The impact of wildfire smoke exposure on cardiovascular health in the western US

项目总结 心血管疾病(CVD)是美国的主要疾病负担。广泛的研究已经建立了 暴露于PM2.5和臭氧等空气污染物是心血管疾病死亡率增加的原因或可能原因 发病率。虽然美国在减少工业和汽车造成的空气污染方面取得了稳步进展 排放，最近野火的增加减缓了甚至逆转了这一进展，特别是在西部 我们。火灾烟雾中的大量空气污染物可以传播数百公里，对 远离最初大火的州的人口聚居区。由于气候变化预计将大幅增加 在该地区已经很高的野火风险下，有一个重要的公共卫生需要确定 暴露在烟雾中，其化学成分与周围空气污染截然不同，与心血管疾病有关 风险。了解吸烟成分的心脏毒性作用之间的关键区别对于 设计有效的火灾烟雾应急措施，并评估长期医疗保健需求 在美国西部。我们将调查心血管疾病风险与急性或慢性暴露于 烟雾中的主要空气污染物，包括PM2.5的主要成分(即硫酸盐、硝酸盐、有机碳、 元素碳)和臭氧和甲醛(甲醛)等气体污染物。具体地说，我们将首先 估计2001年至#年间每天的烟雾和无烟PM2.5组分、臭氧和甲醛的分辨率为1公里。 2020年在美国西部使用美国环保局的社区多尺度空气质量模型(CMAQ)，由地面- 在机器学习模型中调整EPA测量和卫星数据的水平。第二，我们将把这个丰富的 暴露数据集，包括来自美国西部六个州的急诊室就诊和住院数据，以评估不良反应 急性暴露于火灾烟雾对包括急性心肌梗死在内的各种心血管疾病结局的健康影响 中风、心力衰竭、房颤和所有心血管事件。第三，我们将此暴露数据集与 美国西部的医疗保险数据，以表征慢性暴露的影响(即，前几周、几个月或 年)对这些心血管疾病的结果进行炮轰。作为子分析，我们将开发一种增强的机器学习 通过包括2016-2020年可用低成本传感器测量来估计每日PM2.5的模型，并使用 对这一数据集进行类似的流行病学分析，主要进行分析。最后，我们将评估未来 2050年至2100年气候变化导致的慢性烟雾暴露的心血管疾病负担 本研究建立了浓度-反应函数。研究人数多，质量高 暴露估计，我们希望对急性和慢性暴露之间的关系产生强有力的估计 野火、烟雾和心血管疾病风险。有了高分辨率的区域气候模型模拟，我们也将能够 预测气候变化造成的与吸烟有关的长期心血管疾病负担。

PROJECT SUMMARY Cardiovascular diseases (CVD) are the primary disease burden in the US. Extensive research has established exposure to air pollutants such as PM2.5 and ozone as cause or likely cause of increased CVD mortality and morbidity. While the US has made steady progress in reducing air pollution from industrial and vehicle emissions, recent increase in wildfires has slowed or even reversed this progress particularly in the Western US. Massive quantities of air pollutants in fire smoke can travel hundreds of kilometers affecting highly populated areas in states far from the original fire. As climate change is projected to substantially increase the already high wildfire risk in this region, there is an important public health need to establish the effects of smoke exposure, which has a distinctly different chemical composition from ambient air pollution, on CVD risks. Understanding the key distinctions between the cardiotoxic effects of smoke constituents is important to design effective emergency response measures to fire smoke, and to assess the long-term health care needs in the Western US. We will investigate the relationship between CVD risks and acute or chronic exposure to major air pollutants in smoke, including major PM2.5 constituents (i.e., sulfate, nitrate, organic carbon, elemental carbon), and gas pollutants such as ozone and formaldehyde (HCHO). Specifically, we will first estimate daily smoke and non-smoke PM2.5 constituents, ozone and HCHO at 1 km resolution from 2001 to 2020 in the Western US using EPA’s Community Multi-scale Air Quality Model (CMAQ) calibrated by ground- level EPA measurements and satellite data in a machine learning model. Second, we will link this rich exposure dataset with ED visits and hospitalization data from six Western US states to assess the adverse health effects of acute exposure to fire smoke on various CVD outcomes including acute myocardial infarction, stroke, heart failure, atrial fibrillation, and all CVD events. Third, we will link this exposure dataset with Medicare data in Western US to characterize the effect of chronic exposure (i.e., previous weeks, months or year) to fire smoke on these CVD outcomes. As a sub-analysis, we will develop an enhanced machine learning model by including available low-cost sensor measurements for 2016 – 2020 to estimate daily PM2.5, and use this dataset to conduct similar epidemiological analysis to the main analysis. Finally, we will assess the future CVD disease burden of chronic smoke exposure due to climate change from 2050 to 2100 using the concentration-response functions developed in this study. With large study populations and high-quality exposure estimates, we expect to generate robust estimates of the associations of acute and chronic exposure to wildfire smoke and CVD risks. With high-resolution regional climate model simulations, we will also be able to project smoke-related long-term CVD burden imposed by climate change.