Influence of the transported Canadian wildfire smoke on the ozone and particle pollution over the Mid-Atlantic United States

Abstract

The integration of observations and models can improve air quality forecasts (in particular ozone (O3) and particulate matter (PM)) for extreme events (e.g., wildfires). We present our work on the Canadian wildfire event on 6–12 June 2015 that impacted the air quality in the Mid-Atlantic region in the U.S. We use the Weather Research and Forecasting model coupled with Chemistry package (WRF-Chem), and various measurements from both ground-based and spaceborne observations, including the U.S. Environmental Protection Agency (EPA) AirNow data, the National Aeronautics and Space Administration (NASA) operated TROPospheric OZone lidar (TROPOZ), wind radar profiler, ceilometer, Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The objective is to understand the physics of the Planetary Boundary Layer (PBL) and its role on the O3 and PM forecast. The findings show that the model captured the O3 diurnal variation and PM spatial distribution both horizontally and vertically by comparing with EPA AirNow and MODIS/CALIOP observations, respectively. Wildfire smoke was transported from central Canada through Lake Michigan, passing the Ohio River Valley and down to the Baltimore-Washington D.C. metropolis. The night-time O3 mixing ratio reached 30 ppbv, while the daytime O3 mixing ratio approached larger than 100 ppbv near AirNow stations in Maryland, due to the mixing of the transported smoke into the PBL. The novel NASA TROPOZ lidar at Beltsville resolved the O3 vertical profile and the ceilometer identified the smoke intrusion at altitudes above 3.5 km, but later mixed down into the PBL and surface which was also resolved by the model. Thus, integrating both model and observations from different platforms confirms the Canadian wildfire source and transport pathway and improves the understanding of the air quality forecast during the extreme wildfire event.