Food security and sustainable development of agriculture has been a key challenge for decades. To support this, nanotechnology in the agricultural sectors increases productivity and food security,... Show moreFood security and sustainable development of agriculture has been a key challenge for decades. To support this, nanotechnology in the agricultural sectors increases productivity and food security, while leaving complex environmental negative impacts including pollution of the human food chains by nanoparticles. Here we model the effects of silver nanoparticles (Ag-NPs) in a food chain consisting of soil-grown lettuce Lactuca sativa and snail Achatina fulica. Soil-grown lettuce were exposed to sulfurized Ag-NPs via root or metallic Ag-NPs via leaves before fed to snails. We discover an important biomagnification of silver in snails sourced from plant root uptake, with trophic transfer factors of 2.0–5.9 in soft tissues. NPs shifts from original size (55–68 nm) toward much smaller size (17–26 nm) in snails. Trophic transfer of Ag-NPs reprograms the global metabolic profile by down-regulating or up-regulating metabolites for up to 0.25- or 4.20- fold, respectively, relative to the control. These metabolites control osmoregulation, phospholipid, energy, and amino acid metabolism in snails, reflecting molecular pathways of biomagnification and pontential adverse biological effects on lower trophic levels. Consumption of these Ag-NP contaminated snails causes non-carcinogenic effects on human health. Global public health risks decrease by 72% under foliar Ag-NP application in agriculture or through a reduction in the consumption of snails sourced from root application. The latter strategy is at the expense of domestic economic losses in food security of $177.3 and $58.3 million annually for countries such as Nigeria and Cameroon. Foliar Ag-NP application in nano-agriculture has lower hazard quotient risks on public health than root application to ensure global food safety, as brought forward by the United Nations Sustainable Development Goals. Show less
Large-scale offshore wind energy developments represent a major player in the energy transition but are likely to have (negative or positive) impacts on marine biodiversity. Wind turbine... Show moreLarge-scale offshore wind energy developments represent a major player in the energy transition but are likely to have (negative or positive) impacts on marine biodiversity. Wind turbine foundations and sour protection often replace soft sediment with hard substrates, creating artificial reefs for sessile dwellers. Offshore wind farm (OWF) furthermore leads to a decrease in (and even a cessation of) bottom trawling, as this activity is prohibited in many OWFs. The long-term cumulative impacts of these changes on marine biodiversity remain largely unknown. This study integrates such impacts into characterization factors for life cycle assessment based on the North Sea and illustrates its application. Our results suggest that there are no net adverse impacts during OWF operation on benthic communities inhabiting the original sand bottom within OWFs. Artificial reefs could lead to a doubling of species richness and a two-order-of-magnitude increase of species abundance. Seabed occupation will also incur in minor biodiversity losses in the soft sediment. Our results were not conclusive concerning the trawling avoidance benefits. The developed characterization factors quantifying biodiversity-related impacts from OWF operation provide a stepping stone toward a better representation of biodiversity in life cycle assessment. Show less
Zhang, C.; Hu, M.; Meide, M. van der; Di Maio, F.; Yang, X.; Gao, X.; ... ; Li, C. 2023
Metals have an important role in the global economy. With the energy transition, the demand for many metals is expected to sharply increase in the future. Although many studies apply prospective... Show moreMetals have an important role in the global economy. With the energy transition, the demand for many metals is expected to sharply increase in the future. Although many studies apply prospective LCA to assess future environmental impacts of metal supply, the methods have not yet converged to a common approach. This study aims to provide an overview of these studies and their approaches, following 2 research questions: 1. Which metals have been addressed by previous prospective LCA studies and what are their expected future supply impacts according to the identified studies? 