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Long-Term Performance of Five Air Sensor Models Across Seven U.S. Sites
Citation:
Johnson, K., S. Frederick, C. Johnson, R. Yaga, B. Thomas, W. Schoppman, AND A. Clements. Long-Term Performance of Five Air Sensor Models Across Seven U.S. Sites. Air Sensors International Conference - Fall Virtual Series, NA, NC, October 01, 2020.
Impact/Purpose:
Interest and concern about air quality has grown in recent years. Simultaneously, growth in the popularity and use of air sensors across the US has also occurred. However, adoption of this technology is limited due to uncertainty and variation in the quality of the data provided. This work evaluates the performance of a multiple low-cost air sensor models across the U.S.. It seeks to inform sensor users of potential data accuracy issues and long term performance applicable to both these sensor and other similar low-cost sensors. This work focuses on performance over a broader range of locations and longer time periods than much previous air sensor work. This abstract is for a presentation that will be made to the community of sensor users attending the Air Sensors International Conference (ASIC) fall webinar series (Oct 1st 2020).
Description:
For any single project, air sensor performance is typically evaluated at a single site or within a single region over a short time period (weeks to months). However, sensor performance often depends on environmental conditions, pollutant concentrations, and particle properties. In addition, performance may change due to seasonal changes in pollutant mixtures or environmental conditions and degredation of the sensor performance. This makes it difficult for potential users to decipher expected performance for their location and conditions. With this in mind, in August 2019 five types of air sensors were set up for a yearlong collocation at seven air monitoring stations across the United States. Sensors were located at stations across climate regions of the U.S. including North Carolina, Georgia, Delaware, Arizona, Colorado, Oklahoma, and Wisconsin. The five sensor models were the Clarity Node from Clarity Movement, PA-II-SD from PurpleAir, AQY1 from Aeroqual, Maxima from Applied Particle Technologies, and the RAMP from SENSIT. The selected sensors measure either PM, or a combination of PM and some gas pollutants. The performance of the PM2.5 and O3 measurements will be discussed. Sensors were evaluated for accuracy, precision, and the influences of environmental conditions including temperature and relative humidity. Accuracy was evaluated monthly at each site so that comparisons could be made over time and across sites. The results from the first six months of the project provide valuable insights into the performance of air sensors in different climates, including changing seasons. In addition, broader lessons learned about in-field sensor management, data completeness, and data management will be discussed.