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Energy systems are regularly subject to major disruptions affecting economic activities, operation of infrastructure and the society as a whole. Resilience assessment comprises the pre-event oriented classical risk assessment as a central element, but it goes beyond that because it also includes and evaluates post-event strategies to improve the functioning of the system during its future operation. First, an overview of resilience definitions used across various scientific disciplines is presented, followed by an in-depth analysis of resilience assessment and quantification for energy systems. The relevant literature is classified by approach and according to four key functions of resilience: resist, restabilize, rebuild, and reconfigure. Findings show that irrespective of the research field, a resilient system always operates with an aim to minimize the potential consequences resulting from a disruptive event and to efficiently recover from a potential system performance loss., Sustainable and Resilient Infrastructure, 6 (5), ISSN:2378-9697, ISSN:2378-9689

Nano-silica, the engineered nanomaterial with one of the largest production volumes, has a wide range of applications in consumer products and industry. This study aimed to quantify the exposure of nano-silica to the environment and to assess its risk to surface waters. Concentrations were calculated for four environmental (air, soil, surface water, sediments) and two technical compartments (wastewater, solid waste) for the EU and Switzerland using probabilistic material flow modeling. The corresponding median concentration in surface water is predicted to be 0.12 μg/l in the EU (0.053-3.3 μg/l, 15/85% quantiles). The concentrations in sediments in the complete sedimentation scenario were found to be the largest among all environmental compartments, with a median annual increase of 0.43 mg/kg · y in the EU (0.19-12 mg/kg · y, 15/85% quantiles). Moreover, probabilistic species sensitivity distributions (PSSD) were computed and the risk of nano-silica in surface waters was quantified by comparing the predicted environmental concentration (PEC) with the predicted no-effect concentration (PNEC) distribution, which was derived from the cumulative PSSD. This assessment suggests that nano-silica currently poses no risk to aquatic organisms in surface waters. Further investigations are needed to assess the risk of nano-silica in other environmental compartments, which is currently not possible due to a lack of ecotoxicological data.

Material flow modeling constitutes an important approach to predicting and understanding the flows of materials through the anthroposphere into the environment. The new "Dynamic Probabilistic Material Flow Analysis (DPMFA)" method, combining dynamic material flow modeling with probabilistic modeling, is presented in this paper. Material transfers that lead to particular environmental stocks are represented as systems of mass-balanced flows. The time-dynamic behavior of the system is calculated by adding up the flows over several consecutive periods, considering changes in the inflow to the system and intermediate delays in local stocks. Incomplete parameter knowledge is represented and propagated using Bayesian modeling. The method is implemented as a simulation framework in Python to support experts from different domains in the development of their application models. After the introduction of the method and its implementation, a case study is presented in which the framework is applied to predict the environmental concentrations of carbon nanotubes in Switzerland. Presentation of a probabilistic modeling method for representing dynamic mass flows.Implementation as simulation framework.Case study: Exposure modeling of carbon nanotubes for Switzerland.

For the environmental risk assessment of engineered nanomaterials (ENM) knowledge about environmental concentrations is crucial. Soils and sediments are considered sinks for ENM and thus a better understanding of the spatial and temporal variability of concentrations is needed. In this work we use South Australia as a case study for a region with significant biosolids and treated wastewater application on soils, representing a system with almost "closed loops". The probabilistic material flow modelling approach was extended to include a temporal modelling of ENM production and biosolids handling and transfer onto soils, focusing on nano-TiO2, nano-ZnO, nano-Ag, Carbon Nanotubes(CNT) and fullerenes. The results thus not only incorporate the uncertainty on ENM flows but also the spatial and temporal variability of ENM concentrations between 2005 and 2012. The ENM concentrations in different waste amended soils vary by more than 2 orders of magnitude due to different biosolids and wastewater application rates. Because of the almost complete transformation of nano-ZnO and nano-Ag during wastewater treatment, we also modelled the total flows of Zn and Ag derived from the nanoparticles and compared their modelled concentrations to measured total Ag and Zn concentration in biosolids and soils in South Australia. The modelled Ag concentration derived from nano-Ag is 50-times smaller than measured Ag in soils and 10-times in biosolids. For Zn the respective values are 250 and 7. If in the future the accumulation continues with the same rate as in 2012 it would take about 170 years until a regulatory threshold value of 500 ug Ag per kg of soil would be reached. For Zn, it will take 930 years. The results from this modelling highlight that regional and site-specific conditions need to be considered when assessing the environmental risks of nanomaterials. Refereed/Peer-reviewed

The need for an environmental risk assessment for engineered nanomaterials (ENM) necessitates the knowledge about their environmental emissions. Material flow models (MFA) have been used to provide predicted environmental emissions but most current nano-MFA models consider neither the rapid development of ENM production nor the fact that a large proportion of ENM are entering an in-use stock and are released from products over time (i.e., have a lag phase). Here we use dynamic probabilistic material flow modeling to predict scenarios of the future flows of four ENM (nano-TiO2, nano-ZnO, nano-Ag and CNT) to environmental compartments and to quantify their amounts in (temporary) sinks such as the in-use stock and (“final”) environmental sinks such as soil and sediment. In these scenarios, we estimate likely future amounts if the use and distribution of ENM in products continues along current trends (i.e., a business-as-usual approach) and predict the effect of hypothetical trends in the market development o...

