What is your summer research project?
I was drawn to the topic of wildfires because while they are an extremely common and natural occurrence, they have become far more frequent and threatening to both human and animal life in the recent years, with multitudes of research pointing to anthropogenic climate change as the reason behind this. Moreover, while a lot of data has emphasized the effect of wildfires on physical/respiratory health, there is much less on that of wildfires on mental health. Under the mentorship of Dr. Hwang, I used R to investigate the connection between poor mental health days (dependent variable) and several fire indicators, such as percent risk of fire damage, PM2.5, poor AQI days, etc (independent variables). The figure that is attached shows that there is a positive correlation between bad mental health and increased risk of wildfire damage – a relationship that is more prominent when variation due to poverty level as well as education is removed.
What are the implications of your research?
My methods and project was fairly straightforward and simple in its design. I think similar or more advanced models could be used to make great strides in health monitoring and improving treatment for populations that are most strongly disadvantaged by climate change, because comprehensive health services (including mental health services) are significantly more inaccessible to subgroups facing climate disasters in regions that are less equipped to treat human health and recover following major environmental events.
What new skills have you gained through your research?
At the start of this program, I was definitely very unfamiliar with statistical analytics, and essentially all programming languages. While I would consider myself a beginner still, I have gained a lot more experience working with large quantities of data in R, and because I have spent all past research summers in a physical wetlab, I’ve also become more comfortable with the remote, computer-based side of research. Finally, I learned a lot about how to present data and explain statistically significant results to an audience of my peers and mentors.
