Date of Award

Summer 2017

Degree Type

Thesis

Degree Name

Departmental Honors

Department

Biology

Abstract

Air pollution poses one of the largest environmental risks to human health, and greatly contributes to increased mortality within populations. Of the different types of pollutants, fine particulate matter (PM2.5) has the most adverse health effects. Long-term exposure to PM2.5 is known to have serious health outcomes; however, evidence has indicated that even short-term exposure to moderate concentrations of PM2.5 is detrimental to human health. While PM2.5 does contribute to various respiratory conditions by affecting lung function, it also significantly affects the cardiovascular system. Elevated PM2.5 exposure increases risk for cardiovascular disease, congestive heart failure, and cardiac arrhythmias. To assess risk for these conditions, PM2.5 exposure levels must be accurately measured. This is most commonly done through centrally located air monitoring stations that are dispersed throughout the U.S. In Utah, these stations are managed by the state’s Department of Environmental Quality (DEQ) and they collect hourly PM2.5 readings 24/7. It is believed that PM2.5 level readings obtained from the DEQ do not accurately reflect personal exposure to the pollutant. Without accurate measurements of PM2.5 exposure, it is not possible to elucidate the role PM2.5 plays in lung and cardiovascular functional decline. This study aimed to determine whether published DEQ data strongly correlates to individual’s exposure to PM2.5 by comparing readings from personal air monitors. We hypothesized that both within and across a population of individuals, the personal air monitor PM2.5 readings would correlate poorly with the published PM2.5 concentrations. For the study, 20 volunteer residents of Cache Valley wore an AirBeam personal environmental monitor for a period of 8-10 hours as they went about their typical days. The AirBeam PM2.5readings from each individual were adjusted using calibration equations to account for inter-instrument variability and deviations in accuracy from the DEQ monitors. The hourly averages of the corrected values were then compared to the published DEQ data for the specific time frame the monitors were worn. For each participant, the DEQ data was plotted against the recorded AirBeam readings and linear regression equations were generated for each of the correlation graphs. Within subject R2 values from all 20 correlation graphs were low, with an average of 0.10 ± 0.02 and range from 0.004 to 0.38. These low values indicate that within the group of volunteers, the DEQ published data did not accurately reflect individual PM2.5 hourly exposures. Additionally, plotting the daily DEQ averages versus the AirBeam daily PM2.5 averages generated a linear regression equation with a between subject R2 value of 0.27. This, too, exhibited a moderately low R2 value, which demonstrates a poor correlation between the DEQ and AirBeam data across subjects. These findings illustrate a need for the use of personal environmental monitors to accurately assess individual PM2.5 exposure levels and possible cause and effect relationships to certain health outcomes.

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Faculty Mentor

Michael Lefevre

Departmental Honors Advisor

Heidi Wengreen