A Semi-parametric Regression Model to Estimate Variability of NO2

Mieczyslaw Szyszkowicz, Mamun Mahmud, Neil Tremblay


The purpose of this analysis was to derive a land-use regression (LUR) model using a semi-parametric method (based on penalized splines) to estimate the geographical characteristics that influence ambient concentrations of nitrogen dioxide (NO2) in Montreal, Quebec, Canada. Such estimations are often used to assess exposure to traffic-related pollution in epidemiologic studies. In May 2003, levels of NO2 were measured for 14 consecutive days at 67 sites across the city, using Ogawa passive-diffusion samplers. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb). This work is re-analyzing of these data. Linear and semi-parametric multivariate regression analyses were conducted to assess the dependency between logarithms of concentrations of NO2 and land-use variables. In the published multiple linear regression analyses for this study, distance from the nearest highway, length of highways and major roads within 100 m, traffic count on the nearest highway, and population density showed significant associations with NO2. The best-fitting linear model had a R2=0.54. The most important variable in the model was traffic count on the nearest highway. The next most important variable was distance from the nearest highway, which has a negative association with NO2 concentration. This work used a semi-parametric model with a nonparametric part incorporating the variables “area of open space within 100 m” and “length of minor roads within 500 m”. These variables were non-significant in the linear regression model and showed nonlinear associations with the level of NO2. The semi-parametric model improves the fit of the model for land-use regression when comparing observed and predicted results.

Full Text:


DOI: http://dx.doi.org/10.5539/ep.v2n1p46

Environment and Pollution   ISSN 1927-0909 (Print)   ISSN 1927-0917 (Online)

Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the 'ccsenet.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.