Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
Department of Civil and Mineral Engineering, University of Toronto, Ontario, Canada.
Res Rep Health Eff Inst. 2024 Jul;2024(217):1-63.
Numerous studies support an important relationship between long-term exposure to outdoor fine particulate air pollution (PM) and both nonaccidental and cause-specific mortality. Less is known about the long-term health consequences of other traffic pollutants, including ultrafine particles (UFPs, <0.1 μm) and black carbon (BC), which are often present at elevated concentrations in urban areas but are not currently regulated. Knowledge is lacking largely because these pollutants generally are not monitored by governments and vary greatly over small spatial scales, hindering the evaluation of long-term exposures in population-based studies.
We aimed to estimate associations between long-term exposures to outdoor UFPs and BC and nonaccidental and cause-specific mortality in Canada's two largest cities, Montreal and Toronto. We considered several approaches to exposure assessment: (1) land use regression (LUR) models based on large-scale year-long mobile monitoring campaigns combined with detailed land use and traffic information; (2) machine learning (i.e., convolutional neural networks [CNN]) models trained by combining mobile monitoring data with aerial images; and (3) the combined use of these two approaches. We also examined exposure models with and without backcasting based on historical trends in vehicle emissions (to capture potential trends in pollutant concentrations over time) and with and without accounting for neighborhood-level mobility patterns (based on travel demand surveys). These exposure models were linked to members of the Canadian Census Health and Environment Cohorts (CanCHEC) residing in Montreal or Toronto (including census years 1991, 1996, 2001, and 2006) with mortality follow-up from 2001 (or cohort entry for the 2006 cohort) to 2016. Cox proportional hazard models were used to estimate associations between long-term exposures to outdoor UFPs and BC, adjusting for sociodemographic factors and co-pollutants identified as potential confounding factors. Concentration-response relationships for outdoor UFPs and BC were also examined for nonaccidental and cause-specific mortality using smoothing splines.
Our cohort study included approximately 1.5 million people with 174,200 nonaccidental deaths observed during the follow-up period. Combined LUR and machine learning model predictions performed slightly better than LUR models alone and were used as the main exposure models in all epidemiological analyses. Long-term exposures to outdoor UFP number concentrations were consistently positively associated with nonaccidental and cause-specific mortality. Importantly, hazard ratios (HRs) for outdoor UFP number concentrations were sensitive to adjustment for UFP size: UFP size was inversely related to number concentrations and independently associated with mortality, resulting in underestimation of mortality risk for outdoor UFP number concentrations when UFP size was excluded. HRs for outdoor UFP number concentrations were robust to backcasting and mobility weighting but varied slightly in analyses using LUR and machine learning models alone, with stronger associations typically observed for the machine learning models. Associations between outdoor BC concentrations and mortality were generally weak or null, but a positive association was observed for cardiovascular mortality.
Outdoor UFP number concentrations were consistently associated with increased risks of nonaccidental and cause-specific mortality in Montreal and Toronto. Our results suggest that UFP size should be considered in epidemiological analyses of outdoor UFP number concentrations, as excluding size can lead to an underestimation of health risks. Our results suggest that outdoor UFP number concentrations are positively associated with mortality independent of other outdoor air pollutants, including PM mass concentrations and oxidant gases (i.e., nitrogen dioxide [NO] and ozone [O]). As outdoor UFPs are currently unregulated, interventions targeting these pollutants could significantly affect population health.
许多研究支持长期暴露于户外细颗粒物空气污染（PM）与非意外和特定原因死亡率之间存在重要关系。对于其他交通污染物（包括超细颗粒（UFPs，<0.1μm）和黑碳（BC））的长期健康后果知之甚少，这些污染物通常在城市地区以较高浓度存在，但目前尚未受到监管。由于这些污染物通常不受政府监测，并且在小空间尺度上变化很大，这使得在基于人群的研究中评估长期暴露变得困难，因此知识的缺乏主要是由于这个原因。
我们旨在估计加拿大两个最大城市蒙特利尔和多伦多的户外 UFPs 和 BC 长期暴露与非意外和特定原因死亡率之间的关联。我们考虑了几种暴露评估方法：（1）基于大型年度移动监测运动与详细的土地使用和交通信息相结合的土地使用回归（LUR）模型；（2）通过将移动监测数据与航空图像相结合进行训练的机器学习（即卷积神经网络[CNN]）模型；以及（3）这两种方法的结合使用。我们还研究了具有和不具有基于车辆排放历史趋势的回溯（以捕捉污染物浓度随时间的潜在趋势）以及具有和不具有考虑邻里移动模式（基于出行需求调查）的暴露模型。这些暴露模型与居住在蒙特利尔或多伦多的加拿大人口普查健康和环境队列（CanCHEC）成员相关联（包括 1991 年、1996 年、2001 年和 2006 年的人口普查年份），并从 2001 年（或 2006 年队列的队列进入）开始进行死亡率随访至 2016 年。使用 Cox 比例风险模型调整社会人口因素和被确定为潜在混杂因素的共同污染物，估计户外 UFPs 和 BC 长期暴露与非意外和特定原因死亡率之间的关联。还使用平滑样条检查户外 UFPs 和 BC 与非意外和特定原因死亡率之间的浓度-反应关系。
我们的队列研究包括大约 150 万人，在随访期间观察到 174,200 例非意外死亡。组合 LUR 和机器学习模型预测的表现略优于单独的 LUR 模型，并且作为所有流行病学分析的主要暴露模型。户外 UFP 数量浓度的长期暴露与非意外和特定原因死亡率呈持续正相关。重要的是，户外 UFP 数量浓度的危害比（HR）对 UFP 大小的调整敏感：UFP 大小与数量浓度呈反比，与死亡率独立相关，当排除 UFP 大小时，户外 UFP 数量浓度的死亡率风险估计值会低估。户外 UFP 数量浓度的 HR 在回溯和移动权重调整方面是稳健的，但在仅使用 LUR 和机器学习模型进行的分析中略有差异，通常观察到机器学习模型的关联更强。户外 BC 浓度与死亡率之间的关联通常较弱或为零，但心血管死亡率呈正相关。
户外 UFP 数量浓度与蒙特利尔和多伦多的非意外和特定原因死亡率风险增加始终存在关联。我们的结果表明，在户外 UFP 数量浓度的流行病学分析中应考虑 UFP 大小，因为排除大小会导致健康风险的低估。我们的结果表明，户外 UFP 数量浓度与死亡率呈正相关，独立于其他户外空气污染物（即 PM 质量浓度和氧化剂气体（即二氧化氮[NO]和臭氧[O]））。由于户外 UFPs 目前不受监管，针对这些污染物的干预措施可能会对人口健康产生重大影响。
