How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity.

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

Ordered weighted average based grouping of nanomaterials with Arsinh and dose response similarity models.

In the context of the EU GRACIOUS project, we propose a novel procedure for similarity assessment and grouping of nanomaterials. This methodology is based on the (1) Arsinh transformation function for scalar properties, (2) full curve shape comparison by application of a modified Kolmogorov-Smirnov metric for bivariate properties, (3) Ordered Weighted Average (OWA) aggregation-based grouping distance, and (4) hierarchical clustering. The approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. To facilitate the application of the proposed methodology, a software script was developed by using the R programming language which is currently under migration to a web tool. The presented approach was tested against a dataset, derived from literature review, related to immobilization of Daphnia magna and reporting information on several nanomaterials and properties.

Bayesian based similarity assessment of nanomaterials to inform grouping.

Nanoforms can be manufactured in plenty of variants by differing their physicochemical properties and toxicokinetic behaviour which can affect their hazard potential. To avoid testing of each single nanomaterial and nanoform variation and subsequently save resources, grouping and read-across strategies are used to estimate groups of substances, based on carefully selected evidence, that could potentially have similar human health and environmental hazard impact. A novel computational similarity method is presented aiming to compare dose-response curves and identify sets of similar nanoforms. The suggested method estimates the statistical model that best fits the data by leveraging pairwise Bayes Factor analysis to compare pairs of curves and evaluate whether each of the nanoforms is sufficiently similar to all other nanoforms. Pairwise comparisons to benchmark materials are used to define threshold similarity values and set the criteria for identifying groups of nanoforms with comparatively similar toxicity. Applications to use case data are shown to demonstrate that the method can support grouping hypotheses linked to a certain hazard endpoint and route of exposure.

A Decision Support System for preclinical assessment of nanomaterials in medical products: the REFINE DSS.

The application of nanomaterials in medicine has led to novel pharmaceuticals and medical devices that have demonstrated a strong potential for increasing the efficacy/performance and safety of therapeutic and diagnostic procedures to address a wide range of diseases. However, the successful translation of these technologies from their inception (proof-of-concept) to clinical practice has been challenged by substantial gaps in the scientific and technical capacity of R&D companies, especially SMEs, to keep up with the ever-evolving regulatory expectations in the emerging area of nanomedicine. To address these challenges, the EU Horizon 2020 project REFINE has developed a Decision Support System (DSS) to support developers of nanotechnology-enabled health products in bringing their products to the clinic. The REFINE DSS has been developed to support experts, innovators, and regulators in the implementation of intelligent testing strategies (ITS) for efficient preclinical assessment of nanotechnology-enabled health products. The DSS applies logical rules provided by REFINE experts which generate prioritized lists of assays to be performed (i.e. ITSs) for physicochemical characterisation and for immunotoxicological endpoints. The DSS has been tested against several case studies and was validated by internal project experts as well as external ones.

A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging Regulation criteria.

In the context of the European Union (EU) Horizon 2020 GRACIOUS project (Grouping, Read-Across, Characterisation and classification framework for regulatory risk assessment of manufactured nanomaterials and Safer design of nano-enabled products), we proposed a quantitative Weight of Evidence (WoE) approach for hazard classification of nanomaterials (NMs). This approach is based on the requirements of the European Regulation on Classification, Labelling and Packaging of Substances and Mixtures (the CLP regulation), which implements the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (UN GHS) in the European Union. The goal of this WoE methodology is to facilitate classification of NMs according to CLP criteria, following the decision trees defined in ECHA's CLP regulatory guidance. In the WoE, results from heterogeneous studies are weighted according to data quality and completeness criteria, integrated, and then evaluated by expert judgment to obtain a hazard classification, resulting in a coherent and justifiable methodology. Moreover, the probabilistic nature of the proposed approach enables highlighting the uncertainty in the analysis. The proposed methodology involves the following stages: (1) collection of data for different NMs related to the endpoint of interest: each study related to each NM is referred as a Line of Evidence (LoE); (2) computation of weighted scores for each LoE: each LoE is weighted by a score calculated based on data quality and completeness criteria defined in the GRACIOUS project; (3) comparison and integration of the weighed LoEs for each NM: A Monte Carlo resampling approach is adopted to quantitatively and probabilistically integrate the weighted evidence; and (4) assignment of each NM to a hazard class: according to the results, each NM is assigned to one of the classes defined by the CLP regulation. Furthermore, to facilitate the integration and the classification of the weighted LoEs, an online R tool was developed. Finally, the approach was tested against an endpoint relevant to CLP (Aquatic Toxicity) using data retrieved from the eNanoMapper database, results obtained were consistent to results in REACH registration dossiers and in recent literature.

