Austrian Odour Dispersion Model Research Articles

Determining Separation Distances to Avoid Odour Annoyance With Two Models for a Site in Complex Terrain

Direction-dependent separation distances to avoid odour annoyance, calculated with the Gaussian Austrian Odour Dispersion Model AODM and the Lagrange particle diffusion model LASAT for a site in complex terrain, will be analysed and compared. The relevant short-term peak odour concentrations are calculated with a stability-dependent peak-to-mean algorithm. For both models, the same emission and meteorological data, but model-specific atmospheric stability classes have been used. The estimate of atmospheric stability is obtained from three-axis ultrasonic anemometers using the standard deviations of the three wind components and the Obukhov stability parameter. In addition, separation distances are also determined for an odour threshold of 0.25 (factor 4) of the German TA Luft (Technical Guideline for Clean Air) uniquely applied over all stability conditions and distances. The results are demonstrated for the Austrian village Weissbach in the narrow Saalach valley.Meteorological data at this site are provided by a three-axis ultrasonic anemometer from which also stability information (Obukhov stability parameter, OSP) is directly deduced. Separation distances are determined fortwo odour impact criteria, namely exceedence probabilities of 3 and 8 %, each in combination with the odour threshold of 1 ouE/m3. The form of the separation distances around the fictitious odour source depends strongly on the prevailing meteorological conditions (wind and stability), on the odour impact criteria and on the peak-to-mean factors.

Factors influencing separation distances against odour annoyance calculated by Gaussian and Lagrangian dispersion models

Direction-dependent separation distances to avoid odour annoyance, calculated with the Gaussian Austrian Odour Dispersion Model AODM and the Lagrangian particle diffusion model LASAT at two sites, are analysed and compared. The relevant short-term peak odour concentrations are calculated with a stability-dependent peak-to-mean algorithm. The same emission and meteorological data, but model-specific atmospheric stability classes are used. The estimate of atmospheric stability is obtained from three-axis ultrasonic anemometers using the standard deviations of the three wind components and the Obukhov stability parameter. The results are demonstrated for the Austrian villages Reidling and Weissbach with very different topographical surroundings and meteorological conditions. Both the differences in the wind and stability regimes as well as the decrease of the peak-to-mean factors with distance lead to deviations in the separation distances between the two sites. The Lagrangian model, due to its model physics, generally calculates larger separation distances. For worst-case calculations necessary with environmental impact assessment studies, the use of a Lagrangian model is therefore to be preferred over that of a Gaussian model. The study and findings relate to the Austrian odour impact criteria.

A comparison of separation distances against odour annoyance calculated with two models

Dispersion models are a common means to calculate separation distances around odour sources to protect the neighbourhood from odour nuisance. As the models usually calculate half-hourly or hourly averages of concentrations, they have to account somehow for the ability of the human nose to perceive odour within a single breath. For this purpose, the authors have developed and already published a peak-to-mean approach used with the Austrian Odour Dispersion Model (AODM), a Gaussian model adapted for the prediction of odour sensation. This approach is here used also with the Lagrangian particle diffusion model LASAT in a post-processing mode. Both models can now calculate direction-dependent separation distances for a prescribed combination of odour threshold and exceedence probability which are a function of the prevailing atmospheric stability conditions. This is demonstrated for a rural site in the Austrian flatlands east of Vienna. In addition, separation distances are also determined for an odour threshold of 0.25 (factor 4) of the German TA Luft (Technical Guideline for Clean Air) uniquely applied over all stability conditions and distances. The resulting separation distances and their implications when using these approaches with the two models are presented and discussed.

