Adjusting Tropical Marine Water Quality Guideline Values for Elevated Ocean Temperatures.
Andrew P NegriRachael A SmithOlivia KingJulius FrangosMichael St J WarneSven UthickePublished in: Environmental science & technology (2019)
Increased frequency of summer heatwaves and poor water quality are two of the most prevalent and severe pressures faced by coral reefs. While these pressures often co-occur, their potential risks to tropical marine species are usually considered independently. Here, we extended the application of multisubstance-Potentially Affected Fraction (ms-PAF) to a nonchemical stressor, elevated sea surface temperature. We then applied this method to calculate climate-adjusted water quality guideline values (GVs) for two reference toxicants, copper and the herbicide diuron, for tropical marine species. First, we developed a species sensitivity distribution (SSD) for thermal stress based on published experimental data for 41 tropical benthic marine species using methods adapted from water quality GV derivation. This enabled quantitative predictions of community effects as temperatures exceeded acclimation values. The resulting protective temperature values (PTx) were similar to temperatures known to initiate coral bleaching and are therefore relevant for application in multistressor risk assessments. The extended ms-PAF method enabled the adjustment of current water quality GVs to account for thermal stress events. This approach could be applied to other ecosystems and other non-contaminant stressors (e.g., sediment, low salinity, anoxia, and ocean acidification), offering an alternative approach for deriving environmental GVs, reporting and assessing the risk posed by multiple stressors.
Keyphrases
- water quality
- climate change
- human health
- mass spectrometry
- healthcare
- multiple sclerosis
- genetic diversity
- mental health
- heavy metals
- heat stress
- high resolution
- emergency department
- randomized controlled trial
- nitric oxide
- systematic review
- microbial community
- electronic health record
- big data
- deep learning
- drug induced
- data analysis