Scientific consensus predicts that the worldwide use of engineered nanomaterials (ENM) leads to their release into the environment. We reviewed the available literature concerning environmental concentrations of six ENMs (TiO2 ZnO Ag fullerenes CNT and CeO2) in surface waters wastewater treatment plant effluents biosolids sediments soils and air. Presently a dozen modeling studies provide environmental concentrations for ENM and a handful of analytical works can be used as basis for a preliminary validation. There are still major knowledge gaps (e.g. on ENM production application and release) that affect the modeled values but over all an agreement on the order of magnitude of the environmental concentrations can be reached. True validation of the modeled values is difficult because trace analytical methods that are specific for ENM detection and quantification are not available. The modeled and measured results are not always comparable due to the different forms and sizes of particles that these two approaches target. © 2013 Elsevier Ltd. All rights reserved.

The need for an environmental risk assessment for engineered nanomaterials (ENM) necessitates the knowledge about their environmental concentrations. Despite significant advances in analytical methods, it is still not possible to measure the concentrations of ENM in natural systems. Material flow and environmental fate models have been used to provide predicted environmental concentrations. However, almost all current models are static and consider neither the rapid development of ENM production nor the fact that many ENM are entering an in-use stock and are released with a lag phase. Here we use dynamic probabilistic material flow modeling to predict the flows of four ENM (nano-TiO2, nano-ZnO, nano-Ag and CNT) to the environment and to quantify their amounts in (temporary) sinks such as the in-use stock and ("final") environmental sinks such as soil and sediment. Caused by the increase in production, the concentrations of all ENM in all compartments are increasing. Nano-TiO2 had far higher concentrations than the other three ENM. Sediment showed in our worst-case scenario concentrations ranging from 6.7 μg/kg (CNT) to about 40 000 μg/kg (nano-TiO2). In most cases the concentrations in waste incineration residues are at the "mg/kg" level. The flows to the environment that we provide will constitute the most accurate and reliable input of masses for environmental fate models which are using process-based descriptions of the fate and behavior of ENM in natural systems and rely on accurate mass input parameters.

Background The use of gold nanoparticles (Au-NP) based medical applications is rising due to their unique physical and chemical properties. Diagnostic devices based on Au-NP are already available in the market or are in clinical trials and Au-NP based therapeutics and theranostics (combined diagnostic and treatment modality) are in the research and development phase. Currently, no information on Au-NP consumption, material flows to and concentrations in the environment are available. Therefore, we estimated prospective maximal consumption of Au-NP from medical applications in the UK and US. We then modelled the Au-NP flows post-use and predicted their environmental concentrations. Furthermore, we assessed the environment risks of Au-NP by comparing the predicted environmental concentrations (PECs) with ecological threshold (PNEC) values. Results The mean annual estimated consumption of Au-NP from medical applications is 540 kg for the UK and 2700 kg for the US. Among the modelled concentrations of Au-NP in environmental compartments, the mean annual PEC of Au-NP in sludge for both the UK and US was estimated at 124 and 145 μg kg−1, respectively. The mean PEC in surface water was estimated at 468 and 4.7 pg L−1, respectively for the UK and US. The NOEC value for the water compartment ranged from 0.12 up to 26,800 μg L−1, with most values in the range of 1000 μg L−1. Conclusion The results using the current set of data indicate that the environmental risk from Au-NP used in nanomedicine in surface waters and from agricultural use of biosolids is minimal in the near future, especially because we have used a worst-case use assessment. More Au-NP toxicity studies are needed for the soil compartment., Journal of Nanobiotechnology, 13 (1), ISSN:1477-3155

The aim of this chapter is to evaluate what information is needed in order to quantify the flows of ENM to the environment by reviewing the current state of knowledge. The life cycle thinking forms the basis of the evaluation. The first step in release assessment is the knowledge about the production and use of ENM. Data on production are crucial for the assessment, because they determine the maximal amount that could potentially be released. The different life cycles of products containing the ENM are determining the release potential. The knowledge about the product distribution is therefore key to release estimation. The three important life cycle steps that need to be considered are production/manufacturing, the use phase, and the end of life (EoL) treatment. Release during production and manufacturing to the environment may occur because large amounts of pure material are handled. During the use and EoL phase, experimental data from real-world release studies are preferred; however, in most cases release has been estimated or guessed based on standard knowledge about product use and behavior. The mass flows discussed in this chapter provide the input data to derive environmental concentrations needed for environmental risk assessment of ENM. The mass flows to the environment will also be needed for environmental fate models that are based on mechanistic description of the reactions and the behavior of the released ENM in environmental compartments such as water or soils.

Concerns about the environmental risks of engineered nanomaterials (ENM) are growing however currently very little is known about their concentrations in the environment. Here we calculate the concentrations of five ENM (nano TiO2 nano ZnO nano Ag CNT and fullerenes) in environmental and technical compartments using probabilistic material flow modelling. We apply the newest data on ENM production volumes their allocation to and subsequent release from different product categories and their flows into and within those compartments. Further we compare newly predicted ENM concentrations to estimates from 2009 and to corresponding measured concentrations of their conventional materials e.g. TiO2 Zn and Ag. We show that the production volume and the compounds' inertness are crucial factors determining final concentrations. ENM production estimates are generally higher than a few years ago. In most cases the environmental concentrations of corresponding conventional materials are between one and seven orders of magnitude higher than those for ENM. © 2013 Elsevier Ltd. All rights reserved.