Comparison between bioconcentration factor (BCF) data provided by industry to the European Chemicals Agency (ECHA) and data derived from QSAR models

Synergistic effect of KI on the corrosion inhibition of a poly(diallylammonium chloride)-based cyclocopolymer containing bis-cationic motifs for mild steel corrosion in 20% formic acid

This study entails the syntheses of a homopolymer, poly(diallylammonium chloride) (3), and copolymers (8a-c) containing hydrophilic/hydrophobic pendants and their role in mitigating mild steel in aggressive 20% formic acid, a type of corrosion that is not frequently discussed in the literature. The synthesized homopolymer and copolymers were characterized by FTIR, NMR, viscometry, and TGA. Inhibitor 8b was found to be the most potent, with 81.8% inhibition efficiency (IE) registered via the potentiodynamic polarization method for 100 ppm of inhibitor concentration at 30 °C. Inhibitor 8b, mixed with 2 mmol KI, showed more than 90% IE for a meager 1 ppm inhibitor concentration. For a synergism of 50 ppm inhibitor and 2 mmol KI, the IE reached a high value of 99.1%. The synergism was so good that it helped the inhibitor retain ∼100% of its original IE even after a 24 h weight loss study at 60 °C. The adsorption isotherm study showed that 8b followed the Langmuir adsorption isotherm and adsorbed via chemisorption. A very high value (2.48 × 105 L mol-1) of the equilibrium adsorption constant (Kads) indicated strong adsorption. XPS and SEM surface studies provided evidence of the inhibitor found on the metal surface. Some toxicological parameters, such as LC50, bioaccumulation factor, and developmental toxicity, have been measured computationally. A brief mechanistic insight into how the inhibitors functioned has been offered along with the DFT study.

Effects of water quality on palladium-induced olfactory toxicity and bioaccumulation in rainbow trout (Oncorhynchus mykiss)

Through emission processes, palladium (Pd) particulates from industrial sources are introduced into a range of ecosystems including freshwater environments. Despite this, research on Pd-induced bioaccumulation, uptake, and toxicity is limited for freshwater fishes. Unlike other metals, there are currently no regulations or protective guidelines to limit Pd release into aquatic systems, indicating a global absence of measures addressing its environmental impact. To assess the olfactory toxicity potential of Pd, the present study aimed to explore Pd accumulation in olfactory tissues, olfactory disruption, and oxidative stress in rainbow trout (Oncorhynchus mykiss) following waterborne Pd exposure. Olfactory sensitivity, measured by electro-olfactography, demonstrated that Pd inhibits multiple pathways of the olfactory system following 96 h of Pd exposure. In this study, the concentrations of Pd for inhibition of olfactory function by 20% (2.5 μg/L; IC20) and 50% (19 μg/L; IC50) were established. Rainbow trout were then exposed to IC20 and IC50 Pd concentrations in combination with varying exposure conditions, as changes in water quality alter the toxicity of metals. Independent to Pd, increased water hardness resulted in decreased olfactory perception owing to ion competition at the olfactory epithelium. No other environmental parameter in this study significantly influenced Pd-induced olfactory toxicity. Membrane-associated Pd was measured at the olfactory rosette and gill following exposure; however, this accumulation did not translate to oxidative stress as measured by the production of malondialdehyde. Our data suggest that Pd is toxic to rainbow trout via waterborne contamination near field-measured levels. This study further demonstrated Pd bioavailability and uptake at water-adjacent tissues, adding to our collective understanding of the toxicological profile of Pd. Taken together, our results provide novel insights into the olfactory toxicity in fish following Pd exposure. Integr Environ Assess Manag 2024;00:1-13. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Concepts on Accumulation of Pesticides and Veterinary Drugs in Fish: A Review with Emphasis in Tilapia

