Enhancing the use of exposure science across EU chemical policies as part of the European Exposure Science Strategy 2020-2030.

BACKGROUND: A scientific framework on exposure science will boost the multiuse of exposure knowledge across EU chemicals-related policies and improve risk assessment, risk management and communication across EU safety, security and sustainability domains. OBJECTIVE: To stimulate public and private actors to align and strengthen the cross-policy adoption of exposure assessment data, methods and tools across EU legislation. METHODS: By mapping and analysing the EU regulatory landscape making use of exposure information, policy and research challenges and key areas of action are identified and translated into opportunities enhancing policy and scientific efficiency. RESULTS: Identified key areas of actions are to develop a common scientific exposure assessment framework, supported by baseline acceptance criteria and a shared knowledge base enhancing exchangeability and acceptability of exposure knowledge within and across EU chemicals-related policies. Furthermore, such framework will improve communication and management across EU chemical safety, security and sustainability policies comprising sourcing, manufacturing and global trade of goods and waste management. In support of building such a common framework and its effective use in policy and industry, exposure science innovation needs to be better embedded along the whole policymaking cycle, and be integrated into companies' safety and sustainability management systems. This will help to systemically improve regulatory risk management practices. SIGNIFICANCE: This paper constitutes an important step towards the implementation of the EU Green Deal and its underlying policy strategies, such as the Chemicals Strategy for Sustainability.

Modelling the bioaccumulation of persistent organic pollutants in agricultural food chains for regulatory exposure assessment.

New models for estimating bioaccumulation of persistent organic pollutants in the agricultural food chain were developed using recent improvements to plant uptake and cattle transfer models. One model named AgriSim was based on K OW regressions of bioaccumulation in plants and cattle, while the other was a steady-state mechanistic model, AgriCom. The two developed models and European Union System for the Evaluation of Substances (EUSES), as a benchmark, were applied to four reported food chain (soil/air-grass-cow-milk) scenarios to evaluate the performance of each model simulation against the observed data. The four scenarios considered were as follows: (1) polluted soil and air, (2) polluted soil, (3) highly polluted soil surface and polluted subsurface and (4) polluted soil and air at different mountain elevations. AgriCom reproduced observed milk bioaccumulation well for all four scenarios, as did AgriSim for scenarios 1 and 2, but EUSES only did this for scenario 1. The main causes of the deviation for EUSES and AgriSim were the lack of the soil-air-plant pathway and the ambient air-plant pathway, respectively. Based on the results, it is recommended that soil-air-plant and ambient air-plant pathway should be calculated separately and the K OW regression of transfer factor to milk used in EUSES be avoided. AgriCom satisfied the recommendations that led to the low residual errors between the simulated and the observed bioaccumulation in agricultural food chain for the four scenarios considered. It is therefore recommended that this model should be incorporated into regulatory exposure assessment tools. The model uncertainty of the three models should be noted since the simulated concentration in milk from 5th to 95th percentile of the uncertainty analysis often varied over two orders of magnitude. Using a measured value of soil organic carbon content was effective to reduce this uncertainty by one order of magnitude.

Assessment and improvement of biotransfer models to cow's milk and beef used in exposure assessment tools for organic pollutants.

The aim of this study was to assess and improve the accuracy of biotransfer models for the organic pollutants (PCBs, PCDD/Fs, PBDEs, PFCAs, and pesticides) into cow's milk and beef used in human exposure assessment. Metabolic rate in cattle is known as a key parameter for this biotransfer, however few experimental data and no simulation methods are currently available. In this research, metabolic rate was estimated using existing QSAR biodegradation models of microorganisms (BioWIN) and fish (EPI-HL and IFS-HL). This simulated metabolic rate was then incorporated into the mechanistic cattle biotransfer models (RAIDAR, ACC-HUMAN, OMEGA, and CKow). The goodness of fit tests showed that RAIDAR, ACC-HUMAN, OMEGA model performances were significantly improved using either of the QSARs when comparing the new model outputs to observed data. The CKow model is the only one that separates the processes in the gut and liver. This model showed the lowest residual error of all the models tested when the BioWIN model was used to represent the ruminant metabolic process in the gut and the two fish QSARs were used to represent the metabolic process in the liver. Our testing included EUSES and CalTOX which are KOW-regression models that are widely used in regulatory assessment. New regressions based on the simulated rate of the two metabolic processes are also proposed as an alternative to KOW-regression models for a screening risk assessment. The modified CKow model is more physiologically realistic, but has equivalent usability to existing KOW-regression models for estimating cattle biotransfer of organic pollutants.

