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Community Multiscale Air Quality (CMAQ) model simulations utilizing the volatility basis set (VBS) treatment for organic aerosols (CMAQ-VBS) were evaluated against measurements collected at routine monitoring networks (Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE)) and those collected during the 2010 California at the Nexus of Air Quality and Climate Change (CalNex) field campaign to examine important sources of organic aerosol (OA) in southern California. CMAQ-VBS (OA lumped by volatility, semivolatile POA) underpredicted total organic carbon (OC) at CSN (-25.5% Normalized Median Bias (NMdnB)) and IMPROVE (-63.9% NMdnB) locations and total OC was underpredicted to a greater degree compared to the CMAQ-AE6 (9.9 and -55.7% NMdnB, respectively; semi-explicit OA treatment, SOA lumped by parent hydrocarbon, nonvolatile POA). However, comparisons to aerosol mass spectrometer (AMS) measurements collected at Pasadena, CA indicated that CMAQ-VBS better represented the diurnal profile and the primary/secondary split of OA. CMAQ-VBS secondary organic aerosol (SOA) underpredicted the average measured AMS oxygenated organic aerosol (OOA, a surrogate of SOA) concentration by a factor of 5.2 (4.7 µg m-3 measured vs. 0.9 µg m-3 modeled), a considerable improvement to CMAQ-AE6 SOA predictions, which were approximately 24× lower than the average AMS OOA concentration. We use two new methods, based on species ratios and on a simplified SOA parameterization from the observations, to apportion the SOA underprediction for CMAQ-VBS to too slow photochemical oxidation (estimated as 1.5× lower than observed at Pasadena using - log (NOx: NOy)), low intrinsic SOA formation efficiency (low by 1.6 to 2× for Pasadena), and too low emissions or too high dispersion for the Pasadena site (estimated to be 1.6 to 2.3× too low/high). The first and third factors will be similar for CMAQ-AE6, while the intrinsic SOA formation efficiency for that model is estimated to be too low by about 7×. For CMAQ-VBS, 90% of the anthropogenic SOA mass formed was attributed to aged secondary semivolatile vapors (70% originating from volatile organic compounds (VOCs) and 20% from intermediate volatility compounds (IVOCs)). From source-apportioned model results, we found most of the CMAQ-VBS modeled POA at the Pasadena CalNex site was attributable to meat cooking emissions (48%, and consistent with a substantial fraction of cooking OA in the observations), compared to 18% from gasoline vehicle emissions, 13% from biomass burning (in the form of residential wood combustion), and 8% from diesel vehicle emissions. All "other" inventoried emission sources (e.g. industrial/point sources) comprised the final 13%. The CMAQ-VBS semivolatile POA treatment underpredicted AMS hydrocarbon-like OA (HOA) + cooking-influenced OA (CIOA) at Pasadena by a factor of 1.8 (1.16 µg m-3 modeled vs. 2.05 µg m-3 observed) compared to a factor of 1.4 overprediction of POA in CMAQ-AE6, but did well to capture the AMS diurnal profile of HOA and CIOA, with the exception of the midday peak. We estimated that using the National Emission Inventory (NEI) POA emissions without scaling to represent SVOCs underestimates SVOCs by ~1.7×. [ABSTRACT FROM AUTHOR]

Community Multiscale Air Quality (CMAQ) model simulations utilizing the volatility basis set (VBS) treatment for organic aerosols (CMAQ-VBS) were evaluated against measurements collected at routine monitoring networks (Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE)) and those collected during the 2010 California at the Nexus of Air Quality and Climate Change (CalNex) field campaign to examine important sources of organic aerosol (OA) in southern California. CMAQ-VBS (OA lumped by volatility, semivolatile POA) underpredicted total organic carbon (OC) at CSN (-25.5% Normalized Median Bias (NMdnB)) and IMPROVE (-63.9% NMdnB) locations and total OC was underpredicted to a greater degree compared to the CMAQ-AE6 (9.9 and -55.7% NMdnB, respectively; semi-explicit OA treatment, SOA lumped by parent hydrocarbon, nonvolatile POA). However, comparisons to aerosol mass spectrometer (AMS) measurements collected at Pasadena, CA indicated that CMAQ-VBS better represented the diurnal profile and the primary/secondary split of OA. CMAQ-VBS secondary organic aerosol (SOA) underpredicted the average measured AMS oxygenated organic aerosol (OOA, a surrogate of SOA) concentration by a factor of 5.2 (4.7 μgm-3 measured vs. 0.9 μgm-3 modeled), a considerable improvement to CMAQ-AE6 SOA predictions, which were approximately 