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.