The air quality and fire management communities are faced with increasingly difficult decisions regarding critical fire management activities, given the potential contribution of wildland fires to fine particulate matter (PM2.5). Unfortunately, in model frameworks used for air quality management, the ability to represent PM2.5 from fires is severely limited. This is due in part to incomplete identification and quantification of compounds emitted from fires and uncertainties in mechanisms leading to PM2.5 formation in smoke plumes. Thus there is great need for improved emissions inventories and validated smoke models that better capture emissions of organic compounds emitted from fires and subsequent chemical and physical transformations to form PM2.5. In this work two-dimensional gas chromatography with time-of-flight mass spectrometry has been used to develop the most comprehensive emissions inventory to date of gaseous organic compounds emitted from fires. For any given fire, hundreds of organic compounds can be identified (based on first and second dimension retention time, and mass spectra); compound identification and quantification varies as a function of fuel species, fuel component, and burn characteristics. The relative importance of these variables on organic compound emissions (identity and quantity), and the existence of unique chemical fingerprints for fuel types, are being explored using statistical analysis and dimensionality reduction techniques. These efforts are essential to developing predictive models that link fuel and fire characteristics with pollutant production in smoke plumes.

Prof. Kelley Barsanti