24× lower than the average AMS OOA concentration. We use two new methods, based on species ratios and on a simplified SOA parameterization from the observations, to apportion the SOA underprediction for CMAQ-VBS to too slow photochemical oxidation (estimated as 1.5× lower than observed at Pasadena using -log(NOx : NOy )), low intrinsic SOA formation efficiency (low by 1.6 to 2× for Pasadena), and too low emissions or too high 25 dispersion for the Pasadena site (estimated to be 1.6 to 2.3× too low/high). The first and third factors will be similar for CMAQ-AE6, while the intrinsic SOA formation efficiency for that model is estimated to be too low by about 7×. For CMAQ-VBS, 90% of the anthropogenic SOA mass formed was attributed to aged secondary semivolatile vapors (70% originating from volatile organic compounds (VOCs) and 20% from intermediate volatility compounds (IVOCs)). From source-apportioned model results, we found most of the CMAQ-VBS modeled POA at the Pasadena CalNex site was attributable to meat cooking emissions (48%, and consistent with a substantial fraction of cooking OA in the observations), compared to 18% from gasoline vehicle emissions, 13% from biomass burning (in the form of residential wood combustion), and 8% from diesel vehicle emissions. All "other" inventoried emission sources (e.g. industrial/point sources) comprised the final 13 %. The CMAQ-VBS semivolatile POA treatment underpredicted AMS hydrocarbon-like OA (HOA)+cooking-influenced OA (CIOA) at Pasadena by a factor of 1.8 (1.16 μgm-3 modeled vs. 2.05 μgm-3 observed) compared to a factor of 1.4 overprediction of POA in CMAQ-AE6, but did well to capture the AMS diurnal profile of HOA and CIOA, with the exception of the midday peak. We estimated that using the National Emission Inventory (NEI) POA emissions without scaling to represent SVOCs underestimates SVOCs by ~ 1.7×. [ABSTRACT FROM AUTHOR]

Utilizing an aircraft-specific parameterization based on smog chamber data in the Community Multiscale Air Quality (CMAQ) model with the volatility basis set (VBS), we estimated contributions of non-traditional secondary organic aerosols (NTSOA) for aircraft emissions during landing and takeoff (LTO) activities at the Hartsfield-Jackson Atlanta International Airport. NTSOA, formed from the oxidation of semi-volatile and intermediate volatility organic compounds (S/IVOCs), is a heretofore unaccounted component of fine particulate matter (PM2.5) in most air quality models. We expanded a prerelease version of CMAQ with VBS implemented for the Carbon Bond 2005 (CB05) chemical mechanism to use the Statewide Air Pollution Research Center 2007 (SAPRC-07) chemical mechanism and added species representing aircraft S/IVOCs and corresponding NTSOA oxidation products. Results indicated that the maximum monthly average NTSOA contributions occurred at the airport and ranged from 2.4 ng m-3 (34% from idle and 66% from non-idle aircraft activities) in January to 9.1 ng m-3 (33 and 67%) in July. This represents 1.7% (of 140 ng m-3) in January and 7.4% in July (of 122 ng m-3) of aircraft-attributable PM2.5 compared to 41.0-42.0% from elemental carbon and 42.8-58.0% from inorganic aerosols. As a percentage of PM2.5, impacts were higher downwind of the airport, where NTSOA averaged 4.6-17.9% of aircraft-attributable PM2.5 and, considering alternative aging schemes, was as high as 24.0% - thus indicating the increased contribution of aircraft-attributable SOA as a component of PM2.5. However, NTSOA contributions were generally low compared to smog chamber results, particularly at idle, due to the considerably lower ambient organic aerosol concentrations in CMAQ compared to those in the smog chamber experiments. [ABSTRACT FROM AUTHOR]

Co-located measurements of fine particulate matter (PM2.5) organic carbon (OC), elemental carbon, radiocarbon (14C), speciated volatile organic compounds (VOCs), and OH radicals during the CalNex field campaign provide a unique opportunity to evaluate the Community Multiscale Air Quality (CMAQ) model's representation of organic species from VOCs to particles. Episode average daily 23 h average 14C analysis indicates PM2.5 carbon at Pasadena and Bakersfield during the CalNex field campaign was evenly split between contemporary and fossil origins. CMAQ predicts a higher contemporary carbon fraction than indicated by the 14C analysis at both locations. The model underestimates measured PM2.5 organic carbon at both sites with very little (7% in Pasadena) of the modeled mass represented by secondary production, which contrasts with the ambient-based SOC/OC fraction of 63% at