The quality of the aquatic environment can be compromised by the practice of intensive use of pesticides in agriculture and by the misuse of veterinary drugs. Therefore, organisms that live in aquatic ecosystems may be affected due to the presence of these chemicals, through runoff, leaching and other processes. Exposure of aquatic organisms to these xenobiotics could pose health risks. Consequently, there is a growing interest in predicting the bioaccumulation of these substances in aquatic biota from experiments conducted under laboratory conditions. Studies on fish have been performed due to its importance as human food and their wide distribution in most of the aquatic environment. Thus, this article reviews the concepts on determining the accumulation of pesticides and veterinary drugs in fish. The risk regarding the consumption of fish containing residues of these chemical agents, the acceptable daily intake, the testing protocols and the analytical techniques used to determine the residues of these substances in fish tissues are discussed. An emphasis on studies involving tilapia as the test organism was included because, according to Food and Agricultural Organization (FAO), this species is one of the most cultivated in the world.

Per- and polyfluoroalkyl substances (PFASs) registered under REACH-What can we learn from the submitted data and how important will mobility be in PFASs hazard assessment?

The EU is planning to restrict the manufacture, placing on the market and use of per- and polyfluoroalkyl substances (PFASs) as a class. For such a broad regulatory approach, a lot of different data are required, including data on the hazardous properties of PFASs. Here, we analyze substances that fulfill the OECD definition of PFASs and that are registered under the regulation on Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) in the EU to obtain a better data basis for PFASs and to elucidate the range of PFASs on the market in the EU. As of September 2021, at least 531 PFASs had been registered under REACH. Our hazard assessment of the PFASs registered under REACH shows that the currently available data are not sufficient to identify those PFASs that are persistent, bioaccumulative and toxic (PBT) or very persistent and very bioaccumulative (vPvB). Using some basic assumptions - which are 1) PFASs or their metabolites do not mineralize, 2) neutral hydrophobic substances bioaccumulate unless they are metabolized and 3) all chemicals exhibit baseline toxicity, and effect concentrations cannot be above effect concentrations for baseline toxicity - shows that at least 17 of the 177 PFASs with full registration are PBT substances, 14 more than currently identified. Moreover, if mobility is considered as a hazard criterion, at least 19 additional substances will need to be considered hazardous. The regulation of persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances would therefore also affect PFASs. However, many of the substances that have not been identified as PBT, vPvB, PMT or vPvM are either persistent and toxic, persistent and bioaccumulative or persistent and mobile. The planned PFASs restriction will therefore be important for a more effective regulation of these substances.

Affinity of Compounds for Phosphatydylcholine-Based Immobilized Artificial Membrane-A Measure of Their Bioconcentration in Aquatic Organisms

The BCF (bioconcentration factor) of solutes in aquatic organisms is an important parameter because many undesired chemicals enter the ecosystem and affect the wildlife. Chromatographic retention factor log k_w^IAM obtained from immobilized artificial membrane (IAM) HPLC chromatography with buffered, aqueous mobile phases and calculated molecular descriptors obtained for a group of 120 structurally unrelated compounds were used to generate useful models of log BCF. It was established that log k_w^IAM obtained in the conditions described in this study is not sufficient as a sole predictor of bioconcentration. Simple, potentially useful models based on log k_w^IAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR), partial least squares (PLS) regression and artificial neural networks (ANN). The models proposed in the study were tested on an external group of 120 compounds and on a group of 40 compounds with known experimental log BCF values. It was established that a relatively simple MLR model containing four independent variables leads to satisfying BCF predictions and is more intuitive than PLS or ANN models.

Predicting bioconcentration factor and estrogen receptor bioactivity of bisphenol a and its analogues in adult zebrafish by directed message passing neural networks

