Compartment-Specific Screening Tools for Persistence: Potential Role and Application in the Regulatory Context

Moving persistence assessments into the 21st century: A role for weight-of-evidence and overall persistence

Assessing the persistence of chemicals in the environment is a key element in existing regulatory frameworks to protect human health and ecosystems. Persistence in the environment depends on many fate processes, including abiotic and biotic transformations and physical partitioning, which depend on substances' physicochemical properties and environmental conditions. A main challenge in persistence assessment is that existing frameworks rely on simplistic and reductionist evaluation schemes that may lead substances to be falsely assessed as persistent or the other way around-to be falsely assessed as nonpersistent. Those evaluation schemes typically assess persistence against degradation half-lives determined in single-compartment simulation tests or against degradation levels measured in stringent screening tests. Most of the available test methods, however, do not apply to all types of substances, especially substances that are poorly soluble, complex in composition, highly sorptive, or volatile. In addition, the currently applied half-life criteria are derived mainly from a few legacy persistent organic pollutants, which do not represent the large diversity of substances entering the environment. Persistence assessment would undoubtedly benefit from the development of more flexible and holistic evaluation schemes including new concepts and methods. A weight-of-evidence (WoE) approach incorporating multiple influencing factors is needed to account for chemical fate and transformation in the whole environment so as to assess overall persistence. The present paper's aim is to begin to develop an integrated assessment framework that combines multimedia approaches to organize and interpret data using a clear WoE approach to allow for a more consistent, transparent, and thorough assessment of persistence. Integr Environ Assess Manag 2022;18:868-887. © 2021 ExxonMobil Biomedical Sciences, Inc. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Computational material flow analysis for thousands of chemicals of emerging concern in European waters

Knowledge of exposure to a wide range of chemicals, and the spatio-temporal variability thereof, is urgently needed in the context of protecting and restoring aquatic ecosystems. This paper discusses a computational material flow analysis to predict the occurrence of thousands of man-made organic chemicals on a European scale, based on a novel temporally and spatially resolved modelling framework. The goal was to increase understanding of pressures by emerging chemicals and to complement surface water monitoring data. The ambition was to provide a first step towards a "real-life" mixture exposure situation accounting for as many chemicals as possible. Comparison of simulated concentrations and chemical monitoring data for 226 substance/basin combinations showed that the simulated concentrations were accurate on average. For 65% and 90% of substance/basin combinations the error was within one and two orders of magnitude respectively. An analysis of the relative importance of uncertainties revealed that inaccuracies in use volume or use type information contributed most to the error for individual substances. To resolve this, we suggest better registration of use types of industrial chemicals, investigation of presence/absence of industrial chemicals in wastewater and runoff samples and more scientific information exchange.

Publicly available QSPR models for environmental media persistence

The evaluation of persistency of chemicals in environmental media (water, soil, sediment) is included in European Regulations, in the context of the Persistence, Bioaccumulation and Toxicity (PBT) assessment. In silico predictions are valuable alternatives for compounds screening and prioritization. However, already existing prediction tools have limitations: narrow applicability domains due to their relatively small training sets, and lack of medium-specific models. A dataset of 1579 unique compounds has been collected, merging several persistence data sources annotated by, at least, one experimental dissipation half-life value for the given environmental medium. This dataset was used to train binary classification models discriminating persistent/non-persistent (P/nP) compounds based on REACH half-life thresholds on sediment, water and soil compartments. Models were built using ISIDA (In SIlico design and Data Analysis) fragment descriptors and support vector regression, random forest and naïve Bayesian machine-learning methods. All models scored satisfactory performances: sediment being the most performing one (BA_ext = 0.91), followed by water (BA_ext = 0.77) and soil (BA_ext = 0.76). The latter suffer from low detection of persistent ('P') compounds (Sn_ext = 0.50), reflecting discrepancies in reported half-life measurements among the different data sources. Generated models and collected data are made publicly available.