Analysis of distributed ledger technologies for industrial manufacturing.

In recent years, industrial manufacturing has undergone massive technological changes that embrace digitalization and automation towards the vision of intelligent manufacturing plants. With the aim of maximizing efficiency and profitability in production, an important goal is to enable flexible manufacturing, both, for the customer (desiring more individualized products) and for the manufacturer (to adjust to market demands). Manufacturing-as-a-service can support this through manufacturing plants that are used by different tenants who utilize the machines in the plant, which are offered by different providers. To enable such pay-per-use business models, Distributed Ledger Technology (DLT) is a viable option to establish decentralized trust and traceability. Thus, in this paper, we study potential DLT technologies for efficient and intelligent integration of DLT-based solutions in manufacturing environments. We propose a general framework to adapt DLT in manufacturing, and then we introduce the use case of shared manufacturing, which we utilize to study the communication and computation efficiency of selected DLTs in resource-constrained wireless IoT networks.

Stochastic Ex-Ante LCA under Multidimensional Uncertainty: Anticipating the Production of Undiscovered Microalgal Compounds in Europe.

Due to their biodiversity, microalgae represent a promising source of high-value compounds that bioprospecting is aiming to reveal. Performing an ex-ante Life Cycle Assessment (LCA) to anticipate and potentially minimize the environmental burden associated with the European production of a bioprospected microalgal compound is subject to substantial and multi-factorial uncertainty as the compound remains undiscovered. Given that any microalgal strain could potentially host the compound of interest, the ex-ante LCA should consider this bioprospecting uncertainty together with the uncertainty on the technology and the production mix. Using a parameterized cultivation simulation and consequential LCA model and an extensive stochastic pseudo Monte Carlo approach, we define and propagate techno-operational, bioprospecting, and production mix uncertainties for a microalgal compound being currently bioprospected in Europe. We perform global sensitivity analysis using different sampling strategies to identify the main contributors to the total output variance. Overall, the uncertainty propagation allowed us to define and analyze the probabilistic scope for the potential environmental impacts in the emerging production of high-value microalgal compounds in Europe based on current knowledge. These findings can support policy-making as well as actors in the microalgal sector toward technological paths with lower environmental impact.

Health benefits of microalgae and their microbiomes.

Microalgae comprise a phylogenetically very diverse group of photosynthetic unicellular pro- and eukaryotic organisms growing in marine and other aquatic environments. While they are well explored for the generation of biofuels, their potential as a source of antimicrobial and prebiotic substances have recently received increasing interest. Within this framework, microalgae may offer solutions to the societal challenge we face, concerning the lack of antibiotics treating the growing level of antimicrobial resistant bacteria and fungi in clinical settings. While the vast majority of microalgae and their associated microbiota remain unstudied, they may be a fascinating and rewarding source for novel and more sustainable antimicrobials and alternative molecules and compounds. In this review, we present an overview of the current knowledge on health benefits of microalgae and their associated microbiota. Finally, we describe remaining issues and limitation, and suggest several promising research potentials that should be given attention.

Stochastic LCA Model of Upscaling the Production of Microalgal Compounds.

Microalgae are currently being investigated for their promising metabolites but assessing the environmental impact of producing these compounds remains a challenge. Microalgae cultivation performance results from the complex interaction of biological, technological, geographical, and physical factors, which bioengineers try to optimize during the upscaling process. The path from the discovery of a microalgal compound to its industrial production is therefore highly uncertain. Nonetheless, it is key to anticipate the potential environmental impacts associated with the future production of a microalgal target compound. This is achieved in this study by developing an ex-ante, parameterized, and consequential LCA model that performs dynamic simulations of microalgae cultivation. The model is applied to calculate the environmental impacts of 9000 stochastically generated combinations of photobioreactor geometries and operational setups. The demonstration of the model is done for a fictive microalgal strain, parameterized to resemble Chlorella vulgaris, and a fictive target compound assumed to be a carbohydrate. The simulations are performed in Aalborg, Denmark, and Granada, Spain to appreciate geographical variability, which highly affects the requirements for thermoregulation. Open-source documentation allows full reproducibility and further use of the model for the ex-ante assessment of microalgal products.

Methodological review and detailed guidance for the life cycle interpretation phase.

