American Association for Aerosol Research - Abstract Submission

AAAR 38th Annual Conference
October 5 - October 9, 2020

Virtual Conference

Abstract View


Yearlong Performance of Six PM Air Sensor Models across Seven U.S. Sites

KAROLINE BARKJOHN, Cortina Johnson, Samuel Frederick, Robert Yaga, Brittany Thomas, William Schoppman, Andrea Clements, ORISE fellow, U.S. EPA Office of Research and Development

     Abstract Number: 61
     Working Group: Instrumentation and Methods

Abstract
Particulate air sensor performance is often evaluated at a single site or in a single region over a time period from weeks to a few months. However, sensor performance is typically dependent on environmental conditions, pollutant concentrations, and particle properties. In addition, performance may change due to seasonal variations in particle properties, environmental conditions, and degradation of the sensor. To study these impacts on air sensor performance, five models of gas and particulate sensors, were set up in August 2019 for a yearlong study. An additional sensor type was deployed March 2020. Sensors were collocated alongside a Federal Equivalent Method Monitor for PM2.5 at seven air monitoring stations throughout the United States and across climate regions. The sensor models were the Maxima from Applied Particle Technology, Clarity Node from Clarity Movement, PA-II-SD from PurpleAir, the AQY1 from Aeroqual, and the RAMP from SENSIT, with QuantAQ’s ARISense sensor deployed at the later date. PM2.5 sensors were evaluated for accuracy, precision, and the influences of environmental conditions, including temperature and relative humidity. Prior to deployment, sensors were evaluated and normalized based on a collocation period in Durham, North Carolina so that performance could be compared across sites independent of differences among sensors of the same type. Accuracy was evaluated monthly at each site so that comparisons could be made over time. Initial results suggest significant seasonal changes in performance, low precision between sensors of the same type for some sensor models, and large differences in sensor performance across sensor types. This work provides insights to improve sensor accuracy across optical PM sensors across the United States.

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