RESUMO
The vulnerability of aquatic ecosystems due to the entry of cadmium (Cd) is a concern of public and environmental health. This work explores the ability of tissues and symbiotic corpuscles of Pomacea canaliculata to concentrate and depurate Cd. From hatching to adulthood (4 months), snails were cultured in reconstituted water, which was a saline solution in ASTM Type I water. Then, adult snails were exposed for 8 weeks (exposure phase) to Cd (5 µg/L) and then returned to reconstituted water for other 8 weeks (depuration phase). Cadmium concentration in the digestive gland, kidney, head/foot and viscera (remaining of the snail body), symbiotic corpuscles, and particulate excreta was determined by electrothermal atomic absorption spectrometry. After exposure, the digestive gland showed the highest concentration of Cd (BCF = 5335). Symbiotic corpuscles bioaccumulated Cd at a concentration higher than that present in the water (BCF = 231 for C symbiotic corpuscles, BCF = 8 for K symbiotic corpuscles). No tissues or symbiotic corpuscles showed a significant change in the Cd levels at different time points of the depuration phase (weeks 8, 9, 10, 12, and 16). The symbiotic depuration through particulate excreta was faster between weeks 8 and 10, and then slower after on. Our findings show that epithelial cells of the digestive gland of P. canaliculata and their symbiotic C corpuscles are sensitive places for the bioindication of Cd in freshwater bodies.
Assuntos
Cádmio , Poluentes Químicos da Água , Animais , Cádmio/química , Ecossistema , Biomarcadores Ambientais , Água Doce/análise , Água Doce/química , Caramujos , SimbioseRESUMO
A dispersive liquid-liquid microextraction (DLLME) method was developed based on the application of a magnetic ionic liquid (MIL) used as extractant phase for trace As determination in honey samples by electrothermal atomic absorption spectrometry (ETAAS). The procedure was simple, efficient and did not require a centrifugation stage. The As(III) species was preconcentrated by chelation with ammonium diethyldithiophosphate under acidic conditions at 3â¯molâ¯L-1 HCl, followed by the extraction of the chelated analyte with the MIL trihexyl(tetradecyl)phosphonium tetrachloroferrate (III) ([P6,6,6,14]FeCl4) and acetonitrile as dispersant. The MIL phase containing the analyte was separated simply by a magnet. The collected aliquot of the MIL phase was injected directly into the graphite furnace of ETAAS for As determination. Under optimal experimental conditions, an extraction efficiency of 99% and a sensitivity enhancement factor of 110 were obtained. The limit of detection was 12â¯ngâ¯L-1 As and the relative standard deviation 3.9% (at 1⯵gâ¯L-1 As and nâ¯=â¯10), calculated from the peak height of the absorbance signals. The linear range obtained was 0.02-5.0⯵gâ¯L-1. This work reports the first application of the MIL [P6,6,6,14]FeCl4 along with the DLLME technique for the determination of As in honeys.
Assuntos
Arsênio/análise , Mel/análise , Líquidos Iônicos/química , Microextração em Fase Líquida , Líquidos Iônicos/síntese química , Campos Magnéticos , Espectrofotometria Atômica , TemperaturaRESUMO
The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-nine elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr). Once the analytical data were collected, supervised pattern recognition techniques such as linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), support vector machine (SVM) and Random Forest (RF) were applied to construct classification/discrimination rules. The results indicated that nonlinear methods, RF and SVM, perform best with up to 98% and 93% accuracy rate, respectively, and therefore are excellent tools for classification of grapes.