General Overview of the Environmental Quality of the Mediterranean Sea

1993 ◽  
pp. 33-179
Author(s):  
F. B. Walle ◽  
M. Nikolopoulou-Tamvakli ◽  
W. J. Heinen
Keyword(s):  
Mediterranean Sea ◽  
Environmental Quality ◽  
General Overview ◽  
The Mediterranean

Environmental Quality of Coastal Areas in the Mediterranean Sea and Potential Risks to Human Health

2021 ◽  
pp. 103-143
Author(s):  
Marion Pillet ◽  
Michel Marengo ◽  
Sylvie Gobert ◽  
Pierre Lejeune ◽  
Michèle Leduc ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Human Health ◽  
Environmental Quality ◽  
Coastal Areas ◽  
Potential Risks ◽  
The Mediterranean

scholarly journals Gymnorhynchus gigas in Lepidopus caudatus (Actinopterygii: Perciformes: Trichiuridae): prevalence and related effects on fish quality

2014 ◽  
Vol 32 (No. 4) ◽  
pp. 320-325 ◽  
Author(s):  
F. Giarratana ◽  
D. Muscolino ◽  
Ch. Beninati ◽  
G. Ziino ◽  
A. Giuffrida ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Total Volatile ◽  
Fish Quality ◽  
Total Volatile Basic Nitrogen ◽  
Basic Nitrogen ◽  
Hygienic Quality ◽  
High Prevalence ◽  
The Mediterranean

We evaluated the effects of Gymnorhynchus gigas on the freshness and hygienic quality of Lepidopus caudatus. Total Volatile Basic Nitrogen (TVB-N), Trimethylamine Nitrogen (TMA-N), as well as Specific Spoilage Organisms (SSOs) are the most important freshness indicators in fish. Our study was carried-out on 65 specimens of L. caudatus kept in ice and stored at 2°C for different period of time. The microbiological charge of SSOs recovered on a portion of parasitised muscles (MP) was compared with those recovered on portions of parasite-free muscles (M). The contents of TVB-N and TMA-N on MP, M, and G. gigas larva/ae were measured using the Conway microdiffusion method. High prevalence (72.31%) of G. gigas in the specimens of L. caudatus from the Mediterranean sea was observed. No statistically significant differences (P < 0.05) between M and MP were found during storage. However, massive infestation of G. gigas on the muscle of the silver scabbardfish could negatively influence TVB-N values, without compromising the sensorial characteristic of fish.

scholarly journals Nutritional Quality of the European Spiny Lobster Palinurus elephas (J.C. Fabricius, 1787) (Achelata, Palinuridae) and the Non-Indigenous Northern Brown Shrimp Penaeus aztecus Ives, 1891 (Dendrobranchiata, Penaeidae)

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2480
Author(s):  
Thodoros E. Kampouris ◽  
Adamantia Asimaki ◽  
Dimitris Klaoudatos ◽  
Athanasios Exadactylos ◽  
Ioannis T. Karapanagiotidis ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Proximate Composition ◽  
Spiny Lobster ◽  
Brown Shrimp ◽  
Penaeus Aztecus ◽  
The Mediterranean ◽  
First Time ◽  
Established Species ◽  
Palinurus Elephas

The European spiny lobster is a species of great commercial value, yet a limited scientific knowledge exists on its biology, ecology, and physiology, especially for the stocks from east Mediterranean waters. The northern brown shrimp, a non-indigenous established species, is commercially exploited in regions of the Mediterranean Sea. Both species’ proximate composition and fatty acid profile were assessed for the first time in the Mediterranean region, exhibiting an overall significant statistical difference. Protein, fat, and energy contents were significantly higher in the northern brown shrimp, whereas moisture and ash contents were significantly higher in the European spiny lobster. The proximate composition for both species was well within the reported range for other lobster and prawn species in the Mediterranean Sea.

scholarly journals Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts

Ocean Science ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 997-1022 ◽  
Author(s):  
Stefano Salon ◽  
Gianpiero Cossarini ◽  
Giorgio Bolzon ◽  
Laura Feudale ◽  
Paolo Lazzari ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Reference Data ◽  
Robust Statistics ◽  
Marine Ecosystem ◽  
Global Ocean ◽  
Argo Data ◽  
Argo Floats ◽  
Biogeochemical Models ◽  
The Mediterranean

