Pollution has been linked to increases in both Cardiovascular Disease and respiratory illness, but little investigation has been performed to date regarding the impact of daily fluctuations in Particulate Matter (PM) on COVID-19 case counts, hospitalizations, deaths and recovery times. Due to the smaller size of PM 2.5, it is able to efficiently bypass our defenses, enter our bloodstream and damage our cells causing increased susceptibility to viral infection contraction. This study aims to perform a short-term time series analysis of COVID-19 event rates in relation to PM 2.5 levels based on both state-level and county-level scales. Environmental Protection Agency (EPA) approved and Volunteered Geographic Information (VGI) air quality data was collected and compared in terms of reliability and consistency, from which correlation between COVID-19 incidents and PM 2.5 was deduced through various statistical and data visualization analyses. Ultimately, long-term PM 2.5 level averages were found to have a more direct impact on cumulative COVID-19 positive cases, but daily collated COVID-19 data played a role in training and testing Machine Learning (ML) algorithms to predict its occurrence based on PM 2.5 and other variables included within the study.