THE IMPORTANCE OF DIRECTLY EMITTED NITROGEN DIOXIDE FROM ROAD VEHICLES TO URBAN AIR QUALITY IN THE UK

ABSTRACT Recent analyses of comprehensive ambient air pollution measurements in London have quantified the proportion of nitrogen oxides (NO X ) in vehicle exhausts that is emitted as nitrogen dioxide (NO 2 ). The analyses show that a greater proportion of NO X is emitted directly as NO 2 than previously thought. For the 43 monitoring sites considered, the mean primary NO 2 volume fraction was calculated to be 11.2 %. Emissions of primary NO 2 of this magnitude appear to explain approximately 21 % of measured NO 2 concentrations on average. However, at many congested locations with a high proportion of diesel vehicles, primary NO 2 emissions are thought to explain over 30 % of observed concentrations. For high percentile values of NO 2 , the primary NO 2 contribution can dominate ambient concentrations. These results have implications for the management of air quality in urban areas since it is likely that directly emitted NO 2 would respond differently to NO X control measures compared with that chemically produced in the atmosphere. In particular, the source apportionment of NO 2 concentrations can be very different to NO X close to roads in London. The results also have implications for dispersion modelling studies of NO 2 , where it is generally assumed that a fixed 5.0 % of the NO X emitted by vehicles is in the form of NO 2 . The implications of the increased use of particle traps on the London bus fleet that produce NO 2 to assist in the oxidation of particles is also assessed, together with the potential effects of the London Congestion Charging Scheme

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

London Hybrid Exposure Model: Improving Human Exposure Estimates to NO2 and PM2.5 in an Urban Setting.

Here we describe the development of the London Hybrid Exposure Model (LHEM), which calculates exposure of the Greater London population to outdoor air pollution sources, in-buildings, in-vehicles, and outdoors, using survey data of when and where people spend their time. For comparison and to estimate exposure misclassification we compared Londoners LHEM exposure with exposure at the residential address, a commonly used exposure metric in epidemiological research. In 2011, the mean annual LHEM exposure to outdoor sources was estimated to be 37% lower for PM2.5 and 63% lower for NO2 than at the residential address. These decreased estimates reflect the effects of reduced exposure indoors, the amount of time spent indoors (∼95%), and the mode and duration of travel in London. We find that an individual's exposure to PM2.5 and NO2 outside their residential address is highly correlated (Pearson's R of 0.9). In contrast, LHEM exposure estimates for PM2.5 and NO2 suggest that the degree of correlation is influenced by their exposure in different transport modes. Further development of the LHEM has the potential to increase the understanding of exposure error and bias in time-series and cohort studies and thus better distinguish the independent effects of NO2 and PM2.5

Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents

Knowledge about early risk factors for major depressive disorder (MDD) is critical to identify those who are at high risk. A multivariable model to predict adolescents’ individual risk of future MDD has recently been developed however its performance in a UK sample was far from perfect. Given the potential role of air pollution in the aetiology of depression, we investigate whether including childhood exposure to air pollution as an additional predictor in the risk prediction model improves the identification of UK adolescents who are at greatest risk for developing MDD. We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative UK birth cohort of 2,232 children followed to age 18 with 93% retention. Annual exposure to four pollutants – nitrogen dioxide (NO(2)), nitrogen oxides (NO(X)), particulate matter <2.5μm (PM(2.5)) and <10μm (PM(10)) – were estimated at address-level when children were aged 10. MDD was assessed via interviews at age 18. The risk of developing MDD was elevated most for participants with the highest (top quartile) level of annual exposure to NO(X) (adjusted OR=1.43, 95% CI=0.96-2.13) and PM(2.5) (adjusted OR=1.35, 95% CI=0.95-1.92). The separate inclusion of these ambient pollution estimates into the risk prediction model improved model specificity but reduced model sensitivity – resulting in minimal net improvement in model performance. Findings indicate a potential role for childhood ambient air pollution exposure in the development of adolescent MDD but suggest that inclusion of risk factors other than this may be important for improving the performance of the risk prediction model

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

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&apos;s largest cities. We assessed smallarea-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. Translational perspective Road traffic noise has been associated with hypertension but evidence for the long-term effects on hospital admissions and mortality is limited. Our results suggest small increased population risks of all-cause mortality and cardiovascular morbidity and mortality, particularly of stroke in the elderly, at moderate levels of road noise exposure. Findings are consistent with the larger body of evidence linking traffic noise exposure with hypertension

Spatially resolved flux measurements of NOx from London suggest significantly higher emissions than predicted by inventories

To date, direct validation of city-wide emissions inventories for air pollutants has been difficult or impossible. However, recent technological innovations now allow direct measurement of pollutant fluxes from cities, for comparison with emissions inventories, which are themselves commonly used for prediction of current and future air quality and to help guide abatement strategies. Fluxes of NOx were measured using the eddy-covariance technique from an aircraft flying at low altitude over London. The highest fluxes were observed over central London, with lower fluxes measured in suburban areas. A footprint model was used to estimate the spatial area from which the measured emissions occurred. This allowed comparison of the flux measurements to the UK's National Atmospheric Emissions Inventory (NAEI) for NOx, with scaling factors used to account for the actual time of day, day of week and month of year of the measurement. The comparison suggests significant underestimation of NOx emissions in London by the NAEI, mainly due to its under-representation of real world road traffic emissions. A comparison was also carried out with an enhanced version of the inventory using real world driving emission factors and road measurement data taken from the London Atmospheric Emissions Inventory (LAEI). The measurement to inventory agreement was substantially improved using the enhanced version, showing the importance of fully accounting for road traffic, which is the dominant NOx emission source in London. In central London there was still an underestimation by the inventory of 30-40% compared with flux measurements, suggesting significant improvements are still required in the NOx emissions inventory

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