CEA presented 17 posters and platforms at SETAC Copenhagen in May 2022. Since then we have been showcasing each of these presentations in a series of 'SETAC Spotlight' articles. This week it is:
Analysis of public environmental monitoring datasets for the listing and delisting of priority substances
Author: Greg Hughes
The procedure for analysing public environmental monitoring data to assess the listing and delisting of substances as priority substances (PS) under the associated directive (2455/2001/EC amended by 2013/39/EU) draws on the Watch List approach (Carvalho et al., 2016). This compounds a series of worst-case assumptions resulting in an unrealistic risk assessment, notably, the manner in which it (i) deals with outliers and non-detections; (ii) considers all available data to assess the current state of the environment whilst ignoring changes in policy and associated changes in product registrations/usage and their impact on environmental residues; (iv) compares a single worst case predicted no-effect concentration (PNEC) against the 95th percentile measured environmental concentration when deriving the hazard quotient ignoring associated Water Framework Directive guidance and the updated annual average (AA) and maximum acceptable concentration (MAC) environmental quality standards (EQS).
This poster uses a surrogate dataset for an example compound, compiled using readily available and more recent public monitoring datasets (given the dataset used in the draft PS dossier preparation is not available owing to Member State data access restrictions). We outline key issues relating to (i) data quality and harmonisation, (ii) data processing e.g. outlier definition and dealing with non-detects, (iii) sample number for confidence in site/country exceedance results and (iv) continuity of data for assessment of trends. The method of analysis explores the implications of using the proposed methodology, for example, using the substitution approach for dealing with non-detections which is acknowledged to introduce bias/error (Helsel, 2005; 2012) instead of the Kaplan Meier, Robust Regression on Ordered Statistics and/or Maximum Likelihood Estimation (MLE) approaches, depending on the rate of left-censored data and sample size.
The analysis presented demonstrates how compounding these overly precautionary assumptions may lead to an assessment of risk that will require many MS to invest precious resources monitoring for a compound that does not present a risk to their surface waterbodies. More dialogue between regulatory agencies, industry and data analysis experts is required to define an approach that makes the most of the public monitoring data available but that also acknowledges the inherent challenges when doing so.
Download the poster for free.
You can find all of the other posters and platforms that CEA presented at SETAC Copenhagen here. You can also find all of our publications from previous conferences and links to journal articles we have authored on our library page.