Creating National Air Pollution Models for Population Exposure Assessment in Canada

• DOI: 10.1289/ehp.1002976
• Publication Date: 2011-03-31T18:07:58Z
• Authors: Perry Hystad; Eleanor Setton; Alejandro Cervantes; Karla Poplawski; Steeve Deschenes; Michael Brauer; Aaron van Donkelaar; Lok Lamsal; Randall Martin; Michael Jerrett; Paul Demers
• Keywords: Inverse distance weighting

Background: Population exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, regulatory purposes. Currently, such limited.Methods: We created 2006 national models fine particulate matter [PM with aerodynamic diameter ≤ 2.5 μm (PM2.5)], nitrogen dioxide (NO2), benzene, ethylbenzene, 1,3-butadiene from routinely collected fixed-site monitoring data in Canada. In multiple regression models, we incorporated satellite estimates geographic predictor variables to background regional variation used deterministic gradients variation. The NO2 benzene evaluated independent measurements previous land use were conducted seven Canadian cities. National applied census block-face points, each of which represents the location approximately 89 individuals, produce population exposure.Results: model explained 73% monitor concentrations, PM2.5 46%, 62%, ethylbenzene 67%, 68%. predicted, on average, 43% within-city compared 18% when using inverse distance weighting data. Benzene performed poorly predicting variability. Based our estimated ambient annual average population-weighted exposures (in micrograms per cubic meter) 8.39 PM2.5, 23.37 NO2, 1.04 0.63 0.09 1,3-butadiene.Conclusions: here improve traditional monitor-based approaches by capturing both pollution Applying can extend modeling techniques informing regulation.