Sean Beevers

Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport.

We used Comparative Risk Assessment methods to estimate the health effects of alternative urban land transport scenarios for two settings-London, UK, and Delhi, India. For each setting, we compared a business-as-usual 2030 projection (without policies for reduction of greenhouse gases) with alternative scenarios-lower-carbon-emission motor vehicles, increased active travel, and a combination of the two. We developed separate models that linked transport scenarios with physical activity, air pollution, and risk of road traffic injury. In both cities, we noted that reduction in carbon dioxide emissions through an increase in active travel and less use of motor vehicles had larger health benefits per million population (7332 disability-adjusted life-years [DALYs] in London, and 12 516 in Delhi in 1 year) than from the increased use of lower-emission motor vehicles (160 DALYs in London, and 1696 in Delhi). However, combination of active travel and lower-emission motor vehicles would give the largest benefits (7439 DALYs in London, 12 995 in Delhi), notably from a reduction in the number of years of life lost from ischaemic heart disease (10-19% in London, 11-25% in Delhi). Although uncertainties remain, climate change mitigation in transport should benefit public health substantially. Policies to increase the acceptability, appeal, and safety of active urban travel, and discourage travel in private motor vehicles would provide larger health benefits than would policies that focus solely on lower-emission motor vehicles.

Air pollution and mortality benefits of the London Congestion Charge: spatial and socioeconomic inequalities

Objectives: To alleviate traffic congestion in Central London, the Mayor introduced the Congestion Charging Scheme (CCS) in February 2003. We modelled the impact of the CCS on levels of traffic pollutants, life expectancy and socioeconomic inequalities. Methods: Annual average NO2 and PM10 were modelled using an emission-dispersion model. We assumed the meteorology and vehicle fleet remained constant during the pre- and post-CCS periods to isolate changes due to traffic flow. Air pollution concentrations were linked to small area socioeconomic, population and mortality data. Associated changes in life expectancy were predicted using life table analysis and exposure-response coefficients from the literature. Results: Before the introduction of the CCS, annual average NO2 was 39.9 μg/m3 and PM10 was 26.2 μg/m3 across Greater London. Concentrations were 54.7 μg/m3 for NO2 and 30.3 μg/m3 for PM10 among census wards within or adjacent to the charging zone. Absolute and relative reductions in concentrations following the introduction of the CCS were greater among charging zone wards compared to remaining wards. Predicted benefits in the charging zone wards were 183 years of life per 100 000 population compared to 18 years among the remaining wards. In London overall, 1888 years of life were gained. More deprived areas had higher air pollution concentrations; these areas also experienced greater air pollution reductions and mortality benefits compared to the least deprived areas. Conclusions: The CCS, a localised scheme targeting traffic congestion, appears to have modest benefit on air pollution levels and associated life expectancy. Greater reductions in air pollution in more deprived areas are likely to make a small contribution to reducing socioeconomic inequalities in air pollution impacts.

Aircraft noise and cardiovascular disease near Heathrow airport in London: small area study

Objective To investigate the association of aircraft noise with risk of stroke, coronary heart disease, and cardiovascular disease in the general population. Design Small area study. Setting 12 London boroughs and nine districts west of London exposed to aircraft noise related to Heathrow airport in London. Population About 3.6 million residents living near Heathrow airport. Risks for hospital admissions were assessed in 12 110 census output areas (average population about 300 inhabitants) and risks for mortality in 2378 super output areas (about 1500 inhabitants). Main outcome measures Risk of hospital admissions for, and mortality from, stroke, coronary heart disease, and cardiovascular disease, 2001-05. Results Hospital admissions showed statistically significant linear trends (P<0.001 to P<0.05) of increasing risk with higher levels of both daytime (average A weighted equivalent noise 7 am to 11 pm, LAeq,16h) and night time (11 pm to 7 am, Lnight) aircraft noise. When areas experiencing the highest levels of daytime aircraft noise were compared with those experiencing the lowest levels (>63 dB v ≤51 dB), the relative risk of hospital admissions for stroke was 1.24 (95% confidence interval 1.08 to 1.43), for coronary heart disease was 1.21 (1.12 to 1.31), and for cardiovascular disease was 1.14 (1.08 to 1.20) adjusted for age, sex, ethnicity, deprivation, and a smoking proxy (lung cancer mortality) using a Poisson regression model including a random effect term to account for residual heterogeneity. Corresponding relative risks for mortality were of similar magnitude, although with wider confidence limits. Admissions for coronary heart disease and cardiovascular disease were particularly affected by adjustment for South Asian ethnicity, which needs to be considered in interpretation. All results were robust to adjustment for particulate matter (PM10) air pollution, and road traffic noise, possible for London boroughs (population about 2.6 million). We could not distinguish between the effects of daytime or night time noise as these measures were highly correlated. Conclusion High levels of aircraft noise were associated with increased risks of stroke, coronary heart disease, and cardiovascular disease for both hospital admissions and mortality in areas near Heathrow airport in London. As well as the possibility of causal associations, alternative explanations such as residual confounding and potential for ecological bias should be considered.

