Little work has accounted for congestion, using data that reflects driving patterns, traffic volume, and speed, to examine the association between traffic emissions and human health. In this study, we performed a health risk assessment of PM_2.5 emissions during congestion periods in the Greater Toronto and Hamilton Area (GTHA), Canada. Specifically, we used a micro-level approach that combines the Stochastic User Equilibrium Traffic Assignment Algorithm with a MOVES emission model to estimate emissions considering congestion conditions. Subsequently, we applied a concentration-response function to estimate PM_2.5-related mortality, and the associated health costs. Our results suggest that traffic congestion has a substantial impact on human health and the economy in the GTHA, especially at the most congested period (7:00 am). Considering daily mortality, our results showed an impact of 206 (boundary test 95%: 116; 297) and 119 (boundary test 95%: 67; 171) deaths per year (all-cause and cardiovascular mortality, respectively). The economic impact from daily mortality is approximately $1.3 billion (boundary test 95%: 0.8; 1.9), and $778 million (boundary test 95%: 478; 981), for all-cause and cardiovascular mortality, respectively. Our study can guide reliable projections of transportation and air pollution levels, improving the capability of the medical community to prepare for future trends.

使用反映驾驶模式，交通量和速度的数据来检查交通排放与人类健康之间的关系，很少有工作可以解决交通拥堵的问题。在这项研究中，我们对加拿大大多伦多地区和汉密尔顿地区（GTHA）拥堵期间的PM _2.5排放进行了健康风险评估。具体来说，我们使用了一种微观方法，该方法结合了随机用户均衡流量分配算法和MOVES排放模型，以在考虑拥堵条件的情况下估算排​​放量。随后，我们应用浓度响应函数估算PM _2.5相关的死亡率，以及相关的健康费用。我们的结果表明，交通拥堵对GTHA中的人类健康和经济具有重大影响，尤其是在最拥挤的时段（上午7:00）。考虑到每日死亡率，我们的结果显示每年有206人（边界测试95％：116； 297）和119（边界测试95％：67； 171）死亡（分别是全因死亡率和心血管死亡率）。对于全因死亡率和心血管疾病死亡率，每日死亡率带来的经济影响分别约为13亿美元（边界检验95％：0.8； 1.9）和7.78亿美元（边界检验95％：478； 981）。我们的研究可以指导运输和空气污染水平的可靠预测，提高医学界为未来趋势做准备的能力。