Abstract Air Quality Models Research Articles

Evaluation of the Variability in Chemical Transport Model Performance for Deposition and Ambient Concentrations of Nitrogen and Sulfur Compounds

Air quality models are increasingly used to develop estimates of dry and wet deposition of sulfate and nitrate in watersheds (because of lack of measurements) in an effort to determine the acidifying deposition load into the aquatic systems. These models need to be rigorously evaluated to ensure that one can rely on the modeled quantities instead of the measured quantities. In the United State (U.S.), these models have been proposed for use in establishing national standards based on modeled quantities. The U.S. Environmental Protection Agency (EPA) is considering aquatic acidification as the main ecological endpoint of concern in determining the secondary national ambient air quality standards for nitrogen oxides and sulfur oxides. Acidification is tied to depositions of sulfur and nitrogen, which are linked to ambient concentrations of the elements. As EPA proposes to use a chemical transport model in linking deposition to ambient concentration, it is important to investigate how the currently used chemical transport models perform in predicting depositions and ambient concentrations of relevant chemical species and quantify the variability in their estimates. In this study, several annual simulations by multiple chemical transport models for the entire continental U.S. domain are evaluated against available measurement data for depositions and ambient concentrations of sulfur oxides and reactive nitrogen species. The model performance results vary by evaluation time-scale and geographical region. Evaluation of annualized quantities (annual average ambient concentrations and annual total depositions) suppresses the large variances shown in the evaluation using the observation’s native shorter-term time-scales (e.g., weekly). In addition, there is a large degree of bias and error (especially for deposition fluxes) in the modeling results that brings to question the suitability of using air quality models to provide estimates of deposition loads. Variability in the ratio of deposition to ambient concentration, so-called the Transference Ratio that EPA has proposed to use in linking deposition to ambient concentration, is also examined. Our study shows that the Transference Ratios as well as total reduced nitrogen deposition, another modeled parameter EPA proposed to use in the process of determining the new secondary standard, vary considerably by geographical region and by model simulation.

Comparison of Lagrangian Process Analysis tools for Eulerian air quality models

Abstract Air quality models (AQM) are used to understand the complex relationships between sources of air pollutants and ambient concentrations. Two new AQM diagnostic tools, the Lagrangian Process Analysis (LPA) tool and the Python-based Process Analysis (pyPA), have recently been created that allow users to track a plume within the AQM, and then calculate the chemical and physical process rates that occur within it. These two new process analysis tools perform their functions differently. The LPA in-model algorithm operates at the computational timestep of the AQM, and pyPA is a post-processor tool dependent on the temporal resolution of the AQM output, typically 1 h. This work compares process rates calculated by these tools, using as a case study the simulation of a rapidly evolving plume that resulted from an industrial hydrocarbon release. Releases from industrial sources are of regulatory significance in Houston and their accurate simulation of great importance. Results show that the largest differences in the outputs of the tools occur early in the life of the plume when it is rapidly expanding. During this time, the plume encounters NO x sources that significantly impact chemical and physical process rates that are not seen in the pyPA post-processing of hourly AQM output.

SAGA: a decision support system for air pollution management around a coal-fired power plant

Air quality models are currently feasible approaches to prevent air pollution episodes. From one of the first source-oriented modelling approaches for air pollution forecasting (Souto et al., 1994, 1996, 1998), a new decision support system for air quality management, SAGA, was developed to provide support to As Pontes Power Plant (APPP) staff. SAGA can provide air pollution forecasts and manage meteorological and air quality measurements. Power plant decisions are supported by the results of a non-hydrostatic meteorological model (ARPS, Xue et al., 2001) to produce Meteorological Forecasts (MFs), and to be coupled to different Lagrangian dispersion models.

Validation of Mobile Source Emission Estimates Using Mass Balance Techniques

Air quality models must consider error in the emissions estimates as well as in the model itself. Currently, the accepted method for determining emission factors for mobile sources is through the use of the EPA model MOBILE3. In this paper, emission factor calculations from a technique based on the conservation of mass are presented from experimental data. These emission factors are then compared to MOBILE3 simulations using the experimental conditions and two different methods of obtaining the vehicle scenario. In both cases, the mass balance gave emission factors that were from 1.5 to 2.2 times those predicted by MOBILE3. Next, mass balance and MOBILE3 emission factors were used in the dispersion models CALINE3 and TXLINE in an effort to predict the observed CO concentrations. In both models, enhanced performance was observed when mass balance data were implemented.