Revising Environmental Quality Standards: Lessons Learned

An environmental quality standard (EQS) is a threshold value applied in regulatory monitoring for retrospective environmental risk assessment. However, an EQS may vary with time and between countries with shared water bodies, challenging coherent risk management. This study aimed to analyze the underlying reasons for changes in EQS values following a revision of previously derived EQSs for 62 substances. Relevant data were retrieved from publicly accessible databases, available literature, registration dossiers, and, in some cases, provided by manufacturers. Ecotoxicological data were assessed regarding reliability and relevance. As in previous studies, EQS derivation followed the European Union guideline. Overall, 61 annual average EQSs (AA-EQS) and 58 maximum acceptable concentration EQSs (MAC-EQS) were derived. Size and completeness of data sets generally increased due to the revision. AA-EQSs increased in 13 cases and decreased in 21 cases. MAC-EQSs increased in 22 cases and decreased in 11 cases. Most EQSs were derived using the deterministic assessment factor (AF) method. The number of substances for which EQSs were derived probabilistically by reference to the species sensitivity distribution (SSD) method increased from 2 to 5 AA-EQSs and from 6 to 11 MAC-EQSs. For AA-EQS derivation, AFs were reduced in 14 cases and increased in 6 cases. For MAC-EQS derivation, AFs were reduced in 9 cases and increased in 2 cases. Results demonstrate that the revisions did not generally lead to either lower or higher EQSs. The majority of EQSs (>93%) changed less than 10-fold. Clearly, EQSs based on small or incomplete data sets with large AFs were more prone to considerable changes in their numeric values when revised than EQSs based on SSDs. Thus, revision can reduce uncertainty and increase robustness of an EQS. In this study, however, available data continued to be insufficient to construct SSDs for the majority of substances. This was mostly due to a lack of reliable data. Integr Environ Assess Manag 2019;00:1-13. © 2019 SETAC.