Comparison of two peak-to-mean approaches for use in odour dispersion models

In this paper, two approaches to estimate odour concentrations in dispersion models are compared. The approaches differ in the estimation of the momentary (peak) odour concentration for the time interval of a single human breath (approximately 5 s). The Austrian Odour Dispersion Model (AODM) is a Gaussian model with peak-to-mean factors depending on wind speed and atmospheric stability. The German Lagrange code AUSTAL2000 uses a constant factor 4 in all meteorological conditions to derive the maximum odour concentration over a short integration time. As the Lagrange model, in contrast to the Gauss model, can be applied also in complex topography and with isolated buildings, the implementation of the Austrian peak-to-mean approach in AUSTAL2000 would enable for more realistic separation distances in these environments. In a current scientific project, this implementation will be carried out, and a comparison of separation distances with AODM and AUSTAL2000 will be undertaken.

A new empirical model to calculate separation distances between livestock buildings and residential areas applied to the Austrian guideline to avoid odour nuisance

Abstract In Austria a new guideline is under development to calculate the separation distance between livestock and residential areas to avoid odour annoyance. On the basis of dispersion model calculations for 6 sites by the Austrian odour dispersion model (AODM) a regression model is developed, using a power function S = a Eb. The power function is defined by four input parameters. The basis of the power function is the odour emission rate E (ouE s−1) in the range between 400 ouE s−1 ≤ E ≤ 24 000 ouE s−1. The factor a and the exponent b of the power function are defined by two meteorological parameters, the relative frequency of the wind direction F, the mean wind velocity W of the wind direction for 10° sectors as well as the odour exceedance probability P of the odour impact criterion. One of the requirements for this empirical model is the aspiration to substitute the complex calculation with a dispersion model by the new empirical model. The empirical model can be used in a paper and pencil mode, which enables an evaluation of a planned livestock building in a simple way. For a more detailed assessment, a dispersion model can be applied with the entire meteorological information (e.g., stability of the atmosphere, wind field models), the geometry of the emission source, a time depending odour emission rate, and the orography of the site.

Regional Differences of Separation Distances Between Livestock Buildings and Residential Areas in Austria and Comparison to the Austrian Odour Dispersion Model

Regional Differences of Separation Distances Between Livestock Buildings and Residential Areas in Austria and Comparison to the Austrian Odour Dispersion Model

A sensitivity study of separation distances calculated with the Austrian Odour Dispersion Model (AODM)

The Austrian Odour Dispersion Model (AODM) is a Gaussian model adapted for the prediction of odour sensation. It estimates the daily and seasonal variation of the odour emission, the average, ambient odour concentration and the momentary (peak) concentration for the time-interval of a single human breath (approx. 5 s). Peak concentrations, further downwind, are modified by use of an exponential attenuation function for which the ratios of the standard deviations of the wind components to the average wind speed have either to be taken from the literature or to be calculated, e.g. from ultrasonic anemometer data. AODM calculates direction-dependent separation distances for a combination of odour threshold and exceedence probability, which are a function of the prevailing wind velocity and atmospheric stability conditions. Meteorological time series from one site in Styria in southern Austria and one site in the Austrian flatlands, North of the Alps, both rural, are used for a sensitivity study of separation distances. One aspect is, how two different schemes to determine atmospheric stability influence the separation distances. Another source of uncertainty of the calculated separation distances results from the use of measured or literature values for the ratios mentioned above. Decisions on which schemes or ratios to be used have a decisive influence on the separation distances.

Odour episodes in the vicinity of livestock buildings: A qualitative comparison of odour complaint statistics with model calculations