Abstract. The quality of the upgraded version of the Copernicus Marine Environment Monitoring Service (CMEMS) biogeochemical operational system of the Mediterranean Sea (MedBFM) is assessed in terms of consistency and forecast skill, following a mixed validation protocol that exploits different reference data from satellite, oceanographic databases, Biogeochemical Argo floats, and literature. We show that the quality of the MedBFM system has been improved in the previous 10 years. We demonstrate that a set of metrics based on the GODAE (Global Ocean Data Assimilation Experiment) paradigm can be efficiently applied to validate an operational model system for biogeochemical and ecosystem forecasts. The accuracy of the CMEMS biogeochemical products for the Mediterranean Sea can be achieved from basin-wide and seasonal scales to mesoscale and weekly scales, and its level depends on the specific variable and the availability of reference data, the latter being an important prerequisite to build robust statistics. In particular, the use of the Biogeochemical Argo floats data proved to significantly enhance the validation framework of operational biogeochemical models. New skill metrics, aimed to assess key biogeochemical processes and dynamics (e.g. deep chlorophyll maximum depth, nitracline depth), can be easily implemented to routinely monitor the quality of the products and highlight possible anomalies through the comparison of near-real-time (NRT) forecasts skill with pre-operationally defined seasonal benchmarks. Feedbacks to the observing autonomous systems in terms of quality control and deployment strategy are also discussed.

scholarly journals Development of super-ensemble techniques for ocean analyses: the Mediterranean Sea case

2016 ◽  
Vol 16 (8) ◽  
pp. 1807-1819 ◽  
Author(s):  
Jenny Pistoia ◽  
Nadia Pinardi ◽  
Paolo Oddo ◽  
Matthew Collins ◽  
Gerasimos Korres ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mediterranean Sea ◽  
Ad Hoc ◽  
Spatial Filter ◽  
Ensemble Member ◽  
Training Period ◽  
Data Set ◽  
Ensemble Techniques ◽  
The Mediterranean

Abstract. A super-ensemble methodology is proposed to improve the quality of short-term ocean analyses for sea surface temperature (SST) in the Mediterranean Sea. The methodology consists of a multiple linear regression technique applied to a multi-physics multi-model super-ensemble (MMSE) data set. This is a collection of different operational forecasting analyses together with ad hoc simulations, created by modifying selected numerical model parameterizations. A new linear regression algorithm based on empirical orthogonal function filtering techniques is shown to be efficient in preventing overfitting problems, although the best performance is achieved when a simple spatial filter is applied after the linear regression. Our results show that the MMSE methodology improves the ocean analysis SST estimates with respect to the best ensemble member (BEM) and that the performance is dependent on the selection of an unbiased operator and the length of training. The quality of the MMSE data set has the largest impact on the MMSE analysis root mean square error (RMSE) evaluated with respect to observed satellite SST. The MMSE analysis estimates are also affected by training period length, with the longest period leading to the smoothest estimates. Finally, lower RMSE analysis estimates result from the following: a 15-day training period, an overconfident MMSE data set (a subset with the higher-quality ensemble members) and the least-squares algorithm being filtered a posteriori.

scholarly journals Development of Super-Ensemble techniques for ocean analyses: the Mediterranean Sea case

2016 ◽  
Author(s):  
Jenny Pistoia ◽  
Nadia Pinardi ◽  
Paolo Oddo ◽  
Matthew Collins ◽  
Gerasimos Korres ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mediterranean Sea ◽  
Ad Hoc ◽  
Spatial Filter ◽  
Ensemble Member ◽  
Least Square ◽  
Training Period ◽  
Ensemble Techniques ◽  
The Mediterranean

Abstract. A super-ensemble methodology is proposed to improve the quality of short-term ocean analyses for Sea Surface Temperature (SST) in the Mediterranean Sea. The methodology consists in a multi-linear regression technique applied to a Multi-Physics Multi-Model Super-Ensemble (MMSE) dataset. This is a collection of different operational forecasting analyses together with ad-hoc simulations, created by modifying selected numerical model parameterizations. A new linear regression algorithm based on Empirical Orthogonal Function filtering techniques is shown to be efficient in preventing overfitting problems, although the best performance is achieved when a simple spatial filter is applied after the linear regression. Our results show that the MMSE methodology improves the ocean analysis SST estimates with respect to the Best Ensemble Member (BEM) and that the performance is dependent on the selection of an unbiased operator and the length of training. The quality of the MMSE dataset has the largest impact on the MMSE analysis Root Mean Square Error (RMSE) evaluated with respect to observed satellite SST. The MMSE analysis estimates are also affected by changing the training period with the longest period leading to the smoothest estimates. Finally, lower RMSE analysis estimates result from the following: a 15 day training period, an overconfident MMSE dataset (a subset with the higher quality ensemble members), and the least square algorithm being filtered a posteriori.

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