The bioconcentration factor (BCF) is a key parameter for bioavailability assessment of environmental pollutants in regulatory frameworks. The comparative toxicology and mechanism of action of congeners are also of concern. However, there are limitations to acquire them by conducting field and laboratory experiments while machinelearning is emerging as a promising predictive tool to fill the gap. In this study, the Direct Message Passing Neural Network (DMPNN) was applied to predict logBCFs of bisphenol A (BPA) and its four analogues (bisphenol AF (BPAF), bisphenol B (BPB), bisphenol F (BPF) and bisphenol S (BPS)). For the test set, the Pearson correlation coefficient (PCC) and mean square error (MSE) were 0.85 and 0.52 respectively, suggesting a good predictive performance. The predicted logBCFs values by the DMPNN ranging from 0.35 (BPS) to 2.14 (BPAF) coincided well with those by the classical EPI Suite (BCFBAF model). Besides, estrogen receptor α (ERα) bioactivity of these bisphenols was also predicted well by the DMPNN, with a probability of 97.0 % (BPB) to 99.7 % (BPAF), which was validated by the extent of vitellogenin (VTG) induction in male zebrafish as a biomarker except BPS. Thus, with little need for expert knowledge, DMPNN is confirmed to be a useful tool to accurately predict logBCF and screen for estrogenic activity from molecular structures. Moreover, a gender difference was noted in the changes of three endpoints (logBCF, ER binding affinity and VTG levels), the rank order of which was BPAF > BPB > BPA > BPF > BPS consistently, and abnormal amino acid metabolism is featured as an omics signature of abnormal hormone protein expression.

Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency

Under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) analysis of alternatives (AoA) process, quantitative structure–activity relationship (QSAR) models play an important role in expanding information gathering and organizing frameworks. Increasingly recognized as an alternative to testing under registration. QSARs have become a relevant tool in bridging data gaps and supporting weight of evidence (WoE) when assessing alternative substances. Additionally, QSARs are growing in importance in integrated testing strategies (ITS). For example, the REACH ITS framework for specific endpoints directs registrants to consider non-testing results, including QSAR predictions, when deciding if further animal testing is needed. Despite the raised profile of QSARs in these frameworks, a gap exists in the evaluation of QSAR use and QSAR documentation under authorization. An assessment of the different uses (e.g., WoE and ITS) in which QSAR predictions play a role in evidence gathering and organizing remains unaddressed for AoA. This study approached the disparity in information for QSAR predictions by conducting a substantive review of 24 AoA through May 2017, which contained higher-tier endpoints under REACH. Understanding the manner in which applicants manage QSAR prediction information in AoA and assessing their potential within ITS will be valuable in promoting regulatory use of QSARs and building out future platforms in the face of rapidly evolving technology while advancing information transparency.

Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric

Solute descriptors have been widely used to model chemical transfer processes through poly-parameter linear free energy relationships (pp-LFERs); however, there are still substantial difficulties in obtaining these descriptors accurately and quickly for new organic chemicals. In this research, models (PaDEL-DNN) that require only SMILES of chemicals were built to satisfactorily estimate pp-LFER descriptors using deep neural networks (DNN) and the PaDEL chemical representation. The PaDEL-DNN-estimated pp-LFER descriptors demonstrated good performance in modeling storage-lipid/water partitioning coefficient (log K_{storage-lipid/water}), bioconcentration factor (BCF), aqueous solubility (ESOL), and hydration free energy (freesolve). Then, assuming that the accuracy in the estimated values of widely available properties, e.g., logP (octanol-water partition coefficient), can calibrate estimates for less available but related properties, we proposed logP as a surrogate metric for evaluating the overall accuracy of the estimated pp-LFER descriptors. When using the pp-LFER descriptors to model log K_{storage-lipid/water}, BCF, ESOL, and freesolve, we achieved around 0.1 log unit lower errors for chemicals whose estimated pp-LFER descriptors were deemed "accurate" by the surrogate metric. The interpretation of the PaDEL-DNN models revealed that, for a given test chemical, having several (around 5) "similar" chemicals in the training data set was crucial for accurate estimation while the remaining less similar training chemicals provided reasonable baseline estimates. Lastly, pp-LFER descriptors for over 2800 persistent, bioaccumulative, and toxic chemicals were reasonably estimated by combining PaDEL-DNN with the surrogate metric. Overall, the PaDEL-DNN/surrogate metric and newly estimated descriptors will greatly benefit chemical transfer modeling.

