Assessment of the odour concentration in
the
nearfield of small sources
Introduction
In administrative procedures, small odour
sources place the expert however also the authority in many cases in front of
the problem of the proportionality. Small sources can be livestock buildings,
snackrooms, small businesses and so on. From the complexity of geometry and
meteorological situation, microscale models would be appropriate. In many
cases the necessary application cannot not be justified through the expenses.
In the following we present a method to
assess the odour concentration in the near field of a building. The near field
is the zone where the source structure directly affects plume dispersion and
structure. The near field is typically 10 to 20 times the reference length,
often estimated by the building height or width [1,2].
First we discuss the adaptation of
dispersion models to mimic the odour sensation of humans due to their nonlinear
doseresponse relationship. Second we present a simplified method to calculate
odour concentration in the near field of buildings, based on box models.
Adaptation of dispersion models for the odour perception of
humans
Most dispersion models are calculating mean
values for an integration time of 30 to 60 minutes (eg [3]). For odour
sensation the relevant integration time is about the duration of one single
breath (about 5s). Therefore the fluctuations of the odour concentration has to
be taken into account.
For the assessment of the expected maximum
concentration at a receptor point a correction factor F_{P/M } can be
used, which is defined by the concentration for an integration time of the
dispersion model and the concentration for an integration time of one single
breath, often called peaktomean ratio.
The maximum expected concentration for a
single breath C_{P} can be calculated by
_{
} (1)
with the concentrations of the dispersion
model C and a distance depending factor F_{P/M}. Close to the source a
factor F_{P/M}^{0}_{ } is calculated by [4], which has
to be attenuated as a function of distance
_{
} (2)
with the exponent a of the power function,
depending on the stability of the atmosphere (Tab. 1), the integration
time for the mean value t_{m} = 1800 s (depending on the used
dispersion model between t_{m} = 1800 s and 3600 s) and
the short time integration time t_{P}, here assumed as t_{P} =
5 s as duration of a single breath.
Tab. 1: Exponent a of Eq.
2, to assess the short term concentration C_{P} as a function of the
stability classes of the atmosphere SC (ÖNorm [3] and Pasquill) and the
correspondent peaktomean factors F_{P/M}^{0}
SC

