Abstract
BACKGROUND: Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies. AIMS: This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression. METHODS: High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression. KEY RESULTS: The combined dataset provides up to 71% more PM(2.5) emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information. CONCLUSIONS: The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates. IMPLICATIONS: The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.