Pasadena. Measurements and predictions of gas-phase anthropogenic species, such as toluene and xylenes, are generally within a factor of 2, but the corresponding SOC tracer (2,3-dihydroxy-4-oxo-pentanoic acid) is systematically underpredicted by more than a factor of 2. Monoterpene VOCs and SOCs are underestimated at both sites. Isoprene is underestimated at Pasadena and overpredicted at Bakersfield and isoprene SOC mass is underestimated at both sites. Systematic model underestimates in SOC mass coupled with reasonable skill (typically within a factor of 2) in predicting hydroxyl radical and VOC gas-phase precursors suggest error(s) in the parameterization of semivolatile gases to form SOC. Yield values (α) applied to semivolatile partitioning species were increased by a factor of 4 in CMAQ for a sensitivity simulation, taking into account recent findings of underestimated yields in chamber experiments due to gas wall losses. This sensitivity resulted in improved model performance for PM2.5 organic carbon at both field study locations and at routine monitor network sites in California. Modeled percent secondary contribution (22% at Pasadena) becomes closer to ambient-based estimates but still contains a higher primary fraction than observed. [ABSTRACT FROM AUTHOR]

Co-located measurements of fine particulate matter (PM2.5) organic carbon, elemental carbon, radiocarbon (14C), speciated volatile organic compounds (VOCs), and OH radical during the CalNex field campaign provide a unique opportunity to evaluate the Community Multiscale Air Quality (CMAQ) model's representation of organic species from VOCs to particles. Episode averaged daily 23 h average 14C analysis indicate PM2.5 carbon at Pasadena and Bakersfield during the CalNex field campaign was evenly split between contemporary and fossil origin. CMAQ predicts a higher contemporary carbon fraction than indicated by the 14C analysis at both locations. The model underestimates measured PM2.5 organic carbon at both sites with very little (7% in Pasadena) of the modeled mass represented by secondary production, which contrasts with the ambient based SOC/OC fraction of 63% at Pasadena. Measurements and predictions of gas-phase anthropogenic species, such as toluene and xylenes, are generally within a factor of 2, but the corresponding secondary organic carbon (SOC) tracer (2,3-dihydroxy-4-oxo-pentanioc acid) is systematically underpredicted by more than a factor of 2. Monoterpene VOCs and SOCs are underestimated at both sites. Isoprene is underestimated at Pasadena and over predicted at Bakersfield and isoprene SOC mass is underestimated at both sites. Systematic model underestimates in SOC mass coupled with reasonable skill (typically within a factor of 2) in predicting hydroxyl radical and VOC gas phase precursors suggests error(s) in the parameterization of semi-volatile gases to form SOC. Yield values (α) applied to semi-volatile partitioning species were increased by a factor of 4 in CMAQ for a sensitivity simulation, taking in account recent findings of underestimated yields in chamber experiments due to gas wall losses. This sensitivity resulted in improved model performance for PM2.5 organic carbon at both field study locations and at routine monitoring network sites in California. Modeled percent secondary contribution (22% at Pasadena) becomes closer to ambient based estimates but is still too primary compared with ambient estimates at the CalNex sites. [ABSTRACT FROM AUTHOR]

This study examined the impacts of aircraft emissions during the landing and takeoff cycle on PM2.5 concentrations during the months of June and July 2002 at the Hartsfield-Jackson Atlanta International Airport. Primary and secondary pollutants were modeled using the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). AMSTERDAM is a modified version of the Community Multiscale Air Quality (CMAQ) model that incorporates a plumein- grid process to simulate emissions sources of interest at a finer scale than can be achieved using CMAQ's model grid. Three fundamental issues were investigated: the effects of aircraft on PM2.5 concentrations throughout northern Georgia, the differences resulting from use of AMSTERDAM's plume-in-grid process rather than a traditional CMAQ simulation, and the concentrations observed in aircraft plumes at subgrid scales. Comparison of model results with an air quality monitor located in the vicinity of the airport found that normalized mean bias ranges from -77.5% to 6.2% and normalized mean error ranges from 40.4% to 77.5 %, varying by species. Aircraft influence average PM2.5 