This project, a collaboration between Colorado State University (CSU), Carnegie Mellon University (CMU), the University of Washington (UW), and the National Park Service (NPS), investigated the atmospheric aging of biomass burning plumes in order to examine changes in both primary particle emissions and the production of additional, secondary organic aerosol (SOA). Included in the project were chamber studies to directly study smoke aging as well as analyses of ambient samples to look for evidence of smoke aging and SOA formation in the ambient atmosphere. CMU conducted smog chamber studies to investigate the atmospheric evolution of fine particle and organic aerosol emissions in fire plumes. The experiments were conducted at the USDA/FS Fire Science Laboratory (FSL) in Missoula, MT as part of the FLAME-3 campaign organized by CSU and FSL; similar experiments were also conducted in the Air Quality Laboratory at CMU. The experiments investigated emissions from laboratory fires from fuels representing different regions in North America commonly impacted by prescribed burning and wildfires, including the Southeast (e.g., gallberry and pocosin), southern California (e.g., sagebrush and chamise) and forest regions of the western United States and Canada (e.g., ponderosa pine, lodgepole pine, and black spruce). The results from the chamber experiments are described in six papers published in peer-reviewed archival literature on the atmospheric stability of the primary smoke marker levoglucosan (Hennigan et al., 2010), the secondary organic aerosol formation and primary organic aerosol emissions processing biomass burning plumes (Hennigan et al., 2011); the formation and growth of new particles in biomass burning plumes (Hennigan et al. 2012); the evolution of cloud condensation nuclei in biomass burning plumes (Engelhart et al. 2012); the evolution of organic aerosol optical properties in biomass burning plumes (Saleh et al. 2013); and the gas-particle partitioning and volatility distribution of primary organic aerosol emissions from fires (May et al. 2013). Filter samples from the chamber experiments were analyzed by CSU. CSU also studied the chemical composition of ambient aerosol and smoke-impacted cloud water samples. This overall sample set was used to identify markers of secondary organic aerosol production: nitrocatechols. Detailed chemical analysis of aerosol samples collected at several different locations in the U.S., representing urban, rural, continental and coastal environments, showed good correlation between nitrocatechols and levoglucosan, a known marker of biomass burning. Primary smoke emissions; however, contain low levels of nitrocatechols; these compounds are formed during aging of smoke plumes during transport making them good markers of SOA formation. While the nitrocatechols were initially identified using liquid chromatography with time of flight mass spectrometric detection, a simpler, more practical analysis method using high-performance liquid chromatography with absorbance detection was developed and successfully used for analysis of nitrocatechols in aerosol samples collected using a Hi-Vol air sampler (Desyaterik et al., in prep). Nitrocatechols and other nitrogen-containing organic matter were also documented as important contributors to visible light absorption (“brown carbon”) in biomass burning aerosols and in smoke-impacted cloud water (Desyaterik et al., 2013), accounting for nearly half of the visible light absorption in clouds impacted by biomass burning. UW and NPS collaborated on further developing source apportionment tools to apportion the contribution of biomass burning to fine particulate matter. The focus was on hybrid receptor models that incorporated source attribution estimates derived from a chemical transport model (CTM) into receptor-oriented frameworks based on measured air quality data. In this work, a simple semi-empirical, backward Lagrangian particle chemical transport model was refined, making an operational tool used by NPS to apportion biomass burning and other sources to fine particulate carbon measured at IMPROVE monitoring sites (Schichtel et al., 2012a). The model was used to apportion IMPROVE data from 2006-08 and these results were refined by incorporation into the hybrid model based on a synthesis inversion method (Schichtel et al., 2012b). A hybrid source apportionment modeling tool was also developed by incorporating CTM modeling results into the Positive Matrix Factorization (PMF) receptor model. This model was tested at two IMPROVE sites impacted by wildfires (Sturtz et al., 2013 b). A user friendly version of this model was implemented in the Multilinear Engine (ME-2) optimization package and will be used by NPS personnel. Elements of this receptor model were used to apportionment coarse particulate matter in three cities (Sturtz et al., 2013a).