Long-term traffic air and noise pollution in relation to mortality and hospital readmission among myocardial infarction survivors.

BACKGROUND: There is relatively little evidence of health effects of long-term exposure to traffic-related pollution in susceptible populations. We investigated whether long-term exposure to traffic air and noise pollution was associated with all-cause mortality or hospital readmission for myocardial infarction (MI) among survivors of hospital admission for MI. METHODS: Patients from the Myocardial Ischaemia National Audit Project database resident in Greater London (n = 1 8,138) were followed for death or readmission for MI. High spatially-resolved annual average air pollution (11 metrics of primary traffic, regional or urban background) derived from a dispersion model (resolution 20 m × 20 m) and road traffic noise for the years 2003-2010 were used to assign exposure at residence. Hazard ratios (HR, 95% confidence interval (CI)) were estimated using Cox proportional hazards models. RESULTS: Most air pollutants were positively associated with all-cause mortality alone and in combination with hospital readmission. The largest associations with mortality per interquartile range (IQR) increase of pollutant were observed for non-exhaust particulate matter (PM(10)) (HR = 1.05 (95% CI 1.00, 1.10), IQR = 1.1 μg/m(3)); oxidant gases (HR = 1.05 (95% CI 1.00, 1.09), IQR = 3.2 μg/m(3)); and the coarse fraction of PM (HR = 1.05 (95% CI 1.00, 1.10), IQR = 0.9 μg/m(3)). Adjustment for traffic noise only slightly attenuated these associations. The association for a 5 dB increase in road-traffic noise with mortality was HR = 1.02 (95% CI 0.99, 1.06) independent of air pollution. CONCLUSIONS: These data support a relationship of primary traffic and regional/urban background air pollution with poor prognosis among MI survivors. Although imprecise, traffic noise appeared to have a modest association with prognosis independent of air pollution

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

New NOx and NO2 vehicle emission curves, and their implications for emissions inventories and air pollution modelling

Emissions of NOx and primary NO2 from road transport sources are highly influential in NO2 exposure at both local and regional scales; quantifying these accurately is therefore an important but challenging component of emissions inventory and air pollution model development. Results are presented from an urban air pollution model, after creation of new speed-emissions curves for NOx through the combination of available vehicle drive cycles and nearly 500,000 UK-based remote sensing measurements of exhaust emissions. Vehicle power-based relationships are applied to 1 Hz drive cycle datasets, with random sampling of the outputs allowing generation of the new curves. These demonstrate significantly higher emissions than those predicted by existing curves for most Euro VI HGVs, and among successive petrol and diesel passenger cars; this may be partly explained by relatively low UK ambient temperatures, as well as an underestimation of the level of tampering with HGV SCR systems. Implementation of the curves in a detailed emissions inventory for London, UK in 2019 leads to substantially improved air pollution model performance for NOx/NO2; normalised mean bias reduces in magnitude, changing from −0.18 to +0.01 for NOx and −0.12 to +0.01 for NO2. The curves developed are widely applicable, and the novel approach outlined has the potential to improve source apportionment and future model predictions under differing policy scenarios, produce better exposure estimates for health-related studies and revise NOx emissions budgets for compliance with the NEC Directive, all of which are important for the development of mitigation policies

Eddy covariance measurements highlight sources of nitrogen oxide emissions missing from inventories for central London

During March–June 2017 emissions of nitrogen oxides were measured via eddy covariance at the British Telecom Tower in central London, UK. Through the use of a footprint model the expected emissions were simulated from the spatially resolved National Atmospheric Emissions Inventory for 2017 and compared with the measured emissions. These simulated emissions were shown to underestimate measured emissions during the daytime by a factor of 1.48, but they agreed well overnight. Furthermore, underestimations were spatially mapped, and the areas around the measurement site responsible for differences in measured and simulated emissions were inferred. It was observed that areas of higher traffic, such as major roads near national rail stations, showed the greatest underestimation by the simulated emissions. These discrepancies are partially attributed to a combination of the inventory not fully capturing traffic conditions in central London and both the spatial and temporal resolution of the inventory not fully describing the high heterogeneity of the urban centre. Understanding of this underestimation may be further improved with longer measurement time series to better understand temporal variation and improved temporal scaling factors to better simulate sub-annual emissions

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

Development of an open-source road traffic noise model for exposure assessment

This paper describes the development of a model for assessing TRAffic Noise EXposure (TRANEX) in an open-source geographic information system. Instead of using proprietary software we developed our own model for two main reasons: 1) so that the treatment of source geometry, traffic information (flows/speeds/spatially varying diurnal traffic profiles) and receptors matched as closely as possible to that of the air pollution modelling being undertaken in the TRAFFIC project, and 2) to optimize model performance for practical reasons of needing to implement a noise model with detailed source geometry, over a large geographical area, to produce noise estimates at up to several million address locations, with limited computing resources. To evaluate TRANEX, noise estimates were compared with noise measurements made in the British cities of Leicester and Norwich. High correlation was seen between modelled and measured LAeq,1hr (Norwich: r = 0.85, p = .000; Leicester: r = 0.95, p = .000) with average model errors of 3.1 dB. TRANEX was used to estimate noise exposures (LAeq,1hr, LAeq,16hr, Lnight) for the resident population of London (2003–2010). Results suggest that 1.03 million (12%) people are exposed to daytime road traffic noise levels ≥ 65 dB(A) and 1.63 million (19%) people are exposed to night-time road traffic noise levels ≥ 55 dB(A). Differences in noise levels between 2010 and 2003 were on average relatively small: 0.25 dB (standard deviation: 0.89) and 0.26 dB (standard deviation: 0.87) for LAeq,16hr and Lnight