Predicting single‐species toxicity in natural water systems

Abstract

A methodology is proposed to predict single‐species toxicity in natural waters by using laboratory bioassay data which relate effect to a tissue concentration of toxicant or to injury accumulation. Such relationships should be independent of test conditions and therefore be transferable from lab to field. Data for endrin and copper are presented which illustrate relationships between tissue concentrations and mortality. A mathematical model of uptake and elimination of toxicants by fish is used to relate exposure concentration to tissue concentration and then to effect. Prediction of toxicity in a laboratory test in which the exposure concentration varies in time is presented as a first step in testing the methodology. Using a dose‐response relationship determined from published data for zinc toxicity to rainbow trout in a constant exposure toxicity test, the survival time under time‐varying exposure is calculated and compared to observed data. The model predicted survival times that were consistent with the observed times. Whole‐body residue is used as the estimate of dose. A methodology for considering chemicals for which tissue concentration is not a useful estimate of dose is also presented. Diazinon‐induced acetylcholinesterase inhibition data are used to demonstrate this methodology.

Influence of in‐stream diel concentration cycles of dissolved trace metals on acute toxicity to one‐year‐old cutthroat trout (Oncorhynchus clarki lewisi)

“…In addition, some data suggested reduced toxicity for fluctuating concentrations compared to constant concentrations, but these trends were not statistically conclusive. These findings agree with the conclusion drawn by Brown et al [14] and subsequent modeling studies [44–46]. None of our results confirm that fluctuating concentrations of Zn and Cd produce increased toxicity, which is what Hodson et al [15] concluded about Pb.…”

“…Thus, they hypothesized that the level of biological response is more closely related to net accumulation rather than to the exposure concentration. Later modeling [44–46] of the Zn toxicity data of Brown et al [14] also indicated that detoxification outpaced uptake when toxicant concentrations were lower or decreasing such that fluctuating concentrations tended to have reduced toxicity compared to constant concentrations equivalent to the average of the fluctuating concentration. Hodson et al [15], who studied Pb, however, came to the opposite conclusion—that is, that rates of uptake are large relative to decay rates and, therefore, that exposure to intermittent, high waterborne concentrations leads to excess toxicity, because accumulated toxicant remains on the tissue even after the high‐concentration exposure is removed.…”

Time-Dependent Toxicity of Neonicotinoids and Other Toxicants:Implications for a New Approach to Risk Assessment

“…While more information is necessary to prove that other metallic and metalloid elements (e.g arsenic) produce time-dependent toxic e ects, the evidence so far obtained from toxicity experiments carried out with sh (e.g. [52][53][54]) illustrates a toxicity pattern similar to that observed with neonicotinoid insecticides. is is likely to result from metal accumulation in the body, which increases with time until the organisms reach a su cient dose to cause a toxic e ect (see review in [55]).…”

Overcoming Challenges of Incorporating Higher Tier Data in Ecological Risk Assessments and Risk Management of Pesticides in the United States: Findings and Recommendations from the 2017 Workshop on Regulation and Innovation in Agriculture

“…Regulators have recognized the need to incorporate population models into risk assessment and management processes since the 1990s (Barton ), and this prompted a Society of Environmental Toxicology and Chemistry (SETAC) Workshop on Population Ecology and Wildlife Toxicology of Agricultural Pesticide Use: A Modeling Initiative for Avian Species, held at Kiawah Island, South Carolina, USA on 22–27 July 1990 (Kendall and Lacher ). These early models were beginning to estimate effects on terrestrial (Lari et al ; Madrigal et al ) and aquatic organisms (Connolly ; Barnthouse ). Increased computational power and agreement on probabilistic risk paradigms allowed regional scale assessments to emerge (Solomon et al , ; Purucker et al ).…”