Weighted road density: A simple way of assigning traffic-related air pollution exposure

Background ng traffic variables used for predicting NO2 in epidemiological studies are either difficult to acquire or explain only a small proportion of the variance. The purpose of this study was to develop and validate a new predictor, weighted road density, which combines the maximum amount of information related to traffic into a single variable without the requirement of obtaining traffic counts for a given area. ek NO2 samples were collected using the readings of up to 32 passive samplers on 3 separate rounds between September and December 2006 and again in 2007. Several types of traffic related explanatory variables based on traffic counts, distance to main road and the proposed weighted road density were constructed using GIS software, and tested for association with the NO2 samplers. Assessment of the best model was based on R2 values, as well as leave-one-out cross validation. s ighted road density variable and the density variable based on traffic counts resulted in a similar R2 (0.59) for predicting NO2, although weighted road density was much easier to construct and outperformed other variables such as distance to main road. sion l as being a powerful predictor for use in a land use regression model, weighted road density can be used as a proxy for exposure to traffic-related pollution, for use in circumstances where direct measurements of pollutant levels are not feasible or are not required.