Risk Management Framework for Nano-Biomaterials Used in Medical Devices and Advanced Therapy Medicinal Products.

The convergence of nanotechnology and biotechnology has led to substantial advancements in nano-biomaterials (NBMs) used in medical devices (MD) and advanced therapy medicinal products (ATMP). However, there are concerns that applications of NBMs for medical diagnostics, therapeutics and regenerative medicine could also pose health and/or environmental risks since the current understanding of their safety is incomplete. A scientific strategy is therefore needed to assess all risks emerging along the life cycles of these products. To address this need, an overarching risk management framework (RMF) for NBMs used in MD and ATMP is presented in this paper, as a result of a collaborative effort of a team of experts within the EU Project BIORIMA and with relevant inputs from external stakeholders. The framework, in line with current regulatory requirements, is designed according to state-of-the-art approaches to risk assessment and management of both nanomaterials and biomaterials. The collection/generation of data for NBMs safety assessment is based on innovative integrated approaches to testing and assessment (IATA). The framework can support stakeholders (e.g., manufacturers, regulators, consultants) in systematically assessing not only patient safety but also occupational (including healthcare workers) and environmental risks along the life cycle of MD and ATMP. The outputs of the framework enable the user to identify suitable safe(r)-by-design alternatives and/or risk management measures and to compare the risks of NBMs to their (clinical) benefits, based on efficacy, quality and cost criteria, in order to inform robust risk management decision-making.

Comparison of species sensitivity distribution modeling approaches for environmental risk assessment of nanomaterials - A case study for silver and titanium dioxide representative materials.

Species sensitivity distributions (SSDs) are used in chemical safety assessments to derive predicted-no-effect-concentrations (PNECs) for substances with a sufficient amount of relevant and reliable ecotoxicity data available. For engineered nanomaterials (ENMs), ecotoxicity data are often compromised by poor reproducibility and the lack of nano-specific characterization needed describe an ENM under test exposure conditions. This may influence the outcome of SSD modelling and hence the regulatory decision-making. This study investigates how the outcome of SSD modelling is influenced by: 1) Selecting input data based on the nano-specific "nanoCRED" reliability criteria, 2) Direct SSD modelling avoiding extrapolation of data by including long-term/chronic NOECs only, and 3) Weighting data according to their nano-specific quality, the number of data available for each species, and the trophic level abundance in the ecosystem. Endpoints from freshwater ecotoxicity studies were collected for the representative nanomaterials NM-300 K (silver) and NM-105 (titanium dioxide), evaluated for regulatory reliability and scored according to the level of nano-specific characterization conducted. The compiled datasets are unique in exclusively dealing with representative ENMs showing minimal batch-to-batch variation. The majority of studies were evaluated as regulatory reliable, while the degree of nano-specific characterization varied greatly. The datasets for NM-300 K and NM-105 were used as input to the nano-weighted n-SSWD model, the probabilistic PSSD+, and the conventional SSD Generator by the US EPA. The conventional SSD generally yielded the most conservative, but least precise HC5 values, with 95 % confidence intervals up to 100-fold wider than the other models. The inclusion of regulatory reliable data only, had little effect on the HC5 generated by the conventional SSD and the PSSD+, whereas the n-SSWD estimated different HC5 values based on data segregated according to reliability, especially for NM-105. The n-SSWD weighting of data significantly affected the estimated HC5 values, however in different ways for the sub-datasets of NM-300 K and NM-105. For NM-300 K, the inclusion of NOECs only in the weighted n-SSWD yielded the most conservative HC5 of all datasets and models (a HC5 based on NOECs only could not be estimated for NM-105, due to limited number of data). Overall, the estimated HC5 values of all models are within a relatively limited concentration range of 25-100 ng Ag/L for NM-300 K and 1-15 µgTiO2/L for NM-105.

Use of multiple indicators to compare sustainability performance of organic vs conventional vineyard management.

