Login / Signup

Heavy Metal Concentration in Neotropical Aquatic Snakes (Helicops pastazae) and Its Potential as a Bioindicator of Water Pollution.

María José Hurtado-MoralesManuel Rodriguez SusaAdolfo Amézquita
Published in: Archives of environmental contamination and toxicology (2022)
The purpose of this study was to test the potential role of the aquatic snake Helicops pastazae as an indicator of water pollution caused by heavy metals. In particular, we tested whether the total heavy metal concentration is related to (1) the position (upstream vs downstream) of the sampling point and its distance from the point where wastewater is discharged; (2) the taxonomic group studied: piscivorous snakes vs characid fish that occupy the same habitats; and (3) the organ or tissue examined: snake liver versus muscle. We used atomic absorption spectrophotometry with electrothermal atomization to quantify cadmium (Cd), chromium (Cr) and lead (Pb) and found significant differences between some of the sampling points, with particularly high metal concentrations detected upstream at point 1. However, we found no clear spatial pattern nor any significant differences in the concentration of any of the metals in fish and snake muscle, suggesting that both species accumulate similar amounts of the sampled elements. With regard to interactions, snake liver had the highest concentrations of Cd, while muscle had the highest concentrations of Pb and Cr, which may indicate tissue affinity differences for certain metals. Altogether, our results indicate that H. pastazae accumulates contaminants differentially, depending on the tissue and location, which highlights their potential as bioindicators of water contamination. Further research is necessary to understand their role as bioindicators based on extensive sampling and environmental contaminant data.
Keyphrases
  • heavy metals
  • risk assessment
  • human health
  • health risk assessment
  • health risk
  • skeletal muscle
  • sewage sludge
  • drinking water
  • climate change
  • wastewater treatment
  • air pollution
  • deep learning
  • genetic diversity