Date of Award

January 2021

Document Type


Degree Name

Medical Doctor (MD)



First Advisor

L. Drew Hill

Second Advisor

Krystal Pollitt


The detection and measurement of particulate matter (PM) in ambient air samples is an important tool in air quality monitoring for public health. Multiple methods exist for identifying the chemical and physical properties of PM, though these methods vary in their spatial and temporal resolution as well as their expense and ease of implementation. As such, it is important to understand how these different methods relate to one another and whether different approaches can be used to approximate the same information. By comparing various outputs of the thermo-optical attenuation (TOA) method with light attenuation data collected from optical methods of PM detection, this study identifies that the most strongly correlated measurements are between those of elemental carbon (EC) via thermo-optical reflectance (TOR) and transmission (TOT) and the measurement of black carbon (BC) via light absorption at infrared and ultraviolent frequencies, findings consistent with the available literature. Knowing that these methods have measurable correlation, it was feasible to use the optical data to calculate a variable that is specific for biomass combustion, known as Delta-C, to see if this approach can predict West Coast wildfire smoke identified in East Coast air samples. Using logistic regression to test variables’ ability to predict a set of days identified to have wildfire smoke plumes mixing from the upper atmosphere into the air closer to the planetary surface, Delta-C was a statistically significant predictor of remote sensing of wildfire smoke even when controlling for weather variables known to affect PM detection. The health impact of general small particulate matter of size < 2.5 µm/m3 (PM2.5) and of wildfire smoke in particular is reviewed hereafter to explore the impact this detected wildfire smoke may have on population health both locally and nationally.


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