Library of tools

This tool is designed to construct nano-specific species sensitivity distributions (SSD) used in environmental hazard assessment. The nano-specific Species Sensitivity Weighted Distribution (nSSWD) can be accessed as part in the SUN Decision Support System (SUNDS). The approach is based on three species weighting criteria (i.e. species relevance, trophic level abundance and data quality) as well as weighting factors for data quality and the extent of physico-chemical characterisation of the tested nanomaterial in each study. SSD curves are calculated for hazard concentrations (HCx) for 5%, 50% and 95% of species.

SimpleBox4nano is a regulatory-relevant multimedia environmental fate model that is specifically fit for use with nanomaterials. The tool predicts background concentrations of nanomaterials in air, water, sediment and soil. SimpleBox4Nano does so by simultaneously solving mass balance equations for each environmental compartment box in the model. It is a first-principles model in the sense that it internally derives mass flow rates from physical and chemical substance properties, and characteristics of the environment modeled. It takes user-specified release rates as input, producing exposure concentrations in the environment as output.

The SUNDS web tool covers risk management and sustainability of nano-enabled products along their life cycle. It is based on a tiered approach including a decision support tool and a risk assessment and control part. It is based on REACH guidelines and focuses on assessment models. Besides traditional risk assessment and risk management information, the system also asks for the inclusion of sustainability data supporting safer-by-design nano-enabled products (this includes life cycle assessment, social and economic impact assessments). The tool covers human and environmental exposure, hazard and risk assessment.

The LICARA Tool supports SMEs in their decision-making process. It does this by scanning both the benefits and risks over the nanoproduct's life time. It uses a structured life cycle approach which enables the evaluation of the benefits and risks qualitatively with low and manageable efforts, over the nanoproduct's life time. It further allows a comparison with the risks and benefits of the conventional (non-nano) products. The tool stimulates economic, environmental and social opportunities. This tool is specifically intended for use by SMEs to support them in communicating with regulators, and potential clients and investors.

The precautionary matrix for synthetic nanomaterials is geared toward industry and trade. The precautionary matrix is a method for assessing the nano-specific health and environmental risks of nanoproducts. The precautionary matrix enables the structured assessment of the “nano-specific need for precautions” when handling synthetic nanomaterials. The precautionary matrix is designed to help industry and trade comply with their due diligence and their duty to exercise self-control opposite employees, consumers and the environment.

GUIDEnano is a risk assessment model that allows the assessment and mitigation of human and environmental risks related to nanomaterials and nano-enabled product, considering their whole life cycle. Using the GUIDEnano Tool, different stakeholders can evaluate and efficiently mitigate possible health risks for workers, consumers and the environment.

The ConsExpo nano tool can be used to estimate inhalation exposure to nanomaterials in consumer spray products. To run the model, user input on different exposure determinants such as the product and its use, the nanomaterial and the environmental conditions is required. Exposure is presented in different measures. The outcome of the assessment is an alveolar load in the lungs as one of the most critical determinants of inflammation of the lungs is both the magnitude and duration of the alveolar load of a nanomaterial. To estimate the alveolar load arising from the use of nano-enabled spray products, ConsExpo nano combines models that estimate the external aerosol concentration in indoor air, with models that estimate the deposition in and clearance of inhaled aerosol from the alveolar region.

This module allows you to qualitatively assess occupational health risks from inhalation exposure to manufactured nanomaterials. Risk management measures may be selected or included in the action plan. “Stoffenmanager Nano” is an extension of Stoffenmanager, which is a knowledge-based platform aimed at reducing exposure risks to hazardous substances and biological agents in the workplace.

NanoSafer is a combined control-banding and risk management tool that enables assessment of the risk level and recommended exposure control associated with production and use of manufactured nanomaterials (e.g., nanoparticles, nanoflakes, nanofibers, and nanotubes) in specific work scenarios. In addition to manufactured nanomaterials, the tool can also be used to assess and manage emissions from nanoparticle-forming processes.

RISKOFDERM is a quantitative model for estimating potential dermal exposure, i.e. the total amount of a substance coming into contact with the protective clothing, work clothing and exposed skin. It includes six dermal exposure operation (DEO) units, where each unit is a cluster of exposure scenarios involving general chemical substances. In the context of nanomaterials, its applicability domain is not yet established. In the present study, the performance of the model, while estimating the dermal exposure to nanomaterials, is tested by comparing its output with experimentally measured dermal exposure levels of nanomaterials on hands.

