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Abstract
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 halfhour 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 peaktomean ratio with increasing wind speed, stability, and distance from the source.
Odour emission model
The emission model is based on a steadystate 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
The ventilation systems in livestock buildings are mainly designed as temperaturecontrolled 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 T_{C} and the proportional range DT_{C} describe the course of the volume flow depending on the indoor air temperature T_{i} 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 T_{i}.
Parameter  
Mean total energy release of an animal Q_{A} (continuous fattening between 30 and 100 kg) 

Minimum volume flow V_{min}. Design value for the ventilation system taking into account the maximum accepted indoor CO_{2} concentration of 3000 ppm 

Maximum volume flow V_{max}. Design value for the ventilation system taking into account the maximum temperature difference between indoor and outdoor for summer (T_{i}=30°C) of 3 K 

Area of the building (ceiling, walls, windows, doors) per animal 

Thermal transmission coefficient U 

Set point temperature of the control unit T_{C} 

Bandwidth of the control unit DT_{C} 

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 e_{m} 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 T_{o }(Oldenburg, 1989; Kowalewsky, 1981). The linear regression of Oldenburg (1989) was adapted to assess the influence of the temperature T_{0} on odour flow E_{m} by Eqn (3).
The concentration of odoriphores can be handled like other volatile pollutants The dispersion of such substances can be described by wellknown 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. halfhour, 3hours 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 northsouth oriented valleys, main wind directions are west and east.
b
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).
Stability classes SC are determined
as a function of halfhourly 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 LinzHörsching, 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 twoyear 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 wellmixed 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.


in m/s 



5  6  7  




41  71  




35  59  




30  22  7  




25  12  




4  










Sum 



59  110  137 










































































































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:
These values are only valid close to the odour source. Due to turbulent mixing, the peaktomean 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 peaktomean ratio in equation (6) is modified by an exponential attenuation function of T/t_{L}, where T=x/u is the time of travel with the distance x and the mean wind speed u, and t_{L} is a measure of the Lagrangian time scale (Mylne, 1992):
The time scale t_{L }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:






















The peak concentration C_{p} is calculated by the following equation:
b
c
d
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)
Acknowledgement
This work was partly funded by the Austrian Federal Ministry for Environment, Youth and Family (GZ. 14 4444/10I(4/29.
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