During the 2013 Southern Oxidant and Aerosol Study, Fourier Transform Infrared Spectroscopy (FTIR) and Aerosol Mass Spectrometer (AMS) measurements of submicron mass were collected at Look Rock (LRK), Tennessee, and Centreville (CTR), Alabama. Carbon monoxide and submicron sulfate and organic mass concentrations were 15-60% higher at CTR than at LRK but their time series had moderate correlations (r~0.5). However, NOx had no correlation (r=0.08) between the two sites with nighttime-to-early-morning peaks 3~10 times higher at CTR than at LRK. Organic mass (OM) sources identified by FTIR Positive Matrix Factorization (PMF) had three very similar factors at both sites: Fossil Fuel Combustion (FFC) related organic aerosols, Mixed Organic Aerosols (MOA), and Biogenic Organic Aerosols (BOA). The BOA spectrum from FTIR is similar (cosine similarity > 0.6) to that of lab-generated particle mass from the photochemical oxidation of both isoprene and monoterpenes under high NOx conditions from chamber experiments. The BOA mass fraction was highest during the night at CTR but in the afternoon at LRK. AMS PMF resulted in two similar pairs of factors at both sites and a third nighttime NOx-related factor (33% of OM) at CTR but a daytime nitrate-related factor (28% of OM) at LRK. NOx was correlated with BOA and LO-OOA for NOx concentrations higher than 1 ppb at both sites, producing 0.5 ± 0.1 μg m-3 for CTR-LO-OOA and 1.0 ± 0.3 μg m-3 for CTR-BOA above 1 ppb additional biogenic OM for each 1 ppb increase of NOx.

Mounting evidence from field and laboratory observations coupled with atmospheric model analyses shows that primary combustion emissions of organic compounds dynamically partition between the vapor and particulate phases, especially as near-source emissions dilute and cool to ambient conditions. The most recent version of the Community Multiscale Air Quality model version 5.2 (CMAQv5.2) accounts for the semivolatile partitioning and gas-phase aging of these primary organic aerosol (POA) compounds consistent with experimentally derived parameterizations. We also include a new surrogate species, potential secondary organic aerosol from combustion emissions (pcSOA), which provides a representation of the secondary organic aerosol (SOA) from anthropogenic combustion sources that could be missing from current chemical transport model predictions. The reasons for this missing mass likely include the following: (1) unspeciated semivolatile and intermediate volatility organic compound (SVOC and IVOC, respectively) emissions missing from current inventories, (2) multigenerational aging of organic vapor products from known SOA precursors (e.g., toluene, alkanes), (3) underestimation of SOA yields due to vapor wall losses in smog chamber experiments, and (4) reversible organic compounds-water interactions and/or aqueous-phase processing of known organic vapor emissions. CMAQ predicts the spatially averaged contribution of pcSOA to OA surface concentrations in the continental United States to be 38.6 and 23.6 % in the 2011 winter and summer, respectively. Whereas many past modeling studies focused on a particular measurement campaign, season, location, or model configuration, we endeavor to evaluate the model and important uncertain parameters with a comprehensive set of United States-based model runs using multiple horizontal scales (4 and 12 km), gas-phase chemical mechanisms, and seasons and years. The model with representation of semivolatile POA improves predictions of hourly OA observations over the traditional nonvolatile model at sites during field campaigns in southern California (CalNex, May-June 2010), northern California (CARES, June 2010), the southeast US (SOAS, June 2013; SEARCH, January and July, 2011). Model improvements manifest better correlations (e.g., the correlation coefficient at Pasadena at night increases from 0.38 to 0.62) and reductions in underprediction during the photochemically active afternoon period (e.g., bias at Pasadena from -5.62 to -2.42 μg m-3). Daily averaged predictions of observations at routine-monitoring networks from simulations over the continental US (CONUS) in 2011 show modest improvement during winter, with mean biases reducing from 1.14 to 0.73μg m-3, but less change in the summer when the decreases from POA evaporation were similar to the magnitude of added SOA mass. Because the model-performance improvement realized by including the relatively simple pcSOA approach is similar to that of more-complicated parameterizations of OA formation and aging, we recommend caution when applying these more-complicated approaches as they currently rely on numerous uncertain parameters. The pcSOA parameters optimized for performance at the southern and northern California sites lead to higher OA formation than is observed in the CONUS evaluation. This may be due to any of the following: variations in real pcSOA in different regions or time periods, too-high concentrations of other OA sources in the model that are important over the larger domain, or other model issues such as loss processes. This discrepancy is likely regionally and temporally dependent and driven by interferences from factors like varying emissions and chemical regimes.

