Performance of Vehicle Add-on Mobile Monitoring System PM2.5 measurements during wildland fire episodes†
Abstract
Fine particulate matter (PM2.5) resulting from wildland fire is a significant public health risk in the United States (U.S.). The existing stationary monitoring network and the tools used to alert the public of smoke conditions, such as the Air Quality Index or NowCast, are not optimized to capture actual exposure concentrations in impacted communities given that wildland fire smoke plumes have characteristically steep exposure concentration gradients that can vary over fine spatiotemporal scales. In response, we developed and evaluated a lightweight, universally attachable mobile PM2.5 monitoring system to provide supplemental, real-time air quality information during wildfire incidents and prescribed burning activities. We retroactively assessed the performance of the mobile monitor compared to nearby (100–1500 m) stationary low-cost sensors and regulatory monitors using 1 minute averaged data collected during two large wildfires in the western U.S. and during one small, prescribed burn in the Midwest. The mobile measurements were highly correlated (R2 > 0.85) with the stationary network during the large wildfires. Further, 1 minute averaged mobile measurements differed from three collocated stationary instruments by <25% on average for fourteen out of fifteen total passages. For the small, prescribed burn, rapidly changing conditions near the fire border complicated the comparison of mobile and stationary measurements but the spatial maximum concentrations measured by both instruments were consistent. In general, this work highlights the value of using portable sensor technologies to address the monitoring challenges presented by dynamic wildland fire conditions and demonstrates the value in combining mobile monitoring with stationary data where possible.
- This article is part of the themed collections: Wildfire impacts on atmospheric composition - Topic Highlight and A collection on dense networks and low-cost sensors, including work presented at ASIC 2022