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1.
Environ Res ; 214(Pt 1): 113757, 2022 11.
Artigo em Inglês | MEDLINE | ID: mdl-35753380

RESUMO

The study of metals mobility derived from mining activities in an ultramafic lithology is limited. This study investigates the effects of distance on potentially toxic metals such as Co, Cu, Fe, Mn, Ni, Pb, and Zn pollution, and the geochemical processes of fluvial system downstream of an ex-copper mine (Mamut River). The toxicity level of the river was evaluated using various sediment quality guidelines, ecotoxicological risks (ecological risk and risk index) and pollution indices. The geochemical behavior and stability of these toxic metals in the solid-phase samples were also examined. The results show that elevated concentrations of Ni, Cu, and Fe in the sediments can be linked to the adsorption and precipitation of metals from the aqueous-phase samples. We found that the metal scavenging rate as a function of distance is more evident in tropical environments than it was previously thought (10 km downstream). Such an inference could be explained by the greater amount of rainfall (pH 5.5-6.5) received in the tropics and higher weathering products that could react and form stable complexes. Geochemical analysis of the river sediment indicates that Ni, Cu, and Fe in the river sediment have increased 44-, 81-, and 90-fold compared to the background values, respectively. A significant decrease in the concentration of the potentially toxic metals was found at 5.5 km downstream. The scavenging rate of Fe is the highest (1485.82 µg g-1 km-1) followed by Cu (141.48 µg g-1 km-1), Ni (10.23 µg g-1 km-1), Pb (8.12 µg g-1 km-1) and Zn (5.01 µg g-1 km-1) in the tropical river system. In contrast, the concentration of Co and Mn in the river sediments doubled as the river flows approximately 5 km downstream due to the higher mineral solubility and weaker metal partition coefficient. This study also discusses the possibility of asbestos (mainly as chrysotile in the X-ray diffraction) as a potential hidden risk present within the ultramafic setting. This case study can be extrapolated to explain the dispersion of inorganic pollutants in an ultramafic environment in a global context.


Assuntos
Metais Pesados , Poluentes Químicos da Água , China , Monitoramento Ambiental , Sedimentos Geológicos , Chumbo , Medição de Risco , Rios
2.
Pak J Biol Sci ; 17(1): 62-7, 2014 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-24783779

RESUMO

Perna viridis (P. viridis) has been identified as a good biological indicator in identifying environmental pollution, especially when there are various types of Heavy Metals Accumulations (HMA) inside its tissue. Based on the potential of P. viridis to accumulate heavy metals and the data on its physical properties, this study proffers to determine the relationships between both properties. The similarities of the physical properties are used to mathematical model their relationships, which included the size (length, width, height) and weight (wet and dry) of P. viridis, whilst the heavy metals are focused on concentrations of Pb, Cu, Cr, Cd and Zn. The concentrations of metal elements are detected by using Flame Atomic Adsorption Spectrometry. Results show that the mean concentration of Pb, Cu, Cr, Cd, Zn, length, width, height, wet weight and dry weight are: 1.12 +/- 1.00, 2.36 +/- 1.65, 2.12 +/- 2.74, 0.44 +/- 0.41 and 16.52 +/- 10.64 mg kg(-1) (dry weight), 105.08 +/- 14.35, 41.64 +/- 4.64, 28.75 +/- 3.92 mm, 14.56 +/- 3.30 and 2.37 +/- 0.86 g, respectively. It is also found out that the relationships between the Heavy Metals Concentrations (HMA) and the physical properties can be represented using Multiple Linear Regressions (MLR) models, relating that the HMA of Zinc has affected significantly the physical growth properties of P. viridis.


Assuntos
Monitoramento Ambiental , Metais Pesados/metabolismo , Metais Pesados/toxicidade , Perna (Organismo)/efeitos dos fármacos , Perna (Organismo)/metabolismo , Fenômenos Físicos , Animais , Poluentes Ambientais/metabolismo , Poluentes Ambientais/toxicidade , Malásia , Modelos Estatísticos
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