Towards the design of organosilicon compounds for environmental degradation by using structure biodegradability relationships

Organosilicon compounds have numerous applications in consumer products. After entering the environment most of them are resistant against microbial degradation and they persist in the environment. Accordingly, they are ubiquitously present in the environment. Therefore, better environmentally degradable organosilicon compounds are urgently needed. A systematic investigation of environmental degradability of organosilicon compounds allows to derive some general design principles, which in turn would enable chemists to reduce or better avoid environmental persistence of organosilicon compounds in the environment. Therefore, in this study, all organosilicon substances registered in the European Chemicals Agency (ECHA) database were evaluated for their environmental biodegradability. Results of own experiments with different organosilicon substances were added to extend the data basis. A dataset was generated. An assessment of all data was done and invalid data were excluded. The remaining 182 substances were grouped regarding their structure to derive general rules for the environmental biodegradability of organosilicon compounds. Non-biodegradable at all were for example cyclic, linear and branched siloxanes. Groups like ethers, esters, oximes, amines, and amides were prone to hydrolysis, which can result in readily biodegradable intermediates if they do not contain silicon functional groups anymore. This knowledge could be used for the design of better degradable organosilicon compounds as non-degradable substances should be avoided if they enter the environment after their usage.

Bioconcentration and Metabolism of the New Herbicide Methiozolin in Ricefish (Oryzias latipes)

Methiozolin is a novel herbicide used to control annual bluegrass. It has low vapor pressure and high hydrophobicity, which could result in persistence in water and bioaccumulation. We measured the bioconcentration factors (BCFs) of methiozolin in ricefish (Oryzias latipes). Two radiolabels were used to quantify the parent compound and identify its metabolites. Ricefish were exposed to 2.0 and 20.0 ng/L methiozolin for 28 days in the uptake phase with a 96-h LC₅₀ of 2.2 mg/L(95% confidence limit: 2.1-2.5 mg/L) and water solubility of 4.2 mg/L after 48 h was observed. On the basis of total radioactivity residues (TRRs), BCF_ss and BCF_k values of 797.0-851.9 and 992.9-1077.4 were observed, respectively, while BCF_ss values for methiozolin were 251.9-257.5. Several minor metabolites with TRR < 3.4% were detected. Among them, 4-(2,6-difluorobenzyloxy-methyl)-3-hydroxy-3-methyl-1-(3-methylthiophen-2-yl)butan-1-one, 2,6-difluorobenzyl alcohol, and 4,5-dihydro-5-methyl-3-(3-methylthiophen-2-yl)isoxazol-5-yl)methanol were identified. Methiozolin is metabolized into numerous minor metabolites with potentially low bioaccumulation capacity in ricefish. These findings can facilitate risk assessments regarding methiozolin use, particularly its movements and final stages in aquatic environments.

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

Examining Uncertainty in In Vitro-In Vivo Extrapolation Applied in Fish Bioconcentration Models

In vitro biotransformation rates were determined for 30 chemicals, mostly fragrance ingredients, using trout liver S9 fractions (RT-S9) and incorporated into in vitro-in vivo extrapolation (IVIVE) models to predict bioconcentration factors (BCFs). Predicted BCFs were compared against empirical BCFs to explore potential major uncertainties involved in the in vitro methods and IVIVE models: (i) in vitro chemical test concentrations; (ii) different gill uptake rate constant calculations (k₁); (iii) protein binding (different calculations and measurement of the fraction of unbound chemical, f_U); (iv) species differences; and (v) extrahepatic biotransformation. Predicted BCFs were within 0.5 log units for 44% of the chemicals compared to empirical BCFs, whereas 56% were overpredicted by >0.5 log units. This trend of overprediction was reduced by alternative k₁ calculations to 32% of chemicals being overpredicted. Moreover, hepatic in vitro rates scaled to whole body biotransformation rates (k_B) were compared against in vivo k_B estimates. In vivo k_B was underestimated for 79% of the chemicals. Neither lowering the test concentration, nor incorporation of new measured f_U values, nor species matching avoided the tendency to overpredict BCFs indicating that further improvements to the IVIVE models are needed or extrahepatic biotransformation plays an underestimated role.

