Robustness of Land-Use Regression Models Developed from Mobile Air Pollutant Measurements.

Abstract

Land-use regression (LUR) models are useful for resolving fine scale spatial variations in average air pollutant concentrations across urban areas. With the rise of mobile air pollution campaigns, characterized by short-term monitoring and large spatial extents, it is important to investigate the effects of sampling protocols on the resulting LUR. In this study a mobile lab was used to repeatedly visit a large number of locations (∼1800), defined by road segments, to derive average concentrations across the city of Montreal, Canada. We hypothesize that the robustness of the LUR from these data depends upon how many independent, random times each location is visited (Nvis) and the number of locations (Nloc) used in model development and that these parameters can be optimized. By performing multiple LURs on random sets of locations, we assessed the robustness of the LUR through consistency in adjusted R2 (i.e., coefficient of variation, CV) and in regression coefficients among different models. As Nloc increased, R2adj became less variable; for Nloc = 100 vs Nloc = 300 the CV in R2adj for ultrafine particles decreased from 0.088 to 0.029 and from 0.115 to 0.076 for NO2. The CV in the R2adj also decreased as Nvis increased from 6 to 16; from 0.090 to 0.014 for UFP. As Nloc and Nvis increase, the variability in the coefficient sizes across the different model realizations were also seen to decrease.