In this paper we compare odour complaint statistics of various sites in Europe, South Africa and North America with calculated odour sensation at a site in Austria, which can be used as a typical example of a well-ventilated area with prevailing west wind and a superimposed weak influence of a mountain wind system. A major goal of this work is the discussion of the general time pattern of complaint statistics and the discrepancy with the occurrence of odour sensation calculated by a dispersion model. The Austrian odour dispersion model (AODM) is a Gaussian model suitable for the prediction of ambient odour concentrations. Based on cumulative frequency distributions of calculated odour concentrations at receptor points, separation distances are obtained defined by odour impact criteria chosen as a combination of odour threshold and probability of threshold exceedance. At these separation distances, depending on the wind direction, the occurrence of odour sensation is analysed and compared with the well-known time pattern of the complaint statistics for odour. Here, the AODM is used to calculate separation distances for an odour threshold of 1 odour unit (OU) per cubic metre exceeded in 3% of the year. At a site in the Austrian North-alpine foreland, direction-dependent separation distances for a 1000 head pig unit (calculated on the basis of a 2-year time series of meteorological data) lie between 99 m for northerly winds and 362 m for westerly winds. For these direction-dependent separation distances we analysed when odour sensation can be expected in relation to meteorological parameters as well time of the day and year. For the main wind directions, West and East and odour sensation can be expected more often for higher wind velocities and a neutrally or stably stratified atmosphere around sunset. North and South winds show the typical diurnal variation of a local valley wind system with predominantly northerly daytime up-valley and southerly night-time down-valley winds. Odour sensation is therefore most likely around noon for North wind and during night-time for South wind. This time pattern of the calculated odour sensation does not fit to the time pattern of the complaint statistics, which shows complaints to occur predominantly in the afternoon and evening hours of the warm season when residents are outside. The presented comparison of odour complaint statistics with the calculated odour episodes is a helpful tool to find out when odour is perceived as most annoying. As a result, the evaluation of these values by the odour impact criteria should not only be based on statistical limits as it is done today but also by considering the annoying potential of odour due to the behaviour of the neighbours. Therefore the odour episode should be weighted by the time of the day and time of the year, as it is done with the limit values for noise.

A COMPARISON OF ODOUR COMPLAINT STATISTICS WITH MODEL CALCULATIONS OF ODOUR EPISODES

The Austrian odour dispersion model (AODM) is a Gaussian model suitable for the prediction of ambient odour concentrations. Based on cumulative frequency distributions of calculated odour concentrations at receptor points, separation distances are obtained defined by odour impact criteria chosen as a combination of odour threshold and probability of threshold exceedance. At these separation distances, depending on the wind direction, the occurrence of odour sensation is analysed and compared with the well-known time pattern of the complaint statistics for odour. Here, the AODM is used to calculate separation distances for an odour threshold of 1 odour unit (OU) per cubic metre exceeded in 3% of the year. At a site in the Austrian North-alpine foreland, direction-dependent separation distances for a 1000 head pig unit (calculated on the basis of a two-year time series of meteorological data) lie between 99 m for northerly winds and 362 m for westerly winds. For these directiondependent separation distances we analysed when odour sensation can be expected in relation to meteorological parameters as well time of the day and year. For the main wind directions, West and East, odour sensation can be expected more often for higher wind velocities and a neutrally or stably stratified atmosphere around sunset. North and South winds show the typical diurnal variation of a local valley wind system with predominantly northerly daytime up-valley and southerly night-time down-valley winds. Odour sensation is therefore most likely around noon for North wind and during night time for South wind. This time pattern of the calculated odour sensation doesn't fit to the time pattern of the complaint statistics which shows complaints to occur predominantly in the afternoon and evening hours of the warm season when residents are outside. The presented comparison of odour complaint statistics with the calculated odour episodes is a helpful tool to find out when odour is perceived as most annoying. As a result, the evaluation of these values by the odour impact criteria should not only be based on statistical limits as it is done today but also by considering the annoying potential of odour due to the behaviour of the neighbours. Therefore the odour episode should be weighted by the time of the day and time of the year, as is done with the limit values for noise.

IT—Information Techonology and the Human Interface: Calculating Direction-dependent Separation Distance by a Dispersion Model to avoid Livestock Odour Annoyance

