Austrian odour dispersion model (AODM)

Further information: Interessengemeinschaft Geruch igG

published in:

SCHAUBERGER G., PIRINGER M., PETZ E.: Diurnal and annual variation of the sensation distance of odour emitted by livestock buildings calculated by the Austrian odour dispersion model (AODM). Atmospheric Environment 34(2000)28: 4839-4851.


The diurnal and annual variation of distances for different odour thresholds is investigated by the dynamic Austrian odour dispersion model (AODM) consisting of an emissions module, a dispersion module, and a module to calculate instantaneous concentrations. Daily variations of the odour emission in parallel to the total heat release of the animals and the ventilation rate depending on the characteristics of the control unit of the ventilation system and the calculated indoor temperature are taken into account. The ambient half-hour odour concentrations calculated by a regulatory Gauss 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 speed, stability, and distance from the source.

Odour emission model

The emission model is based on a steady-state balance of the sensible heat fluxes to calculate the indoor temperature and the related volume flow of the ventilation system. The corresponding odour flow is assessed by a simple model of the odour release. The model has been described extensively in Schauberger et al. (1999a and 1999b), and therefore only its main features are reported here.

The air temperature inside a mechanically ventilated livestock building is calculated by using a balance equation of the sensible heat (Schauberger, 1988; CIGR 1984, Albright, 1990, ASHRAE, 1972; Sallvik and Pedersen, 1999). The indoor air temperature (equal to the temperature of the outlet air) and the volume flow are calculated as a function of the outdoor temperature.

The balance equation (Eqn 1) consists of three terms describing the sensible heat flux of the livestock building as

SA + SB + SV = 0 (1) with the sensible heat release of one animal SA, the loss of sensible heat caused by the transmission through the building SB, and the sensible heat flow caused by the ventilation system SV.

The ventilation systems in livestock buildings are mainly designed as temperature-controlled variable volume flow systems. The control unit uses the indoor air temperature as the control value. The supply voltage of the fans and therefore the resulting volume flow is the output of the control unit. Two parameters, the set point temperature TC and the proportional range DTC describe the course of the volume flow depending on the indoor air temperature Ti as a control value (eg Bruce, 1999). For an indoor air temperature less than the set point temperature, the minimum volume flow is supplied. In the proportional range above the set point temperature, the volume flow is increased until the maximum ventilation rate is reached. Above this range, the livestock building is supplied by the maximum ventilation flow. Equation (2) gives the volume flow V as a function of the indoor air temperature Ti.

(2) The lower Vmin and the upper Vmax limit of the volume flow are design values according to the guide lines for the indoor climate for animals (CIGR, 1984; ASHRAE 1972; Albright, 1990; Bruce, 1999) (Table 1). Table 1: System parameters of the indoor climate (model calculation) per animal; the parameters are representative for an unit of about 1000 fattening pigs.
Mean total energy release of an animal QA (continuous fattening between 30 and 100 kg)
188 W
Minimum volume flow Vmin. Design value for the ventilation system taking into account the maximum accepted indoor CO2 concentration of 3000 ppm
13.1 m³/h
Maximum volume flow Vmax. Design value for the ventilation system taking into account the maximum temperature difference between indoor and outdoor for summer (Ti=30°C) of 3 K
66.0 m³/h
Area of the building (ceiling, walls, windows, doors) per animal 
1.35 m²
Thermal transmission coefficient U
2.0 W/m² K
Set point temperature of the control unit TC
18 °C
Bandwidth of the control unit DTC
4 K


The model calculations were done for a pig fattening unit of 1000 pigs with a forced ventilation. The livestock building is moderately isolated, described by the U value (Table 1). The assumed space per animal is 0.75 m² according to the welfare guide lines. The chosen system parameters for a livestock building with these specifications, typical for middle Europe, are summarised in Table 1.

The odour release from the livestock building originates from the animals, polluted surfaces and the feed. Outdoor odour sources such as slurry tanks or feed storage facilities are not taken into account. The emission of the livestock building at the outlet air is quantified by the odour flow E in OU/s and the specific odour flow e in OU/s LU related to the livestock (livestock unit (LU) equivalent to 500 kg live mass of the animals). The specific odour flow depends on the kind of animals and how they are kept. Available data are summarised by a literature review of Martinec et al. (1998). For the model calculation presented here, a mean specific odour flow em of 100 OU/s LU and a mean live mass of 60 kg per fattening pig (M = 0.12 LU) were used.

As odour production is a biochemical process, the temperature has an important influence. Most authors select the appropriate value for the outdoor air temperature To (Oldenburg, 1989; Kowalewsky, 1981). The linear regression of Oldenburg (1989) was adapted to assess the influence of the temperature T0 on odour flow Em by Eqn (3).

