We present an application of a Lagrangian Stochastic Model (LSM) to turbulent dispersion over complex terrain, where turbulent coherent structures are known to play a crucial role. We investigate the case of a vegetated canopy by using semi-empirical parameterizations of turbulence profiles in the region inside and above a canopy layer. The LSM is based on a 4-dimensional Fokker-Planck (4DFP) equation, which extends the standard Thomson87 Lagrangian approach. The 4DFP model is derived by means of a Random Field description of the turbulent velocity field. The main advantage of this approach is that not only the experimental Eulerian one-point statistics, but also the Eulerian two-point two-time covariance structure can be included explicitly in the LSM. At variance with the standard Thomson87 approach, the 4DFP model allows to consider explicit parameterizations of the turbulent coherent structures as it explicitly includes both spatial and temporal correlation functions. In order to investigate the effect of the turbulent geometrical structure on a scalar concentration profile, we performed numerical simulations with two different covariance parameterizations, the first one isotropic and the second anisotropic. We show that the accumulation of scalars near the ground is due to the anisotropic geometrical properties of the turbulent boundary layer.

We briefly review the Trajectory Statistical Methods (TSMs) most used in literature for source identification, essentially based on the concept of Residence Time. Then, we introduce a statistical methodology that, starting from the Concentration Field method, takes into account only the peak values in the concentration time series measured at multiple receptor sites. We use virtual simulations to evaluate the performance of our approach. In order to derive concentration time series at multiple receptors, the Lagrangian Dispersion Model (LSM) FLEXPART is used, in the time forward mode, to simulate dispersion from a known emission source. Then, virtual concentration data are available in the receptor sites. As in many TSMs, Residence Times need to be computed and, to this goal, we use FLEXPART, but in the backward mode, that is, FLEXPART is applied to compute the backward trajectories from the receptor sites. Then, our proposed statistical method is applied to the computed Residence Times and to the concentration data to reconstruct the spatial distribution of emission sources. The numerical results show that our approach could overcome the problem of ghost sources. Further, the proposed method requires simulation times shorter that those required in other methods, since it makes use of a relatively small set of trajectories. This could be of some interest in the characterization of impact studies and local climatic scenarios.

Scaling laws for the diffusion generated by three different random walk models are reviewed. The random walks, defined on a one-dimensional lattice, are driven by renewal intermittent events with non-Poisson statistics and inverse power-law tail in the distribution of the inter-event or waiting times, so that the event sequences are characterized by self-similarity. Intermittency is a ubiquitous phenomenon in many complex systems and the power exponent of the waiting time distribution, denoted as complexity index, is a crucial parameter characterizing the system's complexity. It is shown that different scaling exponents emerge from the different random walks, even if the self-similarity, i.e. the complexity index, of the underlying event sequence remains the same. The direct evaluation of the complexity index from the time distribution is affected by the presence of added noise and secondary or spurious events. It is possible to minimize the effect of spurious events by exploiting the scaling relationships of the random walk models. This allows to get a reliable estimation of the complexity index and, at the same time, a confirmation of the renewal assumption. An application to turbulence data is shown to explain the basic ideas of this approach.

Emissions of atmospheric pollutants from ships andharbour activities are a growing concern at international level giventheir potential impacts on air quality and climate. These close-to-landemissions have potential impact on local communities in terms of airquality and health. Recent studies show that the impact of maritimetraffic to atmospheric particulate matter concentrations in severalcoastal urban areas is comparable with the impact of road traffic of amedium size town. However, several different approaches have beenused for these estimates making difficult a direct comparison ofresults. In this work, an integrated approach based on emissioninventories and dedicated measurement campaigns has been appliedto give a comparable estimate of the impact of maritime traffic toPM2.5 and particle number concentrations in three major harbours ofthe Adriatic/Ionian Seas. The influences of local meteorology and ofthe logistic layout of the harbours are discussed.

