Protectiveness of Cu water quality criteria against impairment of behavior and chemo/mechanosensory responses: An update

Meyer, Joseph S.; DeForest, David K.

doi:10.1002/etc.4096

Cited by 11 publications

(14 citation statements)

References 67 publications

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“…Most seriously, in flow-through tests with a short hydraulic residence, free Cu will be elevated throughout the test (Figure 9D). Similarly, Meyer and DeForest (2018) invoked the Ma et al (1999) kinetic model to argue that a lack of adequate equilibration could explain the apparently very low threshold effect concentrations (<2 μg Cu/L) reported by Dew et al (2012) for olfactory impairment in fathead minnows exposed to Cu for short durations (1-24 h), as an alternative to the damage-repair hypothesis proposed by Dew et al (2012) for explaining the decrease of olfactory impairment as exposure time increased.…”

Section: Equilibrium Issuesmentioning

confidence: 99%

“…For example, for waterborne Cu, inhibitory effects are almost immediate, Ca provides little protection, the log K value for Cu at the olfactory rosette is lower than at the gill, and there is evidence of recovery/acclimation from olfactory inhibition during chronic exposure (e.g., McIntyre et al ; Mirza et al ; Green et al ; Dew et al ). Furthermore, there are different viewpoints (e.g., Green et al and Dew et al vs Meyer and Adams , DeForest et al , and Meyer and DeForest ) on whether or not mechanistic bioavailability models based on concepts of ionoregulatory disturbance, such as the BLM‐based US Cu criterion (US Environmental Protection Agency ), are protective against olfactory effects such as behavioral disturbance. If they are protective, it would appear that this is because highly sensitive taxa are included (e.g., cladocerans) for criteria derivation, not because the bioavailability models are mechanistically correct for behavioral endpoints (i.e., the comparison is of apples vs oranges).…”

Section: Evidence For the Mechanistic Approachmentioning

confidence: 99%

See 1 more Smart Citation

Metal Bioavailability Models: Current Status, Lessons Learned, Considerations for Regulatory Use, and the Path Forward

Mebane

Chowdhury²,

Schamphelaere

et al. 2019

Enviro Toxic and Chemistry

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Since the early 2000s, biotic ligand models and related constructs have been a dominant paradigm for risk assessment of aqueous metals in the environment. We critically review 1) the evidence for the mechanistic approach underlying metal bioavailability models; 2) considerations for the use and refinement of bioavailability-based toxicity models; 3) considerations for the incorporation of metal bioavailability models into environmental quality standards; and 4) some consensus recommendations for developing or applying metal bioavailability models. We note that models developed to date have been particularly challenged to accurately incorporate pH effects because they are unique with multiple possible mechanisms. As such, we doubt it is ever appropriate to lump algae/plant and animal bioavailability models; however, it is often reasonable to lump bioavailability models for animals, although aquatic insects may be an exception. Other recommendations include that data generated for model development should consider equilibrium conditions in exposure designs, including food items in combined waterborne-dietary matched chronic exposures. Some potentially important toxicity-modifying factors are currently not represented in bioavailability models and have received insufficient attention in toxicity testing. Temperature is probably of foremost importance; phosphate is likely important in plant and algae models. Acclimation may result in predictions that err on the side of protection. Striking a balance between comprehensive, mechanistically sound models and simplified approaches is a challenge. If empirical bioavailability tools such as multiple-linear regression models and look-up tables are employed in criteria, they should always be informed qualitatively and quantitatively by mechanistic models. If bioavailability models are to be used in environmental regulation, ongoing support and availability for use of the models in the public domain are essential.

