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Quality Assurance Considerations to Deliver Credible Data from Air Sensors: Workshop Summary
Citation:
Barkjohn, K., A. Clements, C. Mocka, Samuel Barrette, A. Bittner, W. Champion, B. Gantt, E. Good, A. Holder, B. Hillis, M. Landis, M. Kumar, M. MacDonald, E. Thoma, T. Dye, J. Archer, M. Bergin, W. Mui, B. Feenstra, M. Ogletree, C. Chester Schroeder, AND N. Zimmerman. Quality Assurance Considerations to Deliver Credible Data from Air Sensors: Workshop Summary. Northeast States for Coordinated Air Use Management Meeting, North Chelmsford, MA, April 02 - 03, 2024.
Impact/Purpose:
Sensor use has increased in recent years but quality assurance is needed to ensure data is useful for the intended purpose. In July 2023, US EPA hosted an international workshop bringing together experts to discuss air sensor quality assurance strategies. This presentation will summarize key concepts from the workshop for attendees of the Northeast States for Coordinated Air Use Management meeting on April 2nd, 2024.
Description:
This presentation summarizes perspectives from the U.S. Environmental Protection Agency’s 2023 Air Sensors Quality Assurance Workshop (https://www.epa.gov/air-sensor-toolbox/quality-assurance-air-sensors#QAworkshop). Air sensors can provide valuable non-regulatory and supplemental data, however different sensor types have different limitations and quality assurance (QA) steps are needed to produce credible data. Understanding current air sensor limitations is critical to effectively using the data. Particulate matter (PM) sensors that detect by ensemble particle light scattering mainly detect 0.3 – 1 µm particles. These measurements typically strongly correlate with PM2.5 but will underestimate during dust impacts; good agreement between these sensors and regulatory PM10 measurements only occurs when fine particles dominate. Optical particle counting (OPC) technology is more accurate for sensor-based dust studies. Volatile organic compound (VOC) sensors estimate hydrocarbon levels in the atmosphere, but current lower-cost VOC sensors cannot quantify specific air pollutants, instead producing an integrated response to the ensemble of hydrocarbon compounds present. Most gas sensors (e.g., ozone, nitrogen dioxide) have cross sensitivities, environmental influences, and drift that must be addressed. All project stages require QA. During planning, define the project objective, select a technology to meet objectives, determine the QA most well-suited to the project, provide training, and engage the community and stakeholders throughout the project. QA approaches to consider during sensor projects include harmonization to the mean/median of the sensor fleet, laboratory, and field evaluation, correction, and accounting for drift and sensor lifespan. Sensors can be used for a variety of applications and the intended use of the data dictates the data quality and QA required. Workshop presentations suggested that air sensor data users need tools including, but not limited to, automated QA protocols and data processing, improved total VOC (TVOC) interpretation, and speciated VOC sensors. Users also need well documented data streams and hardware. Communities using air sensors need training and resources, timely data, accessible QA approaches, and shared responsibility with other stakeholders. This work addresses a key need as better more universal quality assurance will allow stakeholders to leverage air sensor data more effectively to protect human health.