State of Texas^{1}

Smith^{2}

AODM^{3}




a

F_{P/M}^{0}

a

F_{P/M}^{0}

a

F_{P/M}^{0}

2

A

0.68

54.74

0.65

45.88

0.640

43.25

3

B

0.55

25.47

0.52

21.34

0.510

20.12

4

C

0.43

12.57

0.52

21.34

0.380

9.36

5

D

0.30

5.85

0.35

6.58

0.250

4.36

6

E

0.18

2.88





0.000

1.00

7

F

0.18

2.88





0.000

1.00














^{1} [5]; ^{2}
[4]; ^{3} [7]
The former TA Luft guide line selected a
constant correction factor F_{P/M}=10. In this respect it has to taken
into mind that the Gaussian dispersion model, which was used with this
correction factor, is limited to distances above 100 m. A recently
presented version of the dispersion model for odour (AUSTAL2000G), which has to
be used in accordance to TA Luft 2002 (AUSTAL 2000, www.austal2000.de) is using
a constant factor 4 [6].
For a simplified assessment we suggest an
interpolation between the correction factor close to the source F_{P/M}^{0}_{
}and at a distance above 100m F_{P/M,max}. The constant value
above 100 m can be selected according to the TA Luft (F_{P/M,max }= 10)
or by a less conservative approach according to the ODIF or AUSTAL200G model (F_{P/M,max }= 4).
Beside this two constant values a more sophisticated attenuation function can
be used, which is based on the travel time of the pollution and the Lagrangian
time scale describing the stability of the atmosphere [7]. Fig. 1 depicts the
correction factor F_{P/M }calculated for the exponent a used in the
State of Texas (Tab. 1, [5]) and a maximum value of 4 (ODIF or AUSTAL2000G
model) as an example.
The correction factor F_{P/M} for
distances below 100m can be calculated by
_{
} (3)
Fig. 1: Correction factor F_{P/M}
as a function of the distance from the source x (m). The graph shows a maximum
for stability class SC = 2 and a value F_{P/M,max }= 4
according to ODIF or AUSTAL 2000G.
The ratio between emission concentration C_{e}
and the perception threshold of 1 OU/m³ lies in the range between 100 and
10 000. This means, that in the close vicinity of odour source the
expected dilution can be below this ratio. Therefore the maximum expected odour
concentration is limited by the odour emission concentration.
_{
}
(4)
Dispersion models for the near field
In the close vicinity of buildings and
other obstacles close to a stack, plume dispersion is disturbed. For flat areas
the dilution process can be solved relatively simple (eg Gaussian models). For
complex terrain due to bluff bodies like buildings and other obstacles the
windfield itself is changed in the near field but also the spatial
distribution of turbulence. Here we want to give an overview how to treat
building effects.
Fig. 2: Schematic view of the
building effect in the leeward. The reference length L (in this case the
building height h_{B}) characterises the size of the recirculation zone
(after [8])
The dilution process in the close vicinity
of buildings is strongly depending on the scale. In Fig. 2 the influence
of a building is schematically shown. The cavity zone is characterised by the
recirculation. Behind there is the downwash zone. The size of the building
effect can be assessed by the reference length L.
The reference length L can be expressed by
the building height h_{B} and the crosswind width h_{A}
according to ÖNorm [3].
(5)
Fig. 3: Model concepts in the near
field of a building
In the near field various model concepts
are in use (Fig. 3). Box models need only few input information, therefore they
can be used easily as a first estimation. The Gaussian concept can be adopted
for the near field by an interpolation scheme, taking into account building and
downwash effects (eg regulatory dispersion model in The Netherlands).
An other modification is the virtual
source. It is based on the idea, that the source is split into two different
plums to simulate the extra dispersion which is caused by the building effect.
Initially this was achieved by shifting the location of the source upwind, to
model the broadening of the plume due to the building effect [9,10]. More
complex models (microscale nonhydrostatic models, eg MISKAM) were not
included in this overview.
For a box model it is assumed that the
concentration C inside the cavity zone can be expressed by
_{}
(6)
with a dimensionless concentration
coefficient K, odour concentration C (OU/m³), wind velocity u (m/s), building
area cross section A_{B}, which can be expressed by A_{B}=L²,
and the source strength Q (OU/s).
Simple box model
For the simple box model two areas can be
distinguished [8], depending on the normalised distance X = x/L. For
distances closer than x/L = 2.5 a constant concentration coefficient
is assumed, which means, that inside the cavity zone a constant odour
concentration is expected. In the range between x/L = 2.5 and 10 the
concentration coefficient and therefore also the concentration C goes with the
inverse square of the normalised distance. For distances above x/L = 10
this model cannot be applied (see also Fig. 3). For the concentration
coefficient K (for x/L<2.5) various values are in use, in the range between
0.5 and 5.0 [8]. For higher distances values between 4 and 20 can be found in
literature. For a maximum estimation we selected a value of 3.0. For distances
x/L ³ 2.5 a coefficient was selected that the function show a smooth
behaviour for the limit value of x/L = 2.5 by
_{
} (7)
with the distance x (m) in wind direction
from the source and the reference length L (m).
Meroney box model
In the previous concept only the reference
length L as a source depending parameter was used. In the following assessment of
the concentration coefficient K, based on Meroney [8], also the ratio of stack
height h_{e} and the building height h_{B} is used. Fig. 4
shows the influence of the stack height compared to the building height. Only
