ALBERT

Description: We briefly present in this short paper a new SIze REsolved Aerosol Model (SIREAM) which simulates the evolution of atmospheric aerosol by solving the General Dynamic Equation (GDE). SIREAM segregates the aerosol size distribution into sections and solves the GDE by splitting coagulation and condensation/evaporation. A moving sectional approach is used to describe the size distribution change due to condensation/evaporation and a hybrid method has been developed to lower the computational burden. SIREAM uses the same physical parameterizations as those used in the Modal Aerosol Model, MAM sartelet05development. It is hosted in the modeling system POLYPHEMUS (Mallet et al., 2006) but can be linked to any other three-dimensional Chemistry-Transport Model.

Description: The impact of two recent gas-phase chemical kinetic mechanisms (CB05 and RACM2) on the formation of secondary inorganic and organic aerosols is compared for simulations of PM2.5 over Europe between 15 July and 15 August 2001. The host chemistry transport model is Polair3D of the Polyphemus air-quality platform. Particulate matter is modeled with SIREAM, which is coupled to the thermodynamic model ISORROPIA and to the secondary organic aerosol module MAEC. Model performance is satisfactory with both mechanisms for speciated PM2.5. The monthly-mean difference of the concentration of PM2.5 is less than 1 μg/m3 (6%) over the entire domain. Secondary chemical components of PM2.5 include sulfate, nitrate, ammonium and organic aerosols, and the chemical composition of PM2.5 is not significantly different between the two mechanisms. Monthly-mean concentrations of inorganic aerosol are higher with RACM2 than with CB05 (+16% for sulfate, +11% for nitrate, and +12% for ammonium), whereas the concentrations of organic aerosols are slightly higher with CB05 than with RACM2 (+26% for anthropogenic SOA and +1% for biogenic SOA). Differences in the inorganic and organic aerosols result primarily from differences in oxidant concentrations (OH, O3 and NO3). Nitrate formation tends to be HNO3-limited over land and differences in the concentrations of nitrate are due to differences in concentration of HNO3. Differences in aerosols formed from aromatics SVOC are due to different aromatics oxidation between CB05 and RACM2. The aromatics oxidation in CB05 leads to more cresol formation, which then leads to more SOA. Differences in the aromatics aerosols would be significantly reduced with the recent CB05-TU mechanism for toluene oxidation. Differences in the biogenic aerosols are due to different oxidant concentrations (monoterpenes) and different particulate organic mass concentrations affecting the gas-particle partitioning of SOA (isoprene).

Description: The impact of two recent gas-phase chemical kinetic mechanisms (CB05 and RACM2) on the formation of secondary inorganic and organic aerosols is compared for simulations of PM2.5 over Europe between 15 July and 15 August 2001. The host chemistry transport model is Polair3D of the Polyphemus air-quality platform. Particulate matter is modeled with a sectional aerosol model (SIREAM), which is coupled to the thermodynamic model ISORROPIA for inorganic species and to a module (MAEC) that treats both hydrophobic and hydrophilic species for secondary organic aerosol (SOA). Modifications are made to the gas-phase chemical mechanisms to handle the formation of SOA. In order to isolate the effect of the original chemical mechanisms on PM formation, the addition of reactions and chemical species needed for SOA formation was harmonized to the extent possible between the two gas-phase chemical mechanisms. Model performance is satisfactory with both mechanisms for speciated PM2.5. The monthly-mean difference of the concentration of PM2.5 is less than 1 μg m−3 (6%) over the entire domain. Secondary chemical components of PM2.5 include sulfate, nitrate, ammonium and organic aerosols, and the chemical composition of PM2.5 is not significantly different between the two mechanisms. Monthly-mean concentrations of inorganic aerosol are higher with RACM2 than with CB05 (+16% for sulfate, +11% for nitrate, and +10% for ammonium), whereas the concentrations of organic aerosols are slightly higher with CB05 than with RACM2 (+22% for anthropogenic SOA and +1% for biogenic SOA). Differences in the inorganic and organic aerosols result primarily from differences in oxidant concentrations (OH, O3 and NO3). Nitrate formation tends to be HNO3-limited over land and differences in the concentrations of nitrate are due to differences in concentration of HNO3. Differences in aerosols formed from aromatic SVOC are due to different aromatic oxidation between CB05 and RACM2. The aromatic oxidation in CB05 leads to more cresol formation, which then leads to more SOA. Differences in the aromatic aerosols would be significantly reduced with the recent CB05-TU mechanism for toluene oxidation. Differences in the biogenic aerosols are due to different oxidant concentrations (monoterpenes) and different particulate organic mass concentrations affecting the gas-particle partitioning of SOA (isoprene). These results show that the formulation of a gas-phase chemical kinetic mechanism for ozone can have significant direct (e.g., cresol formation) and indirect (e.g., oxidant levels) effects on PM formation. Furthermore, the incorporation of SOA into an existing gas-phase chemical kinetic mechanism requires the addition of reactions and product species, which should be conducted carefully to preserve the original mechanism design and reflect current knowledge of SOA formation processes (e.g., NOx dependence of some SOA yields). The development of chemical kinetic mechanisms, which offer sufficient detail for both oxidant and SOA formation is recommended.