We present a modelling approach to investigate the impact of ship emissions in the port of Brindisi (IT) on local air quality. The focus is on the impact on pollutant concentrations due to the implementation of the MARPOL Annex VI and the associated NOx technical code 2008 (concerning NOx emissions) and the Directives 2005/33/EU-2012/33/EU (concerning the sulphur content of maritime fuels). Emissions are estimated through an adapted MEET methodology using appropriate emission factors for manoeuvring and hotelling phases. Numerical simulations of NOx, SO2 and primary PM10 are performed by means of the mesoscale model BOLCHEM coupled off-line with ADMS-Urban. The impact of present and future ship emissions on air quality in the port area is evaluated. After the implementation of the Directives 2005/33/EU-2012/33/EU for the year 2012 SO2 showed a significant concentration reduction especially close to the port area, while primary PM10 concentration reduction was minor, as well as that of NOx, as a consequence of the NOx technical code. No significant reductions were found for the year 2020.

We present a modelling system for the estimation of forest fireemissions (prebolchem-fire) and their inclusion in the atmospheric compositionmodel BOLCHEM. Emission fluxes have been estimated following themethodology proposed by Seiler and Crutzen (1980) and using MODIS 'burnedarea product'. Then they are modulated following the WRAP approach(WRAP, 2005). This approach is also used for the estimations of fire emissionheight. Model simulations have been performed for the period 22-30 August2007, in which fires were most severe in Greece, Albania and Algeria. Theestimated emission fluxes have been compared with those estimated by theglobal model FINNv1 (fire inventory from NCAR) and the difference betweenthe two emissions have been discussed. The modelled concentration of blackcarbon aerosol is compared with measurements at PEARL station lidar, TitoScalo, Potenza (40.63°N, 15.80°E, 760 m asl), Italy, on 30 August 2007. Wediscuss the discrepancies between the measured and modelled verticaldistribution concentrations of black carbon aerosol, probably due touncertainties related to the estimation of the total flux and of the injectionheight of the smoke.

We investigate the time intermittency of turbulent transport associated with the birth-death of self-organized coherent structures in the atmospheric boundary layer. We apply a threshold analysis on the increments of turbulent fluctuations to extract sequences of rapid acceleration events, which is a marker of the transition between self-organized structures. The inter-event time distributions show a power-law decay psi(t) proportional to  1/?, with a strong dependence of the power-law index ? on the threshold. A recently developed method based on the application of event-driven walking rules to generate different diffusion processes is applied to the experimental event sequences. At variance with the power-law index ? estimated from the inter-event time distributions, the diffusion scaling H, defined by hX2it2H , is independent from the threshold. From the analysis of the diffusion scaling it can also be inferred the presence of different kind of events, i.e. genuinely transition events and spurious events, which all contribute to the diffusion process but over different time scales. The great advantage of event-driven diffusion lies in the ability of separating different regimes of the scaling H. In fact, the greatest H, corresponding to the most anomalous diffusion process, emerges in the long time range, whereas the smallest H can be seen in the short time range if the time resolution of the data is sufficiently accurate. The estimated diffusion scaling is also robust under the change of the definition of turbulent fluctuations and, under the assumption of statistically independent events, it corresponds to a self-similar point process with a well-defined power-law index ?_D= 2.1, where D denotes that ?_D is derived from the diffusion scaling. We argue that this renewal point process can be associated to birth and death of coherent structures and to turbulent transport near the ground, where the contribution of turbulent coherent structures becomes dominant.

Bursting and intermittent behavior is a fundamental feature of turbulence, especially in the vicinity of solid obstacles. This is associated with the dynamics of turbulent energy production and dissipation, which can be described in terms of coherent motion structures. These structures are generated at random times and remain stable for long times, after which they become suddenly unstable and undergo a rapid decay event. This intermittent behavior is described as a birth-death point process of self-organization, i.e., a sequence of critical events. The Inter-Event Time (IET) distribution, associated with intermittent self-organization, is typically a power-law decay, whose power exponent is known as complexity index and characterizes the complexity of the system, i.e., the ability to develop self-organized, metastable motion structures. We use a method, based on diffusion scaling, for the estimation of system's complexity. The method is applied to turbulence velocity data in the atmospheric boundary layer. A neutral condition is compared with a stable one, finding that the complexity index is lower in the neutral case with respect to the stable one. As a consequence, the crucial birth-death events are more rare in the stable case, and this could be associated with a less efficient transport dynamics.