Life cycle interpretation is the fourth and last phase of life cycle assessment (LCA). Being a "pivot" phase linking all other phases and the conclusions and recommendations from an LCA study, it represents a challenging task for practitioners, who miss harmonized guidelines that are sufficiently complete, detailed, and practical to conduct its different steps effectively. Here, we aim to bridge this gap. We review available literature describing the life cycle interpretation phase, including standards, LCA books, technical reports, and relevant scientific literature. On this basis, we evaluate and clarify the definition and purposes of the interpretation phase and propose an array of methods supporting its conduct in LCA practice. The five steps of interpretation defined in ISO 14040-44 are proposed to be reorganized around a framework that offers a more pragmatic approach to interpretation. It orders the steps as follows: (i) completeness check, (ii) consistency check, (iii) sensitivity check, (iv) identification of significant issues, and (v) conclusions, limitations, and recommendations. We provide toolboxes, consisting of methods and procedures supporting the analyses, computations, points to evaluate or check, and reflective processes for each of these steps. All methods are succinctly discussed with relevant referencing for further details of their applications. This proposed framework, substantiated with the large variety of methods, is envisioned to help LCA practitioners increase the relevance of their interpretation and the soundness of their conclusions and recommendations. It is a first step toward a more comprehensive and harmonized LCA practice to improve the reliability and credibility of LCA studies.

Life Cycle Assessment of Bitcoin Mining.

This study estimates the environmental impact of mining Bitcoin, the most well-known blockchain-based cryptocurrency, and contributes to the discussion on the technology's supposedly large energy consumption and carbon footprint. The lack of a robust methodological framework and of accurate data on key factors determining Bitcoin's impact have so far been the main obstacles in such an assessment. This study applied the well-established Life Cycle Assessment methodology to an in-depth analysis of drivers of past and future environmental impacts of the Bitcoin mining network. It was found that, in 2018, the Bitcoin network consumed 31.29 TWh with a carbon footprint of 17.29 MtCO2-eq, an estimate that is in the lower end of the range of results from previous studies. The main drivers of such impact were found to be the geographical distribution of miners and the efficiency of the mining equipment. In contrast to previous studies, it was found that the service life, production, and end-of-life of such equipment had only a minor contribution to the total impact, and that while the overall hashrate is expected to increase, the energy consumption and environmental footprint per TH mined is expected to decrease.

Contingent valuation of health and mood impacts of PM2.5 in Beijing, China.

Air pollution from PM2.5 affects many cities worldwide, causing both health impacts and mood depression. One of the obstacles to implementing environmental regulations for PM2.5 reduction is that there are limited studies of PM2.5 welfare loss and few investigations of mood depression caused by PM2.5. This article describes a survey study conducted in Beijing, China to estimate the welfare loss due to PM2.5. In total, 1709 participants completed either a face-to-face or online survey. A contingent valuation method was applied to elicit people's willingness to pay to avoid PM2.5 pollution and willingness to accept a compensation for such pollution. The payment/compensation was evaluated for two outcome variables: perceived health impacts and mood depression caused by PM2.5 pollution. This is one of few papers that explicitly studies the effects of PM2.5 on subjective well-being, and to the authors' knowledge, the first to estimate welfare loss from PM2.5 using a random forest model. Compared to the standard Turnbull, probit, and two-part models, the random forest model gave the best fit to the data, suggesting that this may be a useful tool for future studies too. The welfare loss due to health impacts and mood depression is CNY 1388.4/person/year and CNY 897.7/person/year respectively, indicating that the public attaches great importance to mood, feelings and happiness. The study provides scientific support to the development of economic policy instruments for PM2.5 control in China.

External costs of PM2.5 pollution in Beijing, China: Uncertainty analysis of multiple health impacts and costs.

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO2 and NOx. Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society. The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both the value of statistical life (VSL) and the amended human capital (AHC) approach were applied for external costs estimation, which could provide the upper and lower bound of the external costs due to PM2.5. To fully understand the uncertainty levels, the external cost distribution was determined via Monte Carlo simulation based on the uncertainty of the parameters such as PM2.5 concentration, exposure-response coefficients, and economic cost per case. The results showed that the external costs were equivalent to around 0.3% (AHC, China's guideline: C0 = 35 µg/m3) to 0.9% (VSL, WHO guideline: C0 = 10 µg/m3) of regional GDP depending on the valuation method and on the assumed baseline PM2.5 concentration (C0). Among all the health impacts, the economic loss due to premature deaths accounted for more than 80% of the overall external costs. The results of this study could help policymakers prioritizing the PM2.5 pollution control interventions and internalize the external costs through the application of economic policy instruments.

