Annoyance due to environmental odour exposure is in many jurisdictions evaluated by a yes/no decision. Such a binary decision has been typically achieved via odour impact criteria (OIC) and, when applicable, the resultant separation distances between emission sources and residential areas. If the receptors lie inside the required separation distance, odour exposure is characterised with the potential of causing excessive annoyance. The state-of-the-art methodology to determine separation distances is based on two general steps: (i) calculation of the odour exposure (time series of ambient odour concentrations) using dispersion models and (ii) determination of separation distances through the evaluation of this odour exposure by OIC. Regarding meteorological input data, dispersion models need standard meteorological observations and/or atmospheric stability typically on an hourly basis, which requires expertise in this field. In the planning phase, and as a screening tool, an educated guess of the necessary separation distances to avoid annoyance is in some cases sufficient. Therefore, empirical equations (EQs) are in use to substitute the more time-consuming and costly application of dispersion models. Because the separation distance shape often resembles the wind distribution of a site, wind data should be included in such approaches. Otherwise, the resultant separation distance shape is simply given by a circle around the emission source. Here, an outline of selected empirical equations is given, and it is shown that only a few of them properly reflect the meteorological situation of a site. Furthermore, for three case studies, separation distances as calculated from empirical equations were compared against those from Gaussian plume and Lagrangian particle dispersion models. Overall, our results suggest that some empirical equations reach their limitation in the sense that they are not successful in capturing the inherent complexity of dispersion models. However, empirical equations, developed for Germany and Austria, have the potential to deliver reasonable results, especially if used within the conditions for which they were designed. The main advantage of empirical equations lies in the simplification of the meteorological input data and their use in a fast and straightforward approach.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited