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    Generalized lambda distribution for the expression of measurement uncertainty

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    The generalized lambda distribution family fits the probability distributions of a wide variety of data sets, including the most important distributions encountered in the measurement applications (normal, uniform, Student's t, U-shaped, exponential). This paper illustrates how the four parameters needed for such distribution can be exploited in the expression of measurement uncertainty and to extend the information related to a measurement. The obtained representation allows an immediate calculation of coverage intervals and is particularly useful to support the techniques commonly applied in the estimation of the combined uncertainty. Moreover, in order to include the classical measurement information, a novel parameterization of the distribution is proposed

    A new approach for roughness representation within urban dispersion models

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    The effects of green infrastructure on pollutant concentrations are greatly variable, essentially depending on the surrounding built-up environment and on local meteorological conditions. To simulate the effects of the presence of trees at urban scale, a reliable methodology is the Computational Fluid Dynamics (CFD) approach, however it needs high calculation costs. An alternative integral dispersion model is given by provided that a suitable parameterization for vegetation is included. In this work, we have developed and demonstrated a novel methodology, based on aerodynamic parameters, to include the aerodynamic effect of trees in an operational dispersion model, the ADMS-Urban. The aerodynamic parameters were derived using the morphometric method starting from open data containing information on buildings and trees. The new roughness parameter calculation method has produced the urban spatially varying roughness (USVR) and it was evaluated in different scenarios at the urban and neighborhood scale. The numerical outputs of the simulations were compared with observations from reference air quality stations collected within an ad-hoc intensive field campaign conducted in 2017 in the city of Bologna, Italy. The results of the comparison highlight that the introduction of the aerodynamic effects of buildings lead to great improvements in the performance of the model at both spatial scales and for the different study sites considered in this study. Conversely, the inclusion of trees in the calculation produces significant improvements only when conducting studies at high spatial resolution and for densely vegetated areas
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