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

Levy, Ilan, Levin, Noam, Yuval, Schwartz, Joel D. and Kark, Jeremy D. (2015) Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution. Environmental Science and Technology, 49 6: 3603-3610. doi:10.1021/es505707e


Author Levy, Ilan
Levin, Noam
Yuval
Schwartz, Joel D.
Kark, Jeremy D.
Title Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution
Journal name Environmental Science and Technology   Check publisher's open access policy
ISSN 1520-5851
0013-936X
Publication date 2015-02
Sub-type Article (original research)
DOI 10.1021/es505707e
Open Access Status Not Open Access
Volume 49
Issue 6
Start page 3603
End page 3610
Total pages 8
Place of publication Washington DC, United States
Publisher American Chemical Society
Collection year 2016
Language eng
Formatted 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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Non HERDC
 
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