Land-use
regression models are built by including predictor variables, such
as traffic, topography, and other geographic variables, in multivariable regression using monitored pollution levels as the out come. Subsequently, levels of pollution may then be estimated for
any point, using the parameter estimates derived from the regression model. This method has been identified as a preferred
approach to estimating small area variations in air pollution effectively, when household level monitoring data are not available