The wine sector is paying more attention to sustainable wine production practices, but this topic is highly debated because organic viticulture aims to a reduction of environmental impacts, while conventional viticulture ensures an increase of yield. This work provides an economic and environmental comparison using different indicators whereas no previous studies on viticulture have faced on both aspects of sustainability. Two distinct vineyards within the same case study farm were considered, where conventional and organic viticulture practices were applied for 5 years. For each type of production, we calculated the economic benefit and environmental indicators such as the Water Footprint, Carbon Footprint, and an indicator of environmental performance associated with the vineyard phase ("Vineyard Management" or "Vigneto" indicator part of the Italian VIVA certification framework). This latter considers six sub-indicators investigating pesticides management, fertilizers management, organic matter content, soil compaction, soil erosion, and landscape quality. The multi criteria approach is a novel framework assessing sustainability on vineyard management using environmental indicators from VIVA calculator and the economic aspect. Main results showed that organic management in viticulture can be applied without having economic losses and with the benefit of better preserving the natural capital.

Controlling the risks of nano-enabled products through the life cycle: The case of nano copper oxide paint for wood protection and nano-pigments used in the automotive industry.

The widespread use of engineered nanomaterials (ENMs) in consumer products and the overwhelming uncertainties in their ecological and human health risks have raised concerns regarding their safety among industries and regulators. There has been an ongoing debate over the past few decades on ways to overcome the challenges in assessing and mitigating nano-related risks, which has reached a phase of general consensus that nanotechnology innovation should be accompanied by the application of the precautionary principle and best practice risk management, even if the risk assessment uncertainties are large. We propose a quantitative methodology for selecting the optimal risk control strategy based on information about human health and ecological risks, efficacy of risk mitigation measures, cost and other contextual factors. The risk control (RC) methodology was developed in the European FP7 research project SUN and successfully demonstrated in two case studies involving real industrial nano-enabled products (NEPs): nano-scale copper oxide (CuO) and basic copper carbonate (Cu2(OH)2CO3) used as antimicrobial and antifungal coatings and impregnations for the preservation of treated wood, and two nanoscale pigments used for colouring plastic automotive parts (i.e. red organic pigment and carbon black). The application of RC for human health risks showed that although nano-related risks could easily be controlled in automotive plastics case study with modifications in production technology or specific type of engineering controls, nano-related risks due to sanding and sawing copper oxide painted wood were non-acceptable in the use lifecycle stage and would need the identification of a more effective risk control strategy.

Risk Governance of Nanomaterials: Review of Criteria and Tools for Risk Communication, Evaluation, and Mitigation.

Nanotechnologies have been increasingly used in industrial applications and consumer products across several sectors, including construction, transportation, energy, and healthcare. The widespread application of these technologies has raised concerns regarding their environmental, health, societal, and economic impacts. This has led to the investment of enormous resources in Europe and beyond into the development of tools to facilitate the risk assessment and management of nanomaterials, and to inform more robust risk governance process. In this context, several risk governance frameworks have been developed. In our study, we present and review those, and identify a set of criteria and tools for risk evaluation, mitigation, and communication, the implementation of which can inform better risk management decision-making by various stakeholders from e.g., industry, regulators, and the civil society. Based on our analysis, we recommend specific methods from decision science and information technologies that can improve the existing risk governance tools so that they can communicate, evaluate, and mitigate risks more transparently, taking stakeholder perspectives and expert opinion into account, and considering all relevant criteria in establishing the risk-benefit balance of these emerging technologies to enable more robust decisions about the governance of their risks.

Comparison of Water-focused Life Cycle Assessment and Water Footprint Assessment: The case of an Italian wine.

