Translocation factor of heavy metals by elephant grass grown with varying concentrations of landfill leachate.

The aim of this study is to obtain the translocation factor by application of landfill leachate (LL) diluted in public irrigation water (IW). Pennisetum purpureum Schum (elephant grass) was cultivated for 83 days in an experimental water reuse unit. The present work was developed at the Experimental Water Reuse Unit (UERA), on the UFERSA campus in Mossoró, RN, Brazil. Plot irrigation was based on water balance and crop evapotranspiration (ETc). The concentration in the plant tissue (root and leaf) of the following heavy metals was measured to determine the respective translocation factors: manganese (Mn), zinc (Zn), copper (Cu), nickel (Ni), cadmium (Cd), and lead (Pb). The experiment was set up in a randomized block design with five treatments (T1, plots irrigated only with IW; T2, 50% of LL dose plus IW; T3, 100% of LL dose plus IW; T4, 150% of LL dose plus IW; and T5, 200% of LL dose plus IW) and five replications. All treatments received LL plus IW depth of 491.02 mm for 83 days of P. purpureum cultivation. The data obtained were submitted to multivariate analysis plus the nonparametric Kruskal-Wallis test to compare the means. Pennisetum purpureum showed a potential to accumulate metals in its tissues, mainly Mn, Zn, and Cu. The treatments that most favored the extraction of these metals were T2 and T5; in this sense, P. purpureum was not efficient in translocating heavy metals, since the translocation factor observed in all treatments was below 1.0, indicating that the species used extract heavy metals from soil solution and keeps in yours roots. This suggests planting P. purpureum may not be a viable option to remediate environments highly contaminated with heavy metals.

Physical-chemical determinant properties of biological communities in continental semi-arid waters.

Throughout human history, water has undergone changes in quality. This problem is more serious in dry areas, where there is a natural water deficit due to climatic factors. The aims of this study, therefore, were (i) to verify correlations between physical attributes, chemical attributes and biological metrics and (ii) from the biological attributes, to verify the similarity between different points of a body of water in a tropical semi-arid region. Samples were collected every 2 months, from July 2009 to July 2011, at seven points. Four physical attributes, five chemical attributes and four biological metrics were investigated. To identify the correlations between the physicochemical properties and the biological metrics, hierarchical cluster analysis (HCA) and canonical correlation analysis (CCA) were applied. Nine classes of phytoplankton were identified, with the predominance of species of cyanobacteria, and ten families of macroinvertebrates. The use of HCA resulted in the formation of three similar groups, showing that it was possible to reduce the number of sampling points when monitoring water quality with a consequent reduction in cost. Group I was formed from the waters at the high end of the reservoir (points P1, P2 and P3), group II by the waters from the middle third (points P4 and P5), and group III by the waters from the lower part of the reservoir (points P6 and P7). Richness of the phytoplanktons Cyanophyceae, Chorophyceae and Bacillariophyceae was the attribute which determined dissimilarity in water quality. Using CCA, it was possible to identify the spatial variability of the physicochemical attributes (TSS, TKN, nitrate and total phosphorus) that most influence the metrics of the macroinvertebrates and phytoplankton present in the water. Low macroinvertebrate diversity, with a predominance of indicator families for deterioration in water quality, and the composition of phytoplankton showing a predominance of cyanobacteria, suggests greater attention to the management of water resources.

Land use effects in groundwater composition of an alluvial aquifer (Trussu River, Brazil) by multivariate techniques.

Multivariate statistical techniques, cluster analysis (CA) and factor analysis/principal component analysis (FA/PCA), were applied to analyze the similarities or dissimilarities among the sampling sites to identify spatial and temporal variations in water quality and sources of contamination (natural and anthropogenic). The aquifer under study is supplied by the Trussu River, which has a general direction from west to east, within Iguatu County, Ceará, Brazil. Groundwater samples were collected in four shallow wells, located at the Trussu River alluvial, from October 2002 to February 2004. The samples were analyzed for 13 parameters: pH, electrical conductivity (EC), Na, Ca, Mg, K, Cl, HCO(3), PO(4), NH(4)-N, NO(3)-N, SO(4), and sodium adsorption ratio (SAR). Two zones were very well differentiated based on cluster analysis results, and implied a relation to geographic position and time variation. One zone called UL-upland region-corresponds to upland of studied area, used mainly for irrigation and livestock activities. The other zone called DL-downland region-corresponds to the region downstream and is occupied by human settlements. These results may be used to reduce the number of samples analyzed both in space and time, without too much loss of information. Three major independent factors that define water quality in the UL region and four in DL region were identified in the PCA. At both regions, rotated component (RC) loadings identified that the variables responsible for water quality composition are mainly related to soluble salts variables (natural process) and nutrients (high loads of NO(3)-N, NH(4)-N), expressing anthropogenic activities. RC also revealed that hydrochemical processes were the major factors responsible for water quality.