Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study
1 School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC V6T 1Z3, Canada
2 Occupational Cancer Research Centre, Cancer Care Ontario, Ontario, Canada
3 Science Integration Division, Centre for Chronic Disease Prevention and Control, Public Health Agency of Canada, Ontario, Canada
4 Air Quality Research Division, Environment, Ontario, Canada
5 Department of Physics and Atmospheric Science, Dalhousie University, Ontario, Canada
6 Atmospheric Chemistry and Dynamics Branch, NASA Goddard Space Flight Center, Greenbelt, USA
7 Department of Physics and Atmospheric Science, Dalhousie University, Canada; Harvard-Smithsonian Center for Astrophysics, Cambridge, USA
8 School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
Environmental Health 2012, 11:22 doi:10.1186/1476-069X-11-22Published: 4 April 2012
Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.
National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.
Calibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 μg/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.
We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.
For submission to: Environmental Health