Case study: a comparison of predicted odour exposure levels using 2D and 3D windfields in CALPUFF model

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sesion04 murguia05  The use of general purpose steady state Gaussian models (e.g. AERMOD) for predicting Odour exposure levels around the vicinity of an industrial site has been considered an accepted practice for many countries around the world for more than a decade now.

Murguia Walter1, Pagans Estel·la2, Heike Hauschildt3

1. Odournet México, Jardines de Madrid 7622, Monterrey, México. T. +52 (55) 5677 1319

2. Odournet SL, Parc de Recerca UAB · Edificio Eureka, Barcelona, Spain.

3. Odournet Gmbh, Frauenhoferstr. 13, Kiel, Germany.

 

   Competing interests: The author has declared that no competing interests exist.

   Academic editor: Carlos N Díaz.

   Content quality: This paper has been peer reviewed by at least two reviewers. See scientific committee here

   Citation: Murguia Walter, Pagans Estel·la, Heike Hauschildt, 2014, Case study: a comparison of predicted odour exposure levels using 2D and 3D windfields in CALPUFF model, Ist International Seminar of Odours in the Environment, Santiago, Chile, www.olores.org

   Copyright: 2015 olores.org. Open Content Creative Commons license., It is allowed to download, reuse, reprint, modify, distribute, and/or copy articles in olores.org website, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

   Keyword: AERMOD, Gaussian model, odour exposure, 2D windfields, 3D windfields, CALPUFF.

Abstract

  The use of general purpose steady state Gaussian models (e.g. AERMOD) for predicting Odour exposure levels around the vicinity of an industrial site has been considered an accepted practice for many countries around the world for more than a decade now.

  This tendency has been reduced lately in Southern Europe due the widely known shortcomings of these models to accurately assess dispersion under a range of ‘complex’ conditions (e.g. extreme topography; coastal wind patterns, calm conditions; cold flows; heterogeneous land usage). In such circumstances, there is a real danger that odour impact risk can be either under or overestimated, which has a substantial influence on the development of a pragmatic, cost efficient odour mitigation plans.

Environmental consultancies at Spain have started using US EPA CALPUFF model as an alternative mean to provide a more effective way of simulating these complex conditions.

  The main difference in the applications of the model among Environmental professionals in Spain seems to be the type of met data input to CALPUFF model. In the experience of the authors three types of met data are commonly used to drive CALPUFF model at Spain: 2D windfields (Aermod met data), and 3D windfields produced by CALMET with Prognostic meteorological data only (e.g.TAPM[1] or MM5 [2]), or 3D windfields produce by CALMET with a mix of Prognostic and Observational met data.

  These configurations are commonly named as: CALPUFF Lite (Aermod surface and upper air met data), CALPUFF Noobs (3D windfields produced by CALMET with prognostic met data only), and CALPUFF Hybrid (3D windfields produced by CALMET with Prognostic and Observational met data).

  This paper evaluates how predictions by each met data type compare for odour assessment purposes for a complex study site, and whether the use of any of the met data sets offers any advantage in gaining a better understanding of odour exposure and impact risk. The modelled odour impact was validated by means of “Odour ambient measurements” using German standard VDI 3940.

  According to the German standard VDI 3940 Part 1, a group of trained Olfactory panellists, selected in compliance to Olfactometry standard EN13725, observes the odour impression at a given measurement grid surrounding an emitting site. This statistical approach gives a reasonable impression of the odour impact in the vicinity of an emitting site and can be correlated to the Odour plume extent.

  The results of this case study provide a compelling case to use a mix of TAPM met data, and Surface Observational met data to define odour management requirements and assessing regulatory compliance.



[1] The Air Pollution Model (TAPM) developed by CSIRO Australia, http://www.csiro.au

[2] The PSU/NCAR mesoscale model (known as MM5), http://www.mmm.ucar.edu/mm5/

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