Case report study on correlation of Covid-19 among PM2.5 & PM10 concentrations between urban & rural Counties within the State of Ohio, USA

• Main Author: Dalanon, Jocian Dominick C.
• Format: text
• Language: English
• Published: Animo Repository 2022
• Subjects: COVID-19 (Disease) > United States (Ohio) > Case studies; Physics
• Online Access: https://animorepository.dlsu.edu.ph/etdb_physics/23; https://animorepository.dlsu.edu.ph/context/etdb_physics/article/1027/viewcontent/2022_Dalanon_Case_Report_Study_on_Correlation_of_COVID_19_among_PM2.5___PM10_Full_text.pdf
• Institution: De La Salle University

On the 5th of December of 2019, an individual from Wuhan, China contracted a virus that would later descend the planet into a lockdown a few months later. COVID-19 still continues to affect the lives of people three years later. Particulate matter (PM) is a type of aerosol that contains both organic and inorganic material that can compromise human health in various degrees of severity. Two variants of this aerosol, size 2.5 μm and 10 μm, will be investigated through a data analysis process to determine a possible correlation among COVID-19 individuals. The public datasets are retrieved from government agencies that are housed in the state of Ohio that are modified towards the timeline of the virus. Thus a study between March 2020 to September 2022 is performed to discover a potential correlation between the two variables. The time frame is then examined between two counties with opposing population sizes, one urban being the second-most populous and the other being the fifth-least populous. Within these counties, available SLAMS (State or Local Air Monitoring Stations) collect and store pollutant data. By developing a formula inspired by COVID-19 positivity reports, the dataset has summarized the average exposure an individual has prior to the reported onset COVID-19 symptoms. Utilizing the Pearson correlation on a statistical program called Statistica it is shown that there is a weak correlation for both counties in regards to the monthly and daily reported incidences. Certain SLAMS provided positive and negative correlations depending on the location of the monitor in relation to the county. The sex of an individual was not significant for both counties and their respective monitors. However due to the data inconsistency of some SLAMS, the data analysis caused a bias toward one particular SLAM in the urban county. This monitor was pivotal in determining a huge particular area in the county. The remaining SLAMS in both counties did not produce a major loss in data when including missing variables. A monthly average concentration of the pollutants was constructed to display when COVID-19 may have run rampant in the state of Ohio through these two counties. Regardless of the results both particulate matter 2.5 and 10 remain significant, albeit a low value, when inferring with the correlation values and events within both counties there is a relevant relationship.