The overall aim of this proposal is to evaluate whether increasingly detailed estimates of long-term individual exposure to pollutants for large scale studies are useful and effective in yielding better estimates of the health effects of exposure to outdoor air pollution. The study will be implemented in London, a city with relatively low air pollution.
Specific objectives are:
1. Develop long-term estimates of personal exposures to outdoor air pollution based on unique highly detailed exposure measurement data sets already available.
2. Use existing models for estimating concentrations, including combinations of dispersion and models based on land use variables and satellite data with the use of machine learning techniques, and models based on combining ambient and micro-environmental modelling with time activity patterns in the studied population to estimate long-term exposure of individuals to PM10, PM2.5, black carbon (BC), NO2 and O3.
3. Assess the impact of measurement error of each method in (2.) on effect estimates using simulated data sets.
4. Apply the different exposure estimation methods in a cohort of London inhabitants, compare their performance and correct for measurement error.
To achieve these objectives investigators with very long experience in air pollution studies, covering several distinct disciplines will collaborate: exposure scientists, modellers, epidemiologists, statisticians and scientists dealing with health impact assessment, based at King's College, St George's and Imperial from the University of London, and the University of Athens, with a consulting role for Dr J. Schwartz from Harvard.
Extensive existing measurement, modelling and methodological resources from on-going and concluded projects within King's College Environmental Research Group and Collaborating Institutes will be used. We will harmonize data from projects with personal measurements for about 600 individuals (from children to elderly; patients to professional drivers; total of > 18,000 days of measurements) calculating their annual personal exposure to pollutants from outdoor sources and estimate their exposure using the various available models described above.
Information from this data base together with exposure/concentration -response functions will be used to develop a simulated data set including data on health outcomes to assess the impact of measurement error on the effect estimates. We will estimate exposure of the London segment of the UK Biobank cohort of over 60,000 participants with all the above methods and compare the health effects estimates as well as the impact of measurement error correction methods. The project will connect to policy and stakeholders and attempt to answer policy-relevant questions and includes an Advisory Board with experienced scientists from academia and policy bodies. The project is directly addressing the RFA 19-1: it is focused on long-term exposure of PM, BC, NO2 and O3; based on technologically sophisticated personal measurement projects, using advanced and accurate sensors and GPS; and also on hybrid models for long-term air-pollution exposure; employs highly experienced scientists to assess the impact of measurement error on estimated health effects of long-term exposures; and uses a large cohort to test these methods.
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