Hazard assessment of chemical constituents in biocide formulations used in offshore oil and gas operations

• Published in: Marine pollution bulletin Vol. 183; p. 114076
• Main Authors: Bejarano, Adriana C., Hughes, Sarah A., Saunders, David
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2022
• Subjects: Biocides; Hazard assessment; Interspecies correlation estimation models; Offshore oil and gas effluents; Species sensitivity distributions; Species sensitivity distributions; Hazard assessment; Interspecies correlation estimation models; Biocides; Offshore oil and gas effluents
• ISSN: 0025-326X; 1879-3363
• DOI: 10.1016/j.marpolbul.2022.114076

Biocides used in offshore oil and gas operations could be present in water discharges, and thus identifying such chemicals and their hazard could help address concerns regarding non-target organisms. Aquatic toxicity data, queried from different sources and augmented with predictive models, were used to develop species sensitivity distributions and their corresponding 5th percentile hazard concentrations (HC5s). Curated data, including over 1000 empirical records for 137 species, indicated no evidence of bias when comparing sensitivity between marine and freshwater species, even when predicted data were used. HC5s facilitated estimation of an acute-to-chronic ratio (ACR = 10), appropriate for most chemicals and useful in filling data gaps. Comparison of chronic-HC5s with the default approach for deriving predicted no effect concentrations showed that the latter systematically overstates aquatic hazard. The present approach shows promise of using acute-to-chronic HC5 ratios for defining assessment factors for different chemical classes, instead of the use of generic assessment factors. [Display omitted] •Biocides used in offshore oil and gas operations could be present in water discharges.•Effluent risk evaluations include assessments of biocides based on aquatic toxicity.•Gaps in acute and chronic toxicity data can be addressed through predictive models.•Predicted data are useful in enhancing species sensitivity distributions.•Integration of predicted data could improve derivation of assessment factors.