Environmental Engineering Reference
In-Depth Information
Table 1
Linear regression coeficients k and k
0
and squared correlation R
2
between observed
and predicted concentrations (µg/m
3
) are computed for all stations. Distance is measured from
the road side and N is number of cases
Monitor
Distance (m)
N
k
k
0
R
2
ST1
7.3
1,038
0.784
31.5
0.765
ST2
16.8
1,038
0.651
28.8
0.732
ST3
46.8
1,038
0.540
23.4
0.661
Table 2
Relative bias RB is computed for observed and predicted concentrations
(mg/m
3
) in four wind speed groups <1 m/s, 1-2 m/s, 2-3 m/s and >3 m/s. N is the
number of cases
Group (m/s)
N
ST1
ST2
ST3
<1
269
0.012
0.227
0.457
1-2
254
0.081
0.214
0.223
2-3
165
−0.172
−0.160
−0.173
>3
350
−0.121
−0.116
−0.151
All
1,038
−0.018
0.105
0.201
current practice in CAR-FMI. On the other hand, the lowest wind speed regime is
clearly outside the application area of analytical solutions of dispersion equations
as commented by several authors.
Conclusion
We propose a procedure for line source dispersion modeling in low wind speed
conditions, when meandering has a strong effect on the dispersion. The comparisons
between predicted and observed NO
x
concentrations show that the suggested mean-
dering procedure applied to an analytical line source model:
•
Improves predictions in situations of nearly parallel winds to the road, which are
caused by lateral fluctuation of wind direction
Wind speed limit for over- or underestimation regimes is about 2 m/s at 10 m
•
height
Allows temporal diffusion to the upwind side of the road by lateral fluctuation,
•
which is important at receptor points locating within the first tens of meters from
the road
References
1. Oettl D, Almbauer RA, Sturm PJ (2001) A new method to estimate diffusion in stable, low
wind conditions. J. Appl. Meteorol. 40:259-268
2. Anfossi D, Oettl D, Degrazia G, Goulart A (2005) An analysis of sonic anemometer observations
in low wind speed conditions. Boundary Layer Meteorol. 114:179-203
