Author(s): Sara Alhassani
Mentor(s): Dr. Jenna Krall, Public Health
AbstractMy primary focus for this project has been on measuring inequity in air pollution exposure using the Atkinson Index inequality measure.
With the alarming changes we’ve been seeing in the environment, it continues to be of utmost importance to study the environment. Researching air pollution, in particular, is crucial due to the detrimental impacts air pollution exposure has on the health of people, animals, and the earth. Increasing understanding of how air pollution impacts the world around us is used to guide and improve standards to protect everyone and everything at risk.
This presentation focuses on fine particulate matter, or PM 2.5, emissions. PM 2.5 are very small particles that can be inhaled deep into the lungs and pose serious health risks.
The objective of this subsection of the DEVA project is to evaluate the inequity in air pollution exposure between different race and ethnic groups. The upcoming analysis shows inequity between White, Black or African American, Asian, and Hispanic or Latino people.
Using past studies as guidance, we decided to use the Atkinson Index to measure inequity. This measure allows analysis to be done between groups. The Atkinson Index value ranges from 0 to 1 with 0 being complete equality and 1 being maximum inequality.
To determine inequity between races, I first obtained and organized data on all counties in Virginia from the 2010 Decennial Census to estimate inequity in 2011 PM 2.5 emissions using R programming.
Next, I combined that data with clean National Emissions Inventory PM 2.5 data provided by DEVA’s engineering team. I focused on 3 pollutant sectors: wildfires, coal combustion, and diesel trucks.
After this, I manipulated the data to obtain the variables needed to run the R Atkinson Index function for two values of epsilon, and obtained the results shown in the figures.
The bar plots show that when using an epsilon value of 0.75, the Atkinson Index values between groups for all 3 sectors is small and close to 0, not indicating a lot of inequality. However, when using epsilon value of 3, which accounts for sensitive analysis, we see much different results. While Diesel stays small, the Atkinson Index value for wildfires reaches almost 0.5, with coal combustion not too far below. The change in epsilon also appears to switch wildfires and coal combustion for which has greater inequality.
These results suggest that air pollution emission regulations need to be adjusted to lower air pollution exposure inequity caused by coal combustion in industrial broilers. While wildfires yielded similar results to coal combustion, their unpredictability makes it difficult to produce legislation to prevent them. This analysis can be enhanced by looking at inequity between different income levels and education attainment, but there is limited data on these factors at the county level.
Moving forward, DEVA hopes to quantify inequity for additional sectors and pollutants, to be used to support improvements in air pollution and exposure of susceptible and vulnerable populations.
I’d like to thank both Dr. Krall and Dr. Henneman for guiding me throughout this research, as well as the Jeffrees Memorial Trust for their support.
Thank you!