concentrations by up to 0.232 µgm-3 near the airport and by 0.001-0.007 µgm-3 throughout the Atlanta metro area. The plume-in-grid process increases concentrations of secondary PM pollutants by 0.005-0.020 µgm-3 (compared to the traditional grid-based treatment) but reduces the concentration of non-reactive primary PM pollutants by up to 0.010 µgm-3, with changes concentrated near the airport. Examination of subgrid-scale results indicates that median aircraft contribution to grid cells is higher than median puff concentration in the airport's grid cell and outside of a 20 km×20 km square area centered on the airport, while in a 12 km×12 km square ring centered on the airport, puffs have median concentrations over an order of magnitude higher than aircraft contribution to the grid cells. Maximum puff impacts are seen within the 12 km×12 km ring, not in the airport's own grid cell, while maximum grid cell impacts occur within the airport's grid cell. Twenty-one (21)% of all aircraft-related puffs from the Atlanta airport have at least 0.1 µgm-3 PM2.5 concentrations. Near the airport, median daily puff concentrations vary between 0.017 and 0.134 µgm-3 (0.05 and 0.35 µgm-3 at ground level), while maximum daily puff concentrations vary between 6.1 and 42.1 µgm-3 (7.5 and 42.1 µgm-3 at ground level) during the 2-month period. In contrast, median daily aircraft contribution to grid concentrations varies between 0.015 and 0.091 µgm-3 (0.09 and 0.40 µgm-3 at ground level), while the maximum varies between 0.75 and 2.55 µgm-3 (0.75 and 2.0 µgm-3 at ground level). Future researchers may consider using a plume-in-grid process, such as the one used here, to understand the impacts of aircraft emissions at other airports, for proposed future airports, for airport expansion projects under various future scenarios, and for other national-scale studies specifically when the maximum impacts at fine scales are of interest. [ABSTRACT FROM AUTHOR]

This study examined the impacts of aircraft emissions during the landing and takeoff cycle on PM2.5 concentrations during the months of June 2002 and July 2002 at the Hartsfield-Jackson Atlanta International Airport. Primary and secondary pollutants were modeled using the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). AMSTERDAM is a modified version of the Community Multiscale Air Quality (CMAQ) model that incorporates a plume-in-grid process to simulate emissions sources of interest at a finer scale than can be achieved using CMAQ's model grid. Three fundamental issues were investigated: the effects of aircraft on PM2.5 concentrations throughout northern Georgia, the differences resulting from use of AMSTERDAM's plume-in-grid process rather than a traditional CMAQ simulation, and the concentrations observed in aircraft plumes at sub-grid scales. Comparison of model results with an air quality monitor located in the vicinity of the airport found that normalized mean bias ranges from -77.5% to 6.2% and normalized mean error ranges from 40.4% to 77.5 %, varying by species. Aircraft influence average PM2.5 concentrations by up to 0.232 µgm-3 near the airport and by 0.001-0.007 µgm-3 throughout the Atlanta metro area. The plume-in-grid process increases concentrations of secondary PM pollutants by 0.005-0.020 µgm-3 (compared to the traditional grid-based treatment) but reduces the concentration of non-reactive primary PM pollutants by up to 0.010 µgm-3, with changes concentrated near the airport. Examination of sub-grid scale results indicates that puffs within 20 km of the airport often have average PM2.5 concentrations one order of magnitude higher than aircraft contribution to the grid cells containing those puffs, and within 1-4 km of emitters, puffs may have PM2.5 concentrations 3 orders of magnitude greater than the aircraft contribution to their grid cells. 21% of all aircraft-related puffs from the Atlanta airport have at least 0.1 µgm-3 PM2.5 concentrations. Median daily puff concentrations vary between 0.017 and 0.134 µgm-3, while maximum daily puff concentrations vary between 6.1 and 42.1 µgm-3 during the 2-month period. In contrast, median daily grid concentrations vary between 0.015 and 0.091 µgm-3, while maximum daily grid concentrations vary between 0.751 and 2.55 µgm-3. Future researchers may consider using AMSTERDAM to understand the impacts of aircraft emissions at other airports, for proposed future airports, for airport expansion projects under various future scenarios, and for other national-scale studies specifically when the maximum impacts at fine 5 scales are of interest. [ABSTRACT FROM AUTHOR]