In recent decades, the debate on how to implement and measure sustainability in food production gained increasing importance and interest for agriculture. In the wine sector, producers are increasingly pursuing sustainable practices, including measures for water preservation from degradation and overuse. But methodologies for assessing and communicating the impacts on water resources need to be understood in detail to guide the selection of the most appropriate management practices, support environmental labelling and promote environmental-friendly products to consumers. This work focuses on the impacts on water resources associated with the production of Italian wine by comparing two methodologies: the Water-focused Life Cycle Assessment and the "Water" indicator included in the Italian "VIVA" certification framework, which is based on the Water Footprint Assessment. The two methodologies address the impact on freshwater consumption and degradation from a life cycle perspective. VIVA is based on a water balance method that reflects a volumetric measure of water consumption, while the LCA-based approach investigates both the freshwater consumption and depletion using different impact indicators. The study goal is to compare the two methodologies to understand how their outcomes can support and improve the management of water-related issues in wine production. One main conclusion is that the WATER indicator within VIVA framework can provide more precise recommendations for the optimal management of water use during the vineyard phase, while LCA approach highlights impact hotspots related to both direct and indirect use of water resources (e.g., it points out the relevant contribution of the bottling stage to different impact indicators). The comparative application of both methodologies can provide useful insights into the water-related impacts of different wine production processes and stages and support a comprehensive assessment of the best management practices, unless the differences in the methodological approaches and goals are well understood by assessors.

"AMORE" Decision Support System for probabilistic Ecological Risk Assessment - Part II: Effect assessment of the case study on cyanide.

Ecotoxicological data are highly important for risk assessment processes and are used for deriving environmental quality criteria, which are enacted for assuring the good quality of waters, soils or sediments and achieving desirable environmental quality objectives. Therefore, it is of significant importance the evaluation of the reliability and relevance of available data for analysing their possible use in the aforementioned processes. In this context, a new methodology which has been developed based on Multi-Criteria Decision Analysis (MCDA) techniques, is being used, demonstrated and tested for analysing the reliability and relevance of ecotoxicological data of cyanide (which are produced through laboratory biotests for individual effects). The proposed methodology is also used for the production of Weighted by Data Quality Species Sensitivity Distributions (SSD-WDQ), as a component of the Ecological Risk Assessment of chemicals in aquatic systems. The SSD-WDQ production resulted in the estimation of environmental quality criteria (hazard concentration affecting 5% and 50% of the species). The proposed work is part of the development of the AMORE Decision Support System (DSS) for the application of probabilistic Ecological Risk Assessment (ERA), presented in the companion paper (Isigonis et al., 2019). The DSS has been tested through a case study on ERA of cyanide in the watershed of river Selune in France. The paper presents the 'Effect Assessment' of cyanide, based on the aforementioned methodologies. The main results presented in the paper are the probabilistic analysis of the estimated species sensitivity on cyanide (Effect Assessment) and the calculation of Hazardous Concentration (HCx) of the same contaminant in the Selune river area, based on the functionalities of the DSS. The results are described and discussed in detail, with the use of various graphs and indices. The indices are calculated for all the available ecotoxicological data, as well as for the data on trophic levels or taxonomic groups separately. An effect comparison is presented between the innovative methodologies included in the DSS and the currently existing methodologies.

"AMORE" Decision Support System for probabilistic Ecological Risk Assessment - Part I: Exposure and risk assessment of the case study on cyanide.

Ecological Risk Assessment of chemicals in fluvial systems is a highly researched topic, but its importance for the environmental protection of our planet is vital. Thus, new developments and improvements to existing methodologies are proposed constantly, for providing more advanced tools and more accurate results to researchers and other interested parties. In the field of probabilistic Ecological Risk Assessment, a new Decision Support System is proposed, developed, tested and evaluated. The AMORE DSS is a modular DSS, which incorporates a series of new methodologies, and is built upon the notions of 'Exposure Assessment', 'Effect Assessment' and 'Risk Assessment'. The AMORE Decision Support System has been developed as part of the AMORE research project (French National Research Agency project). The DSS provides a set of tools for analysing and integrating both exposure and effect information in order to evaluate the risk for species living on a given contaminated aquatic system in terms of the Potentially Affected Fraction. The DSS has been tested through a case study on ERA of cyanide in the watershed of river Selune in France. The paper presents the 'Exposure Assessment' and 'Risk Assessment' of the cyanide case study, as well as the complete functionalities of the AMORE DSS. The main results presented in the paper are the statistical analysis of the measured environmental concentrations of cyanide (Exposure Assessment) and the probabilistic 'Risk assessment' of the same contaminant in the area of interest, based on the functionalities of the DSS. The results are described and discussed in detail with the use of various graphs and risk indices. The risk indices are calculated for all the available ecotoxicological data, as well as for the data on trophic levels or taxonomic groups separately. A risk comparison is presented between the innovative methodologies included in the DSS and the currently existing methodologies.

