Comparison of land-use regression models between Great Britain and the Netherlands
Introduction
Various studies have documented health effects related to current day levels of outdoor air pollution (Brunekreef and Holgate, 2002). Health effects related to long-term exposure to outdoor air pollution have been less studied than health effects of short-term exposure, partly because of the difficulty in generating valid estimates of long-term exposure over large study areas. A variety of methods have been used in recent years to represent spatial variation of outdoor air pollution, including indicator variables, interpolation of measurements, dispersion modelling and land-use regression models (Jerrett et al., 2005, Briggs, 2005). Land-use regression (LUR) models were initially devised as a means of modelling spatial variations in urban air pollution as part of epidemiological studies of traffic-related pollution and health (Briggs et al., 1997). They involve developing multiple regression models by analysing associations between measured pollutant concentrations at a number of monitoring sites spread over the study area and predictor variables relating to nearby emission sources and the local dispersion environment (e.g. traffic flows, road length, land cover, topography). Since then the approach has been tested and applied in a range of different urban environments (Briggs, 2005, Hoek et al., 2008), and it has been used for exposure assessment in a number of epidemiological studies (Brauer et al., 2003, Beelen et al., 2007, Morgenstern et al., 2007, Jerrett et al., 2009). Recently, Hoek et al. (2008) have reviewed the methods and applications, and shown that, although performance of the method is generally good and remarkably consistent in different study areas, the resulting models vary considerably. Whether these differences are artefacts of the available data, are due to arbitrary choices made by the researchers, or are a reflection of underlying differences in the configuration of cities and their effects on air pollution processes is not clear.
Most studies to date have focused on the use of LUR for local (usually city-scale) applications. Few attempts have been made to apply the technique at a broader, for example national or trans-national, scale (Stedman et al., 1997, Beelen et al., 2007, Beelen et al., 2009, Janssen et al., 2008). This poses a dilemma because the small relative risks generally associated with ambient air pollution imply the need for large population studies in order to provide sufficient statistical power to obtain reliable risk estimates. At the same time, these studies need to be undertaken at the individual level. Increasing attention is therefore being given to the implementation of large multicenter studies, often across different countries. There is also a growing demand for consistency in methods of exposure assessment between different studies, in order to facilitate comparison and pooling of risk estimates (HEI, in press). In addition, there is interest in using relatively simple screening methods to map air pollution at the national and continental scale, both to inform policy and as a basis for risk assessment. LUR techniques would seem to have great potential in this context. To be effective, however, it must be possible to apply the method in a consistent form, with readily available and uniform data, across relatively large study areas – and to do so without paying an unacceptable penalty either in terms of spatial resolution or model performance.
This paper explores the capability to achieve this. It tests the application of LUR techniques to map air pollution in Great Britain (GB) and the Netherlands (NL), under different data conditions: using only consistent data available in both countries, and using country-specific data to derive locally optimised models. It thus assesses the impact both on model performance and exposure estimates of restricting the method to the use of common data sets, and evaluates the transferability of models from one country to another.
Section snippets
Methods
Models were developed in the two countries for two pollutants – NO2 and PM10. These were selected both because they are recognised as priority pollutants under EU air quality regulations, and because of their widespread use in epidemiological studies either as important exposures in their own right, or as markers for traffic-related pollution more generally (Brunekreef and Holgate, 2002). Models were constructed on the basis of monitored data representing annual mean concentrations for the year
Model structure and performance
The regression models obtained for each pollutant and each country, together with their performance statistics are presented in Table 3, Table 4. The locally optimised model (model 3) for NO2 is shown in Fig. 1.
For NO2 (Table 3), the combined model (model 0) comprises three variables in addition to an indicator variable for country: population within 3000 m, major road length within 200 m, and urban influence (total built-up land within 20 km). The overall R2 during model building is 0.64 and
Discussion
The growing use of land-use regression models for air pollution mapping has resulted in models of differing structure and performance in different study areas. Developing locally optimised models may improve the estimates of air pollution, but it also increases the difficulty of comparing results from different studies. The observed inconsistencies in the models may be due to artefacts of data and methodology or may reflect underlying differences in source or dispersion characteristics. If the
Conclusion
This paper provides evidence of the capability to use LUR models as a basis for air pollution mapping and exposure assessment at the national level. This potential is substantially better for NO2 than for PM10, probably related to the more limited density of particulate monitoring networks and the smaller spatial variations in PM10 concentrations. In the countries studied here, LUR models developed on the basis of widely available and common data do not perform markedly worse than models
Acknowledgements
The work was supported by grant RGI-137 from the Dutch program Ruimte voor Geoinformatie supported by the Ministry of Housing, Spatial Planning and the Environment, and also partly supported though the EU 6th Framework Programme INTARESE project (018385-2).
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