Aquatic environments are often contaminated with various compounds, with potential toxicity towards aquatic organisms, which may enter the food chain. Azo dyes are used in various industries and contamination of waters has been reported. The present paper assesses the toxicity of the synthetic, water soluble Congo Red dye towards Lemna minor from a physiological and cytogenetical point of view. The dye was tested in 5-5000 ppm concentrations. Total frond surface, root growth and fresh mass reductions were registered from 5 ppm dye concentration, with a concentration-dependent response and calculated EC50 of 1530 ppm. Plant growth was completely inhibited above 2500 ppm. Dye accumulation was observed in tissues, along with necrosis formation. Chlorophyll a contents decreased, while carotenoid contents increased above 2500 ppm. Significant inhibition of PSII efficiency was recorded above 1000 ppm. Mitotic indices were decreased at 5 and 1000 ppm dye and were 0 at 5000 ppm. The number of chromosomal aberrations significantly increased at 5 and 1000 ppm dye. The growth medium was decontaminated up to 56% at 250 ppm dye concentration by Lemna plants. Congo Red azo dye presented toxicity towards Lemna minor, from a physiological and cyotgenetical point of view, especially at higher concentrations. In the same time, a phytoremediation potential of duckweed with respect to the tested dye was demonstrated.
This paper describes the best way to improve the optimization of spatial databases: through spatial indexes. The most commune and utilized spatial indexes are R-tree and Quadtree and they are presented, analyzed and compared in this paper. Also there are given a few examples of queries that run in Oracle Spatial and are being supported by an R-tree spatial index. Spatial databases offer special features that can be very helpful when needing to represent such data. But in terms of storage and time costs, spatial data can require a lot of resources. This is why optimizing the database is one of the most important aspects when working with large volumes of data.
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