Disparities in exposure to traffic-related air pollution have been widely reported. However, little work has been done to simultaneously assess the impact of various vehicle types on populations of different socioeconomic/ethnic backgrounds. In this study, we employed an extreme gradient-boosting approach to spatially distribute light-duty vehicle (LDV) and heavy-duty truck emissions across the city of Toronto from 2006 to 2020. We examined associations between these emissions and different marginalization indices across this time span. Despite a large decrease in traffic emissions, disparities in exposure to traffic-related air pollution persisted over time. Populations with high residential instability, high ethnic concentration, and high material deprivation were found to reside in regions with significantly higher truck and LDV emissions. In fact, the gap in exposure to traffic emissions between the most residentially unstable populations and the least residentially unstable populations worsened over time, with trucks being the larger contributor to these disparities. Our data also indicate that the number of trucks and truck emissions increased substantially between 2019 and 2020 whilst LDVs decreased. Our results suggest that improvements in vehicle emission technologies are not sufficient to tackle disparities in exposure to traffic-related air pollution.

中文翻译：

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十五年的社区暴露于重型排放：捕捉空间和时间上的差异

广泛报道了与交通相关的空气污染暴露的差异。然而，很少有人同时评估各种车辆类型对不同社会经济/种族背景人群的影响。在这项研究中，我们采用极端梯度提升方法在 2006 年至 2020 年期间在整个多伦多市空间分布轻型车辆 (LDV) 和重型卡车排放。我们研究了这些排放与不同边缘化指数之间的关联时间跨度。尽管交通排放量大幅减少，但随着时间的推移，与交通相关的空气污染暴露的差异仍然存在。居住不稳定、种族集中度高和物质匮乏程度高的人口被发现居住在卡车和轻型汽车排放量明显较高的地区。事实上，居住最不稳定的人群和居住最不稳定的人群之间的交通排放暴露差距随着时间的推移而恶化，卡车是造成这些差异的更大因素。我们的数据还表明，卡车数量和卡车排放量在 2019 年至 2020 年间大幅增加，而 LDV 则有所下降。我们的结果表明，车辆排放技术的改进不足以解决与交通相关的空气污染暴露的差异。我们的数据还表明，卡车数量和卡车排放量在 2019 年至 2020 年间大幅增加，而 LDV 则有所下降。我们的结果表明，车辆排放技术的改进不足以解决与交通相关的空气污染暴露的差异。我们的数据还表明，卡车数量和卡车排放量在 2019 年至 2020 年间大幅增加，而 LDV 则有所下降。我们的结果表明，车辆排放技术的改进不足以解决与交通相关的空气污染暴露的差异。