1 McGill University , Department of Epidemiology, Biostatistics and Occupational Health , Montreal , Quebec H3A 1A2 , Canada.
2 University of Toronto , Department of Civil Engineering , Toronto , Ontario M5S 1A4 , Canada.
3 McGill University , Department of Geography , Montreal , Quebec H3A 2K6 , Canada.
4 Concordia University , Department of Geography, Planning and Environment , Montreal , Quebec HG3 1M8 , Canada.

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

Environ Sci Technol. 2018 Sep 18;52(18):10777-10786

Authors: Fallah-Shorshani M, Hatzopoulou M, Ross NA, Patterson Z, Weichenthal S

Abstract

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.

PMID: 30119601 [PubMed - in process]