External costs of atmospheric lead emissions from a waste-to-energy plant: a follow-up assessment of indirect exposure via topsoil ingestion.

In this study the Impact Pathway Approach (IPA) was used to calculate the external costs associated with indirect exposure, via topsoil ingestion, to atmospheric emissions of lead (Pb) from a waste-to-energy plant in Denmark. Three metal-specific models were combined to quantify the atmospheric dispersion of lead, its deposition and accumulation in topsoil, and the increase in blood lead concentration for children resulting from lead intake via topsoil ingestion. The neurotoxic impact of lead on children was estimated using a lead-specific concentration-response function that measures impaired cognitive development in terms of IQ points lost per each incremental µg/dl of lead in blood. Since IQ loss during childhood can be associated with a percent decrease in expected lifetime earnings, the monetary value of such an impact can be quantified and the external costs per kg of lead emitted from the plant were then calculated. The costs of indirect exposure calculated over a time horizon of 100 years, for the sub-population of children of 0-3 years, and discounted at 3%, were in the range of 15-30 €/kg. Despite the continued accumulation of lead in topsoil resulting in increasing future indirect exposure, the results indicate that costs associated with this exposure pathway are of the same order of magnitude as costs associated with direct exposure via inhalation, calculated at 45-91 €/kg. Moreover, when the monetary value of future impacts is discounted to the present, the differences between the two exposure pathways are diminished. Finally, setting a short time horizon reduces the uncertainties but excludes part of the costs of indirect exposure from the assessment.

Network Analysis as a tool for assessing environmental sustainability: applying the ecosystem perspective to a Danish Water Management System.

New insights into the sustainable use of natural resources in human systems can be gained through comparison with ecosystems via common indices. In both kinds of system, resources are processed by a number of users within a network, but we consider ecosystems as the only ones displaying sustainable patterns of growth and development. This study aims at using Network Analysis (NA) to move such "ecosystem perspective" from theory into practice. A Danish municipal Water Management System (WMS) is used as case study to test the NA methodology and to discuss its generic applicability. We identified water users within the WMS and represented their interactions as a network of water flows. We computed intensive and extensive indices of system-level performance for seven different network configurations illustrating past conditions (2004-2008) and future scenarios (2015 and 2020). We also computed the same indices for other 24 human systems and for 12 ecosystems, by using information from the existing scientific literature on NA. The comparison of these results reveals that the WMS is similar to the other human systems and that human systems generally differ from ecosystems. The WMS is highly efficient at processing the water resource, but the rigid and almost linear structure makes it vulnerable in situations of stress such as heavy rain events. The analysis of future scenarios showed a trend towards increased sustainability, but differences between past and expected future performance of the WMS are marginal. We argue that future interventions should create alternative pathways for reusing rainwater within the WMS, increasing its potential to withstand the occurrence of flooding. We discuss advantages, limitations, and general applicability of NA as a tool for assessing environmental sustainability in human systems.

Indirect human exposure assessment of airborne lead deposited on soil via a simplified fate and speciation modelling approach.

In order to estimate the total exposure to the lead emissions from a municipal waste combustion plant in Denmark, the indirect pathway via ingestion of lead deposited on soil has to be quantified. Multi-media fate models developed for both Risk Assessment (RA) and Life Cycle Assessment (LCA) can be used for this purpose, but present high uncertainties in the assessment of metal's fate. More sophisticated and metal-specific geochemical models exist, that could lower the uncertainties by e.g. accounting for metal speciation, but they require a large amount of data and are unpractical to combine broadly with other fate and dispersion models. In this study, a Simplified Fate & Speciation Model (SFSM) is presented, that is based on the parsimony principle: "as simple as possible, as complex as needed", and that can be used for indirect human exposure assessment in different context like RA and regionalized LCA. SFSM couples traditional multi-media mass balances with empirical speciation models in a tool that has a simple theoretical framework and that is not data-intensive. The model calculates total concentration, dissolved concentration, and free ion activity of Cd, Cu, Ni, Pb and Zn in different soil layers, after accounting for metal deposition and dispersion. The model is tested for these five metals by using data from peer reviewed literature. Results show good accordance between measured and calculated values (factor of 3). The model is used to predict the human exposure via soil to lead initially emitted into air by the waste combustion plant and both the lead cumulative exposure and intake fraction are calculated.

