Environmental Science and Pollution Research, 2019
Recently, the residues of some common and widely used herbicides (acetochlor, bispyribac-sodium, ... more Recently, the residues of some common and widely used herbicides (acetochlor, bispyribac-sodium, bentazon, bensulfuronmethyl, halosulfuron-methyl, and quinclorac) were detected in the surface water, soil, sediments, and fish tissues as the agricultural drainage problems. In this study, juveniles of Nile tilapia Oreochromis niloticus were exposed to sub-lethal concentrations of these herbicides as 2.625, 0.800, 36.00, 2.50, 1.275, and 11.250 mg/l for acetochlor, bispyribac-sodium, bentazon, bensulfuronmethyl, halosulfuron-methyl, and quinclorac respectively for 96 h. Some hemato-biochemical parameters were evaluated. In comparison with the control group, sub-lethal concentrations of all tested herbicides induced alterations in the shape of erythrocytes. Also, in all tested herbicides, hematological parameters of exposed fish exhibited a significant decrease in red blood cell count except bentazon. However, all tested herbicides showed an insignificant reduction in mean corpuscular hemoglobin concentration and total white blood cells except bensulfuron-methyl. For biochemical parameters, most tested herbicides induced a significant increase in levels of cholesterol, albumin, globulin, albumin/globulin ratio, activity of alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total plasma protein (only with acetochlor), urea, and creatinine (except bentazon and halosulfuron-methyl that exhibited nonsignificant decrease in creatinine level) compared with the control. In conclusion, the fish blood profiles can be used as good biomarkers for laboratory study to assess the toxicity of the tested rice herbicides at a sub-acute level especially acetochlor on O. niloticus.
The Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. ... more The Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new eme...
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