Publication Date

5-2024

Advisor(s) - Committee Chair

Ngoc Nguyen, Lukun Zheng, Melanie Autin

Degree Program

Department of Mathematics

Degree Type

Master of Science

Abstract

Air pollution is a crucial factor that affects both the environment and public health. Various methods are available for assessing air quality and pollution levels, such as regression models, principal component analysis, and factor analysis tools. However, some of these methods present issues in multicollinearity and the nature of collected data. It is important to recognize that air pollution data is often uncertain, incomplete, and contains limited valid data points. Weather conditions and economic activities are also factors that can affect air pollution. With growing communities in Kentucky (KY), it is essential to address these factors as the state has unique economic imports and exports. Air pollution has a significant impact on both morbidity and mortality (Z. Song, Deng, & Ren, 2020). Therefore, monitoring and regulating air quality is necessary to mitigate these harmful effects. In this study, Grey Relational Analysis (GRA), Dynamic Grey Relational Analysis (DGRA), and Canonical Correlation Analysis (CCA) methodologies are used to analyze economic and meteorological factors and their relationship with three criteria pollutants: Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Ozone (O3). Data is gathered from 2016-2019 from various sources and localized to 3 counties in Kentucky.

Disciplines

Life Sciences | Medicine and Health Sciences | Physical Sciences and Mathematics

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