title: A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.
abstract: Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure.
authors.affiliation: Air Health Science Division, Health Canada, Ottawa, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada. Electronic address: scott.weichenthal@hc-sc.gc.ca.
authors.forename: Scott
authors.initials: S
authors.lastname: Weichenthal
authors.affiliation: Air Health Science Division, Health Canada, Ottawa, Canada.
authors.forename: Keith Van
authors.initials: KV
authors.lastname: Ryswyk
authors.affiliation: School of Urban Planning, McGill University, Montreal, Canada.
authors.forename: Alon
authors.initials: A
authors.lastname: Goldstein
authors.affiliation: School of Urban Planning, McGill University, Montreal, Canada.
authors.forename: Scott
authors.initials: S
authors.lastname: Bagg
authors.affiliation: Department of Civil Engineering, McGill University, Montreal, Canada.
authors.forename: Maryam
authors.initials: M
authors.lastname: Shekkarizfard
authors.affiliation: Department of Civil Engineering, University of Toronto, Toronto, Canada.
authors.forename: Marianne
authors.initials: M
authors.lastname: Hatzopoulou
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D016018:Least-Squares Analysis; D016014:Linear Models; D000069550:Machine Learning; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D011792:Quebec; D012621:Seasons
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Built environment; Land use regression; Traffic; Ultrafine particles