(3) Instead of a constant odour release in previous model calculations (Schauberger and Piringer 1999a and 1999b), the diurnal variation of the odour release was assessed by the measurements of Rieß et al. (1999) of the odour concentration inside a pig fattening unit by an electronic nose. The diurnal variation of the odour release E(t) is taken into account by a sinusoidal function with the period t of 24 hours, proposed by Pedersen and Takai (1997) on the basis of the variation of the animal activity over the time of the day t. The odour release was calculated by Eqn. 4 with the relative amplitude of 20% related to the daily mean of Em(T0) according to Eqn. 3. The phase of the time course of the energy release and the odour release was assumed as the same, triggered by the animal activity. The minimum of the animal activity of fattening pigs occurs around 01:15 local time at night (Pedersen, 1996; Pedersen and Takai, 1997). (4) The odour flow of the livestock building depends on the odour release and the volume flow of the ventilation system. As a result of the model calculation, the odour concentration C of the outlet air is taken as the parameter to describe the odour release. The concentration is calculated by the odour flow E in OU/s according to Eq. (4) divided by the volume flow V of the ventilation system in m³/s. (5) Dispersion model and meteorological conditions

The concentration of odoriphores can be handled like other volatile pollutants The dispersion of such substances can be described by well-known dispersion models, like a Gaussian one (e. g. Kolb, 1981; ÖNorm M 9440, 1992/96). Then the concentration at a receptor point is calculated as a mean value of the concentration of odoriphores for a defined period (e.g. half-hour, 3-hours mean value). The calculation of the odorant concentration itself is not meaningful if odour has to be evaluated. This is due to the fact that odour is not an attribute of an odoriphor, but a reaction of humans (Summer, 1970). To apply a dispersion model to odour emissions, the odour concentration and the volume flow of the outlet air have to be known. In many cases, these two parameters are assumed to be constant over time, although it is well known that the ventilation system of animal houses is designed to vary the air exchange in a range of 1:5 to 1:10 between the minimum and the maximum volume flow (Table 1, Vmin:Vmax=1:5). This time, appropriate variations of these parameters are taken into account (section 2.1).

The odour concentration of the centre line of the plume is calculated by the Austrian regulatory dispersion model (ÖNorm M 9440, 1992/96; Kolb, 1981) by making use of a statistics of stability classes representative for the Austrian flatlands north of the Alps. The model has been validated internationally with generally good results (e.g. Pechinger and Petz, 1995).

The regulatory model is a Gaussian plume model applied for single stack emissions and distances up to 15 km. Plume rise formulae used in the model are a combination of formulae suggested by Carson and Moses (1969) and Briggs (1975). The model uses a traditional discrete stability classification scheme with dispersion parameters developed by Reuter (1970).

The Austrian flatlands north of the Alps (200 to 400 m above sea level) are characterised by a moderate climate with both maritime and continental influences. The annual average temperature is 9 to 10°C. Precipitation occurs all the year round, culminating in summer storms, and yearly precipitation totals amount from 700 to 1000 mm from east to west. In general, there is a good ambient air movement, with mean wind speeds ranging from about 2 to 4 m/s. Except for north-south oriented valleys, main wind directions are west and east.



Fig. 1. Frequency distribution of (a) the wind direction and (b) wind speed at Wels;
- - - - - -, Calm conditions according to the Austrian regulatory dispersion model with wind speed less than 0.7 m/s (ÖNorm, 1992/1996).

The meteorological data were collected at Wels, a site representative of the Austrian flatlands north of the Alps. The sample interval was 30 minutes for a two-year period between January 30, 1992 and January 31, 1994. The city of Wels in Upper Austria is a regional shopping and business centre of about 50,000 inhabitants. The surroundings are rather flat and consist mainly of farmland. The mean wind speed in undisturbed environment is 2.2 m/s, maximum speeds amounting to about 13 m/s. The distribution of wind directions and wind speeds are shown in Fig. 1. The prevailing wind directions at Wels are west and WSW, as well as east and ENE. Calm conditions according to the Austrian regulatory dispersion model with wind speed of less than 0.7 m/s amount to 18.2%; weak winds (wind speeds less than 1 m/s) comprise 26.5% of all cases. Less than 10% of all wind speeds are larger than 5 m/s. The annual mean temperature at Wels is 9.7 °C, the temperature range (two-year period) is from –14.9 °C to 35.3 °C. The annual precipitation amounts to 838 mm (mean over the period 1961 – 1990).

Stability classes SC are determined as a function of half-hourly mean wind speed and a combination of sun elevation angle and cloud cover (Table 2). The cloud cover was monitored by the meteorological station at the airport Linz-Hörsching, in a distance of about 13 km. Within the Reuter scheme, classes 2 to 7 can occur in Austria. As seen from Table 2, some combinations of stability class and wind speed are not possible by definition (ÖNorm M 9440, 1992/1996). Stability class 4, representative of cloudy and/or windy conditions including precipitation or fog, is by far the most common dispersion category because it occurs day and night. Its occurrence peaks at wind speeds of 2 and 3 m/s. Wind speeds larger than 6 m/s are almost entirely connected with class 4 (since a frequency of 1‰ is equal to about 17 half hours in the two-year statistics, smaller occurrences do not show up in Table 2). Stability classes SC=2 and SC=3, which by definition occur only during daylight hours in a well-mixed boundary layer, class 3 allowing also for cases of high wind velocity and moderate cloud cover, peak slightly below or around the average wind speed. They cover 26% of all cases. Class 5 occurs with higher wind speeds during nights with low cloud cover, a situation which is not observed frequently at Wels. Classes 6 and 7 are relevant for clear nights, when a surface inversion, caused by radiative cooling, traps pollutants near the ground. Such situations occur in 25% of all cases.

