A Comparison of Model Performance between AERMOD and AUSTAL2000

Abstract

In this study the performance of the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD), a Gaussian plume model, is compared in five test cases with the German Dispersion Model according to the Technical Instructions on Air Quality Control (Ausbreitungsmodell gemäbeta der Technischen Anleitung zur Reinhaltung der Luft) (AUSTAL2000), a Lagrangian model. The test cases include different source types, rural and urban conditions, flat and complex terrain. The predicted concentrations are analyzed and compared with field data. For evaluation, quantile-quantile plots were used. Further, a performance measure is applied that refers to the upper end of concentration levels because this is the concentration range of utmost importance and interest for regulatory purposes. AERMOD generally predicted concentrations closer to the field observations. AERMOD and AUSTAL2000 performed considerably better when they included the emitting power plant building, indicating that the downwash effect near a source is an important factor. Although AERMOD handled mountainous terrain well, AUSTAL2000 significantly underestimated the concentrations under these conditions. In the urban test case AUSTAL2000 did not perform satisfactorily. This may be because AUSTAL2000, in contrast to AERMOD, does not use any algorithm for nightly turbulence as caused by the urban heat island effect. Both models performed acceptable for a nonbuoyant volume source. AUSTAL2000 had difficulties in stable conditions, resulting in severe underpredictions. This analysis indicates that AERMOD is the stronger model compared with AUSTAL2000 in cases with complex and urban terrain. The reasons for that seem to be AUSTAL2000's simplification of the meteorological input parameters and imprecision because of rounding errors.

Using Numerical Simulation Technology to Assess the Contribution of Key Polluters to Air Pollution in the Huancui District, Weihai, China

“…Not only can it sort the pollutant concentrations, but it can make three types of contour maps, including the annual average, the daily average and the hourly value, using the predicted results. Langner and Klemm's experiments have shown that the predictions of the model are closer to field observations [7]. In cases with complex and urban terrain, AERMOD is the stronger model compared with AUSTAL2000.…”

Atmospheric Chemistry in Lagrangian Models-Overview

“…Lagrangian plume models are wellestablished regulatory tools for environmental impact assessments. Examples of regulatory models applied widely also within the research community are the Italian dispersion models SPRAY [Tinarelli et al, 1994;Gariazzo et al, 2004] and MicroSpray [Anfossi et al, 2010], the German regulatory model AUSTAL2000 [Janicke et al, 2004;Langner and Klemm, 2011], the UK Met Office model NAME [Ryall and Maryon, 1998;Jones et al, 2007], or the Gaussian puff model CALPUFF [Scire et al, 2000]. The NAME model is a particularly versatile model used operationally for environmental emergency response and volcanic ash cloud modeling as well as for various research applications [Jones et al, 2007].…”

Using CALPUFF to determine the environmental impact of a coal mine open pit

“…The results of Table 4 are framed within the acceptance criteria of dispersion models of air quality. To determine the reliability of the model, the criteria was used in previous studies [29,32,33] The NMSE, indicator of variance, is ˂ 0.5 for the weekly average of all stations, indicating that the observed concentrations and the estimated are agreed. The FB was positive for all environmental receptors and is within the accepted range, indicating that the estimated results are close to the values observed.…”