Abstract View
Evaluation Methods for Low-Cost Particulate Matter Sensors in Rural Oklahoma
JEFF BEAN, Phillips 66
Abstract Number: 422
Working Group: Instrumentation and Methods
Abstract
Understanding and improving the quality of low-cost sensor data is a crucial step before low-cost sensors can be used to fill gaps for air quality management. This study focused on methods for understanding and improving data quality for low-cost particulate matter sensors using the results of 10 months of side-by-side measurements between reference instruments and low-cost sensors in Bartlesville, Oklahoma. At this site in rural Oklahoma the instruments typically encountered only low (under 20 µg/m3) concentrations of particulate matter, however higher concentrations (50-400 µg/m3) were observed on three different days during what were likely agricultural burning events. The data offered insights on how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can impact performance indicators (R2 and root mean square error) for a sensor evaluation. The influence of these factors on performance metrics should be considered when comparing one sensor to another or when determining whether a sensor can produce data that fits a specific need. Though R2 and root mean square error remain the dominant metrics in sensor evaluations, an alternative approach using a prediction interval may offer more consistency between evaluations and a more direct interpretation of sensor data following an evaluation. Ongoing quality assurance for sensor data is needed to ensure data continues to meet expectations. Observations of trends in linear regression parameters and sensor bias were used to analyze calibration and other quality assurance techniques.