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Assessing spatial variability of ambient nitrogen dioxide in Montréal, Canada, with a land-use regression model
Nicolas L Gilbert1, Mark S Goldberg, Bernardo Beckerman
1Air Health Effects Division, Health Canada, Ottawa, Ontario, Canada. nicolas_gilbert@hc-sc.gc.ca
Abstract:
The purpose of this study was to derive a land-use regression model to estimate on a geographical basis ambient concentrations of nitrogen dioxide (NO2) in Montreal, Quebec, Canada. These estimates of concentrations of NO2 will be subsequently used to assess exposure in epidemiologic studies on the health effects of traffic-related air pollution. In May 2003, NO2 was 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). Linear regression analysis was used to assess the association between logarithmic concentrations of NO2 and land-use variables derived using the ESRI Arc 8 geographic information system. In univariate analyses, NO2 was negatively associated with the area of open space and positively associated with traffic count on nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads within 750 m, and population density within 2000 m. Industrial land-use and the length of minor roads showed-no association with NO2. In multiple regression analyses, distance from the nearest highway, traffic count on the nearest highway, length of highways and major roads within 100 m, and population density showed significant associations with NO2; the best-fitting regression model had a R2 of 0.54. These analyses confirm the value of land-use regression modeling to assign exposures in large-scale epidemiologic studies.
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Linear Approximations
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Levels of Use of a GIS
