Indicadores para avaliação dos impactos ambientais e sociais das nano-cápsulas e nanopartículas na agricultura.

Resumo: A Nanotecnologia oferece a perspectiva de grandes avanços que permitirão melhorar a qualidade de vida e preservar o meio ambiente e os nano-produtos agrícolas ganham espaço com as descobertas de novas aplicações, muitas das quais já disponíveis no mercado. Este projeto visa desenvolver indicadores para avaliação dos impactos ambientais e sociais das nano-cápsulas e nano-partículas utilizadas na agricultura. Estes serão futuramente validados através de consultas a especialistas de áreas correlatas à Nanotecnologia na Agricultura e utilizados no software Impactos NanoAgri

Seismic oceanography imaging of thermal intrusions in strong frontal regions

The Naval Research Laboratory and collaborating partners carried out two dedicated seismic oceanography field experiments in two very different strong frontal regions. ADRIASEISMIC took seismic oceanography measurements at the confluence of North Adriatic Dense Water advected along the Western Adriatic Current and Modified Levantine Intermediate Water advected around the topographic rim of the Southern Adriatic basin. ARC12 took seismic oceanography measurements in and around the Agulhas Return Current as it curved northwards past the Agulhas Plateau and interacted with a large anticyclone that had collided with the current. Despite one study focused on coastal boundary currents and the other focused on a major Western Boundary Current extension, the complex horizontal structures seen through seismic imaging are tied to the processes of thermal intrusions and interleaving in both systems. Seismic Oceanography provides a unique capability of tracking the fine-scale horizontal extent of these intrusions

Preface: Oceanographic processes on the continental shelf: observations and modeling

No abstract available

Evaluating meteorological climate model inputs to improve coastal hydrodynamic studies

Abstract. This work compares meteorological results from different regional climate model (RCM) implementations in the Mediterranean area, with a focus on the northern Adriatic Sea. The need to use these datasets as atmospheric forcings (wind and atmospheric pressure fields) for coastal hydrodynamic models to assess future changes in the coastal hydrodynamics, is the basis of the presented analysis. It would allow the assessment of uncertainties due to atmospheric forcings in providing coastal current, surge and wave climate changes from future implementations of hydrodynamic models. Two regional climate models, with different spatial resolutions, downscaled from two different global climate models (whose atmospheric components are, respectively, ECHAM4 and ECHAM5), were considered. In particular, the RCM delivered wind and atmospheric pressure fields were compared with measurements at four stations along the Italian Adriatic coast. The analyses were conducted using a past control period, 1960–1990, and the A1B IPCC future scenario (2070–2100). The chosen scenario corresponds to a world of very rapid economic and demographic growth that peaks in mid-century, with a rapid introduction of new efficient technologies, which balance fossil and non-fossil resources (IPCC, 2007). Consideration is given to the accuracy of each model at reproducing the basic statistics and the trends. The role of models' spatial resolution in reproducing global and local scale meteorological processes is also discussed. The Adriatic Sea climate is affected by the orography that produces a strengthening of north-eastern katabatic winds like bora. Therefore, spatial model resolution, both for orography and for a better resolution of coastline (Cavaleri et al., 2010), is one of the important factors in providing more realistic wind forcings for future hydrodynamic models implementations. However, also the characteristics in RCM setup and parameterization can explain differences between the datasets. The analysis from an ensemble of model implementation would provide more robust indications on climatic wind and atmospheric pressure variations. The scenario-control comparison shows a general increase in the mean atmospheric pressure values while a decrease in mean wind speed and in extreme wind events is seen, particularly for the datasets with higher spatial resolution

Wave climate of the Adriatic Sea: a future scenario simulation

Abstract. We present a study on expected wind wave severity changes in the Adriatic Sea for the period 2070–2099 and their impact on extremes. To do so, the phase-averaged spectral wave model SWAN is forced using wind fields computed by the high-resolution regional climate model COSMO-CLM, the climate version of the COSMO meteorological model downscaled from a global climate model running under the IPCC-A1B emission scenario. Namely, the adopted wind fields are given with a horizontal resolution of 14 km and 40 vertical levels, and they are prepared by the Italian Aerospace Research Centre (CIRA). Firstly, in order to infer the wave model accuracy in predicting seasonal variability and extreme events, SWAN results are validated against a control simulation, which covers the period 1965–1994. In particular, numerical predictions of the significant wave height Hs are compared against available in-situ data. Further, a statistical analysis is carried out to estimate changes on wave storms and extremes during the simulated periods (control and future scenario simulations). In particular, the generalized Pareto distribution is used to predict changes of storm peak Hs for frequent and rare storms in the Adriatic Sea. Finally, Borgman's theory is applied to estimate the spatial pattern of the expected maximum wave height Hmax during a storm, both for the present climate and that of the future scenario. Results show a future wave climate in the Adriatic Sea milder than the present climate, even though increases of wave severity can occur locally

Identification of Seismo-Volcanic Regimes at Whakaari/White Island (New Zealand) Via Systematic Tuning of an Unsupervised Classifier

We present an algorithm based on Self-Organizing Maps (SOM) and k-means clustering to recognize patterns in a continuous 12.5-year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano-seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre-eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes