Shekarrizfard Maryam, Faghih-Imani Ahmadreza, Hatzopoulou Marianne
Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke St. W., Room 492, Montréal, Québec H3A 2K6, Canada.
Civil Engineering, University of Toronto, 35 St George Street, Toronto ON M5S 1A4, Canada.
Environ Res. 2016 May;147:435-44. doi: 10.1016/j.envres.2016.02.039. Epub 2016 Mar 10.
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.
大都市地区的空气污染主要由交通排放物造成。本研究展示了一个由交通模型、排放模型和大气扩散模型组成的模型链的开发,该模型链通过将个体的日常轨迹与研究区域内空气污染的每小时空间变化相交,用于动态评估个体暴露于空气污染的情况。这种动态方法在加拿大蒙特利尔实施,以突出该方法在暴露分析方面的优势。作为交通相关空气污染标志物的二氧化氮(NO₂)的结果显示,与在家中位置的长期平均NO₂浓度相比,当依赖于空间和时间分辨浓度并结合个体的日常轨迹时,存在显著差异。我们观察到,基于全天出行和活动地点的NO₂暴露通常高于在家中位置的每日NO₂浓度。在家中24小时日均浓度低于其24小时移动暴露的所有个体的百分比为89.6%,其中31%的个体因离开家而使暴露增加超过10%。平均而言,无论家中位置的空气质量如何,个体在通勤和离家进行活动时暴露增加了23 - 44%(与家中每日浓度相比)。我们得出结论,我们提出的动态建模方法显著改善了依赖于家中位置长期平均浓度的传统方法的结果,并且我们阐明了使用个体日常轨迹来理解暴露的重要性。