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Section: Equilibrium Issuesmentioning

confidence: 99%

Section: Evidence For the Mechanistic Approachmentioning

confidence: 99%

Metal Bioavailability Models: Current Status, Lessons Learned, Considerations for Regulatory Use, and the Path Forward

Mebane

Chowdhury²,

Schamphelaere

et al. 2019

Enviro Toxic and Chemistry

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“…Many experiments have tested the effects of Cu exposure on fish olfactory performance either directly using neurophysiological responses such as electroencephalogram (EEG) or electroolfactogram (EOG) techniques or indirectly through behavioral assays using alarm cues, similar to the present study (e.g., Table 4 and Meyer and Deforest 2018). This is the first experiment to test the effects of Cu exposure on the salmonid olfactory system over time using the same fish.…”

Section: Discussionmentioning

confidence: 99%

“…However, notwithstanding the ongoing efforts of Meyer and Adams (2010) and Meyer and DeForest (2018) to model the effects of water chemistry on potential olfactory effects in salmonids (and other species), given the limited amount of data and research on this topic as it pertains to watersheds with chemistry similar to the Bristol Bay watershed (low hardness and DOC), we proceeded to conduct the research described in the present study. The present study was conducted as a follow-on to a preceding 96-h bioassay using rainbow trout exposed to laboratory water with water chemistry adjusted to values that were similar to site water collected from the Bristol Bay watershed as described in our companion paper (Morris et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Copper toxicity in Bristol Bay headwaters: Part 2—Olfactory inhibition in low‐hardness water

Morris

Brinkman

Takeshita

et al. 2018

Enviro Toxic and Chemistry

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We investigated the olfactory toxicity of copper (Cu) to rainbow trout in low‐hardness (27 mg/L as CaCO3) water formulated in the laboratory over a 120‐h period using a flow‐through design. The fish's response to an alarm cue (e.g., reduction in activity) was recorded to determine the exposure concentrations and durations that inhibited olfactory detection of the cue after 3, 24, 48, and 96 h of Cu exposure and after 24 h of clean water recovery following the 96‐h exposure period. Exposures were conducted with a range of Cu concentrations from 0.13 (control) to 7.14 μg Cu/L (dissolved Cu). We observed a dose‐dependent response in olfactory inhibition with a 20% reduction in the probability of responding to the alarm cue, relative to controls, at 2.7 and 2.4 μg Cu/L after 24 or 96 h of exposure, respectively. Olfactory inhibition manifested between 3 and 24 h of exposure. Our 24‐ and 96‐h 20% olfactory inhibition estimates fell between the criteria derived using the biotic ligand model (BLM; criterion maximum concentration [CMC] and criterion continuous concentration [CCC] values were 0.63 and 0.39 μg Cu/L, respectively) and water hardness–based criteria (CMC and CCC values were 3.9 and 2.9 μg Cu/L, respectively). Therefore, the hardness‐based criteria do not appear to be protective and the BLM‐derived criteria do appear to be protective against Cu‐induced olfactory inhibition given our test water chemistry. Neither the hardness‐based criteria nor the BLM‐derived criteria appear to be protective against our estimated Cu behavioral avoidance response concentrations at 24‐ and 96‐h exposures (0.54 and 0.50 μg Cu/L, respectively). Environ Toxicol Chem 2019;38:198–209. © 2018 SETAC

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“…In these analyses, I seek to bring in newly analyzed datasets, or new perspectives on established modeling approaches. A goal is to further vet but not to simply repeat similar analyses presented in the recent reviews by Brix et al, (Brix et al 2017;), Environment Canada (2021, and Meyer and DeForest (2018).…”

Section: Introductionmentioning

confidence: 99%

Bioavailability and toxicity models of copper and zinc to freshwater life: the state of the science and alternatives for water quality criteria

