Extension of twoCaulerpa species along the Tunisian coast

Two species of the green alga genusCaulerpa are considered as invasive in Tunisia:Caulerpa racemosa andC. taxifolia. C. racemosa is found in a many new localities at the central and southern Tunisian coast, and even in the North of the country in colder water. These new observations lead to the hypothesis of the multiplicity of the origins of the introduction of the species and suggest possible invasive potentialities. The general thallus, and more particularly the number, the shape as well as the arrangement of ramelli on fronds allowed us to identify four morphologically different groups. These groups could be either different systematic entities or polymorphic variations of the same taxon due to different ecological conditions.Caulerpa taxifolia was observed for the first time in Tunisia at the roadstead of Sousse in the beginning of the year 2000. It is recognized as identical to that found in the other Mediterranean localities (GenBank number AJ228960). Up to the end of March 2000, a submarine prospecting campaign revealed an affected area of about 350 ha in front of 2 km of coast. Colonies ofCaulerpa taxifolia disseminating in this area appear in isolated spots. form, In the growing stage, they form surfaces varying between 0.5 to 6 m² and located 20 to 100 m from each other. The total covered surface was estimated to be between 0.5 and 1 ha.     

Contamination of polycyclic aromatic hydrocarbons (PAHs) in microlayer and subsurface waters along Alexandria coast,Egypt

The residues of seven polycyclic aromatic hydrocarbons (PAHs) pollutants in microlayer and subsurface seawater samples collected from Alexandria coast, Egypt, were analyzed by gas chromatography–electron-impact mass spectrometry-selected ion monitoring mode (GC–MS-SIM). The pollutants studied were, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene and benzo[a]pyrene. Total PAH levels in microlayer ranged from 103 to 523 ng/l, while it ranged in subsurface samples from 13 to 120 ng/l. The Western Harbor location recorded the highest level of PAHs pollutant over all the other location for both subsurface and microlayer waters. The two major PAHs in microlayer water at the Western Harbor were fluorene and phenanthrene, making up 27% and 20% of the total PAHs, while the two major PAHs in subsurface water at the Eastern Harbor were phenanthrene and fluoranthene recording up 21% each of the total PAHs. The total PAH levels were generally in the nano-gram per liter for microlayer and subsurface seawater samples. The dominant PAHs in both subsurface and microlayer samples were fluoranthene, pyrene and benzo[a]pyrene. The microlayer enrichment factor at Alexandria’s Mediterranean coast was ranged from 29 for fluorene to 3 for phenanthrene and benzo[a]pyrene which showed PAHs concentration in the microlayer with an average of five times more than the total PAH in the subsurface samples.     

Hematological studies on some Nile fishes,Tilapia nilotica,Lates niloticus,and Labeo niloticus

Hemoglobin content of 3 Nile fishes — Tilapia nilotica Linnaeus, Lates niloticus Linnaeus and Labeo niloticus Forskal— were examined. Clear differences in hemoglobin and hematocrit values were observed, and seasonal variation in the hemoglobin content of Lates niloticus and Labeo niloticus were noticed. There is a correlation between hematocrit value and hemoglobin content of the 3 species examined. The chloride content is higher in the species with lower hemoglobin content.     

Foraminiferal biotopes and their distribution control in Ria de Aveiro (Portugal): a multiproxy approach

Ria de Aveiro, which is located in the centre of Portugal (40° 38′ N, 8° 45′ W), is a well-mixed and complex coastal lagoon that is separated from the sea by a sandy barrier and connects with the Atlantic through an artificial inlet. Tidal currents are the main factor controlling the lagoon’s hydrodynamics and, to a great extent, the sedimentary dynamic. The inner lagoonal zones receive input from several rivers and experience the pressure caused by the accumulation of organic matter and pollutants (namely, trace metals) from diverse anthropic activities. This paper is the first piece of work aiming to recognize, characterize and explain the main benthic foraminiferal biotopes in Ria de Aveiro. To provide a broad overview of this kind of setting, our results are compared to those of previous published studies conducted in similar transitional environments. The research is based on an investigation of 225 sites spread throughout this ecosystem. Utilizing a statistical approach, this study analyses the details of dead benthic foraminiferal assemblages composed of 260 taxa, the texture and composition (mineralogical and geochemical) of the sediment and physicochemical data. On the basis of the results of R-mode and Q-mode cluster analyses, several different biotopes can be defined as marsh biotope/near-marsh biotope; marginal urban/marginal urban mixing biotope; inner-outer lagoon biotope or enclosed lagoon; outer lagoon biotope, mixed sub-biotope; and outer lagoon, marine sub-biotope. These biotopes are related to foraminifera assemblages and substrate type and are influenced by local currents, water depth, chemical and physicochemical conditions, river or oceanic proximity, and anthropogenic impact, as evidenced by the mapping of the six factor loadings of the principal component analysis conducted herein. Based on a similar methodology of analysis as that applied in previous studies in the Lagoon of Venice, comparable biotypes were identified in Lagoon of Aveiro.     

A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Use of cellular automata in the study of variables involved in land use changes

The study of changes in land use has been included lately in territorial processes in order to optimize future management decisions. The different functions that the territory plays (production, aesthetic, and natural functions, etc.) make planning choices more difficult. This work focuses on the selection and combination of a set of indicators to analyze the variables through which the changes occur in the land used in viticulture for wine production. The proposed approach makes use of the Geographic Information System (GIS) in the development of a map of land use scenario. It is applied to a case study through a model involving cellular automata (CA) implemented with maps of suitability for viticulture and Markov chains. The use in this case of the CA is aimed at validating the scenario map in order to deduce the variables and the orientations of the farmers in the field of wine production.