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In the companion paper (Lee et al., 2012), it was showed that CMAQ simulation using a lateral boundary conditions (LBCs) derived from RAQMS-CMAQ linkage, compared to the CMAQ results with the default CMAQ LBCs, improved ozone simulations in the conterminous US domain. In the present paper, the study is extended to investigate the influence of LBCs on PM2.5 simulation. MM5-SMOKE-CMAQ modeling system was used for meteorological field generation, emissions preparation and air quality simulations, respectively. Realtime Air Quality Modeling System (RAQMS) model assimilated with satellite observations were used to generate the CMAQ-ready LBCs. CMAQ PM2.5 simulations with RAQMS LBCs and predefined LBCs were compared with U.S. EPA Air Quality System (AQS) measurements. Mean PM2.5 lateral boundary conditions taken from RAQMS outputs showed strong variations both in the horizontal grid and vertical layers in the northern and western boundaries and affected the results of CMAQ PM2.5 predictions. CMAQ with RAQMS LBCs could improve CMAQ PM2.5 predictions resulting in the improvement of index of agreement from 0.38 to 0.63. [ABSTRACT FROM AUTHOR]

Abstract: Prediction of air quality is an important component of any air quality management programme. Broadly, two approaches are used to predict the ambient air quality – the deterministic and the statistical approach, with each approach having its own merits and demerits. While the models based on the deterministic approach accurately predict the concentrations of air pollutants in the middle percentile range, the statistical models provide a better estimate of concentrations in the extreme percentile ranges. However, the statistical models are site specific and are not able generate ‘what-if’ scenarios; the deterministic models on the other hand are general in character and useful in creating alternative scenarios. An alternative approach – hybrid modelling is a technique which aggregates the benefits of the two techniques and predicts the ‘entire range’ of the distribution. While in the past there were attempts to predict the concentrations of inert pollutants using hybrid modelling approach, this paper shows the hybrid model applications for reactive secondary pollutants like ground level Ozone (GLO). This study presents the development of a hybrid model that concatenates the results of CMAQ (community multi-scale air quality model) as its deterministic component with statistical distribution model (based on the specific area category and timeframe) to predict the entire range of GLO concentrations. Predictions have been made using both purely deterministic and hybrid approaches at a receptor location near a major traffic intersection. The performance of the model has been found to improve from an index of agreement from 0.77 (deterministic model) to 0.91 (hybrid model). In order to assess the predictive capability of the hybrid approach, the model has been tested at an entirely different location for different set of temporal data. The results show an improvement in the predictions using the hybrid model over the deterministic model. [Copyright &y& Elsevier]

Abstract: This paper compares spatial and seasonal variations and temporal trends in modeled and measured concentrations of sulfur and nitrogen compounds in wet and dry deposition over an 18-year period (1988–2005) over a portion of the northeastern United States. Substantial emissions reduction programs occurred over this time period, including Title IV of the Clean Air Act Amendments of 1990 which primarily resulted in large decreases in sulfur dioxide (SO2) emissions by 1995, and nitrogen oxide (NOx) trading programs which resulted in large decreases in warm season NOx emissions by 2004. Additionally, NOx emissions from mobile sources declined more gradually over this period. The results presented here illustrate the use of both operational and dynamic model evaluation and suggest that the modeling system largely captures the seasonal and long-term changes in sulfur compounds. The modeling system generally captures the long-term trends in nitrogen compounds, but does not reproduce the average seasonal variation or spatial patterns in nitrate. [ABSTRACT FROM AUTHOR]

Convective dust storms have significant impacts on atmospheric conditions and air quality and are a major source of dust uplift in summertime. However, regional-to-global models generally do not accurately simulate these storms, a limitation that can be attributed to (1) using a single mean value for wind speed per grid box, i.e., not accounting for subgrid wind variability and (2) using convective parametrizations that poorly simulate cold pool outflows. This study aims to improve the simulation of convective dust storms by tackling these two issues. Specifically, we incorporate a probability distribution function for surface wind in each grid box to account for subgrid wind variability due to dry and moist convection. Furthermore, we use lightning assimilation to increase the accuracy of the convective parameterization and simulated cold pool outflows. This updated model framework is used to simulate a massive convective dust storm that hit Phoenix, AZ, on 6 July 2011. The results show that lightning assimilation provides a more realistic simulation of precipitation features, including timing and location, and the resulting cold pool outflows that generated the dust storm. When those results are combined with a dust model that accounts for subgrid wind variability, the prediction of dust uplift and concentrations are considerably improved compared to the default model results. This modeling framework could potentially improve the simulation of convective dust storms in global models, regional climate simulations, and retrospective air quality studies. [ABSTRACT FROM AUTHOR]