The Safety Observer app template 'NanoObserver' can be used in measuring safe and healthy working conditions and behaviour with nanomaterials. A template for the free smartphone/tablet app ‘Safety Observer’ has been developed for use in proactive safety rounds in industrial and academic workplaces that work with or are exposed to manufactured nanomaterials (MN). The template can be adapted to a local context and language, and be used by students, workers, faculty, managers and OSH professionals. Safe and unsafe working conditions and behaviour regarding MN in a workplace are observed and counted, such as: 1) MN signage, marking and labelling 2) MN handling, storage and transport 3) Ventilation and filters 4) Personal protective equipment 5) Technical aids 6) Order and tidiness 7) Hygiene 8) Waste storage, recycling and disposal 9) First aid equipment. Comments and photos can be included in the observations with the app, and a final report, including a ‘safety index’, is automatically generated and made available in the app and sent to one’s email for immediate use in improving and reinforcing OSH initiatives.

The approach is specifically applicable to similarity assessment as a basis for grouping of (nanoforms of) chemical substances as well as for classification of the substances according to the Classification, Labeling and Packaging regulation. The unique goal of this approach is to assess data quality in such a way that all the steps are automatized, thus reducing reliance on expert judgment. The analysis starts from available (meta)data as provided in the data entry templates developed by the NanoSafety community and used for import into the eNanoMapper database. The methodology is implemented in the templates as a traffic light system—the providers of the data can see in real time the completeness scores calculated by the system for their datasets in green, yellow, or red.

This online tool provides estimates of indoor exposure to airborne particles and is based on the NIST multizone modeling software, CONTAM. It is couple with a size resolved tool, which is an additional physical model that accounts for the properties of nanoparticles that may impact their transport within the built environment including some beyond those that CONTAM is currently capable of modeling, e.g., coagulation.

The BIORIMADS uses advanced models to support the occupational, consumer and environmental risk assessment of nanomaterials and biomaterials along the lifecycle of nano-enabled consumer products and medical applications. The BIORIMADS addresses nano and biomaterials used in medical applications such as medical devices and advanced therapy medicinal products (ATMPs). In situations where the risks are not controlled the BIORIMADS proposes suitable Risk Management Measures (e.g. engineering controls, Personal Protective Equipment) and provides information about the cost of implementing these measures. Risk control can be demonstrated by reducing risk to below threshold levels or by investigating feasible alternatives to the substance. If the risks cannot be adequately controlled and no feasible alternatives can be found, a Socioeconomic Analysis (SEA) can be performed to demonstrate that the benefits of using a certain nano/biomaterial or application significantly outweigh the risks.

The combined dosimetry model (CoDo) can be used to simulate the exposure concentrations in air corresponding to the doses used in in vitro studies in submerged systems. It works by integrating in vitro dosimetry and lung dosimetry, and assuming that the deposited dose per area in vitro corresponds to the deposited dose per area in the lung. The input data include experimental parameters about the in vitro system and lung parameters that define the hypothetical human exposure scenario; the required parameters and the parameters that, if not specified by the user, are calculated by the model.

The nano Benefit Assessment Matrix (nano-BAM) supports the assessment of functional, health and environmental benefits of nanomaterials, nano-enabled manufactured nanomaterials and products from the first innovation stage until the product is on the market. The BAM assists users to summarize the benefits by assessing two aspects: (i) Degree of benefit (DoB) to estimate how achievable the benefits are and (ii) Degree of evidence (DoE) to understand what scientific evidence is available for the benefit identified by users.

The “Socio-Economic Life Cycle-Based Framework for SSbD” is a tool to perform a socio-economic assessment of nanomaterials and nano-enabled products to support decision-making for safe-and sustainable-by-design (SSbD). The main target user group is industries in the early stages of product development. The framework, based on a social life cycle analysis (S-LCA) and multi-criteria decision analysis (MCDA) methodologies, will help users make decisions that would reduce the negative socio-economic impacts of nanomaterials and nano-enabled products.

Dynamic Probabilistic Material Flow analysis (DPMFA) can be used to predict release of nanomaterials to the environment based on an analysis of the mass flows during the full life cycle from nanomaterial production over use to final end-of-life treatment of nanoenabled products. Main input requirements are data about production amounts, uses in products and transfer coefficients between all compartments, e.g. release or behavior during EoL. The model then quantifies flows into environmental compartments such as water, air, soil and the subsurface. These flows can then be used as input for environmental fate models.

NanoDUFLOW is a nanomaterial environmental fate model that links nanomaterial-specific process descriptions to a spatially explicit hydrological model. The link enables the realistic modelling of feedbacks between local flow conditions and nanomaterial fate processes, such as homo- and heteroaggregation, resuspension and sedimentation. Spatially explicit simulations using five size classes of engineered nanomaterials and five size classes of natural solids showed how nanomaterial sediment contamination ‘hot spots’ and nanomaterial speciation can be predicted as a function of place and time.

ECETOC’s NanoApp is a tool designed to define the boundaries of sets of similar nanoforms and to generate a justification for the REACH registration. NanoApp helps registrants follow the European Chemical Agency (ECHA)’s new registration requirements for nanomaterials under the EU’s REACH legislation. It does this by creating and justifying ‘sets of similar nanoforms’ for a joint human health and environmental hazard, exposure and safety assessment. The tool uses established criteria and rules that systematically evaluate similarity between nanoforms. On that basis, it concludes whether a set of nanoforms can be justified or not. Its decision logic follows the ECHA guidance in a transparent and evidence-based manner – covering primarily the ‘Appendix for nanoforms applicable to the Guidance on Registration and Substance Identification’.

