen dioxide on lung function, asthma risk, and symptoms; however, the direction of association varied between studies. The TLR4 single-nucleotide polymorphisms rs1927911, rs10759931, and rs6478317 modified the association of particulate matter and nitrogen dioxide with asthma. The transforming growth factor β1 (TGFB1) polymorphism -509C>T also modified the association of pollutants with asthma. This review indicates that genes controlling innate immune recognition of foreign material (TLR4) and the subsequent inflammatory response (TGFB1, TLR4) modify the associations of exposure to air pollution with respiratory function. The associations observed have biological plausibility; however, larger studies with improved reporting are needed to confirm these findings.

Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.