Assessment of plant uptake models used in exposure assessment tools for soils contaminated with organic pollutants.

The aim of this study was to evaluate and improve the accuracy of plant uptake models for neutral hydrophobic organic pollutants (1 < logK(OW) < 9, -8 < logK(AW) < 0) used in regulatory exposure assessment tools, using uncertainty and sensitivity analyses. The models considered were RAIDAR, EUSES, CSOIL, CLEA, and CalTOX. In this research, CSOIL demonstrated the best performance of all five exposure assessment tools for root uptake from polluted soil in comparison with observed data, but no model predicted shoot uptake well. Recalibration of the transpiration and volatilisation parameters improved the performance of CSOIL and CLEA. The dominant pathway for shoot uptake simulated differed according to the properties of the chemical under consideration; those with a higher air-water partition coefficient were transported into shoots via the soil-air-plant pathway, while chemicals with a lower octanol-water partition coefficient and air-water partition coefficient were transported via the root. The soil organic carbon content was a particularly sensitive parameter in each model and using a site specific value improved model performance.

Potential of aerobic denitrification by Pseudomonas stutzeri TR2 to reduce nitrous oxide emissions from wastewater treatment plants.

In contrast to most denitrifiers studied so far, Pseudomonas stutzeri TR2 produces low levels of nitrous oxide (N(2)O) even under aerobic conditions. We compared the denitrification activity of strain TR2 with those of various denitrifiers in an artificial medium that was derived from piggery wastewater. Strain TR2 exhibited strong denitrification activity and produced little N(2)O under all conditions tested. Its growth rate under denitrifying conditions was near comparable to that under aerobic conditions, showing a sharp contrast to the lower growth rates of other denitrifiers under denitrifying conditions. Strain TR2 was tolerant to toxic nitrite, even utilizing it as a good denitrification substrate. When both nitrite and N(2)O were present, strain TR2 reduced N(2)O in preference to nitrite as the denitrification substrate. This bacterial strain was readily able to adapt to denitrifying conditions by expressing the denitrification genes for cytochrome cd(1) nitrite reductase (NiR) (nirS) and nitrous oxide reductase (NoS) (nosZ). Interestingly, nosZ was constitutively expressed even under nondenitrifying, aerobic conditions, consistent with our finding that strain TR2 preferred N(2)O to nitrite. These properties of strain TR2 concerning denitrification are in sharp contrast to those of well-characterized denitrifiers. These results demonstrate that some bacterial species, such as strain TR2, have adopted a strategy for survival by preferring denitrification to oxygen respiration. The bacterium was also shown to contain the potential to reduce N(2)O emissions when applied to sewage disposal fields.

Streptomyces griseus enhances denitrification by Ralstonia pickettii K50, which is possibly mediated by histidine produced during co-culture.

Ralstonia pickettii K50 (strain K50) is a denitrifying bacterium that produces low levels of N(2)O under aerobic conditions. In this study, we found that co-culturing of strain K50 with Streptomyces griseus significantly enhanced the denitrification activity of strain K50 in an artificial wastewater (AWW) system. Most factors that enhance denitrification activity were in the high molecular weight fraction of the cell-free broth of S. griseus, and were suggested to be extracellular proteases. Further investigation revealed that the cultivation of strain K50 in protease-treated AWW medium fully enhanced denitrification, and that a shortage of amino acids in the medium limited it. Among the 20 standard amino acids tested, only histidine had a significant effect in inducing denitrification by strain K50. Our results indicate that histidine is a novel inducer of bacterial denitrification.