A GIS-based method for modelling air pollution exposures across Europe
Introduction
Studies of air pollution epidemiology face a serious dilemma. On the one hand, most studies show small excess risks: for increases of about 10 µg/m3, in the order of 5–20% for atmospheric particles (Stieb et al., 2002, Pope and Dockery, 2006) and 3–14% for NO2 (Filleul et al., 2005, Gehring et al., 2006, Beelen et al., 2008). On the other hand, the large numbers of people potentially exposed means that these may translate into large public health problems. In Austria, Switzerland and France, combined for example, Künzli et al. (2000) estimate that ambient air pollution accounts for 6% of total mortality amongst adults aged 30+ years, or 40 000 deaths annually. Across Europe, some 13 000 deaths annually of infants (ages 0–4) have also been attributed to outdoor air pollution, making it second in terms of the environmental burden of disease only to physical injuries (Valent et al., 2003). To analyse these effects, air pollution epidemiology typically requires large population studies, with sufficient power to detect the small increases in risk against the background of varying base disease rates. Effective risk management likewise needs to be supported by accurate mapping of exposures, both to identify areas and populations at risk, and to give reliable estimation of the overall health impact of present or future policies.
Herein lie the major challenges. Direct measurement of exposures (e.g. using personal exposure metres or biomarkers) is clearly not feasible for such large study populations. In many epidemiological studies, also, the relevant exposures have already occurred (sometimes many years previously), while in many assessments of risk and health impacts the concern is about potential future exposures. Some form of modelling is therefore essential. The traditional approach has been to extrapolate data from neighbouring (usually routine) monitoring sites (Dockery et al., 1993, Pope et al., 1995), though inevitably this leads to substantial exposure misclassification, and tends to attenuate variations in exposure (and dilute any measured associations with health outcome) by falsely assigning the same concentrations to large numbers of people. In an attempt to reduce this problem, several more sophisticated methods of spatial interpolation have been developed, including geostatistical techniques such as kriging in various forms (Liu and Rossini, 1996, Finkelstein et al., 2003, Jerrett et al., 2005b, Henderson et al., 2007), and Bayesian hierarchical modelling (Elliott et al., 2007, Yanosky et al., 2008). Nevertheless, all of these methods are highly dependent on the spatial coverage and representivity of the available monitoring sites, which are generally too sparse to give accurate characterisation of small-area variations in air pollution.
Alternative methods of modelling are therefore needed, which better reflect the factors (e.g. source distribution and propagation processes) that influence spatial variations in air pollution. Source–receptor (or dispersion) models offer one approach, and a number of long-range atmospheric transport models have been developed for use at the continental scale, including CHIMERE (Bessagnet et al., 2004, Konovalov et al., 2005), EURAD (Hass et al., 1993) and EMEP (Simpson et al., 2003). These, however, typically have a spatial resolution in the order of tens of kilometres, and as such are unsuitable for assessing exposures at the individual or small-area scale. Gaussian dispersion models (Hall et al., 2000a, Hall et al., 2000b, Holmes and Morawska, 2006), in contrast, have been widely proven at the local scale (Nyberg et al., 2000, Bellander et al., 2001, Levy et al., 2002), but their use across large study areas is limited by their hungry data demands and heavy processing requirements. In recent years, a range of GIS-based methods have therefore been developed (Briggs, 2005, Jerrett et al., 2005a) in an attempt to provide a more simple and flexible means of modelling source–receptor relationships. Amongst these, land use regression (LUR) has gained particular attention (Briggs et al., 1997, Briggs et al., 2000, Briggs, 2005, Brauer et al., 2003, Jerrett et al., 2005a, Kanaroglou et al., 2005, Ross et al., 2006, Hoek et al., 2008). Although this has been successfully applied for high resolution mapping across large (continental scale) areas (Beelen et al., 2009), however, its main application has been for local (urban-scale) modelling. In this context, somewhat different models have tended to be developed in different study areas, reflecting differences not only in the modelling strategy by the researchers concerned, but also in data availability and quality and, perhaps, the underlying influences on air pollution concentrations (Briggs, 2007, Hoek et al., 2008). For analysis across large and diverse areas, therefore, methods are needed which bring together the effect of land use and other factors in a more consistent form. One way of doing this explicitly is to translate them into measures of emission, and then use the functionality of GIS to model their contributions to pollutant concentrations at any location, on the basis of their spatial proximity.
This paper describes, demonstrates and tests an approach of this form. Developed as part of the EU-funded APMoSPHERE project (an acronym list is presented in Appendix A), it uses moving window techniques to derive a distance-weighted model of relationships between area-level emissions and monitored concentrations at a set of training sites, and then applies this to estimate concentrations at unsampled locations across the EU. The methodology is demonstrated using nitrogen oxide (NOx) emissions, and monitored NO2 data from the European Airbase database. The resulting NO2 model is compared to two more conventional methods—kriging and land use regression (Beelen et al., 2009).
Section snippets
Principles of the moving window approach
Moving window techniques and associated focal functions are now widely available in many GIS. As the name implies, these techniques involve passing a window, cell-by-cell, across a grid to derive a new value for the central (target) cell as some function of the other cells covered by the window. Several focal functions are available for this purpose. Focalsum, the function used here, sums the cell values covered by the window. The window configuration is determined by a text file called a
Calibration
Table 3 shows the results for the best-fit models at the training sites, obtained using different types of window, both with and without wind speed and direction.
Overall, model performance is highly consistent, notwithstanding the differences in both the distribution of weights and the shape of the annuli. Optimisation of the weights only marginally improves model performance compared to the starting (1/d2) models and circular annuli are only slight improvements on rectangular windows.
Discussion
Despite the relatively small number of available monitoring sites and the large area over which the predictions were made, the focalsum models developed here appear to provide realistic and reliable predictions of NO2 concentrations across the EU. Exposure estimates based on these models differ substantially from those based on the monitoring sites alone, and suggest that the monitoring network may give a somewhat biased indication of exposure distributions. This has important implications for
Conclusion
The contradictory needs for both wide geographical coverage and high spatial resolution in exposure assessment pose severe challenges for exposure modelling. This research demonstrates the use of focalsum methods to model annual average concentrations of nitrogen dioxide across a large continental area, at 1 km resolution. The method is seen to be capable of providing estimates of pollutant concentrations at least as reliable as the other approaches currently available. While it shares with
Acknowledgements
This work was largely undertaken in the framework of the EU-funded APMoSPHERE project (EVK2-2002-00577) between December 2002 and September 2005. It was also partly supported through the EU 6th Framework Programme INTARESE project (018385-2), and by funding from the Ruimte voor Geo-Informatie (RGI), Netherlands. The authors gratefully acknowledge the financial support given by the funders, and the scientific input and advice of colleagues working on these projects. Particular thanks are due to
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