Accurate prediction of trends in marine pollution is strategic, given the negative effects of low water quality on human marine activities. We describe here the computational and functional performance evaluation of a decision making tool that we developed in the context of an operational workflow for food quality forecast and assessment. Our Water Community Model (WaComM) uses a particle-based Lagrangian approach relying on tridimensional marine dynamics field produced by coupled Eulerian atmosphere and ocean models. WaComM has been developed matching the hierarchical parallelization design requirements and tested in Intel X86_64 and IBM P8 multi core environments and integrated in FACE-IT Galaxy workflow. The predicted pollutant concentration and the amount of pollutants accumulated in the sampled mussels are compared in search of coherent trends to prove the correct model behaviour. In the case study shown in this paper, the predicted Lagrangian tracers, acting as pollutant concentration surrogates, tend to spread rapidly and undergo rapid dilution as expected depending on dominant water column integrated currents.
Abstract. Vertical aerosol profiles were directly measured over the city of Milan during three years (2005–2008) of field campaigns. An optical particle counter, a portable meteorological station and a miniaturized cascade impactor were deployed on a tethered balloon. More than 300 vertical profiles were measured, both in winter and summer, mainly in conditions of clear, dry skies. The mixing height was determined from the observed vertical aerosol concentration gradient, and from potential temperature and relative humidity profiles. Results show that inter-consistent mixing heights can be retrieved highlighting good correlations between particle dispersion in the atmosphere and meteorological parameters. Mixing height growth speed was calculated for both winter and summer showing the low potential atmospheric dispersion in winter. Aerosol number size distribution and chemical composition profiles allowed us to investigate particle behaviour along height. Aerosol measurements showed changes in size distribution according to mixing height. Coarse particle profiles (dp>1.6 μm) were distributed differently than the fine ones (dp<1.6 μm) were, at different heights of the mixing layer. The sedimentation process influenced the coarse particle profiles, and led to a reduction in mean particle diameter for those particles observed by comparing data above the mixing height with ground data (−14.9±0.6% in winter and −10.7±1.0% in summer). Conversely, the mean particle diameter of fine particles increased above the mixing height under stable atmospheric conditions; the average increase, observed by comparing data above the mixing height with ground data, was +2.1±0.1% in winter and +3.9±0.3% in summer. A hierarchical statistical model was created to describe the changes in the size distribution of fine particles along height. The proposed model can be used to estimate the typical vertical profile characterising launches within pre-specified groups starting from: aerosol size and meteorological conditions measured at ground-level, and a mixing height estimation. The average increase of fine particle diameter, estimated on the basis of the model, was +1.9±0.5% in winter and +6.1±1.2% in summer, in keeping with experimental findings.
Attribution of nitrogen (N) and carbon (C) origin in atmospheric particulate matter (PM) is one of the main focuses of scientific research in the field of air pollution. Here we show how using multiple pieces of information from different techniques, including concentrations of major ions (NO3-, NH4+, NO-, SO42-, etc…), concentration and isotopic composition of total N (δ15N) and total C (δ13C), characterization of the meteorology, and using state of the art models of atmospheric circulation (Hysplit) and weather prediction (WRF) help understand the causes of PM change in the atmosphere sampled over the historical town of Naples (Italy). PM samples were collected in May 2016 and November 2016 – January 2017 within the ARIASaNa project. The project was led by the Italian National Research Center (CNR), in collaboration with the Parthenope University and was aimed to monitor air pollution in the main towns of the Campania region. Fine particles with diameter < 2.5 μm (PM2.5) and < 10 μm (PM10) were collected for 24h on pre-cleaned (700 °C for 2 h) quartz filters (Whatman, 47 mm diameter) on top of the historical building complex in Largo San Marcellino (lat. 40.85° N; long. 14.26° E, 53 m.a.s.l.). The results show some key features: All species (major ions and isotopic compositions) measured in autumn-winter samples are much less variable than those measured in spring. This seems to be related to a change in weather pattern which is caused by the land-sea breeze mechanism. A significant change of the main species measured is found around the middle of May 2016. This change occurs at the same time as a change in the meteorology of the area, going from high to low pressure. The change found in May 2016 is characterized by a strong positive relationship between ammonium (NH4+) concentration and the isotopic composition of nitrogen (δ15N), suggesting that the dominant factor of change in atmospheric N chemistry is the NH4+ origin. We will discuss the results obtained in terms of influence of the meteorology on atmospheric chemistry of N and C, and will try to disentangle the changes due to secondary atmospheric processes from those caused by a change in the primary source of N and C.
The effective and efficient computing resource allocation is a critical issue in the challenging achievement of realistic, high space and temporal resolution and computing time affordable weather, marine and soil flooding simulation and forecast. Resource allocation relies on matchmaking tool, which must be flexible and straightforward to be deployed, configured and used. Using the Globus Toolkit 4 and our resource broker service, we compared the ClassAd matchmaking algorithm with our matchmaking algorithm and then we integrated the two algorithms in order to avoid their drawbacks.
