Analyzing the Effects of Data Variability & Volume on Predicting Particulate Matter (PM2.5): Insights from a Machine Learning Approach

Author

Jada A. Macharie, CUNY Hunter CollegeFollow

Date of Award

Fall 1-31-2024

Document Type

Thesis

Degree Name

Master of Arts (MA)

Department

Geography

First Advisor

Wenge Ni-Meister

Second Advisor

Shipeng Sun

Third Advisor

Maddalena Romano

Academic Program Adviser

Jochen Albrecht

Abstract

Accurately predicting PM_2.5 concentrations are imperative to the future of public health and environmental policies. Machine learning models incorporating spatial and temporal datasets to predict PM_2.5 are often limited by data availability constraints and poor resolution satellite imagery.

Recommended Citation

Macharie, Jada A., "Analyzing the Effects of Data Variability & Volume on Predicting Particulate Matter (PM2.5): Insights from a Machine Learning Approach" (2024). CUNY Academic Works.
https://academicworks.cuny.edu/hc_sas_etds/1276