Assessing the Accuracy and Reliability of Air Sensors for Quantifying Fine Particulate Matter during Wildfires at the Queens College ASCENT Site
DAVID HAGAN, Eben Cross, David McClosky, Sabrina Westgate, Roya Bahreini, Ann M. Dillner, Armistead G. Russell, Taekyu Joo, Mitchell Rogers, Tori Hass-Mitchell, Drew Gentner, Nga Lee Ng, QuantAQ, Inc.
Abstract Number: 387
Working Group: Coast to Coast Campaigns on Aerosols, Clouds, Chemistry, and Air Quality
Abstract
Air sensors have become widely used for measuring fine aerosol (PM2.5) and are increasingly relied upon by the general public during major air quality events, such as wildfires, to gauge risk and make decisions. While there is a general understanding that optical sensors must be corrected when making measurements of wildfire smoke, it is unclear how the exact physical and chemical properties of the particles impact PM1 and PM2.5 estimates. As biomass burning aerosol ages, the aerosol generally becomes larger and lighter in color as it is chemically processed. These changes in composition and size will affect the optical sensors’ ability to accurately measure aerosol loadings as their calibrations are often based on non-light-absorbing aerosol of known composition. In this work, we present estimates for measurement uncertainty for PM1 and PM2.5 as characterized by air sensors of various types (e.g., nephelometers, optical particle counters, dual-detection, etc.) under varying wildfire smoke conditions across a range in aerosol optical and physical properties. We use a combination of modeled results (using the open-sourced opcsim Python library) and field data collected as part of the ASCENT (Atmospheric Science and Chemistry mEaurement NeTwork) project (e.g., particle size via SMPS, aerosol composition via ACSM, etc.) at the Queens College site during the 2023 wildfires on the US East Coast to support our conclusions.