Energy, energy efficiency, and the built environment

Since the last decades of the 19th century, technological advances have brought substantial improvements in the efficiency with which energy can be exploited to service human needs. That trend has been accompanied by an equally notable increase in energy consumption, which strongly correlates with socioeconomic development. Nonetheless, feasible gains in the efficiency and technology of energy use in towns and cities and in homes have the potential to contribute to the mitigation of greenhouse-gas emissions, and to improve health, for example, through protection against temperature-related morbidity and mortality, and the alleviation of fuel poverty. A shift towards renewable energy production would also put increasing focus on cleaner energy carriers, especially electricity, but possibly also hydrogen, which would have benefits to urban air quality. In low-income countries, a vital priority remains the dissemination of affordable technology to alleviate the burdens of indoor air pollution and other health effects in individuals obliged to rely on biomass fuels for cooking and heating, as well as the improvement in access to electricity, which would have many benefits to health and wellbeing.

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 Europes 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.

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. Display Omitted Adaptation of the Calculation of Road Traffic Noise method for exposure assessment.Freely available open-source software (R with PostgreSQL and GRASS GIS).Model estimates compared well to noise measurements (r: ~0.85-0.95).Noise level exposures modelled for 8.61 million London residents (2003-2010).Over 1 million residents exposed to high daytime and night-time noise levels.

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 NOx). 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.

Traffic-related Air Pollution in Relation to Cognitive Function in Older Adults

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 PM2.5 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 PM2.5 of 1.1 &mgr;g/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.

Impact of Outdoor Air Pollution on Survival After Stroke Population-Based Cohort Study

Background and Purpose— The impact of air pollution on survival after stroke is unknown. We examined the impact of outdoor air pollution on stroke survival by studying a population-based cohort. Methods— All patients who experienced their first-ever stroke between 1995 and 2005 in a geographically defined part of London, where road traffic contributes to spatial variation in air pollution, were followed up to mid-2006. Outdoor concentrations of nitrogen dioxide and particulate matter <10 &mgr;m in diameter modeled at a 20-m grid point resolution for 2002 were linked to residential postal codes. Hazard ratios were adjusted for age, sex, social class, ethnicity, smoking, alcohol consumption, prestroke functional ability, pre-existing medical conditions, stroke subtype and severity, hospital admission, and neighborhood socioeconomic deprivation. Results— There were 1856 deaths among 3320 patients. Median survival was 3.7 years (interquartile range, 0.1 to 10.8). Mean exposure levels were 41 &mgr;g/m3 (SD, 3.3; range, 32.2 to 103.2) for nitrogen dioxide and 25 &mgr;g/m3 (SD, 1.3; range, 22.7 to 52) for particulate matter <10 &mgr;m in diameter. A 10-&mgr;g/m3 increase in nitrogen dioxide was associated with a 28% (95% CI, 11% to 48%) increase in risk of death. A 10-&mgr;g/m3 increase in particulate matter <10 &mgr;m in diameter was associated with a 52% (6% to 118%) increase in risk of death. Reduced survival was apparent throughout the follow-up period, ruling out short-term mortality displacement. Conclusions— Survival after stroke was lower among patients living in areas with higher levels of outdoor air pollution. If causal, a 10-&mgr;g/m3 reduction in nitrogen dioxide exposure might be associated with a reduction in mortality comparable to that for stroke units. Improvements in outdoor air quality might contribute to better survival after stroke.

Characterising and understanding emission sources using bivariate polar plots and k-means clustering

This paper develops the idea of bivariate polar plots as a method for source detection and characterisation. Bivariate polar plots provide a graphical method for showing the joint wind speed, wind direction dependence of air pollutant concentrations. Bivariate polar plots provide an effective graphical means of discriminating different source types and characteristics. In the current work we apply k-means clustering techniques directly to bivariate polar plots to identify and group similar features. The technique is analogous to clustering applied to back trajectories at the regional scale. When applied to data from a monitoring site with high source complexity it is shown that the technique is able to identify important clusters in ambient monitoring data that additional analysis shows to exhibit different source characteristics. Importantly, this paper links identified clusters to known emission characteristics to confirm the inferences made in the analysis. The approaches developed should have wide application to the analysis of air pollution monitoring data and have been made freely available as part of the openair R package.