Mebane¹

2022

Preprint

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The importance of water conditions on the bioavailability and toxicity of metals has long been recognized. In the United States and elsewhere, regulatory criteria to protect aquatic life have likewise acknowledged factors that modify bioavailability, and incorporated water hardness-toxicity regressions into criteria for several metals. Biotic ligand models (BLMs) became a dominant paradigm in predicting the aquatic toxicology of metals as mechanistic functions of modeled metals speciation and cation competition for binding to ionoregulatory sites on the gill or biotic ligand surface, disrupting the internal mineral balance of organisms and leading to stress or death. Following development of software that efficiently executed BLM calculations, in 2007 the U.S. Environmental Protection Agency (USEPA) published national recommended aquatic life criteria for copper (Cu) that was based upon the BLM construct and software. However, there has not been widespread adoption by states of the USEPA’s recommended BLM-based aquatic life criteria into their water quality standards. Reasons for the languishing of the BLM-based Cu criteria presumably include that the BLM-based Cu criteria are far more complex than any previous aquatic life criteria and that the BLM requires much more data than water hardness to calculate the criteria. pH, dissolved organic carbon (DOC), a suite of major ions, alkalinity, and specialized software are required to calculate BLM-based criteria. The purpose of the present study is to compare the leading available aquatic life criteria models for predicting Cu and zinc (Zn) toxicity (or lack of toxicity) to diverse freshwater species and ecosystems: 1) the single linear regression hardness models; 2) Multiple Linear Regression (MLR) models that predict toxicity as a function of DOC, hardness, and pH, and 3) BLM-based criteria. The scope of the review for Zn was truncated relative to that of Cu because no generally applicable MLR model for Zn was available at the time of writing.The comparisons included evaluating the performance of the models for predicting Cu toxicity and the models’ behavior in natural waters. The evaluations of toxicity predictions were limited to freshwater animals and included testing model predictions against literature reports of observed acute and chronic responses with trout, other fish, mussels and other invertebrates, and olfactory impairment or avoidance responses in salmonids. The presumed “safe” criteria concentrations were also compared against effects attributed to Cu in field studies or ecosystem experiments. The comparisons emphasize natural waters in California, however conditions in California waters are diverse and the comparisons are broadly relevant to other freshwaters.The results were consistent with previous work that found that the MLR toxicity predictions were generally more reliable than the BLM for predicting Cu toxicity across the vast majority of natural water types, but that the BLM predictions were more reliable in situations with low or high pH (less than about 5.7 or greater than about 9) or in waters with unusual ionic composition. However, both the MLR and BLM performed well with many datasets and aquatic life criteria calculation procedures both produced criteria concentrations that mostly appeared safe in comparisons with field and ecosystem studies and with particularly sensitive species and endpoints such as chemoreception and avoidance behaviors in salmonids. In marked contrast, the hardness-regression model toxicity predictions performed poorly with all datasets tested, with little correspondences between predicted and observed effects. The comparisons of the hardness-criteria concentrations with Cu effects thresholds estimated from field and ecosystem studies showed that the hardness-based criteria failed to reliably produce protective concentrations. In soft waters with less than about 40 mg/L hardness, the hardness-based criteria would be protective but not at higher hardnesses. In natural waters, the hardness criteria had little correlation to the MLR, which was the most accurate proxy for truly toxic or nontoxic conditions, and in some steams, the hardness-based criteria actually had negative correlations with the MLR criteria.Th performance and protectiveness of the Cu MLR and BLM models and criteria were largely similar. However, the Cu MLR has advantages for adoption into water quality standards over the BLM because of its reduced data requirements, simpler form, ease of calculation, transparency, and non-dependence on specialized computer software. The Cu MLR could be structured in tiers to accommodate missing data by setting conservative default values for pH or DOC, for example, pH 7 and 1 mg/L DOC. Then, only if environmental Cu concentrations exceeded criteria as calculated with default values would actual DOC and pH data be needed.

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Protectiveness of Cu water quality criteria against impairment of behavior and chemo/mechanosensory responses: An update

Cited by 11 publications

References 67 publications

Metal Bioavailability Models: Current Status, Lessons Learned, Considerations for Regulatory Use, and the Path Forward

Metal Bioavailability Models: Current Status, Lessons Learned, Considerations for Regulatory Use, and the Path Forward

Copper toxicity in Bristol Bay headwaters: Part 2—Olfactory inhibition in low‐hardness water

Bioavailability and toxicity models of copper and zinc to freshwater life: the state of the science and alternatives for water quality criteria

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