title: 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 Nlocincreased, R2adjbecame less variable; for Nloc= 100 vs Nloc= 300 the CV in R2adjfor ultrafine particles decreased from 0.088 to 0.029 and from 0.115 to 0.076 for NO2.The CV in the R2adjalso decreased as Nvisincreased from 6 to 16; from 0.090 to 0.014 for UFP. As Nlocand Nvisincrease, the variability in the coefficient sizes across the different model realizations were also seen to decrease.
authors.affiliation: Department of Civil Engineering, University of Toronto , Toronto, Ontario Canada , M5S 1A4.
authors.forename: Marianne
authors.initials: M
authors.lastname: Hatzopoulou
authors.affiliation: Division of Clinical Epidemiology, McGill University , Montreal, Quebec Canada , H4A 3J1.
authors.forename: Marie France
authors.initials: MF
authors.lastname: Valois
authors.affiliation: Air Quality Processes Research Section, Environment and Climate Change Canada , Downsview, Ontario Canada , M3H 5T4.
authors.forename: Ilan
authors.initials: I
authors.lastname: Levy
authors.affiliation: Air Quality Processes Research Section, Environment and Climate Change Canada , Downsview, Ontario Canada , M3H 5T4.
authors.forename: Cristian
authors.initials: C
authors.lastname: Mihele
authors.affiliation: Air Quality Processes Research Section, Environment and Climate Change Canada , Downsview, Ontario Canada , M3H 5T4.
authors.forename: Gang
authors.initials: G
authors.lastname: Lu
authors.affiliation: School of Urban Planning, McGill University , Montreal, Quebec Canada , H3A 0C2.
authors.forename: Scott
authors.initials: S
authors.lastname: Bagg
authors.affiliation: Department of Civil Engineering, University of Toronto , Toronto, Ontario Canada , M5S 1A4.
authors.forename: Laura
authors.initials: L
authors.lastname: Minet
authors.affiliation: Air Quality Processes Research Section, Environment and Climate Change Canada , Downsview, Ontario Canada , M3H 5T4.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Brook
mesh_terms: D000393:Air Pollutants; D000397:Air Pollution; D004784:Environmental Monitoring; D008962:Models, Theoretical; D052638:Particulate Matter; D012044:Regression Analysis
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter