Multiple Regression Model

Particulate Matter (PM) 2.5 in California

Presenter: Therese Azevedo

Co-Presenter(s):
Juelle Citizen

Presenter Status: Undergraduate student

Academic Year: 20-21

Semester: Spring

Faculty Mentor: Elaine Newman

Department: Mathematics

President's Strategic Plan Goal: Sustainability and Environmental Inquiry

Screenshot URL: https://drive.google.com/uc?id=1NeN3iLkTNqJxa6BByRZMZvN0B0rfEdw3

Abstract:
Particulate Matter (PM) 2.5 is the concentration of particulate matter in the air with a diameter of 2.5 or less and can come from outdoor sources such as vehicles, burning of fuels from wood or coal, as well as reactions in the atmosphere between gases and powerplants. Due to the small size of this particulate matter, it can pose adverse health effects; these particulates are small enough to enter the human body and into the lungs or bloodstream. The researchers developed a model that has the ability to predict PM 2.5 by using the variables of pollution burden score, ozone concentration, traffic density, diesel PM, and poverty. The researchers found that both the variables and multiple regression model were statistically significant and useful.