T. C. Landi

Study of atmospheric aerosols and mixing layer by LIDAR

The LIDAR (laser radar) is an active remote sensing technique, which allows for the altitude-resolved observation of several atmospheric constituents. A typical application is the measurement of the vertically resolved aerosol optical properties. By using aerosol particles as a marker, continuous determination of the mixing layer height (MLH) can also be obtained by LIDAR. Some examples of aerosol extinction coefficient profiles and MLH extracted from a 1-year LIDAR data set collected in Milan (Italy) are discussed and validated against in situ data (from a balloon-borne optical particle counter). Finally a comparison of the observation-based MLH with relevant numerical simulations (mesoscale model MM5) is provided.

High black carbon and ozone concentrations during pollution transport in the Himalayas: Five years of continuous observations at NCO-P global GAW station

To study the influence of polluted air-mass transport carrying ozone (O3) and black carbon (BC) in the high Himalayas, since March 2006 the Nepal Climate Observatory at Pyramid (NCO-P) GAW-WMO global station (Nepal, 5079 m a.s.l.) is operative. During the first 5-year measurements, the O3 and BC concentrations have shown a mean value of 48 +/- 12 ppb (+/- standard deviation) and 208 +/- 374 ng/m3, respectively. Both O3 and BC showed well defined seasonal cycles with maxima during pre-monsoon (O3: 61.3 +/- 7.7 ppbV; BC: 444 +/- 433 ng/m3) and minima during the summer monsoon (O3: 40.1 +/- 12.4 ppbV; BC: 64 +/- 101 ng/m3). The analysis of the days characterised by the presence of a significant BC increase with respect to the typical seasonal cycle identified 156 days affected by acute pollution events, corresponding to 9.1% of the entire data-set. Such events mostly occur in the pre-monsoon period, when the O3 diurnal variability is strongly related to the transport of polluted air-mass rich on BC. On average, these acute pollution events were characterised by dramatic increases of BC (352%) and O3 (29%) levels compared with the remaining days.

Influence of open vegetation fires on black carbon and ozone variability in the southern Himalayas (NCO-P, 5079 m a.s.l.)

We analysed the variability of equivalent black carbon (BC) and ozone (O3) at the global WMO/GAW station Nepal Climate Observatory-Pyramid (NCO-P, 5079xa0mxa0a.s.l.) in the southern Himalayas, for evaluating the possible contribution of open vegetation fires to the variability of these short-lived climate forcers/pollutants (SLCF/SLCP) in the Himalayan region. We found that 162 days (9% of the data-set) were characterised by acute pollution events with enhanced BC and O3 in respect to the climatological values. By using satellite observations (MODIS fire products and the USGS Land Use Cover Characterization) and air mass back-trajectories, we deduced that 56% of these events were likely to be affected by emissions from open fires along the Himalayas foothills, the Indian Subcontinent and the Northern Indo-Gangetic Plain. These results suggest that open fire emissions are likely to play an important role in modulating seasonal and inter-annual BC and O3 variability over south Himalayas.

Analysis of Summer Ozone Observations at a High Mountain Site in Central Italy (Campo Imperatore, 2388 m a.s.l.)

Tropospheric ozone (O3) is an important atmospheric pollutant and climate forcer. The Mediterranean basin is a hot-spot region in terms of short-term O3 distribution, with frequent episodes of high tropospheric O3, especially during summer. To improve the characterisation of summer O3 variability in the Mediterranean area, during the period 6–27 August 2009 an experimental campaign was conducted at Campo Imperatore, Mt Portella (CMP), a high mountain site (2,388xa0m a.s.l.) located in the central Italian Apennines. As deduced from analysis of atmospheric circulation, the measurement site was significantly affected by air masses originating over the Mediterranean basin, which affected the measurement site for 32xa0% of the time. Analysis of average values and diurnal and day-to-day variability revealed that CMP O3 observations (average value 60.0xa0±xa05.1 ppbv) were comparable with measurements at other European mountain stations, indicating a prevalent effect of meteorological conditions and atmospheric transport on the synoptic scale. In fact, only a small “reverse” diurnal variation typically characterises diurnal O3 variability because of local thermal wind circulation, which sporadically favours transport of air masses rich in O3 from the foothill regions. Statistical analysis of five-day back-trajectory ensembles indicates that synoptic-scale air-mass transport from the Mediterranean Sea usually results in decreasing O3 concentrations at CMP, whereas the highest hourly O3 values are mostly associated with air masses from central continental Europe, eastern Europe, and northern Italy. High O3 concentrations are also related to downward air-mass transport from higher altitudes. Comparison of in-situ O3 variability with tropospheric O3 satellite-based measurements reveals similar features of the two data sets. Together with the results from back-trajectory analysis, this indicates that CMP measurements might usefully improve characterisation of broad-scale O3 variability over the central Mediterranean basin.

May Weather Types and Wind Patterns Enhance Our Understanding of the Relationship Between the Local Air Pollution and the Synoptic Circulation

Aim of this work is to better understand the connections between synoptic circulation patterns, local wind regimes and air pollution in the Po Valley which is a densely populated and heavily industrialized area. In this study we present a classification of weather types (WTs) in the Alps region performed with an objective method (COST Action 733 “Harmonization and Applications of Weather Type Classification for European regions”) based on hierarchical cluster analysis followed by a k-means cluster analysis, which is applied to the daily 500hPa time series from ERA INTERIM reanalysis. In order to take in account the strong influence of the regional wind regime on the local air quality, a classification of surface wind pattern (WPs) is performed as well with a cluster analysis technique. The link between WTs and WPs is investigated, and the statistical properties of pollutants concentration, aerosol chemical composition and dimensional distribution are analyzed in connection with WTs and WPs.

On the Interplay Between Upper and Ground Levels Dynamics and Chemistry in Determining the Surface Aerosol Budget

We use the WRF/Chem model to interpret observations of the aerosol concentration and its chemical composition both at surface level and along vertical profiles performed during an intensive campaign in July 2007 in Milan urban area. The model is added with a new diagnostic for aerosol budget analysis, building on that available for gas species, in order to study the contribution of upper levels processes on the aerosol formation at ground level. The analysis illustrates a quite variegated evolution of budget terms, which we found to depend strongly on the hour of the day, the vertical level, the aerosol compound, and the aerosol size. Primary components are generally emitted near the ground and rapidly transported by turbulent motions to the upper levels, where they gradually disperse and age. For some secondary components, such as nitrate, we calculate a net chemical destruction in the bottom layers, as opposed to a net chemical production higher in the boundary layer, which supply new material to ground level aerosol through turbulent mixing.

Statistical and Fuzzy Approaches for Atmospheric Boundary Layer Classification

In this work we address the problem of inferring the height of atmospheric boundary layer from lidar data. n nFrom one hand the problem to reconstruct the boundary layer dynamics is addressed using a Bayesian statistical inference method. Both parameter estimation and classification to mixed/residual layer, are studied. Probabilistic specification of the unknown variables is deduced from measurements. Hierarchical Bayesian models are adopted to relax the prior assumptions on the unknowns. Markov chain Monte Carlo (MCMC) simulations are conducted to explore the high dimensional posterior state space. n nOn the other hand a novel neuro-fuzzy model (Fuzzy Relational Neural Network) is used to obtain an IF-THEN reasoning scheme able to classify future observations. Experiments on real data are introduced.