Long-term human exposure to lead from different media and intake pathways.

Lead (Pb) is well known as an environmental pollutant: it can accumulate in various media, so actual lead exposure reflects both historical and present contaminations. Two main challenges then emerge: obtaining updated information to gain an overall picture of the sources of exposure, and predicting the resulting internal body exposure levels and effects that occur under long-term exposure conditions. In this paper, a modeling approach is used to meet these challenges with reference to Danish exposure conditions. Levels of lead content in various media have been coupled with data for lead intake and absorption in the human body, for both children and adults. An age-dependent biokinetic model allows then for determination of the blood lead levels resulting from chronic exposure. The study shows that the actual intake of lead is up to 27% of the Provisional Tolerable Daily Intake (PTDI) for children and around 8% for adults. It is confirmed that the critical route of exposure is via ingestion, accounting for 99% of total lead intake, while inhalation contributes only to 1% of total lead intake. The resulting lead levels in the blood after 2 years of exposure to actual contamination conditions have been estimated as up to 2.2µg/dl in children and almost 1µg/dl in adults. Impacts from lead can occur even at such levels. The role of historical and present sources to lead in the environment is discussed, and, for specific child and adult exposure scenarios, external-internal concentration relationships for the direct linkage between lead in environmental media and resulting concentrations of lead in blood are then presented.

External costs of atmospheric Pb emissions: valuation of neurotoxic impacts due to inhalation.

BACKGROUND: The Impact Pathway Approach (IPA) is an innovative methodology to establish links between emissions, related impacts and monetary estimates. Only few attempts have so far been presented regarding emissions of metals; in this study the external costs of airborne lead (Pb) emissions are assessed using the IPA. Exposure to Pb is known to provoke impacts especially on children's cognition. As cognitive abilities (measured as IQ, intelligence quotient) are known to have implications for lifetime income, a pathway can be established leading from figures for Pb emissions to the implied loss in earnings, and on this basis damage costs per unit of Pb emission can be assessed. METHODS: Different types of models are here linked. It is relatively straightforward to establish the relationship between Pb emissions and consequent increase in air-Pb concentration, by means of a Gaussian plume dispersion model (OML). The exposed population can then be modelled by linking the OML-output to population data nested in geo-referenced grid cells. Less straightforward is to establish the relationship between exposure to air-Pb concentrations and the resulting blood-Pb concentration. Here an Age-Dependent Biokinetic Model (ADBM) for Pb is applied. On basis of previous research which established links between increases in blood-Pb concentrations during childhood and resulting IQ-loss we arrive at our results. RESULTS: External costs of Pb airborne emissions, even at low doses, in our site are in the range of 41-83 euro/kg emitted Pb, depending on the considered meteorological year. This estimate applies only to the initial effects of air-Pb, as our study does not address the effects due to the Pb environmental-accumulation and to the subsequent Pb re-exposure. These are likely to be between one and two orders of magnitude higher. CONCLUSIONS: Biokinetic modelling is a novel tool not previously included when applying the IPA to explore impacts of Pb emissions and related external costs; it allows for more fine-tuned, age-dependent figures for the external costs from low-dose exposure. Valuation of additional health effects and impacts e.g. due to exposure via ingestion appear to be feasible when extending the insights from the present pilot study.

Analysis of voltammetric data for the evaluation of seasonal changes of the Ni, Cd, Pb and Cu content in atmospheric particulate PM2.5.

In this paper, the preliminary results of a study on concentration of heavy metals in PM2.5 (atmospheric particles with aerodynamic diameter less than 2.5 microm) fractions of atmospheric particulate matter, sampled in Milan, are presented. This work aims to develop an electroanalytical method to analyse Pb, Cu, Cd and Ni in PM2.5 and to investigate seasonal and weekly trends in the amount of PM2.5 and its composition for considered metals. The samples have been selected within the seasons ranging from September 2002 to November 2003 so that they represent the possible seasonal changes; the samples within this period have been chosen in order to have data relevant to both working days and holidays. The determination of Cd, Pb and Cu has been carried out by Differential Pulse Anodic Stripping Voltammetry, whereas the concentration of Ni has been determined by Differential Pulse Adsorptive Cathodic Stripping Voltammetry. The concentrations of the metals in the sampled atmosphere and in the PM2.5 have been evaluated; through ANOVA possible seasonal or weekly variations in the above cited concentrations have been investigated.