Neighbourhood scale nitrogen dioxide land use regression modelling with regression kriging in an urban transportation corridor

Land use regression models (LUR) associate observed air pollution concentrations with surrounding land use characteristics for air pollution modelling. This technique is common in urban landscapes focused at a city-wide spatial scale. Our study tested the applicability of LUR modelling at a local scale, defined as multiple air monitors within a neighbourhood. The study area was 15.4 km of an urban transportation corridor in Mississauga, Canada. Nitrogen dioxide (NO2) was sampled at 112 sites during the summer in 2018 and observations ranged from 5.8 ppb to 19.65 ppb. A linear regression LUR model explained 69% of the variation in NO2 concentrations at this local scale, with estimated prediction errors less than 1.61 ppb, which were calculated by three cross-validation methods. Traffic volume, major and minor road lengths were key determinants among the predictor variables, and park area and distance to the nearest major intersection were the only variables with negative coefficients in the local-scale model. Extending the linear model approach with regression kriging improved the model's explanatory ability with a coefficient of determination at 0.91; however, smaller improvements were observed during cross-validation. Leave-one-out cross-validation for the linear model LUR model (RMSE = 1.44 ppb and a R2 = 0.64) and the regression kriging LUR model (RMSE = 1.34 ppb and a R2 = 0.69) were similar. Model performance remained stable when 10-fold cross-validation was performed with the regression kriging LUR model (regression kriging, R2 = 0.68 and RMSE = 1.36 ppb). The predicted air pollution levels ranged from 4.5 ppb to 25.6 ppb. This study demonstrates the ability of LUR modelling to perform well for local scale modelling in transportation dominated local urban environments.