Science surrounding the use of low-cost sensors (LCS) to monitor air quality is rapidly expanding to satisfy the desire to fill in regional air quality data gaps. This project evaluates the suitability of two types of LCS: Modulair-PM and PurpleAir (SD and flex models) as efficient means to measure airborne particulate matter. The study involved physical installation of sensors at a suburban site in Denver, connectivity troubleshooting as necessary, and data analysis/modeling of data over multiple months. PM data was compared between individual sensors of the same type as well as across the two different sensor models and used to draw conclusions about air quality trends at the field site. Comparisons between sensors were generally in good agreement, but an additive bias was observed for several sensors, highlighting the importance of calibration of these types of units. The project concluded with aiding in a recent installation of PurpleAir units at a field site at the Kennedy Mountain Campus. Results from the project create a base level of understanding of LCS functionality to be expanded upon in future project applications.