Evaluating the Protectiveness of a Bioavailability‐Based Environmental Quality Standard for the Protection of Aquatic Communities from Zinc Toxicity Based on Field Evidence

Environmental quality standards (EQS) are typically derived from the results of laboratory studies on single species. There is always uncertainty surrounding the protectiveness of an EQS when applied to real ecosystems containing a multitude of chemical and physical stressors. Quantile regression was used with field biological data on invertebrates in United Kingdom waters to identify taxa that are responsive to bioavailable zinc exposures. A threshold based on the total abundance of eight responsive taxa is used as an indicator of the overall ecosystem sensitivity. The inclusion of some responsive but insensitive taxa in this ecological metric could bias the results toward a higher threshold. The least responsive species were progressively removed from the collective ecological metric, basing the analysis on a progressively smaller number of the more responsive species. Quantile regression analysis at the 95th quantile for the three most responsive taxa resulted in a 10% effect concentration of 14.8 µg L−1 bioavailable zinc, suggesting that the EQS of 10.9 µg L−1 bioavailable zinc is sufficiently protective of sensitive members of the invertebrate community. There is a compromise between the robustness of the analysis and the sensitivity of the subcommunity that it is based on. Analyses based on fewer taxa provide a more sensitive result. This approach assessed real ecosystem data and evaluated the uncertainty associated with the protectiveness of the EQS for zinc. The zinc EQS is sufficiently protective of sensitive members of benthic macroinvertebrate communities under real environmental conditions, including a mix of multiple substances. Environ Toxicol Chem 2023;42:1010–1021. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

[1]  Takehiko I. Hayashi,et al.  The effect of intervention in nickel concentrations on benthic macroinvertebrates: A case study of statistical causal inference in ecotoxicology. , 2020, Environmental pollution.

[2]  C. Schlekat,et al.  Acute and Chronic Toxicity of Nickel and Zinc to a Laboratory Cultured Mayfly (Neocloeon triangulifer) in Aqueous but Fed Exposures , 2020, Environmental toxicology and chemistry.

[3]  Aina Garcia-Raventós,et al.  The European Water Framework Directive facing current challenges: recommendations for a more efficient biological assessment of inland surface waters , 2018, Inland Waters.

[4]  T. Goldschmidt Water mites (Acari, Hydrachnidia): powerful but widely neglected bioindicators – a review , 2016 .

[5]  Brian D. Smith,et al.  Bioaccumulation of arsenic and silver by the caddisfly larvae Hydropsyche siltalai and H. pellucidula: a biodynamic modeling approach. , 2015, Aquatic toxicology.

[6]  W. Walley,et al.  REVISION OF THE BIOLOGICAL MONITORING WORKING PARTY (BMWP) SCORE SYSTEM: DERIVATION OF PRESENT‐ONLY AND ABUNDANCE‐RELATED SCORES FROM FIELD DATA , 2014 .

[7]  Glenn W Suter,et al.  A method for deriving water‐quality benchmarks using field data , 2013, Environmental toxicology and chemistry.

[8]  Anne Courrat,et al.  Three hundred ways to assess Europe's surface waters: An almost complete overview of biological methods to implement the Water Framework Directive , 2012 .

[9]  Brian D. Smith,et al.  Caddisflies as biomonitors identifying thresholds of toxic metal bioavailability that affect the stream benthos. , 2012, Environmental pollution.

[10]  Y. Iwasaki,et al.  Effect of zinc on diversity of riverine benthic macroinvertebrates: Estimation of safe concentrations from field data , 2011, Environmental toxicology and chemistry.

[11]  M. Crane,et al.  Effects of Iron on Benthic Macroinvertebrate Communities in the Field , 2011, Bulletin of Environmental Contamination and Toxicology.

[12]  Mark Crane,et al.  Use of field data to support European Water Framework Directive quality standards for dissolved metals. , 2007, Environmental science & technology.

[13]  W. H. Clement,et al.  Development of bioassessment‐based benchmarks for iron , 2007, Environmental toxicology and chemistry.

[14]  R. D. Evans,et al.  Uptake and Elimination of Lead, Zinc, and Copper by Caddisfly Larvae (Trichoptera: Hydropsychidae) Using Stable Isotope Tracers , 2006, Archives of environmental contamination and toxicology.

[15]  Manoel A. W. Pacheco,et al.  Integrating chemical and biological criteria , 2005, Environmental toxicology and chemistry.

[16]  Jeffrey D. Ostermiller,et al.  Effects of sampling error on bioassessments of stream ecosystems: application to RIVPACS-type models , 2004, Journal of the North American Benthological Society.

[17]  B. Cade,et al.  A gentle introduction to quantile regression for ecologists , 2003 .

[18]  R. Death,et al.  Biological assessment of rivers in the Manawatu‐Wanganui region of New Zealand using a predictive macroinvertebrate model , 2003 .

[19]  P. Paquin,et al.  Biotic ligand model of the acute toxicity of metals. 1. Technical Basis , 2001, Environmental toxicology and chemistry.

[20]  Francis Juanes,et al.  INFERRING ECOLOGICAL RELATIONSHIPS FROM THE EDGES OF SCATTER DIAGRAMS: COMPARISON OF REGRESSION TECHNIQUES , 1998 .

[21]  J. Dorgelo,et al.  Effects of diet and heavy metals on growth rate and fertility in the deposit-feeding snail Potamopyrgus jenkinsi (Smith) (Gastropoda: Hydrobiidae) , 1995, Hydrobiologia.

[22]  M. Willis Experimental studies of the effects of zinc on Ancylus fluviatilis (Müller) (Mollusca; Gastropoda) from the Afon Crafnant, N. Wales , 1988, Archiv für Hydrobiologie.

[23]  Mike T. Furse,et al.  A preliminary classification of running‐water sites in Great Britain based on macro‐invertebrate species and the prediction of community type using environmental data , 1984 .

[24]  Graham Merrington,et al.  Assessment of the effects of nickel on benthic macroinvertebrates in the field , 2013, Environmental Science and Pollution Research.

[25]  Adam Peters,et al.  Accounting for both local aquatic community composition and bioavailability in setting site-specific quality standards for zinc , 2013, Environmental Science and Pollution Research.

[26]  B. Cade,et al.  Estimating effects of limiting factors with regression quantiles , 1999 .