Description: An offline-coupled model (WRF/Polyphemus) and an online-coupled model (WRF/Chem-MADRID) are applied to simulate air quality in July 2001 at horizontal grid resolutions of 0.5° and 0.125° over Western Europe. The model performance is evaluated against available surface and satellite observations. The two models simulate different concentrations in terms of domainwide performance statistics, spatial distribution, temporal variations, and column abundance. WRF/Chem-MADRID at 0.5° gives higher values than WRF/Polyphemus for the domainwide mean and over polluted regions in Central and southern Europe for all surface concentrations and column variables except for the tropospheric ozone residual (TOR). Compared with observations, WRF/Polyphemus gives better statistical performance for daily HNO3, SO2, and NO2 at the European Monitoring and Evaluation Programme (EMEP) sites, maximum 1 h O3 at the AirBase sites, PM2.5 at the AirBase sites, maximum 8 h O3 and PM10 composition at all sites, column abundance of CO, NO2, TOR, and aerosol optical depth (AOD), whereas WRF/Chem-MADRID gives better statistical performance for NH3, hourly SO2, NO2, and O3 at the AirBase and BDQA (Base de données de la qualité de l'air) sites, maximum 1 h O3 at the BDQA and EMEP sites, and PM10 at all sites. WRF/Chem-MADRID generally reproduces well the observed high hourly concentrations of SO2 and NO2 at most sites except for extremely high episodes at a few sites, and WRF/Polyphemus performs well for hourly SO2 concentrations at most rural or background sites where pollutant levels are relatively low, but it underpredicts the observed hourly NO2 concentrations at most sites. Both models generally capture well the daytime maximum 8 h O3 concentrations and diurnal variations of O3 with more accurate peak daytime and minimal nighttime values by WRF/Chem-MADRID, but neither model reproduces extremely low nighttime O3 concentrations at several urban and suburban sites due to underpredictions of NOx and thus insufficient titration of O3 at night. WRF/Polyphemus gives more accurate concentrations of PM2.5, and WRF/Chem-MADRID reproduces better the observations of PM10 concentrations at all sites. The differences between model predictions and observations are mostly caused by inaccurate representations of emissions of gaseous precursors and primary PM species, as well as biases in the meteorological predictions. The differences in model predictions are caused by differences in the heights of the first model layers and thickness of each layer that affect vertical distributions of emissions, model treatments such as dry/wet deposition, heterogeneous chemistry, and aerosol and cloud, as well as model inputs such as emissions of soil dust and sea salt and chemical boundary conditions of CO and O3 used in both models. WRF/Chem-MADRID shows a higher sensitivity to grid resolution than WRF/Polyphemus at all sites. For both models, the use of a finer grid resolution generally leads to an overall better statistical performance for most variables, with greater spatial details and an overall better agreement in temporal variations and magnitudes at most sites. The use of online biogenic volatile organic compound (BVOC) emissions gives better statistical performance for hourly and maximum 8 h O3 and PM2.5 and generally better agreement with their observed temporal variations at most sites. Because it is an online model, WRF/Chem-MADRID offers the advantage of accounting for various feedbacks between meteorology and chemical species. However, this model comparison suggests that atmospheric pollutant concentrations are most sensitive in state-of-the-science air quality models to vertical structure, inputs, and parameterizations for dry/wet removal of gases and particles in the model.

Description: Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e., the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID)) are conducted over Western Europe. Part 1 describes the background information for the model comparison and simulation design, the application of WRF for January and July 2001 over triple-nested domains in Western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°, and the effect of aerosol/meteorology interactions on meteorological predictions. Nine simulated meteorological variables (i.e., downward shortwave and longwave radiation fluxes (SWDOWN and LWDOWN), outgoing longwave radiation flux (OLR), temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. The vertical profiles of temperature, dew points, and wind speed/direction are also evaluated using sounding data. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients and vertical profiles of major meteorological variables. While the domainwide performance of LWDOWN, OLR, T2, Q2, and RH2 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in SWDOWN, WS10, and Precip even at 0.125° or 0.025° in both months and in WD10 in January. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g., lack of soil temperature and moisture nudging), limitations in the physical parameterizations (e.g., shortwave radiation, cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget parameterizations (e.g., snow-related processes, subgrid-scale surface roughness elements, and urban canopy/heat island treatments and CO2 domes). While the use of finer grid resolutions of 0.125° and 0.025° shows some improvements for WS10, WD10, Precip, and some mesoscale events (e.g., strong forced convection and heavy precipitation), it does not significantly improve the overall statistical performance for all meteorological variables except for Precip. The WRF/Chem simulations with and without aerosols show that aerosols lead to reduced net shortwave radiation fluxes, 2 m temperature, 10 m wind speed, planetary boundary layer (PBL) height, and precipitation and increase aerosol optical depth, cloud condensation nuclei, cloud optical depth, and cloud droplet number concentrations over most of the domain. These results indicate a need to further improve the model representations of the above parameterizations as well as aerosol–meteorology interactions at all scales.