Graduation Year

2026

Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts

Department

Biology

Reader 1

Pete Chandrangsu

Reader 2

Jamie Felicitas-Perkins

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Abstract

As the fourth leading cause of death in the world, chronic obstructive pulmonary disorder (COPD) is one of the most severe respiratory disorders characterized by airway inflammation.  For the past two decades, climate change has exacerbated the prevalence of wildfires in California due to rising temperatures and lower precipitation. With the influx of wildfires, poor atmospheric air quality continues to be a serious health problem in the Los Angeles area, especially after the recent Eaton fire in January of 2025. My study hopes to assess whether the air pollutants particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO) increase the risk of COPD-related hospitalization for residents in Los Angeles County. Daily PM2.5, PM10, and CO concentrations were taken from the California Air Resources Board and annual COPD-related hospitalization data was taken from California Health and Human Services (CalHHS) from 2005 to 2024. Socioeconomic status, age, and gender were also taken into account using CalHHS’s 2023 hospitalization data. My analysis shows a decreasing trend over time for the average air quality concentrations and COPD-related hospitalizations, as well as the relationship between socioeconomic status, age, and gender with COPD hospitalization. Based on the distribution, socioeconomic status and age may be factors associated with COPD hospitalization, while gender may not play an active role due to hospitalization counts being relatively comparable. Previous studies on the reliance on pharmaceutical treatment for COPD imply that socioeconomic status is a significant barrier to preventative treatment. As such, the study exemplifies that it is imperative we mitigate the effect of poor air quality in Los Angeles and improve community health programs to best identify and protect individuals at risk. However, there are two major limitations in my study: the time lag and air pollutants not being wildfire-specific. A potential future direction is applying a distributed lag non-linear model (DLNM) to integrate the time lag, followed by a predictive time-series model to predict COPD hospitalization outcomes after the Eaton fire.

Download

Included in

Biology Commons

Share

COinS