Abstract
It has been well documented that air pollution adversely affects health, and epidemiological pollution-health studies utilise pollution data from automatic monitors. However, these automatic monitors are small in number and hence spatially sparse, which does not allow an accurate representation of the spatial variation in pollution concentrations required for these epidemiological health studies. Nitrogen dioxide (NO2) diffusion tubes are also used to measure concentrations, and due to their lower cost compared to automatic monitors are much more prevalent. However, even combining both data sets still does not provide sufficient spatial coverage of NO2 for epidemiological studies, and modelled concentrations on a regular grid from atmospheric dispersion models are also available. This paper proposes the first modelling approach to using all three sources of NO2 data to make fine scale spatial predictions for use in epidemiological health studies. We propose a geostatistical fusion model that regresses combined NO2 concentrations from both automatic monitors and diffusion tubes against modelled NO2 concentrations from an atmospheric dispersion model in order to predict fine scale NO2 concentrations across our West Central Scotland study region. Our model exhibits a 47% improvement in fine scale spatial prediction of NO2 compared to using the automatic monitors alone, and we use it to predict NO2 concentrations across West Central Scotland in 2006.
Highlights
The relationship between air pollution concentrations and ill health has been well documented in the past two decades, with epidemiological studies focussing on the effects of both short-term and long-term exposure
The Bayesian geostatistical fusion model we proposed links the observed and modelled-Pollution Climate Mapping (PCM) NO2 concentrations via a regression relationship, and is similar to existing downscaling models used in the literature (Berrocal et al, 2010a, 2010b)
Using the diffusion tube data in addition to the automatic monitoring data enhances the predictive performance of fine scale NO2 concentrations, compared to using the automatic monitors alone
Summary
The relationship between air pollution concentrations and ill health has been well documented in the past two decades, with epidemiological studies focussing on the effects of both short-term and long-term exposure. The number of monitors is few and their geographical positioning is sparse, which does not allow an accurate representation of the spatial variation in pollution concentrations required for the epidemiological studies, cohort and spatial ecological studies. Concentrations are required at the residence of each member in the cohort, while for spatial ecological studies concentrations are required for each spatial unit at which health data are available. These fine scale pollution data are not available, for example in our Glasgow region there are only 16 monitors covering the 368 square kilometre study region. In the Glasgow study region considered here there are 230 diffusion tubes, which provides greatly enhanced spatial coverage compared with using the 16 automatic monitors alone
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