Timeline

Jan 2022 - Present

Description

Chemical transport models (CTMs) estimate pollutant concentration fields by solving fundamental equations grounded in conservation of mass, fluid dynamics, chemical kinetics, and thermodynamics. The accuracy of these predictions depends heavily on both the quality of input data and the completeness of the model’s chemical and physical formulations.

For emerging pollution sources like wildfires, CTMs often struggle to provide reliable exposure estimates. This is largely due to underrepresented chemical mechanisms, incomplete emissions inventories, and both random and systematic errors that vary across time and location. As a result, modeled concentrations of wildfire-related pollutants often differ from observational data, which limits the accuracy of exposure assessments, reduces confidence in public health risk evaluations, and challenges the development of effective air quality management strategies.

This project aims to improve CTM-based predictions of wildfire pollution fields through three strategies:

  1. Emissions Improvement: We are developing an updated wildfire speciation profile to enhance existing regulatory inventories, with a focus on improving the resolution of air toxics and health risk modeling.
  2. Chemical Mechanism Updates: We are revising the chemical mechanisms for key trace gases to better represent their atmospheric transformations under wildfire conditions.
  3. Data Fusion Approach: We are applying machine learning–based data fusion techniques, integrating EPA monitoring data, PurpleAir sensors, in-situ meteorological observations, and satellite-derived aerosol optical depth (AOD) using a random forest model. This approach is reducing model bias and improving forecast accuracy for wildfire smoke exposure and associated pollutants.

In this project, we use the 2020 Glass Fire as a case study to evaluate smoke exposure risks in Northern California wine valleys. Our analysis focuses on the impacts of wildfire smoke in vineyard regions, including potential effects on grape quality and the health of farm workers.