Enhancing Sediment Bioaccumulation Predictions: Isotopically Modified Bioassay and Biodynamic Modeling for Nickel Assessment.
Qijing SuWenze XiaoStuart L SimpsonQiao-Guo TanRong ChenMin-Wei XiePublished in: Environmental science & technology (2023)
Quantifying metal bioaccumulation in a sedimentary environment is a valuable line of evidence when evaluating the ecological risks associated with metal-contaminated sediments. However, the precision of bioaccumulation predictions has been hindered by the challenges in accurately modeling metal influx processes. This study focuses on nickel bioaccumulation from sediment and introduces an innovative approach using the isotopically modified bioassay to directly measure nickel assimilation rates in sediment. Tested in sediments spiked with two distinct nickel concentrations, the measured Ni assimilation rates ranged from 35 to 78 ng g -1 h -1 in the Low-Ni treatment and from 96 to 320 ng g -1 h -1 in the High-Ni treatment. Integrating these rates into a biodynamic model yielded predictions of nickel bioaccumulation closely matching the measured results, demonstrating high accuracy with predictions within a factor of 3 for the Low-Ni treatment and within a factor of 1 for the High-Ni treatment. By eliminating the need to model metal uptake from various sources, this streamlined approach provides a reliable method for predicting nickel bioaccumulation in contaminated sediments. This advancement holds promise for linking bioaccumulation with metal toxicity risks in sedimentary environments, enhancing our understanding of metal-contaminated sediment risks and providing valuable insights to support informed decision-making in ecological risk assessment and management.
Keyphrases
- heavy metals
- risk assessment
- human health
- health risk assessment
- health risk
- metal organic framework
- reduced graphene oxide
- oxidative stress
- oxide nanoparticles
- combination therapy
- drinking water
- machine learning
- carbon nanotubes
- replacement therapy
- polycyclic aromatic hydrocarbons
- artificial intelligence
- deep learning
- clinical evaluation
- smoking cessation