Back-trajectory techniques are extensively used to identify the most probable source locations, starting from the known pollutants concentration data at some receptor sites. In this paper, we review the trajectory statistical methods (TSMs) that are most used in literature for source identification, which are essentially based on the concept of residence time (RT), and we introduce a novel statistical method. To validate this method, artificial receptor data at two receptor sites are derived from numerical simulations with a given aerial source, using the Lagrangian dispersion model (LSM) FLEXPART in forward mode. Then the RTs are computed using again the model FLEXPART, but in backward mode. Then, the new statistical methodology, which is based on the use of peak concentration events, is applied to reconstruct the spatial distribution of emission sources. Our approach requires simulation times shorter than those required in other methods and could overcome the problem of ghost sources.

L'estate 2007 è stata caratterizzata da grossi incendi nell'Area Mediterranea. E' ormai noto che sia il gas che il particolato che vengono emessi in tali episodi hanno un importante impatto sulla qualità dell' aria e sul clima, sia a scala globale che regionale. Nel presente lavoro verranno analizzate le problematiche legate alla modellizzazione e dipersione dei flussi di inquinanti da incendi boschivi. In particolare, verranno presentati i risultati di simulazioni numeriche, ottenute con il modello BOLCHEM, sia di specie gassose, che di aerosol, riguardanti gli incendi che hanno interessato Grecia, Albania e Algeria nel periodo 22-30 agosto 2007.

We present the model performance of the online Coupled Chemistry-Meteorological Model BOLCHEM on seasonal period in an air pollution hot spot. The simulation domain is the Northern Italy where a large amount of agricultural, livestock, industrial activities are present, together with big city, as Milan, Turin and Bologna.Simulated surface concentration of Particulate Matter (PM 10 and PM 2.5 ) have been compared with measured concentrations at Airbase Stations for a winter period, while for the summer period also Ozone (O 3 ) has been considered. Results show that the model well reproduces observed concentrations, with similar correlation coefficient for particulate and ozone.

The Mediterranean Area is affected by forest fires which can burn thousands of hectares in a few days. Biomass burning produces and releases into the atmosphere gases and particulates, spreading smoke across thousands of kilometres, affecting the air quality and the regional and global climate. Inclusion of gas and particulate emissions from wildfires in atmospheric composition models is a challenging task because of the large uncertainties related to the detection of fires and their spatial and temporal evolution, the emission factors as a function of vegetation cover, and the determination of injection height of the smoke. In this work we present a pre-processor for the estimation of forest-fire emissions and injection height. Emission fluxes have been estimated following the methodology proposed by Seiler and Crutzen (1980). We considered the gas species CO, NOx, SO2 and NH3 and the particulate matter PM2.5 and PM10 mainly composted by black carbon (BC) and organic carbon (CO). The estimate of emission rates depends on fire data, such as burned area, and consequently by associated vegetation cover, fuel loading, carbon fraction of the fuel, and combustion fraction. Most of these factors have large uncertainties, and this has an impact on the modelled concentration field. Emission heights relative to the emitted species have been estimated by means of a lagrangian backward model and the methodology proposed in the WRAP study (2005). Numerical simulations have been performed using the atmospheric composition model BOLCHEM (Mircea et al, 2008).Results of simulations for the forest fire event relative to the summer of 2007, during a period in which fires were most severe in Greece, Albania and Algeria, will be shown and discussed. The estimated pollutants emission will be compared with emission of the data set FINNv1. Afterwards, since the height of injection of smoke has an important impact on the simulated concentration, different model runs were performed with smoke injected into grid columns between altitudes of different height and with different daily modulation.