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

Ilan Levy*, Noam Levin, Y. Yuval, Joel D. Schwartz, Jeremy D. Kark

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


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 models prediction to each year from 1961 to 2011. The models predictions were then validated against independent estimates about the national annual NOx emissions from on-road vehicles in a top-down approach. The models cross validated R2 was 0.74, and the correlation between the models 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.

Original languageAmerican English
Pages (from-to)3603-3610
Number of pages8
JournalEnvironmental Science and Technology
Issue number6
StatePublished - 17 Mar 2015

Bibliographical note

Publisher Copyright:
© 2015 American Chemical Society.


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