Literature DB >> 21751580

A comparison of model performance between AERMOD and AUSTAL2000.

Christian Langner1, Otto Klemm.   

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.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21751580     DOI: 10.3155/1047-3289.61.6.640

Source DB:  PubMed          Journal:  J Air Waste Manag Assoc        ISSN: 1096-2247            Impact factor:   2.235


  3 in total

1.  Modelling of human exposure to air pollution in the urban environment: a GPS-based approach.

Authors:  Daniela Dias; Oxana Tchepel
Journal:  Environ Sci Pollut Res Int       Date:  2013-11-24       Impact factor: 4.223

Review 2.  Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models.

Authors:  Arvind Tiwari; Prashant Kumar; Richard Baldauf; K Max Zhang; Francesco Pilla; Silvana Di Sabatino; Erika Brattich; Beatrice Pulvirenti
Journal:  Sci Total Environ       Date:  2019-03-26       Impact factor: 7.963

3.  Assessment using CFD of the wind direction on the air discharges caused by natural ventilation of a poultry house.

Authors:  Fernando Rojano; Pierre-Emmanuel Bournet; Melynda Hassouna; Paul Robin; Murat Kacira; Christopher Y Choi
Journal:  Environ Monit Assess       Date:  2018-11-14       Impact factor: 2.513

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.