Comparison of heavy metal accumulation ability in rainwater by 10 sponge city plant species

Rainwater heavy metal contamination is a growing problem worldwide, which damages the environment and human health. A primary challenge of sponge city designers is selecting suitable plants capable of surviving the toxic metals present in city rainwater. The concept of a sponge city to tackle urban surface-rainwater flooding and related urban rainwater management issues was established by People's Republic of China in 2014. Therefore, we studied the ability of sponge city plants to accumulate heavy metals from rainwater. Ophiopogon japonicus (Linn. f.) Ker-Gawl., Carex heterostachya Bge., Cornus officinalis Sieb. et Zucc., Sedum spectabile Boreau., Typha orientalis Presl., Lythrum salicaria L., Fatsia japonica (Thunb.) Decne. et Planch., Ilex chinensis Sims., Rosa chinensis Jacq., and Buxus bodinieri Levl. were selected as test plants, and their ability to accumulate four heavy metals (lead [Pb], cadmium [Cd], copper [Cu], and zinc [Zn]) was compared. Growth response and heavy metal accumulation across different species were compared over a 28-day enrichment cycle. The results showed that (1) Plant growth responses to heavy metals were significantly different. The most tolerant to heavy metals was Lythrum salicaria and the least tolerant was Rosa chinensis. (2) Concentrations of the heavy metals differed among sponge city plant species. In general, the concentration of Zn was highest, followed by Cu, Cd, and Pb. (3) The accumulation content of the same metal in different test species was related to the bioconcentration factor of the metal and the plant biomass. At the end of the enrichment cycle, Ophiopogon japonicus had the largest accumulation content for Pb, Cu, and Zn, and Lythrum salicaria had the highest accumulation content for Cd. (4) Considering the growth responses of plants and their final accumulation of heavy metals after the enrichment cycle, we concluded that Lythrum salicaria, Typha orientalis, and Ophiopogon japonicus are suitable for use in sponge cities to restore heavy metal-contaminated rainwater.

QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

The bioconcentration factor (BCF), a key parameter required by the REACH regulation, estimates the tendency for a xenobiotic to concentrate inside living organisms. In silico methods can be valid alternatives to costly data measurements. However, in the industrial context, these theoretical approaches may fail to predict BCF with reasonable accuracy. We analyzed whether models built on public data only have adequate performances when challenged to predict industrial compounds. A new set of 1129 compounds has been collected by merging publicly available datasets. Generative Topographic Mapping was employed to compare this chemical space with a set of new compounds issued from the industry. Some new chemotypes absent in the training set (such as siloxanes) have been detected. A new BCF model has been built using ISIDA (In SIlico design and Data Analysis) fragment descriptors, support vector regression and random forest machine-learning methods. It has been externally validated on: (i) collected data from the literature and (ii) industrial data. The latter also served as benchmark for the freely available tools VEGA, EPISuite, TEST, OPERA. New model performs (RMSE of 0.58 log BCF units) comparably to existing ones but benefits of an extended applicability, covering the industrial set chemical space (78% data coverage).

Bioconcentration factors for hydrocarbons and petrochemicals: Understanding processes, uncertainty and predictive model performance

