Environmental Engineering Reference
In-Depth Information
where
concentration of primary pollutant [SO
2
],gm
−
3
C
p
=
concentration of secondary pollutant [SO
2
4
],gm
−
3
C
s
=
source emission rate of primary pollutant, g s
−
1
Q
=
h
=
average mixing height of modeling domain, m
diffusion coefficient, m
2
s
−
1
D
=
average wind speed of modeling domain, m s
−
1
u
=
r
=
distance between source and receptor, m
θ
=
azimuthal angle from the source to the receptor, degree
ϑ
=
azimuthal direction of prevailing wind in modeling domain, degree
K
0
=
Bessel function of zeroth order
τ
c
=
transformation time constant from primary to secondary pollutant, s
τ
dp
=
dry deposition time constant of primary pollutant, s
τ
ds
=
dry deposition time constant of secondary pollutant, s
τ
w
p
=
wet deposition time constant of primary pollutant, s
τ
w
s
=
wet deposition time constant of secondary pollutant, s
Equations (9.21) and (9.22) give the
concentration
at the receptor of the primary and secondary
pollutants. If we are interested in
wet deposition
at the receptor of the secondary pollutant (i.e., the
wet deposition of sulfate ions), the following equation is used:
C
s
hR
τ
w
s
R
0
D
w
s
=
(9.25)
where
D
ws
is the wet deposition rate in g s
−
1
,
R
is the annual or seasonal rainfall at the receptor,
and
R
0
is the annual or seasonal rainfall averaged over the modeling domain. Because
h
appears in
the denominator of equation (9.22) and the numerator of equation (9.25), the wet deposition rate
is independent of
h
. Similarly, the dry deposition rate of sulfate is obtained from
C
s
h
τ
ds
D
ds
=
(9.26)
The model parameters were derived by an optimization technique. Several years of observed data
of annual wet deposition of sulfate in ENA were compared to model predicted values. Optimized
values were obtained from minimizing the root mean square error:
2
=
(
observation
−
prediction
)
E
2
(9.27)
(
observation
)
2
The optimized model parameters are listed in Table 9.6. The optimized parameters are consistent
with independently derived data in the literature. For different averaging periods (e.g., summer or
winter seasons) or for different regions, different model parameters must be derived.
