The increased availability and improved quality of new sensing technologies has created a growing body of research to evaluate and leverage these tools in order to quantify and describe urban environments.  Air quality in particular has received increased attention because of the well-established links to serious respiratory illnesses and the unprecedented levels of poor air quality in developing countries and cities around the world.  Though numerous laboratory and field evaluation studies have begun to explore the use and potential of low-cost air quality monitoring devices, the long-term performance and stability of these tools has not been adequately evaluated in urban environments and further research is needed.  In this study, we present the design of a low-cost air quality monitoring platform based on the Shinyei PPD42 particulate matter sensor and assess the sensor's performance during a field calibration campaign with a federal equivalent monitor (FEM).  We compare three calibration models and show that a gradient boosting regression model can improve sensor accuracy and potentially serve as a universal calibration curve.