Quantitative human health risk assessment along the lifecycle of nano-scale copper-based wood preservatives.

The use of nano-scale copper oxide (CuO) and basic copper carbonate (Cu2(OH)2CO3) in both ionic and micronized wood preservatives has raised concerns about the potential of these substances to cause adverse humans health effects. To address these concerns, we performed quantitative (probabilistic) human health risk assessment (HHRA) along the lifecycles of these formulations used in antibacterial and antifungal wood coatings and impregnations by means of the EU FP7 SUN project's Decision Support System (SUNDS, www.sunds.gd). The results from the risk analysis revealed inhalation risks from CuO in exposure scenarios involving workers handling dry powders and performing sanding operations as well as potential ingestion risks for children exposed to nano Cu2(OH)2CO3 in a scenario involving hand-to-mouth transfer of the substance released from impregnated wood. There are, however, substantial uncertainties in these results, so some of the identified risks may stem from the safety margin of extrapolation to fill data gaps and might be resolved by additional testing. Our stochastic approach successfully communicated the contribution of different sources of uncertainty in the risk assessment. The main source of uncertainty was the extrapolation from short to long-term exposure, which was necessary due to the lack of (sub)chronic in vivo studies with CuO and Cu2(OH)2CO3. Considerable uncertainties also stemmed from the use of default inter- and intra-species extrapolation factors.

An Information System for Brownfield Regeneration: providing customised information according to stakeholders' characteristics and needs.

In the EU brownfield presence is still considered a widespread problem. Even though, in the last decades, many research projects and initiatives developed a wealth of methods, guidelines, tools and technologies aimed at supporting brownfield regeneration. However, this variety of products had and still has a limited practical impact on brownfield revitalisation success, because they are not used in their entire potential due to their scarce visibility. Also, another problem that stakeholders face is finding customised information. To overcome this non-visibility and not-sufficient customisation of information, the Information System for Brownfield Regeneration (ISBR) has been developed, based on Artificial Neural Networks, which allows understanding stakeholders' information needs by providing tailored information. The ISBR has been tested by stakeholders from the EU project TIMBRE case studies, located in the Czech Republic, Germany, Poland and Romania. Data gained during tests allowed to understand stakeholders' information needs. Overall, stakeholders showed to be concerned first on remediation aspects, then on benchmarking information, which are valuable to improve practices in the complex field of brownfield regeneration, and then on the relatively new issue of sustainability applied to brownfield regeneration and remediation. Mature markets confirmed their interest for remediation-related aspects, highlighting the central role that risk assessment plays in the process. Emerging markets showed to seek information and tools for strategic and planning issues, like brownfield inventories and georeferenced data sets. Results led to conclude that a new improved platform, combining the ISBR functionalities with geo-referenced ones, would be useful and could represent a further research application.

Probabilistic risk assessment of emerging materials: case study of titanium dioxide nanoparticles.

The development and use of emerging technologies such as nanomaterials can provide both benefits and risks to society. Emerging materials may promise to bring many technological advantages but may not be well characterized in terms of their production volumes, magnitude of emissions, behaviour in the environment and effects on living organisms. This uncertainty can present challenges to scientists developing these materials and persons responsible for defining and measuring their adverse impacts. Human health risk assessment is a method of identifying the intrinsic hazard of and quantifying the dose-response relationship and exposure to a chemical, to finally determine the estimation of risk. Commonly applied deterministic approaches may not sufficiently estimate and communicate the likelihood of risks from emerging technologies whose uncertainty is large. Probabilistic approaches allow for parameters in the risk assessment process to be defined by distributions instead of single deterministic values whose uncertainty could undermine the value of the assessment. A probabilistic approach was applied to the dose-response and exposure assessment of a case study involving the production of nanoparticles of titanium dioxide in seven different exposure scenarios. Only one exposure scenario showed a statistically significant level of risk. In the latter case, this involved dumping high volumes of nano-TiO2 powders into an open vessel with no personal protection equipment. The probabilistic approach not only provided the likelihood of but also the major contributing factors to the estimated risk (e.g. emission potential).

Probabilistic approach for assessing infants' health risks due to ingestion of nanoscale silver released from consumer products.

