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Predicted health impacts of urban air quality management
  1. J Mindell,
  2. M Joffe
  1. Department of Epidemiology and Public Health, Imperial College School of Science, Technology and Medicine, London, UK
  1. Correspondence to:
 Dr J Mindell
 Department of Epidemiology and Public Health, Imperial College London, St Mary’s Campus, Norfolk Place, London W2 1PG, UK;


Study objective: The 1995 UK Environment Act required local authorities to review air quality and, where UK National Air Quality Strategy objectives (except ozone) are likely to be exceeded in 2005, to declare local air quality management areas and prepare action plans. This study modelled the impacts on health of reductions from current levels of PM10 to these objectives.

Design: The framework for conducting quantified health impact assessment assessed causality, then, if appropriate, examined the shape and magnitude of the exposure-response relations. The study modelled declines in pollution to achieve the objectives, then modelled the numbers of deaths and admissions affected if air pollution declined from existing levels to meet the objectives, using routine data.

Setting: Westminster, central London.

Main results: Attaining the 2004 PM10 24 hour objective in Westminster results in 1–21 lives no longer shortened in one year (annual deaths 1363). Reducing exceedences from 35 to seven almost doubles the estimates. The 2009 objective for the annual mean requires a substantial reduction in PM10, which would delay 8–20 deaths. About 20 respiratory and 14–20 circulatory admissions would be affected and around 5% of emergency hospital attendances for asthma by attaining the lower annual mean target. The effects of long term exposure to particulates may be an order of magnitude higher: models predict about 24 deaths are delayed by reaching the 2004 annual target (40 μg/m3[gravimetric]) and a hundred deaths by reducing annual mean PM10 to 20 μg/m3[gravimetric].

Conclusions: Modelling can be used to estimate the potential health impacts of air quality management programmes.

  • air pollution
  • health impact assessment
  • APHEA, short term effects of air pollution on health: European approach
  • NMMAPS, national mortality and morbidity air pollution study
  • COPD, chronic obstructive pulmonary disease
  • TEOM, tapered element oscillating microbalance
  • ACS, American Cancer Society study
  • GAM, generalised additive models

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  • * Relative risk (RR) for a 1 μg/m3 increase in pollutant =eβ where β is the regression coefficient for a 1 μg/m3 change in pollutant. Percentage increase in outcome for a 1 μg/m3 increase in pollutant =100(RR–1).

  • 95% confidence intervals (CI) for RR for a 1 μg/m3 increase in pollutant =e(β±1.96SE), where β is the regression coefficient for a 1 μg/m3 change in pollutant and SE is the standard error of β.

  • 4.2 μg/m3×13d per 1 μg/m3 PM10×10/75=7.3d; 19.5 μg/m3×13d per 1 μg/m3 PM10×10/75=34d

  • § 11 μg/m3×13d per 1 μg/m3 PM10×10/75=19d

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