Proxies: the example of traffic-related air pollution

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Administrators

Studies of traffic-related air pollution provide a good example of the need for, and dangers in using, proxies. One of the challenges these studies inevitably face is how to estimate exposures in the study populations. A number of issues make this especially difficult, including:

The complex mix of transport-related emissions, and uncertainties about which pollutant should be targetted and what the relevant exposure metric should be;
The need to obtain exposures for large study populations;
The cost and limited potential to undertake personal monitoring;
The sparseness of routine air pollution monitoring networks (as well as the limited range of pollutants that they cover);
The limited capability for air pollution modelling available to many studies;
The need to allow for the local variability in exposures which is often seen in urban areas;
Concerns about effects of both long- and short-term exposures (and how these might interact).

In the face of these difficulties, studies have often had to make pragmatic decisions about study design, with the consequence that a wide range of measures and proxies have been used to represent exposures to the pollutant(s) of direct concern. Proxies for long-term exposures, for example, include:

Land use category (e.g. city centre, high density residential, suburban);
Distance from the nearest main road;
Traffic volume on the nearest road;
Road density (or traffic volume) in the surrounding neighbourhood;
Self-reported measures of exposure (e.g. to traffic noise);
Monitored concentrations of a marker pollutant (e.g. NO₂, PM₁₀) at the nearest routine monitoring site;
Modelled concentrations of a marker pollutant (e.g. NO₂), using various different methods.

As a result of using these different exposure proxies, a wide variety of different exposure-response functions have been produced. In principle, these different metrics ought to tell a broadly similar story, so that the choice of exposure proxy should not be overly critical. In this case it would also be possible to convert the various measures into a common form, and thereby allow the exposure-response functions to be compared or combined. In practice, however, the different exposure proxies often seem to give very different results. The figure below, for example, illustrates how they can result in markedly different maps of air pollution. The table shows how their relationship with actual concentrations of (or exposures to) the real pollutants of interest (in this case, PM₁₀ concentration) can vary.

Proxies can certainly be useful, especially as a basis for a rough-and-ready approximation at the screening stage. They nevertheless need to be chosen with great care. Ideally, also, their relationship with the real exposure of interest ought to be clearly understood (and, if possible, quantified) before they are used.