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 (N) and the number of locations (N) 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 R (i.e., coefficient of variation, CV) and in regression coefficients among different models. As N increased, R became less variable; for N = 100 vs N = 300 the CV in R for ultrafine particles decreased from 0.088 to 0.029 and from 0.115 to 0.076 for NO The CV in the R also decreased as N increased from 6 to 16; from 0.090 to 0.014 for UFP. As N and N increase, the variability in the coefficient sizes across the different model realizations were also seen to decrease.

Land-use regression (LUR) models of air pollutants are frequently developed on the basis of short-term stationary or mobile monitoring approaches, which raises the question of whether these two data collection protocols lead to similar exposure surfaces. In this study, we measured ultrafine particles (UFP) and black carbon (BC) concentrations in Toronto during summer 2016, using two short-term data collection approaches: mobile, involving 3023 road segments sampled on bicycles, and stationary, involving 92 sidewalk locations. We developed four LUR models and exposure surfaces, for the two pollutants and measurement protocols. Coefficients of determination ( R) varied from 0.434 to 0.525. Various small-scale traffic variables were included in the mobile LUR. Pearson correlation coefficients between the mobile and stationary surfaces were 0.23 for UFP and 0.49 for BC. We also compared the two surfaces using personal exposures from a panel study in Toronto conducted during the same period. The personal exposures differed from the outdoor exposures derived from the combination of GPS information and exposure surfaces. For UFP, the median for personal outdoor exposure was 26 344 part/cm, while the cycling and stationary surfaces predicted medians of 31 201 and 19 057 part/cm. Similar trends were observed for BC, with median exposures of 1764 (personal), 1799 (cycling), and 1469 ng/m (stationary).

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