Table 2: Two-dimensional frequency distribution in ‰ of stability classes SC (2 to 7) and wind speed in m/s at Wels
Stability class SC
Wind speed,
in m/s
5 6 7
< 1.0
  41 71
1.0 - 1.9
  35 59
2.0 - 2.9
30 22 7
3.0 - 3.9
25 12  
4.0 - 4.9
5.0 - 5.9
6.0 - 6.9
³ 7
59 110 137

  Table 3: Relative frequency (%) of the stability classes SC for each month

Stability class SC



The average occurrence of stability classes for each month is given in Table 3.

Table 3 shows a lot of seasonal variation of the occurrence of stability classes. Especially the probability for stability class 2 is about ten times higher during summer than during winter months. The effect of this variation on the distance, where sensation occurs, is discussed in section 3. The occurrence frequencies for stability classes 3 and 4 vary by a factor of 3, those for classes 5 to 7 by a factor of about 2.

Assessment of the expected maximum concentration in an interval of an breath

The regulatory model calculates half hour mean concentrations. The sensation of odour, however, depends on the momentary odour concentration and not on a mean value over a long time of integration. Smith (1973) gives the following relationship:

(6) with the mean concentration Cm calculated for an integration time of tm and the peak concentration Cp for an integration time of tp. Smith (1973) suggests the following values of the exponent u depending on the stability of the atmosphere: 0.35 (SC=4), 0.52 (SC=3) and 0.65 (SC=2). Using tm = 1800 s (calculated half-hour mean value) and tp = 5 s (duration of a single breath), the following peak-to-mean factors, depending on atmospheric stability, are derived by a quadratic function based on the values of Smith (1973): 43.25 (SC=2), 20.12 (SC=3), 9.36 (SC=4), 4.36 (SC=5), 1.00 (SC=6) and 1.00 (SC=7).

These values are only valid close to the odour source. Due to turbulent mixing, the peak-to-mean ratio is reduced with increasing distance from the source. Mylne and Mason (1991) analysed the fluctuation of the plume concentration and developed the following relationship: The peak-to-mean ratio in equation (6) is modified by an exponential attenuation function of T/tL, where T=x/u is the time of travel with the distance x and the mean wind speed u, and tL is a measure of the Lagrangian time scale (Mylne, 1992):

(7) where Y0 is the peak-to-mean factor calculated in equation 6.

The time scale tL is taken to be equal to s/ewhere  is the variance of the wind speed as the mean of the three wind components u, v, and w, respectively, and eis the rate of dissipation of turbulent energy using the following approximation:

(8) where k=0.4 is the von Karman constant and z=2m is the height of the receptor, the human nose. The ratio of the variances of the three components u, v and w to the horizontal windspeed u depending on the stability of the atmosphere is given in Table 4. For stability classes 6 and 7 no change of the peak-to-mean ratio is assumed. For su / u and sv / u, values are taken from (Robins, 1979), and no change with stability is assumed. sw / u is taken to be stability-dependant, using our long-term Sodar experience which suggests an increasing importance of sw compared to u in unstable conditions. Table 4: Variance of the three components of the wind u, v and w as a function of the stability of the atmosphere (details see text)

Variance of the wind velocity
Class of stability
/ u
/ u
/ u

The peak concentration Cp is calculated by the following equation:

(9) The approach leading to equation 9 assures a gradual decrease of the peak to mean – ratio with increasing distance, wind speed and stability, as can be seen from Fig. 2. For classes 2 and 3, Y , starting at rather high values near the source and at low wind speeds, rapidly approaches 1 with increasing wind speed and distance. This is in agreement with ideas that vertical turbulent mixing in weak winds then locally can lead to short periods of high ground-level concentrations, whereas the ambient mean concentrations are low. For class 4, the decrease of the peak to mean – ratio is more gradual with increasing wind speed and distance, because vertical mixing is reduced and horizontal diffusion is dominating the dispersion process. This is even more the case for class 5, when the peak to mean – ratio never exceeds 2. Compared to uncorrected peak to mean the damping is most effective for class 2 and decreases with increasing class number.




Fig. 2: Dependance of the attenuation function Y of the peak to mean ratio with distance for stability class 2 (a), 3 (b), 4 (c) and 5 (d) for all classes of wind velocity which occur at Wels (see also Tab. 2 and 3)

The problem of odour regulation is summarised by Nicell (1994) discussing the whole chain of odour sensation (detection 1 OU/m3), discrimination (3 OU/m3), unmistakable perception (5 OU/m3, complaint level), and as a last step the degree of annoyance. Following this definition, three distances were calculated using these limit values, named sensation distance, discrimination distance, and complaint distance, by linear interpolation of the odour concentration calculated for discrete 41 distances between 50 m and 2000 m


This work was partly funded by the Austrian Federal Ministry for Environment, Youth and Family (GZ. 14 4444/10-I(4/29.


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