期刊
ENVIRONMENTAL RESEARCH
卷 147, 期 -, 页码 435-444出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.envres.2016.02.039
关键词
Traffic emissions; Air pollution; Exposure; Dynamic; Mobility; Nitrogen dioxide (NO2)
资金
- Collaborative Health Research Projects grant by the Government of Canada [336477]
- Montreal Department of Public Health
Air pollution in metropolitan areas is mainly caused by traffic emissions. This study presents the development of a model chain consisting of a transportation model, an emissions model, and atmospheric dispersion model, applied to dynamically evaluate individuals' exposure to air pollution by intersecting daily trajectories of individuals and hourly spatial variations of air pollution across the study domain. This dynamic approach is implemented in Montreal, Canada to highlight the advantages of the method for exposure analysis. The results for nitrogen dioxide (NO2), a marker of traffic related air pollution, reveal significant differences when relying on spatially and temporally resolved concentrations combined with individuals' daily trajectories compared to a long-term average NO2 concentration at the home location. We observe that NO2 exposures based on trips and activity locations visited throughout the day were often more elevated than daily NO2 concentrations at the home location. The percentage of all individuals with a lower 24-hour daily average at home compared to their 24-hour mobility exposure is 89.6%, of which 31% of individuals increase their exposure by more than 10% by leaving the home. On average, individuals increased their exposure by 23-44% while commuting and conducting activities out of home (compared to the daily concentration at home), regardless of air quality at their home location. We conclude that our proposed dynamic modelling approach significantly improves the results of traditional methods that rely on a long-term average concentration at the home location and we shed light on the importance of using individual daily trajectories to understand exposure. (C) 2016 Elsevier Inc. All rights reserved.
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