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Comparing the estimated health impacts of long-term exposure to traffic-related air pollution using fixed-site, mobile, and deep learning models

Principal Investigator: 
,

McGill University, Canada

This study will evaluate health impacts of long-term exposures to traffic-related air pollution using exposure estimates from fixed-site and mobile measurement campaigns, as well as deep learning models, in Toronto and Montreal, Canada. The investigators will compare exposure estimates generated by these models to present-day and historical measurements, and to each other. 

Funded under
Status: 
Ongoing
Abstract

Poster abstract for HEI Annual Conference 2022

Characterizing the Long-Term Health Impacts of Ultrafine Particles and Black Carbon using Mobile and Deep Learning Models

Marshall Lloyd1, Arman Ganji2, Junshi Xu2, Alessya Venuta1, Leora Simon1, Mingqian Zhang2, Shoma Yamanouchi2, Joshua Apte3, Paul Villeneuve4, Eric Lavigne5, Alexandra Schmidt1, Hong Chen2, Mike Tjepkema6, Rick Burnett7, Kris Hong8, Marianne Hatzopoulou2, Scott Weichenthal1

1McGill University, Montreal, Canada; 2University of Toronto, Toronto, Canada; 3University of California Berkeley, Berkeley, USA; 4Carleton University, Ottawa, Canada; 5University of Ottawa, Ottawa, Canada; 6Statistics Canada, Ottawa, Canada; 7Consultant, Ottawa, Canada; 8Consultant, Manchester, United Kingdom

Background: Numerous epidemiological studies support an important relationship between long-term exposure to outdoor fine particulate air pollution (PM2.5) and mortality. However, much less is known about the long-term health impacts of other traffic pollutants including ultrafine particles (UFPs, <0.1 µm) and black carbon (BC) which are often present at elevated concentrations in urban areas. In this study, we will develop and apply new models of within-city spatial variations in outdoor UFP and BC concentrations across Montreal and Toronto, Canada using mobile monitoring data collected throughout 2020-2021.

Methods: Our study has five specific aims. Aim 1 will develop new exposure models for UFPs, BC, and NO2/O3 using mobile monitoring data and will compare model predictions to fixed-site measurements collected over the same time-period. Aim 2 will evaluate the ability of mobile models to estimate past exposures using historical monitoring data. Aim 3 will integrate population-level mobility data into exposure models to capture neighbourhood-level trends in population mobility. Aim 4 will evaluate a novel approach to exposure modelling using digital imagery to estimate spatial variations in UFPs/BC. Aim 5 will apply the new mobile and deep learning models in the Canadian Census Health and Environment Cohort (CanCHEC) to estimate associations between UFPs/BC and nonaccidental and cause-specific mortality. This analysis will include 2-million adults followed over a 15-year period (2001-2016) in Montreal and Toronto pooled across multiple census years (1991, 1996, 2001, 2006). We will conduct a detailed evaluation of how the method of exposure assessment impacts the magnitudes of health risks and the shapes of concentration-response relationships. In addition, we will specifically evaluate how errors in time (i.e., with and without back-casting) and space (i.e., residential vs. mobility-weighted exposure models) impact estimates of long-term health risks.

Results: Hundreds of hours of mobile monitoring data were collected in 2020-2021. Preliminary results in cross-validation procedures for Montreal suggest that our new models explain the majority of spatial variations in outdoor UFP number concentrations (R2 =0.58), UFP size (nm) (R2=0.55), and black carbon concentrations (R2=0.64). Preliminary models also explain the majority of spatial variations in NO2/O3 across each city ( 0.75 < R2 < 0.85).

Conclusions: Mobile data collection was successfully completed across Montreal and Toronto in 2020-2021 and includes hundreds of hours of data over thousands of road segments. Exposure model development is under way and the preliminary results are encouraging. Final exposure models will be completed by the end of the second year of our project as planned.