Abstract View
Impacts of Data Completeness on Hourly Averaged PurpleAir PM2.5 Concentrations and AQI Category Estimation during Smoke Events
SAMUEL FREDERICK, Karoline Barkjohn, Amara Holder, Andrea Clements, U.S. EPA Office of Research and Development
Abstract Number: 512
Working Group: Instrumentation and Methods
Abstract
Air sensors can provide continuous PM measurements advantageous for rapidly changing concentrations due to wildfires. However, there may be increased uncertainty in sensor data due to interruptions in sensor connectivity or power. This work simulates the impact of data gaps in a smoke impacted PurpleAir PM2.5 dataset. Data were collected during August 2018 at the Natchez wildfire in Northern California where a PurpleAir was deployed alongside an E-BAM. The PurpleAir logged data to an SD card every 80 seconds while the E-BAM recorded 1-hr averages. To simulate lowered completeness versions of the PurpleAir dataset, subsets within each 1-hr interval (e.g. 50% completeness=22 80s points/hour) were selected either at random or through an iterative process. Each hourly subset was averaged and corrected with a USEPA correction equation developed to make PurpleAir data more comparable to regulatory grade data making it possible to use the data for health messaging. Corrected PurpleAir data was then compared to both the full PurpleAir and E-BAM hourly averages. The NowCast AQI, a rolling 12-hr weighted average for PM2.5 developed by USEPA, was calculated for datasets simulating ‘worst-case’ completeness (1 point/hour) alongside NowCast AQI values for the full PurpleAir dataset and E-BAM hourly averages. Estimations of NowCast AQI category for the ‘worst-case’ scenario with one data point sampled each hour indicate that the PurpleAir reports within one category of the E-BAM ~90% of the time. These results are important as sensor users develop methods for sensor data cleaning and analysis since applying overly strict completeness criteria could exclude limited data available during smoke events, while applying insufficient completeness criteria could provide misleading results.
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