Toxicity of nickel to tropical freshwater and sediment biota: A critical literature review and gap analysis.
Monique T BinetMerrin S AdamsFrancesca GissiLisa A GoldingChristian E SchlekatEmily R GarmanGraham MerringtonJennifer L StauberPublished in: Environmental toxicology and chemistry (2017)
More than two-thirds of the world's nickel (Ni) lateritic deposits are in tropical regions, and just less than half are within South East Asia and Melanesia (SEAM). With increasing Ni mining and processing in SEAM, environmental risk assessment tools are required to ensure sustainable development. Currently, there are no tropical-specific water or sediment quality guideline values for Ni, and the appropriateness of applying guideline values derived for temperate systems (e.g., Europe) to tropical ecosystems is unknown. Databases of Ni toxicity and toxicity tests for tropical freshwater and sediment species were compiled. Nickel toxicity data were ranked, using a quality assessment, identifying data to potentially use to derive tropical-specific Ni guideline values. There were no data for Ni toxicity in tropical freshwater sediments. For tropical freshwaters, of 163 Ni toxicity values for 40 different species, high-quality chronic data, based on measured Ni concentrations, were found for just 4 species (1 microalga, 2 macrophytes, and 1 cnidarian), all of which were relevant to SEAM. These data were insufficient to calculate tropical-specific guideline values for long-term aquatic ecosystem protection in tropical regions. For derivation of high-reliability tropical- or SEAM-specific water and sediment quality guideline values, additional research effort is required. Using gap analysis, we recommend how research gaps could be filled. Environ Toxicol Chem 2018;37:293-317. © 2017 SETAC.
Keyphrases
- climate change
- heavy metals
- risk assessment
- metal organic framework
- oxidative stress
- electronic health record
- big data
- human health
- oxide nanoparticles
- polycyclic aromatic hydrocarbons
- transition metal
- quality improvement
- reduced graphene oxide
- artificial intelligence
- deep learning
- carbon nanotubes
- gold nanoparticles
- drug induced