Investigation of fire smoke plume injection height sensitivities during the 2017 Northern California wildfires outbreak using a combination of satellite data and air quality modeling: Implications for health risk assessment

Abstract:

Wildfire smoke exerts both short- and long-term impacts on human health and the environment. The recurrence of wildfires in California and other fire prone areas make it vitally important to develop a system that can accurately estimate the impacts of wildfire smoke. Particularly, a system that can not only estimate impacts in terms of the production of fine particulate matter (PM_2.5), but one that can also gauge short-term exposure-response relationships, for emergency response planning for public health protection. Additionally, we strive to address an uncertainty of wildfire smoke due to vertical transport of smoke or plume rise, which determines the distance of transport and area of smoke impact. The October 2017 Northern California wildfires occurred near highly populated areas, coincident with several satellite-based and ground-based instruments measurements, provides a unique opportunity to study these factors. This unique situation presents an opportunity to use a combination of satellite-derived datasets (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIOP), Multi-Angle Imaging Spectroradiometer (MISR), Multi-Angle Implementation of Atmospheric Correction (MAIAC), Cloud-Aerosol Transport System (CATS)) and air quality modeling tools to provide health risk assessment information. Here, we present a novel approach that uses plume tops from two different algorithms and three outputs to infer model sensitivities to plume rise. We test algorithms in the modeling system to determine optimal results for the wildfire event in October 2017. In addition, we run sensitivity studies with the CMAQ model looking at various model inputs and configurations. Lastly, we compare CMAQ model results with satellite-derived aerosol and plume height retrievals.