Using hydrodynamic and water quality variables to assess eutrophication in a tropical hydroelectric reservoir.

Few studies have examined the influence of reservoir hydrodynamics on the water quality of its limnological zones. In this study, the relationships between the operational phases and the water quality of the limnological zones were assessed for the Amazonian reservoir Tucuruí. Limnological zones were clustered by means of an artificial neural network technique, and inputs used were water quality variables, measured at twelve stations between 2006 and 2016. Generalized Linear Models (GLMs) were then used to identify the influence of the operational phases of the reservoir on the water quality of its limnological zones. The GLM with a gamma-distributed response variable indicated that Chlorophyll-a concentrations in the riverine and transitional zones differed notably from those observed in the lacustrine zone. Chlorophyll-a concentrations were significantly lower during the operational falling water phase than in the low water phase (p < 0.05). The GLM with an inverse Gaussian-distributed response variable indicated that Secchi depth was significantly lower in the riverine than in the lacustrine limnological zone (p < 0.05). Our results suggest that more eutrophic conditions occur during the operational rising water phase, and that the area most vulnerable to eutrophication is the transitional zone. We demonstrate that the use of GLMs is suitable for determining areas and operational phases most vulnerable to eutrophication. We envisage that this information will be useful to decision-makers when monitoring the water quality of hydroelectric reservoirs with dendritic patterns and dynamic operational phases.

High CO2 effects on growth and biometal contents in the pioneer species Senna reticulata: climate change predictions.

The aim of the present study consisted in evaluating the effects of CO2 enrichment on the growth and biometal/nutrient content and accumulation in Senna reticulata germinated under two different carbon dioxide concentrations: atmospheric (360 mg L-1) and elevated (720 mg L-1). Biometal/nutrient determinations were performed on three different plant portions (leaflets, stem and root) using flame atomic absorption spectrometry. In general, the biometal and nutrient stoichiometries in roots were increased, probably due to reduced transpiration, and consequent biometal accumulation. An Artifical Neural Network analysis suggests that Mg, Na and Fe display the most different behavior when comparing plants germinated at atmospheric and elevated CO2 conditions. Biomass and growth increases and certain elemental levels indicate that S. reticulata benefits from increased CO2 levels, however some results indicate the contrary, making further studies in this context necessary, as these changes may lead to direct effects on food safety, crop yields, and phytoremediation efficiency.

Categorization of the trophic status of a hydroelectric power plant reservoir in the Brazilian Amazon by statistical analyses and fuzzy approaches.

The Amazon area has been increasingly suffering from anthropogenic impacts, especially due to the construction of hydroelectric power plant reservoirs. The analysis and categorization of the trophic status of these reservoirs are of interest to indicate man-made changes in the environment. In this context, the present study aimed to categorize the trophic status of a hydroelectric power plant reservoir located in the Brazilian Amazon by constructing a novel Water Quality Index (WQI) and Trophic State Index (TSI) for the reservoir using major ion concentrations and physico-chemical water parameters determined in the area and taking into account the sampling locations and the local hydrological regimes. After applying statistical analyses (factor analysis and cluster analysis) and establishing a rule base of a fuzzy system to these indicators, the results obtained by the proposed method were then compared to the generally applied Carlson and a modified Lamparelli trophic state index (TSI), specific for trophic regions. The categorization of the trophic status by the proposed fuzzy method was shown to be more reliable, since it takes into account the specificities of the study area, while the Carlson and Lamparelli TSI do not, and, thus, tend to over or underestimate the trophic status of these ecosystems. The statistical techniques proposed and applied in the present study, are, therefore, relevant in cases of environmental management and policy decision-making processes, aiding in the identification of the ecological status of water bodies. With this, it is possible to identify which factors should be further investigated and/or adjusted in order to attempt the recovery of degraded water bodies.

Fish bile as a biomarker for metal exposure.

Fish accumulate several trace elements in muscle, gills and liver, however studies also indicate that metals can be excreted through bile. Since metal contamination leads to modifications in bile composition, biliary excretion offers an alternative way to evaluate the presence of trace-elements. Bile is easier to obtain than other organs and presents a simpler matrix, making it easier for chemical pre-treatment. To verify if bile can be useful as a biomonitoring tool for metal contamination, liver and bile trace element concentrations were determined and correlated. The Artificial Neural Networks statistical technique was used to verify if liver trace-element quantification could be substituted by bile analysis. Results show that significant correlations were obtained between trace elements in bile and liver and the ANN validated the hypothesis that certain trace-elements in bile could be utilized instead of liver trace-elements. Further studies in this field are of interest to further validate this biomarker.