2. What are the studied parameters of the metal supply chains, the applied scenario modelling approaches, and data sources used? We performed a systematic literature review to identify studies which assess future environmental impacts due to the supply of metals. This includes publications about absolute impacts of global metal demand, but also relative impacts assessed by comparative LCAs of emerging technologies. For these studies, we analysed both the results and the methods to integrate prospective elements in the LCA models focussing on the choice of parameters, background scenarios, data sources and modelling approaches. The literature review yielded 40 papers. We found that the majority of publications investigate bulk metals like Cu, Fe and Al. Most studies investigate relative impacts (i.e. per kg metal produced). Fewer studies also address absolute impacts of the total future demand; however, these mostly agree that absolute environmental impacts associated with global metal demand are likely to increase. Moreover, the results show that the majority of studies assess CO2 emissions, while other impacts are less often investigated. Furthermore, we found that the parameters considered most frequently are future ore grades, recycling shares, and energy efficiency. Background scenarios were primarily energy scenarios, which were most often electricity scenarios from the integrated assessment model IMAGE. Background scenarios modelling other developments are less common. Overall, the review reveals a wide variety of parameter choices, scenario modelling approaches and data sources. This study stresses the necessity to reduce environmental impacts of metal supply. Moreover, it highlights the need for guidelines for prospective LCA as well as for the documentation of modelling choices, LCI and scenario data to facilitate transparency and sharing of LCA scenarios in the community. Show less
The presence of antibiotic resistance genes (ARGs) in the environment poses a threat to human health and therefore their environmental behavior needs to be studied urgently. A systematic study was... Show moreThe presence of antibiotic resistance genes (ARGs) in the environment poses a threat to human health and therefore their environmental behavior needs to be studied urgently. A systematic study was conducted on the photodegradation pathways of the cell-free tetracycline resistance gene (Tc-ARG) under simulated sunlight irradiation. The results showed that Tc-ARG can undergo direct photodegradation, which significantly reduces its horizontal transfer efficiency. Suwannee River fulvic acid (SRFA) promoted the photodegradation of Tc-ARG and further inhibited its horizontal transfer by generating reactive intermediates. The photodegradation of Tc-ARG was attributed to degradation of the four bases (G, C, A, T) and the deoxyribose group. Quantum chemical calculations showed that the four bases could be oxidized by the hydroxyl radical (HO) through addition and H-abstraction reactions. The main oxidative product 8-oxo-dG was detected. This product was generated through the addition reaction of G-C with HO, subsequent to dissolved oxygen initiated H-abstraction and H2O catalyzed H-transfer reactions. The predicted maximum photodegradation rates of Tc-ARG in the Yellow River estuary were 0.524, 0.937, and 0.336 h−1 in fresh water, estuary water, and seawater, respectively. This study furthermore revealed the microscopic photodegradation pathways and obtained essential degradation parameters of Tc-ARG in sunlit surface water. Show less
Offshore wind energy (OWE) is a cornerstone of future clean energy development. Yet, research into global OWE material demand has generally been limited to few materials and/or low technological... Show moreOffshore wind energy (OWE) is a cornerstone of future clean energy development. Yet, research into global OWE material demand has generally been limited to few materials and/or low technological resolution. In this study, we assess the primary raw material demand and secondary material supply of global OWE. It includes a wide assortment of materials, including bulk materials, rare earth elements, key metals, and other materials for manufacturing offshore wind turbines and foundations. Our OWE development scenarios consider important drivers such as growing wind turbine size, introducing new technologies, moving further to deep waters, and wind turbine lifetime extension. We show that the exploitation of OWE will require large quantities of raw materials from 2020 to 2040: 129-235 million tonnes (Mt) of steel, 8.2-14.6 Mt of iron, 3.8-25.9 Mt of concrete, 0.5-1.0 Mt of copper and 0.3-0.5 Mt of aluminium. Substantial amounts of rare earth elements will be required towards 2040, with up to 16, 13, 31 and 20 fold expansions in the current Neodymium (Nd), Dysprosium (Dy), Praseodymium (Pr) and Terbium (Tb) demand, respectively. Closed-loop recycling of end-of-life wind turbines could supply a maximum 3% and 12% of total material demand for OWE from 2020 to 2030, and 2030 to 2040, respectively. Moreover, a potential lifetime extension of wind turbines from 20 to 25 years would help to reduce material requirements by 7-10%. This study provides a basis for better understanding future OWE material requirements and, therefore, for optimizing future OWE developments in the ongoing energy transition. Show less
