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Multi-dimensional Characterization of Particulate Matter Low-cost Sensors in Florida
Jasper Bowles, Marc Compere, Kevin Adkins, MARWA EL-SAYED, Embry-Riddle Aeronautical University
Abstract Number: 697
Working Group: Urban Aerosols
Abstract
Current attempts to measure ground-based atmospheric particulate matter (PM) by the Environmental Protection Agency (US EPA) in its federal reference and federal equivalent methods (FRM and FEM, respectively) are not only cumbersome and costly but also lack high spatiotemporal resolution. Due to their stationary nature, the EPA’s methods are limited in measuring the concentrations of PM at the ground-level, lacking the ability of monitoring concentrations at multiple altitudes. This, hence, hinders our ability to accurately characterize the origin and the sources responsible for the formation of these atmospheric pollutants. Recently, the development of low-cost sensors (LCSs) has been used to address the economic, practical, and technological shortcomings associated with obtaining PM measurements at high spatial and temporal scales. The aim of this study is to characterize the horizontal and vertical profiles of atmospheric PM using LCSs at Daytona Beach, Florida, a site characterized by its diverse atmospheric pollution. To achieve this, we place commercial LCSs in two varying operational modes: stationary, and mobile on unmanned vehicles (UVs) flying up to 400 ft altitude. Two commercial LCSs which rely on light scattering techniques and optical particle counters, namely: PurpleAir and OPC-N3, respectively, are compared in this study and their performance is evaluated across varying environmental and meteorological conditions in summer 2021. At the stationary position, validation of PM concentrations is established by collocating LCSs with an FEM monitor. Validation of the sensors on UVs is conducted by mounting a Vaisala AQT 420 sensor to a tethered balloon collocated with the LCSs on the UVs. This work has implications for the detection of atmospheric pollutants in congested, remote, and endangered areas at fine temporal and spatial resolutions.