Evaluating the Impact of Neighborhood Characteristics on Differences between Residential and Mobility-Based Exposures to Outdoor Air Pollution

• Published in: Environmental science & technology Vol. 52; no. 18; pp. 10777 - 10786
• Main Authors: Fallah-Shorshani, Masoud, Hatzopoulou, Marianne, Ross, Nancy A, Patterson, Zachary, Weichenthal, Scott
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 18.09.2018
• Subjects: Air pollution; Airborne particulates; Canada; cities; Data processing; Employment; epidemiological studies; Epidemiology; Exposure; highways; Human exposure; Impact analysis; income; mobile telephones; Mobility; Neighborhoods; Nitrogen dioxide; Outdoor air quality; Parameter estimation; Particulate matter; particulates; people; Pollutants; Residential areas; Residential location; risk estimate; Roads & highways; Smartphones; traffic; Ultrafines; unemployment; Canada
• ISSN: 0013-936X; 1520-5851; 1520-5851
• DOI: 10.1021/acs.est.8b02260

Epidemiological studies often assign outdoor air pollution concentrations to residential locations without accounting for mobility patterns. In this study, we examined how neighborhood characteristics may influence differences in exposure assessments between outdoor residential concentrations and mobility-based exposures. To do this, we linked residential location and mobility data to exposure surfaces for NO2, PM2.5, and ultrafine particles in Montreal, Canada for 5452 people in 2016. Mobility data were collected using the MTL Trajet smartphone application (mean: 16 days/subject). Generalized additive models were used to identify important neighborhood predictors of differences between residential and mobility-based exposures and included residential distances to highways, traffic counts within 500 m of the residence, neighborhood walkability, median income, and unemployment rate. Final models including these parameters provided unbiased estimates of differences between residential and mobility-based exposures with small root-mean-square error values in 10-fold cross validation samples. In general, our findings suggest that differences between residential and mobility-based exposures are not evenly distributed across cities and are greater for pollutants with higher spatial variability like NO2. It may be possible to use neighborhood characteristics to predict the magnitude and direction of this error to better understand its likely impact on risk estimates in epidemiological analyses.