Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution

Abstract

Land use regression (LUR) models rely on air pollutant measurements for their development, and are therefore limited to recent periods where such measurements are available. Here we propose an approach to overcome this gap and calculate LUR models several decades before measurements were available. We first developed a LUR model for NOx using annual averages of NOx at all available air quality monitoring sites in Israel between 1991 and 2011 with time as one of the independent variables. We then reconstructed historical spatial data (e.g., road network) from historical topographic maps to apply the model's prediction to each year from 1961 to 2011. The model's predictions were then validated against independent estimates about the national annual NOx emissions from on-road vehicles in a top-down approach. The model's cross validated R2 was 0.74, and the correlation between the model's annual averages and the national annual NOx emissions between 1965 and 2011 was 0.75. Information about the road network and population are persistent predictors in many LUR models. The use of available historical data about these predictors to resolve the spatial variability of air pollutants together with complementary national estimates on the change in pollution levels over time enable historical reconstruction of exposures.

Figure 1

Sensitivity analysis of the predictors included in the model. Gray shadings denote prediction errors. Ticks at the bottom mark observations included in the model. Boxplots above each panel provide descriptive statistics for that variable.

Figure 2

Leave-one-out cross validation of the predicted vs. measured NO_x at each site and year. Statistics are for all sites.

Figure 3

Time series of: a) boxplots of the LUR prediction over residential areas (50 m domain) with the mean of the prediction error over that area (gray shading), as well as weighted LUR prediction according to the national emissions and a smoothing weighted LUR prediction; b) reconstructed national NO_x emissions from vehicles; c) annual boxplots of NO_x measured at all available AQM sites, mean NO_x at near road sites (red) and general sites (blue).

Figure 4

Scatter plot of national emissions of NO_x against (a) the annual mean NO_x as measured by all AQMN sites (empty circles), general sites (blue) and near-road sites (red); (b) the mean of model predicted NO_x over the residential areas. Lines represent the linear correlations.