when the emission height h_{e} is 2.5 times above the building height
it can be supposed that the emission lies outside of the building influence.
Fig. 4: Influence of the height of
the odour emission h_{e} compared to the building height h_{B} [after
[8])
The assessment of K is based on a
modification of the dispersion parameters s_{x} and s_{z} of the Gaussian dispersion model.
The modification is based on the broadening of the plume due to the building
effect, parameterised by the crosssection of the building A_{B}
_{}
(8)
with an empirical constant c (between c=0.5
and 2.0).
To take into account the size of the recirculation
zone and the entrainment into this zone the ratio between emission height h_{e}
and building height h_{B} is introduced. For a maximum of the ratio h_{e}/h_{B}=2.5
the concentration coefficient K_{2.5} is calculated by
_{
} (9)
Then the concentration coefficient K can be
calculated by the ratio h_{e}/h_{B}
_{
} (10)
The dispersion parameters,
s_{x} and s_{y}, used in this paper are based on the
ÖNorm [3].
Example
In the following example we apply the simple
box modes and the Meroney box model to a typical small odour source. We
selected an isolated building with a building height h_{B} of about 5 m,
an emission height h_{e} of about 6 m. The odour source was
estimated by an odour flow rate Q = 420 OU/s and an emission concentration
C_{e} = 300 OU/m³ (Fig. 5)
Fig. 5: Example of an isolated
building as a small odour source with typical parameters.
The odour concentration was calculated by
the two box models. For distances x>100m we assume, that beyond this
distance the Gaussian model can be applied. The presented results were
calculated for an wind velocities of 1, 3, and 5 m/s.
In Fig. 6 the odour concentration was
calculated by the Meroney box model for all stability classes 2 to 7 and a wind
velocity of u=1 m/s. Below a distance of L=10, the maximum value over all
stability classes (thick line in Fig. 6) is dominated by SC=2 (unstable), for
higher distances by SC=7 (very stable).
Fig. 6: Odour concentration
calculated by the Meroney box model for a wind velocity of 1 m/s.
In Fig. 7 the calculation for the simple box
model, the Meroney model, and the odour concentration calculated by the
Gaussian model was depicted. For a wind velocity of u =1 m/s and 3m/s
the simple model shows a plateau which is caused by the fact, that the ambient
odour concentration is limited by the odour emission concentration. At the
upper limit of the model domain (L = 10 or x = 50 m) and
u =1 m/s, the odour concentration is assessed by the simple box model
with C = 139 UO/m³, which is about the 10 fold of the Meroney
model. The upper level of distinct odour sensation (C = 10 OU/m³)
is reached at a distance of 20 L, which means x = 100 m. At
distances above 100 m, the odour concentration calculated by the Gaussian
model shows a close agreement with the Meroney assessment.
By using a wind statistics (wind direction
and wind velocity) or a dispersion climatology (stability classes and wind
velocity) these results can be used to assess the annoying potential for a
certain distance. Therefore the exceedance probability for a certain threshold
can be used. In general an odour threshold of 1 OU/m³ is selected. The
exceedance probability for this threshold depends on the protection level which
has to be archived. For Germany
an exceedance probability of 10% (90precentile) is assumed for residential
areas.
a
b
c
Fig. 7: Odour concentration
calculated by the simple box model (Eq. 7), the Meroney box model and by the
Gaussian model (distance > 100 m) for a wind velocity of 1 m/s (a),
3 m/s (b), and 5 m/s.
Discussion and Conclusions
Small odour sources have to be evaluated in
many cases to assess the odour annoyance in the vicinity of the emission.
Especially for administrative procedures, consultants and expert witness have
sometimes to accept restrictions concerning the costs. In this paper we
summarise methods which can be used in a multistep approach. To consider the
sensation characteristics of humans, we suggest an adaptation of the presented
models by a peaktomean approach, as it is used for regulatory dispersion
models. As a first guess, the odour concentration can be calculated by a simple
box model or the Meroney box model. If the calculated odour concentration exceeds
the preselected limit values, more sophisticated solutions can be applied.
References
[1] Environment Protection
Authority Draft Policy: Assessment and Management of Odour from Stationary
Sources In NSW. Technical Note. New South Wales EPA, Sydney: 2001.
[2] Katestone Scientific
Peaktomean ratios for odour assessments. Report from Katestone Scientific to
Environment Protection Authority of New South Wales, Australia: 1998
[3] ÖNorm M 9440: Ausbreitung von luftverunreinigenden Stoffen in der
Atmosphäre; Berechnung von Immissionskonzentrationen und Ermittlung von
Schornsteinhöhen. Österreichisches Normungsinstitut: Wien:
1992/1996
[4] Smith, M.E.: Recommended
Guide for the Prediction of the Dispersion of Airborne Effluents.
ASME, N.Y. : 1973
[5] Trinity consultants:
Atmospheric diffusion notes. Fall: 1976
[6] Janicke L., Janicke U.:The development of the dispersion model AUSTAL2000G. Ingenieurbüro Janicke, Dunum, Germany: 2004
[7] 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: 48394851
[8] Meroney
R.N. : Turbulent diffusion near buildings. In:
Plate E.J. (Ed): Engineering Meteorology. Elsevier, New York: 1982
[9] Olesen H.R., Genikhovich E.
: Building downwash algorithm for the OML atmospheric dispersion model.
National Environmental Research Institute, NERI Research Note No 123, Denmark: 2000
[10] Robins A.G., Apsley D. D.: Modelling of
building effects in ADMS. ADMS 3 pp39: 2003