Analysis of Urban Atmosphere Plume Concentration Fluctuations |
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Authors: | Dennis Finn Kirk L Clawson Roger G Carter Jason D Rich Chris Biltoft Martin Leach |
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Institution: | (1) Department of Environmental Technology Management, College for Women, Kuwait University, P.O. Box 5969, Kuwait, Safat, 13060, State of Kuwait;(2) Faculty of Engineering, Assiut University, Assiut, 71516, Egypt |
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Abstract: | Concentration variability in the fast-response tracer dataset for continuous, near-surface, point source releases in the urban
core from the Joint Urban 2003 field study is analyzed. Concentration variability for conditionally and unconditionally sampled
time series is characterized by probability densities, concentration fluctuation intensity, skewness, and kurtosis. Significant
day-night differences in plume dispersion are observed. Relative to daytime, nighttime plumes were more likely to have reduced
concentration fluctuation intensities, higher normalized surface concentrations, suppressed vertical mixing, and a greater
prevalence of Gaussian-like distributions rather than log-normal or mixed mode distributions. This was in spite of the similar
stability and turbulence conditions in the urban core for day and night. The potential roles of flow meander and thermal stability
in explaining these differences are examined. Probability densities of concentration are found to be a strong function of
fluctuation intensity. There are few differences in probability densities between day and night when classified by fluctuation
intensity. There are no appreciable differences between conditional and unconditional probability densities and only small
differences between conditional and unconditional sampling statistics relative to the larger differences usually observed
in more homogeneous settings. Fluctuation intensity, skewness, and kurtosis are higher for the daytime experiments, and closer
to the source, but show little difference between conditional and unconditional results over most of their range of values.
The log-normal distribution provides a better overall fit to a broader range of the dataset than the exponential or clipped-normal
distributions. |
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