Mounting evidence from field and laboratory observations coupled with atmospheric model analyses shows that primary combustion emissions of organic compounds dynamically partition between the vapor and particulate phases, especially as near-source emissions dilute and cool to ambient conditions. The most recent version of the Community Multiscale Air Quality model version 5.2 (CMAQv5.2) accounts for the semivolatile partitioning and gas-phase aging of these primary organic aerosol (POA) compounds consistent with experimentally derived parameterizations. We also include a new surrogate species, potential secondary organic aerosol from combustion emissions (pcSOA), which provides a representation of the secondary organic aerosol (SOA) from anthropogenic combustion sources that could be missing from current chemical transport model predictions. The reasons for this missing mass likely include the following: (1) unspeciated semivolatile and intermediate volatility organic compound (SVOC and IVOC, respectively) emissions missing from current inventories, (2) multigenerational aging of organic vapor products from known SOA precursors (e.g., toluene, alkanes), (3) underestimation of SOA yields due to vapor wall losses in smog chamber experiments, and (4) reversible organic compounds-water interactions and/or aqueous-phase processing of known organic vapor emissions. CMAQ predicts the spatially averaged contribution of pcSOA to OA surface concentrations in the continental United States to be 38.6 and 23.6 % in the 2011 winter and summer, respectively. Whereas many past modeling studies focused on a particular measurement campaign, season, location, or model configuration, we endeavor to evaluate the model and important uncertain parameters with a comprehensive set of United States-based model runs using multiple horizontal scales (4 and 12 km), gas-phase chemical mechanisms, and seasons and years. The model with representation of semivolatile POA improves predictions of hourly OA observations over the traditional nonvolatile model at sites during field campaigns in southern California (CalNex, May-June 2010), northern California (CARES, June 2010), the southeast US (SOAS, June 2013; SEARCH, January and July, 2011). Model improvements manifest better correlations (e.g., the correlation coefficient at Pasadena at night increases from 0.38 to 0.62) and reductions in underprediction during the photochemically active afternoon period (e.g., bias at Pasadena from -5.62 to -2.42 μg m^-3). Daily averaged predictions of observations at routine-monitoring networks from simulations over the continental US (CONUS) in 2011 show modest improvement during winter, with mean biases reducing from 1.14 to 0.73μg m^-3, but less change in the summer when the decreases from POA evaporation were similar to the magnitude of added SOA mass. Because the model-performance improvement realized by including the relatively simple pcSOA approach is similar to that of more-complicated parameterizations of OA formation and aging, we recommend caution when applying these more-complicated approaches as they currently rely on numerous uncertain parameters. The pcSOA parameters optimized for performance at the southern and northern California sites lead to higher OA formation than is observed in the CONUS evaluation. This may be due to any of the following: variations in real pcSOA in different regions or time periods, too-high concentrations of other OA sources in the model that are important over the larger domain, or other model issues such as loss processes. This discrepancy is likely regionally and temporally dependent and driven by interferences from factors like varying emissions and chemical regimes.

During the 2013 Southern Oxidant and Aerosol Study, Fourier Transform Infrared Spectroscopy (FTIR) and Aerosol Mass Spectrometer (AMS) measurements of submicron mass were collected at Look Rock (LRK), Tennessee, and Centreville (CTR), Alabama. Carbon monoxide and submicron sulfate and organic mass concentrations were 15-60% higher at CTR than at LRK but their time series had moderate correlations (r~0.5). However, NO_x had no correlation (r=0.08) between the two sites with nighttime-to-early-morning peaks 3~10 times higher at CTR than at LRK. Organic mass (OM) sources identified by FTIR Positive Matrix Factorization (PMF) had three very similar factors at both sites: Fossil Fuel Combustion (FFC) related organic aerosols, Mixed Organic Aerosols (MOA), and Biogenic Organic Aerosols (BOA). The BOA spectrum from FTIR is similar (cosine similarity > 0.6) to that of lab-generated particle mass from the photochemical oxidation of both isoprene and monoterpenes under high NO_x conditions from chamber experiments. The BOA mass fraction was highest during the night at CTR but in the afternoon at LRK. AMS PMF resulted in two similar pairs of factors at both sites and a third nighttime NO_x-related factor (33% of OM) at CTR but a daytime nitrate-related factor (28% of OM) at LRK. NO_x was correlated with BOA and LO-OOA for NO_x concentrations higher than 1 ppb at both sites, producing 0.5 ± 0.1 μg m^-3 for CTR-LO-OOA and 1.0 ± 0.3 μg m^-3 for CTR-BOA above 1 ppb additional biogenic OM for each 1 ppb increase of NO_x.

Organic compounds and liquid water are major aerosol constituents in the southeast United States (SE US). Water associated with inorganic constituents (inorganic water) can contribute to the partitioning medium for organic aerosol when relative humidities or organic matter to organic carbon (OM/OC) ratios are high such that separation relative humidities (SRH) are below the ambient relative humidity (RH). As OM/OC ratios in the SE US are often between 1.8 and 2.2, organic aerosol experiences both mixing with inorganic water and separation from it. Regional chemical transport model simulations including inorganic water (but excluding water uptake by organic compounds) in the partitioning medium for secondary organic aerosol (SOA) when RH > SRH led to increased SOA concentrations,· particularly at night. Water uptake to the organic phase resulted in even greater SOA concentrations as a result of a positive feedback in which water uptake increased SOA, which further increased aerosol water and organic aerosol. Aerosol properties· such as the OM/OC and hygroscopicity parameter (κ_org), were captured well by the model compared with measurements during the Southern Oxidant and Aerosol Study (SOAS) 2013. Organic nitrates from monoterpene oxidation were predicted to be the least water-soluble semivolatile species in the model, but most biogenically derived semivolatile species in the Community Multiscale Air Quality (CMAQ) model were highly water soluble and expected to contribute to water-soluble organic carbon (WSOC). Organic aerosol and SOA precursors were abundant at night, but additional improvements in daytime organic aerosol are needed to close the model-measurement gap. When taking into account deviations from ideality, including both inorganic (when RH > SRH) and organic water in the organic partitioning medium reduced the mean bias in SOA for routine monitoring networks and improved model performance compared to observations from SOAS. Property updates from this work will be released in CMAQ v5.2.