Using a dispersion model to calculate ambient odour concentrations, the separation distance between livestock buildings and residential areas is defined by the odour impact criteria incorporating the probability of exceeding a pre-selected odour threshold in odour units (OU) per cubic metre. The dynamic Austrian odour dispersion model (AODM), a Gaussian model, is used to calculate the direction-dependent separation distances for several combinations of these two values, which represent the protection level of various land use categories. The calculated direction-dependent separation distances are a function of the prevailing wind velocity and atmospheric stability conditions. At a site in the Austrian North-alpine foreland, the direction-dependent separation distance for a 1000-head pig unit (calculated on the basis of a 2-year time series of meteorological data) for pure residential areas (3% probability of threshold exceedance over the year for an odour threshold of 1 OU m−3) lies between 99 m (for northerly winds with a probability of less than 3% per year) and 362 m (for westerly winds with a probability of 34%). For the main wind directions, West and East, odour sensation can be expected more often for higher wind velocities and a neutral or stable atmosphere around sunset. North and South winds show the typical diurnal variation of a local valley wind system with predominantly northerly daytime up-valley and southerly nighttime down-valley winds. Odour sensation is therefore most likely around noon for North winds and during nighttime for South winds.

Predicting odour impact using the Austrian odour dispersion model (AODM)

Using a dispersion model to calculate ambient odour concentrations, the separation distance between livestock buildings and residential areas is defined by the odour impact criteria using a combination of a pre-selected odour threshold and an exceeding probability. The dynamic Austrian Odour Dispersion Model (AODM), a Gaussian model, is used to calculate the direction-dependent separation distances for several combinations of these two values, which represent the protection level of various land use categories. The calculated direction-dependent separation distances are a function of the prevailing wind velocity and atmospheric stability conditions. At a site in the Austrian North-alpine foreland, the direction-dependent separation distance (calculated on the basis of a two year time series of meteorological data) for pure residential areas (3% exceeding probability over the year for an odour threshold of 1 OU/m3) lies between 99 m (for northerly winds with a probability of less than 3%) and 362 m (for westerly winds with a probability of 34%). For west and east the main wind directions, odour sensation can be expected more often for higher wind velocities and a neutral or stable atmosphere around sunset. Northerly and southerly winds show the typical diurnal variation of a local valley wind system with predominantly northerly daytime up-valley and southerly night-time down-valley winds.

Separation distance to avoid odour nuisance due to livestock calculated by the Austrian odour dispersion model (AODM)

Odour emission of livestock buildings is major burden for ambient residential areas. Using a dispersion model to calculate ambient odour concentrations, the separation distance between livestock buildings and residential areas was defined by a pre-selected odour threshold and an exceeding probability. The dynamic Austrian odour dispersion model (AODM) was used to calculate the separation distance for several combinations of these two values, which represent the protection level of various land use categories. The AODM consists of three modules: (1) odour release on the basis of a simulation model for the indoor climate of livestock buildings; (2) a regulatory dispersion model (Gauss) to calculate hourly or half-hourly ambient odour concentrations; and (3) a fluctuation module, calculating the instantaneous odour concentration, depending on wind velocity and stability of the atmosphere. The calculated separation distances for a pig fattening unit of 1000 heads were compared with empirical guide lines used in some countries (Austria, Germany, Switzerland, The Netherlands, USA). For most guide lines, the separation distances were smaller compared to the model calculation, except for the German guide line applied for non-agricultural areas. Odour sensation occurred predominantly around sunset, with neutral or slightly stable atmospheric stability. The presented AODM is a useful tool for regulatory purpose.

Diurnal and annual variation of the sensation distance of odour emitted by livestock buildings calculated by the Austrian odour dispersion model (AODM)

The diurnal and annual variation of distances for different odour thresholds is investigated by the dynamic Austrian odour dispersion model (AODM) consisting of an emission module, a dispersion module, and a module to calculate instantaneous odour concentrations. The effect of daily variations in odour production, ventilation rates and indoor air temperature are included in impact assessments. The ambient half-hour odour concentrations calculated by a regulatory Gaussian plume model are transformed to instantaneous values representative for the duration of a single breath by an attenuation function decreasing the peak-to-mean ratio with increasing wind velocity, stability, and distance from the source. The resultant distances for different odour thresholds and their dependence on meteorological parameters are investigated and discussed in detail, focussing on the distance for the detection limit, 1 OU m −3, the so-called sensation distance. The results suggest a stronger dependence of the sensation distance upon variation in meteorological conditions than diurnal and annual variations in odour emission rates.