Air pollution and weather-related health impacts: methodological study based on spatiotemporally disaggregated multi-pollutant models

Lead Research Organisation: St George's, University of London
Department Name: Community Health Sciences

Abstract

There is a large and convincing body of epidemiological evidence linking short term exposure to outdoor air pollutants to adverse health effects. However, most of this evidence is derived from studies that have linked single pollutants to health in urban environments. There is increasing recognition that greater protection against the adverse health effects of air pollution could be achieved by focusing research and policy not on individual pollutants, but by a multi-pollutant approach. Furthermore, the spatial variation in pollutant concentrations and their health impacts, especially in rural areas and areas outside the larger cities where much of the UK population reside, are not-well established. Socio-economic impacts (and related issues of environmental justice) and other geographically-determined factors, including housing characteristics (indoor pollution), are also potential modifiers of exposure to outdoor air pollution. The increasing complexity of the scientific inquiry is matched by the difficulties of formulating, proving and implementing appropriate regulatory policy. This proposal builds upon an existing collaboration between researchers in the environmental and health disciplines, with the addition of investigators and practitioners from the policy and social science fields. Our proposal aims to provide new epidemiological evidence on the health impacts of exposure to multiple pollutants; to examine the implications of such evidence for regulation and control of air quality; and to assess how uncertainties in evidence affect its translation into actionable evidence-based policies and the evaluation of their costs and benefits. There are several unique innovations in our study: 1) the development of long series of high resolution (5 km) datasets for daily concentrations of a range of pollutants and weather data, linked to geo-referenced health data including daily mortality, hospital admissions and data on heart attacks; 2) an examination of the contribution of the indoor environment as a modifier of exposure to outdoor pollutants to provide an integrated assessment of the risks to health of short term exposure to air pollution; 3) an integrated assessment of the health effects of various near-term future air quality and climate policies in 2030 as well as selected emissions reduction policies for the UK; 4) the development of a 'decision analysis' tool that includes assessment of uncertainties and can be used to infer the likely outcomes of these various policy choices.
 
Description Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. Regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data.
Even if correlations between model and monitor data appear reasonably strong, additive classical
measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air
pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that
include statistical simulation may be useful.
Exploitation Route Our published study will contribute to the scientific debate regarding measurement error in time series studies.
Sectors Environment,Healthcare