Are Empirical Equations an Appropriate Tool to Assess Separation Distances to Avoid Odour Annoyance?

Abstract

<jats:p>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.</jats:p>

Environmental Odour Nuisance Assessment in Urbanized Area: Analysis and Comparison of Different and Integrated Approaches

“…A multi-criteria procedure, instead of one with predefined and pre-regulated criteria and relative values (percentile and odour concentration threshold) in any scenarios, that may overestimate or underestimate odour nuisance, is therefore to use to assess the odour nuisance. Furthermore, the adoption of a multi-criteria procedure will allow the introduction of an odour risk-assessment approach, supporting the definition of required protection level and/or minimum separation distances between the odour source and the nearest receptors [51][52][53][54].…”

The screening evaluation of environmental odors: a new dispersion modelling-based tool

“…In parallel with the screening dispersion models, other simpli ed tools for the measurement of odor annoyance have been developed. These methods, known as Empirical Equations (EEs), are in use in various jurisdictions and can support rst-level evaluations (Brancher et al, 2020). They are reported as a valuable screening asset for countries without speci c odor legislation, for a rst-instance estimate of the area affected by odor nuisance (Schauberger et al, 2021).…”

Environmental Odour

“…Countries with no specific requirements for managing environmental odour can promote the use of empirical equations as a first-guess or screening tool to estimate possible areas affected by odour annoyance. Brancher et al [13] compared separation distances obtained from selected empirical equations with those from dispersion models AERMOD and LASAT for sites in Brazil, China and Austria. As the separation distance shape often resembles the wind distribution of a site, wind data should be included in such approaches.…”