Fish bioconcentration factors (BCFs) are often used to assess substance-specific bioaccumulation. However, reliable BCF data are limited given the practical challenges of conducting such tests. The objectives of this paper are to describe nine rainbow trout studies performed in our lab using tailored dosing and test designs for obtaining empirical BCFs for 21 test substances; gain insights into the structural features and processes determining the magnitude and uncertainty in observed BCFs; and assess performance of six quantitative structure property relationships (QSPRs) for correctly categorizing bioaccumulation given current regulatory triggers. Resulting mean steady-state BCFs, adjusted to a 5% lipid content, ranged from 12 Lkg^-1 for isodecanol to 15,448 Lkg^-1 for hexachlorobenzene which served as a positive control. BCFs for hydrocarbons depended on aromatic and saturated ring configurations and position. Uptake clearances appeared to be modulated by gill metabolism and substance bioavailability, while elimination rates were likely influenced by somatic biotransformation. Current approaches for quantifying uncertainty in experimental BCFs, which take into account only variability in measured fish concentrations, were found to underestimate the true uncertainty in this endpoint with important implications for decision-making. The Vega (KNN/Read-Across) QSPR and Arnot-Gobas model yielded the best model performance when compared to measured BCFs generated in this study.

Pre-selection and assessment of green organic solvents by clustering chemometric tools

The study presents the result of the application of chemometric tools for selection of physicochemical parameters of solvents for predicting missing variables - bioconcentration factors, water-octanol and octanol-air partitioning constants. EPI Suite software was successfully applied to predict missing values for solvents commonly considered as "green". Values for logBCF, logK_OW and logK_OA were modelled for 43 rather nonpolar solvents and 69 polar ones. Application of multivariate statistics was also proved to be useful in the assessment of the obtained modelling results. The presented approach can be one of the first steps and support tools in the assessment of chemicals in terms of their greenness.

Environmental status and early warning value of the pollutant Semicarbazide in Jincheng and Sishili Bays, Shandong Peninsula, China

A verified method for measuring Semicarbazide (SEM) in seawater, sediments, and shellfish was developed based on ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). A total of 30 stations were radially distributed in Jincheng and Sishili Bays in the Bohai and Yellow Seas, and 1025 monitoring data were collected in 41 voyages, 615 seawater samples, 320 sediment samples and 90 shellfish samples. The concentration ranged from 0.011μg/L to 0.093μg/L and 0 to 0.75μg/kg in seawater and shellfish respectively, but SEM in sediment was all below the limit of detection. Temporal and spatial distribution of SEM was investigated using multivariate analysis to estimate the degree of SEM pollution. Based on the SEM concentration in the three sample types, together with our previous findings, early warning values were deduced for SEM in seawater, and the developed method overcame shortcomings with existing technologies. The results may be helpful to draft national baseline values for SEM in seawater and sediments, and provide a scientific basis for assessing the impacts of SEM on marine ecology and human health.

Results of a round-robin exercise on read-across

A round-robin exercise was conducted within the CALEIDOS LIFE project. The participants were invited to assess the hazard posed by a substance, applying in silico methods and read-across approaches. The exercise was based on three endpoints: mutagenicity, bioconcentration factor and fish acute toxicity. Nine chemicals were assigned for each endpoint and the participants were invited to complete a specific questionnaire communicating their conclusions. The interesting aspect of this exercise is the justification behind the answers more than the final prediction in itself. Which tools were used? How did the approach selected affect the final answer?

A tiered assessment strategy for more effective evaluation of bioaccumulation of chemicals in fish

There is currently limited guidance available for regulators and risk assessors on how to use data from non-guideline methods when assessing the bioaccumulation potential of a chemical. Furthermore, bioaccumulation assessments can be more subjective than they need to be due to the lack of a guidance framework on how to use/include the range of information that may be available for a substance. Under some circumstances, in silico, in vitro and/or in vivo non-test guideline data may be sufficient to classify whether a substance is bioaccumulative without the need for further animal testing. Classifying the bioaccumulative potential of a substance is especially difficult when the bioconcentration factor (BCF) is close to the threshold for defining it as bioaccumulative/very bioaccumulative (B/vB), and a more structured process is required to reduce uncertainty in the BCF estimates. In these situations, in silico and in vitro data can, and should, be used to provide greater confidence in classifying these substances. To aid future evaluations of bioaccumulation data, a proposed tiered assessment strategy is presented incorporating all available data on the bioaccumulative properties of a substance. In addition, a revised scheme is recommended for improving the classification of the bioaccumulative potential of a substance.