Department of Civil, Maritime, and Environmental Engineering, University of Southampton, Southampton, United Kingdom.
Department of Mathematics and Statistics, McGill University, Montreal, Canada.
PLoS One. 2022 Mar 8;17(3):e0264803. doi: 10.1371/journal.pone.0264803. eCollection 2022.
Traffic is one of the major contributors to PM2.5 in cities worldwide. Quantifying the role of traffic is an important step towards understanding the impact of transport policies on the possibilities to achieve cleaner air and accompanying health benefits. With the aim of estimating potential health benefits of eliminating traffic emissions, we carried out a meta-analysis using the World Health Organisation (WHO) database of source apportionment studies of PM2.5 concentrations. Specifically, we used a Bayesian meta-regression approach, modelling both overall and traffic-related (tailpipe and non-tailpipe) concentrations simultaneously. We obtained the distributions of expected PM2.5 concentrations (posterior densities) of different types for 117 cities worldwide. Using the non-linear Integrated Exposure Response (IER) function of PM2.5, we estimated percent reduction in different disease endpoints for a scenario with complete removal of traffic emissions. We found that eliminating traffic emissions results in achieving the WHO-recommended concentration of PM2.5 only for a handful of cities that already have low concentrations of pollution. The percentage reduction in premature mortality due to cardiovascular and respiratory diseases increases up to a point (30-40 ug/m3), and above this concentration, it flattens off. For diabetes-related mortality, the percentage reduction in mortality decreases with increasing concentrations-a trend that is opposite to other outcomes. For cities with high concentrations of pollution, the results highlight the need for multi-sectoral strategies to reduce pollution. The IER functions of PM2.5 result in diminishing returns of health benefits at high concentrations, and in case of diabetes, there are even negative returns. The results show the significant effect of the shape of IER functions on health benefits. Overall, despite the diminishing results, a significant burden of deaths can be prevented by policies that aim to reduce traffic emissions even at high concentrations of pollution.
交通是全球城市 PM2.5 的主要贡献者之一。量化交通的作用是理解交通政策对实现更清洁空气和伴随的健康益处的影响的重要步骤。为了估计消除交通排放的潜在健康益处，我们使用世界卫生组织（WHO）的 PM2.5 浓度源分配研究数据库进行了荟萃分析。具体来说，我们使用贝叶斯荟萃回归方法，同时对整体和与交通相关的（排气管和非排气管）浓度进行建模。我们获得了全球 117 个城市不同类型的预期 PM2.5 浓度（后验密度）分布。使用 PM2.5 的非线性综合暴露反应（IER）函数，我们针对完全消除交通排放的情景，估计了不同疾病终点的百分比减少。我们发现，只有少数污染浓度已经较低的城市才能通过消除交通排放来实现世卫组织建议的 PM2.5 浓度。由于心血管和呼吸道疾病导致的过早死亡百分比的减少会增加到一定程度（30-40ug/m3），超过这个浓度，它就会趋于平稳。对于与糖尿病相关的死亡率，死亡率的减少百分比随着浓度的增加而减少-这种趋势与其他结果相反。对于污染浓度较高的城市，结果突出了需要采取多部门策略来减少污染。PM2.5 的 IER 函数导致在高浓度下健康益处的回报递减，而在糖尿病的情况下，甚至存在负回报。结果表明 IER 函数的形状对健康益处有显著影响。总体而言，尽管效果递减，但即使在高污染浓度下，旨在减少交通排放的政策仍可以防止大量死亡。