Silver nanoparticles (n-Ag) are widely used in consumer products and many medical applications because of their unique antibacterial properties. Their use is raising concern about potential human exposures and health effects. Therefore, it is informative to assess the potential human health risks of n-Ag in order to ensure that nanotechnology-based consumer products are deployed in a safe and sustainable way. Even though toxicity studies clearly show the potential hazard of n-Ag, there have been few attempts to integrate hazard and exposure assessments to evaluate risks. The underlying reason for this is the difficulty in characterizing exposure and the lack of toxicity studies essential for human health risk assessment (HHRA). Such data gaps introduce significant uncertainty into the risk assessment process. This study uses probabilistic methods to assess the relative uncertainty and potential risks of n-Ag exposure to infants. In this paper, we estimate the risks for infants potentially exposed to n-Ag through drinking juice or milk from sippy cups or licking baby blankets containing n-Ag. We explicitly evaluate uncertainty and variability contained in available dose-response and exposure data in order to make the risk characterization process transparent. Our results showed that individual margin of exposures for oral exposure to sippy cups and baby blankets containing n-Ag exhibited minimal risk.

Targeted selection of brownfields from portfolios for sustainable regeneration: User experiences from five cases testing the Timbre Brownfield Prioritization Tool.

Prioritizing brownfields for redevelopment in real estate portfolios can contribute to more sustainable regeneration and land management. Owners of large real estate and brownfield portfolios are challenged to allocate their limited resources to the development of the most critical or promising sites, in terms of time and cost efficiency. Authorities worried about the negative impacts of brownfields - in particular in the case of potential contamination - on the environment and society also need to prioritize their resources to those brownfields that most urgently deserve attention and intervention. Yet, numerous factors have to be considered for prioritizing actions, in particular when adhering to sustainability principles. Several multiple-criteria decision analysis (MCDA) approaches and tools have been suggested in order to support these actors in managing their brownfield portfolios. Based on lessons learned from the literature on success factors, sustainability assessment and MCDA approaches, researchers from a recent EU project have developed the web-based Timbre Brownfield Prioritization Tool (TBPT). It facilitates assessment and prioritization of a portfolio of sites on the basis of the probability of successful and sustainable regeneration or according to individually specified objectives. This paper introduces the challenges of brownfield portfolio management in general and reports about the application of the TBPT in five cases: practical test-uses by two large institutional land owners from Germany, a local and a regional administrative body from the Czech Republic, and an expert from a national environmental authority from Romania. Based on literature requirements for sustainability assessment tools and on the end-users' feedbacks from the practical tests, we discuss the TBPT's strengths and weaknesses in order to inform and give recommendations for future development of prioritization tools.

Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials.

Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.

Timbre Brownfield Prioritization Tool to support effective brownfield regeneration.

In the last decade, the regeneration of derelict or underused sites, fully or partly located in urban areas (or so called "brownfields"), has become more common, since free developable land (or so called "greenfields") has more and more become a scare and, hence, more expensive resource, especially in densely populated areas. Although the regeneration of brownfield sites can offer development potentials, the complexity of these sites requires considerable efforts to successfully complete their revitalization projects and the proper selection of promising sites is a pre-requisite to efficiently allocate the limited financial resources. The identification and analysis of success factors for brownfield sites regeneration can support investors and decision makers in selecting those sites which are the most advantageous for successful regeneration. The objective of this paper is to present the Timbre Brownfield Prioritization Tool (TBPT), developed as a web-based solution to assist stakeholders responsible for wider territories or clusters of brownfield sites (portfolios) to identify which brownfield sites should be preferably considered for redevelopment or further investigation. The prioritization approach is based on a set of success factors properly identified through a systematic stakeholder engagement procedure. Within the TBPT these success factors are integrated by means of a Multi Criteria Decision Analysis (MCDA) methodology, which includes stakeholders' requalification objectives and perspectives related to the brownfield regeneration process and takes into account the three pillars of sustainability (economic, social and environmental dimensions). The tool has been applied to the South Moravia case study (Czech Republic), considering two different requalification objectives identified by local stakeholders, namely the selection of suitable locations for the development of a shopping centre and a solar power plant, respectively. The application of the TBPT to the case study showed that it is flexible and easy to adapt to different local contexts, allowing the assessors to introduce locally relevant parameters identified according to their expertise and considering the availability of local data.