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| Titel |
A statistical approach for the identification of sources associated with concentration peak events |
| VerfasserIn |
P. Paradisi, C. Pizzigalli, R. Cesari, P. Allegrini, M. D'Isidoro, A. Maurizi, F. Tampieri |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250028613
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| Zusammenfassung |
| In air quality management a crucial aspect to be considered is related to the number of times
that the concentration of some pollutant overcomes a given threshold value.
The impact on human health is in fact related to the number of overcomings, whose annual
maximum number, together with the threshold values, is estimated through health impact and
exposure studies and fixed by European directives, most of which transposed into national
laws.
The reference number of overcomings and the threshold value are related to the
consequent
Consequently, evaluating the contribution of emission sources associated with concentration
peak events becomes an important feature to be considered. In this framework, it
is also interesting to develop a numerical tool being able to estimate the relative
contribution of near and far pollutant sources. This is an important aspect that an
environmetal agency should be able to carry out and that should be considered
in the problem of traffic management associated with the high levels of pollutant
concentration.
In this work we illustrate a statistical methodology, involving also a backward Lagrangian
dispersion model [1,2], to characterize the position of sources that give the main contribution
to concentration peaks. This is made by computing a spatial probability distribution of the
sources, which, for each given spatial point in the considered domain, represents the
probability of having a source in that point. The usefulness of the method is related to
the finding of evident maxima points in the source probability distribution. These
maxima are considered to be reliable if they are at least one order of magnitude
greater than the surrounding regions. In the neighbourhood of a receptor, measuring
the pollutant concentration, a high level of the source probability distribution is
usually found, and the comparison of this level with that of the regions far from
the receptor can be also used to estimate the relative contribution of far and near
sources.
In order to check the capability of the statistical model to estimate the main source regions,
artifical receptor data are derived from numerical simulations performed with an Eulerian
dispersion model [3]. The Eulerian model runs are performed over a computational domain
approximately corresponding to the European continent and with known simplified source
distributions, which are expected to be reproduced by the statistical model. The Eulerian
simulations are performed over a computational domain approximately corresponding to the
European continent. The method is then applied to experimental pollutant concentration
data.
References:
[1] A. Stohl, Atmospheric Environment 32(6), 947 (1998).
[2] A. Stohl et al., Atmospheric Environment 36, 4635 (2002).
[3] M. Mircea et al., Atmospheric Environment 42, 1169 (2008). |
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