Craig, Peter S

Hart, Andy

Hickey, Graeme

University of Liverpool Repository

SIMPLE ASSESSMENT-FACTOR BASED PNEC ESTIMATION:  USING ORDER STATISTICS  Graeme Hickey1*, Peter Craig1 & Andy Hart2 1Durham University, Dept. of Mathematical Sciences, Durham, UK 2The Food and Environment Research Agency, York, UK *Email: g.l.hickey@durham.ac.uk  The standard risk assessment approach for exposure of aquatic ecosystems to plant protection products (PPPs) is based on (using standard REACH notation in the interest of generality) the Risk Characterisation Ratio (RCR):       PNEC = Predicted No Effect Concentration and PEC = Predicted Exposure Concentration.   Notation: X1, ..., Xn are a collection of n toxicity test results (acute EC50 or long-term NOEC) for a pesticide. Let    X(i : n) = i-th numerically ranked value.        Current practice defines PNEC = X(1 : n) / AF — the minimum result from a sample of n  (with n usually 1 or 2)    divided by an assessment factor (AF) provided in Directive 91/414/EEC, Annex VI.   Decision Making Criterion: If the RCR is greater than one, then authorisation may not be granted unless an  appropriate (higher tier) risk assessment demonstrates that the risk is acceptable.   Current level of protection implied by the RCR is undefined.   Testing more species leads to an increasingly more conservative risk assessment if the assessment factor       remains fixed.  Part of the fixed assessment factor is intended to account for interspecies variation.   The Species Sensitivity Distribution (SSD) probabilistically models interspecies variation. For a defined            assemblage of biological species, the SSD predicts the potentially affected fraction (PAF) of species for any given environmental concentration.   The SSD is generally accepted for regulatory application when n ≥ 8-10 (REACH TGD, HARAP).   Define the Generalised PNEC = X(i : n) / AF for n ≥ 2, i.e. the i-th numerically ranked toxicity test result for a     sample of 3 or more.   The statistically expected [mean] PAF for any Generalised PNEC can be determined with respect to a theoretical SSD shape, e.g. log-normal (cf. EFSA, 2005) or log-logistic.   Figure 1 (left): Interpolated plot of mean PAF when PNEC = X(i : n) / AF for various choices of i and n, against 'SSD-standardised' assessment factors for a theoretical log-normal SSD.   For fish, standard practice is to set PNEC = X(1 : 2) / AF. Figure 1 implies setting PNEC as X(2 : 5) / AF [blue-dashed curve] or X(3 : 8) / AF  [yellow-dotted curve] yields a mean PAF uniformly bounded by the current level of protection [black-solid curve].  Key Developments of EFSA (2005)   The revised PNEC estimates are shown to be robust for many “near normal” log-SSDs, e.g. logistic, skew-normal, exponential power and positive skewed bimodal distributions (Hickey, 2010).   Conclusions are strengthened by stronger analytical evidence based on theory of (Second Order) Stochastic Dominance (Hickey, 2010).   Demonstrates mutual benefit between regulators and industry if more data is available for risk assessment.  PNECPECRCR References   EFSA (European Food Safety Authority) 2005. Assessment of the Acute and Chronic Risk to Aquatic Organisms with Regard to the Possibility of Lowering the Uncertainty Factor if Additional Species were Tested, The EFSA Journal, 301: 1-45.   HARAP (Guidance Document on Higher Tier Aquatic Risk Assessment for Pesticides) 1999. Campbell, PJ, Arnold, DJS, Brock, TCM, Grandy, NJ, Heger, W, Heimbach, F, Maund, SJ and Streloke, M Eds. Brussels: SETAC.   Hickey, GL. 2010. Ecotoxicological Risk Assessment: Developments in PNEC Estimation. Ph.D. Thesis. Durham University, UK.   REACH TGD (European Chemicals Agency) 2008. Guidance on Information Requirements and Chemical Safety Assessment Chapter R.10: Characterisation of Dose-Response for Environment. July 2008. Conclusion  If it is reasonable to assume a collection of n distinct species toxicity values are a random sample from an (unknown) 'near-normal' log-SSD, then the RCR decision criterion is statistically expected to provide at least the current (undefined) level of protection implied when based on the Generalised PNEC given by: X(1 : n) / AF     for  2 ≤ n ≤ 4; X(2 : n) / AF     for  5 ≤ n ≤ 7; X(3 : n) / AF     for  8 ≤ n ≤ 10; HC5 / AFSSD  with appropriate AFSSD for sufficiently large n. PNEC =  Available data Assessment factor At least one short-term L(E)C50 from each of three trophic levels of the base set (fish, Daph-nia, algae) 1000 One long-term NOEC (either fish or Daphnia) 100 Two long-term NOECs from species represent-ing two trophic levels of the base set (fish and/or Daphnia and/or algae) 50 Long-term NOECs from at least three species (normally fish, Daphnia, algae) representing three trophic levels 10 Species sensitivity distribution (SSD) method 5-1 Field data or model ecosystems Reviewed on a case by case basis Table 1 (above). Assessment factors for deriving a PNEC for aquatic compartments exposed to industrial substances (REACH TGD, p. 19, Table R.10-4). The latest guidance provides clarification on the sources of uncertainty assessment factors are intended to account for, namely:   intra- and inter-laboratory variation of toxicity data;   intra- and inter-species variation (biological variance);   short-term to long-term toxicity extrapolation;   laboratory data to field impact extrapolation. Fields shaded ███ indicate different (non-order statistic) PNEC derivation tools.   If XRainbow trout = 1.2 mg/L, XAtlantic salmon = 12.5 mg/L, XShortnose sturgeon = 5.9 mg/L and XThreespine stickleback = 8.4 mg/L are n = 4 observed LC50 values for a hypothetical pesticide, then n = 4, X(1 : 4) = 1.2, X(2 : 4) = 5.9, X(3 : 4) = 8.4 and X(4 : 4) = 12.5. NB. PPP technical guidance actually uses the term „Toxicity Exposure Ratio‟ (TER), such that:  TER = AF / RCR. 

Simple assessment factor based PNEC estimation using order statistics

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