The model is very useful for the exposure assessment of products containing nanomaterials during shredding (end-of-life), a part of the life cycle where there is little data available. With a Bayesian probabilistic nature in its core, it uses subjective judgement when data is unavailable or scarce while being able to adapt and update risk forecasts as new information becomes available. Its novelty lies on a simplistic approach which combines the material and process variables of the system to determine the probability of number, size, mass and composition of released particles. It is applicable to the shredding of a wide range of nano-enabled products and it aims to reduce the nanomaterial release by using the safe(r)-by-design approach.

Tool to assess risks associated with nanotechnology operations. The tool estimates an emission probability and severity band and provides advice on what engineering controls to use. It includes nine domains covering handling of liquids, powders and abrasion of solids. Combines hazard “severity” scores and exposure “probability” scores in a matrix to obtain a level of risk and associated controls out of 4 possible levels of increasing risk and associated controls. Control banding (CB) strategies (a qualitative risk characterization and management strategy) offer simplified solutions for controlling worker exposures to constituents that are found in the workplace in the absence of firm toxicological and exposure data.

This framework allows to quantify the readiness of different tools and methods towards their wider regulatory acceptance and downstream use by different stakeholders. The framework diagnoses barriers which hinder regulatory acceptance and wider usability of a tool/method based on their Transparency, Reliability, Accessibility, Applicability and Completeness (TRAAC framework). Each TRAAC pillar consists of criteria which help in evaluating the overall quality of the tools and methods for their (i) compatibility with regulatory frameworks and, (ii) usefulness and usability for end-users, through a calculated TRAAC score based on the assessment. Fourteen tools and methods were assessed using the TRAAC framework as proof-of-concept. The results provide insights into any gaps, opportunities, and challenges in the context of each of the 5 pillars of the TRAAC framework.

The Screening Multiple Criteria Decision Analysis (SMCDA) was developed to rank different material options (not only nanomaterials) according to their acceptability in terms of broader environmental and socioeconomic aspects. The criteria used in SMCDA relate to properties of the material that are specific to the production, use, and end-of-life phases. Its structure aims to provide initial guidance even without the knowledge and resources required for a full life cycle analysis. The input questionnaire is divided into three levels that unfold with the user's experience. SMCDA applies weighting to consider the varying relevance of criteria and to improve differentiation among comparable alternatives. The probabilistic approach accounts for uncertainty and missing knowledge. An analysis requires only semi-quantitative estimates without the need for exact numbers, and results can be obtained even with incomplete inputs. SMCDA is intended to be used by various societal actors to contribute to processes of reflexive innovation.

This is a stochastic probabilistic long-term risk prediction tool (working at its best for up to 100 years and more) targeting product material (ingredient) interaction between product life phases and a variety of media and environments. This tool is not mass-flow oriented but builds on probabilistic predictions of the target material's (product ingredient's) location and transformation over its lifecycle and beyond. It performs without predefined (theoretical and other) probability distributions but generates them itself. Its computations integrate humans and environments' risk and vulnerability by combining the load contamination of potential pollutants with toxicity and ecotoxicity data. This tool aims to predict and target specific product ingredients, including newly engineered nanomaterials and their lifecycle-long (up to 50 years) location in and migration through environmental (human) and technical systems. The target compartments and organisms potentially vulnerable or at risk are air, freshwaters, marine waters, groundwater, saline groundwater, freshwater sediments, marine water sediments, soils, freshwater flora, marine water flora, freshwater fauna, marine water fauna and child and adult humans. PERST's services may be used in target regions covering all EU member states, Switzerland and the UK. The main PERST-output forms are 3D graphics with probabilistic information for long time periods, boxplots summarising these outputs distributions over time and line charts visualising the evolution of the target outputs. A product risk etiquette is under construction for evaluating the total risks computed for a target product (product ingredient). More from this info can be found on https://www.etss.ch/perst/.

This tool is based on the bow-tie model and has been adapted for the the specifics of nanotechnology related risk. The bow-tie model offers a paradigm of engagement across an extended temporal plane as it seeks to engage the risk management function within the insurance industry with mitigation and control measure in the ex-ante phase of analysis. The bow-tie model offers a number of advantages. The fact it is visually represented allows for improved heuristics and this is combined with its ability to deliver quite precise risk metrics. Importantly, for a field such as nanotechnology, the model is open to iterative improvement as more data or more precise probabilistic analysis becomes available. the bow-tie model could be used in conjunction with other methodologies and its adoption in no way precludes the use of other approaches such as control banding. The fact that it is a well understood process by insurers and indeed those in the chemical industry leads us to believe that at this stage it is the best candidate tool or perhaps best method available at this juncture