Assessment of groundwater quality by means of self-organizing maps: application in a semiarid area

The Kohonen neural network was applied to hydrochemical data from the Detritic Aquifer of the Lower Andarax, situated in a semiarid zone in the southeast of Spain. An activation map was obtained for each of the sampling points, in which the spatial distribution of the activated neurons indicated different water qualities. To extract the information contained in the activation maps, they were divided into nine quadrats. Cartesian coordinates were assigned to each quadrant (x, y), and for each sampling point, three derived variables were selected, which were assigned the values x and y of the corresponding quadrat. A classification was defined based on this simple matrix system which allows an easy and rapid means of evaluating the water quality. This assessment highlights the various processes that affect groundwater quality. The method generates output that is easier to interpret than from traditional statistical methods. The information is extracted from the activation maps without significant loss of information. The method is proposed for assessing water quality in hydrogeochemically complex areas, where large numbers of observations are made.     

Bioluminescence in the Monterey Submarine Canyon: image analysis of video recordings from a midwater submersible

Video images of bioluminescence were recorded in situ during a 1985 study of the midwater environment of the Monterey Canyon, using a single-person, untethered submersible. Gelatinous organisms were responsible for the most brilliant bioluminescent displays, often exhibiting elaborate kinetics in response to mechanical stimulation. Images of bioluminescent displays recorded from identified organisms are shown and display patterns are described. All bioluminescence emission spectra from captured specimens were blue, with peak emissions between 460 and 494 nm. Image-analysis of recordings of mechanically stimulated bioluminescence revealed source densities between 43 and 175 m^-3 and intensities between 2.5 and 37.3 W sr^-1 m^-3. The predominant display type at all depths studied (between 100 and 560 m) was luminous secretions. Despite high intensities of mechanically stimulated bioluminescence, no spontaneous light production was recorded in the absence of mechanical stimulation.     

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torr?o reservoir (Tamega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.     

Ecosystems for future generations

Environmental Science and Pollution Research -     

Global variability of food waste chemical composition and its consequences on the production of biofuels and chemical compounds

Journal of Material Cycles and Waste Management - The use of food waste (FW) for the production of biofuels and value-added compounds is growing worldwide. However, the significant chemical...