Assessing mercury pollution in Amazon River tributaries using a Bayesian Network approach

• Autor(a) principal: Bonotto, Daniel Marcos [UNESP]
• Data de Publicação: 2018
• Outros Autores: Wijesiri, Buddhi, Vergotti, Marcelo, da Silveira, Ene Glória, Goonetilleke, Ashantha
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.ecoenv.2018.09.099; http://hdl.handle.net/11449/188125
• topic: Amazon waters; Bayesian Networks; Environmental modelling; Hg contamination; Water pollution

Mercury pollution of water bodies exerts significant human and ecosystem health impacts due to high toxicity. Relatively high levels of mercury have been detected in the Amazon River and its tributaries and associated lakes. The study employed a Bayesian Network approach to investigate the contribution from geogenic sources to mercury pollution of lakes in the Madeira River basin, which is the largest tributary of the Amazon River. It was found that the source indicators of naturally occurring mercury have both, positive and negative relationships with mercury in lake sediments. Although the positive relationships indicated the influence of geological and soil formations, the negative relationships implied that the use of mercury amalgam for gold extraction in artisanal and small-scale mining (ASM), which is the primary anthropogenic source of mercury, also contribute to mercury in Amazon tributaries. This was further evident as mercury concentrations in lake sediments were found to be significantly higher than those in the surrounding rocks. However, potential anthropogenic mercury was attributed to historical inputs from gold mining due to the recent decline of ASM mining practice in the region.