Artificial intelligence (AI) applications and digital technologies (DTs) are increasingly present in the daily lives of citizens, in cities and in industries. These developments generate large... Show moreArtificial intelligence (AI) applications and digital technologies (DTs) are increasingly present in the daily lives of citizens, in cities and in industries. These developments generate large amounts of data and enhance analytical capabilities that could benefit the industrial ecology (IE) community and sustainability research in general. With this communication, we would like to address some of the opportunities, challenges, and next steps that could be undertaken by the industrial ecology community in this realm. This article is an adapted summary of the discussion held by experts in industrial ecology, AI, and sustainability during the 2021 Industrial Ecology Day conference session titled “The Future of Artificial Intelligence in the Context of Industrial Ecology.” In brief, building on previous studies and communications, we advise the industrial ecology community to: (1) create internal committees and working groups to monitor and coordinate AI applications within and outside the community; (2) promote and ensure transdisciplinary efforts; (3) determine optimal infrastructure and governance of AI for IE to minimize undesired effects; and (4) act on effective representation and on reduction of digital divides. Show less
Continuous reduction in the levelized cost of energy is driving the rapid development of offshore wind energy (OWE). It is thus important to evaluate, from an environmental perspective, the... Show moreContinuous reduction in the levelized cost of energy is driving the rapid development of offshore wind energy (OWE). It is thus important to evaluate, from an environmental perspective, the implications of expanding OWE capacity on a global scale. Nevertheless, this assessment must take into account various scenarios for the growth of different OWE technologies in the near future. To evaluate the environmental impacts of future OWE development, this paper conducts a prospective life cycle assessment (LCA) including parameterized supply chains with high technology resolution. Results show that OWE-related environmental impacts, including climate change, marine ecotoxicity, marine eutrophication, and metal depletion, are reduced by similar to 20% per MWh from 2020 to 2040 due to various developments including size expansion, lifetime extension, and technology innovation. At the global scale, 2.6-3.6 Gt CO2 equiv of greenhouse gas emissions are emitted cumulatively due to OWE deployment from 2020 to 2040. The manufacturing of primary raw materials, such as steel and fibers, is the dominant contributor to impacts. Overall, 6-9% of the cumulative OWE-related environmental impacts could be reduced by end-of-life (EoL) recycling and the substitution of raw materials. Show less
Nanoplastics (NPs) have become a new type of pollutant of high concern that is ubiquitous in aqueous environments. However, the transport and transformation of NPs in natural waters are not yet... Show moreNanoplastics (NPs) have become a new type of pollutant of high concern that is ubiquitous in aqueous environments. However, the transport and transformation of NPs in natural waters are not yet fully understood. In this study, the aggregation and photooxidation of NPs were assessed with nanosized polystyrene (PS) as an example, and the effects of dissolved organic matter (DOM) were investigated with Suwannee River fulvic acid (SRFA) as representative DOM. The results showed that simulated sunlight irradiation exhibited negligible effects on the aggregation of PS, while SRFA enhanced its heteroaggregation through hydrophobic interactions. In SRFA solutions, photooxidation of PS with a particle size of 200 nm was observed, which led to an increase in the O/C ratio on its surface at a rate of (2.20 +/- 0.40) x 10(-2) h(-1). This indicates the promotional effect of SRFA on the oxidation of nanosized PS, which is attributed to the generation of the excited triplet state ((3)SRFA*), hydroxyl radicals ((OH)-O-center dot), and singlet oxygen (O-1(2)). Among these reactive species, O-1(2) played a crucial role in the oxidation of PS. The findings in this study are helpful for an in-depth understanding of the environmental behavior of NPs in natural waters. Show less
Countries around the globe have introduced renewable energies (RE) and minimized the dependency of fossil resources in power systems to address extensive environmental risks. However, such large... Show moreCountries around the globe have introduced renewable energies (RE) and minimized the dependency of fossil resources in power systems to address extensive environmental risks. However, such large-scale energy transitions pose a great challenge to power systems due to the volatility of RE. Meanwhile, power demand is increasing over time and it shows temporal characteristics, such as seasonal and peak-valley patterns. Whether the future power system with a larger proportion of RE can meet the surging but fluctuated electricity demand remains problematic. Previous studies on short-term load forecasting focused more on forecasting accuracy than stability. Further, there is a relative paucity of research into temporal patterns. In order to fill in these research gaps, this paper proposes a fuzzy theory-based machine learning model for workdays and weekends short-term load forecasting. Fuzzy time series (FTS) is applied for data mining and back propagation (BP) neural network is used as the main predictor for short-term load forecasting. To exploit the trade-offs between forecasting stability and accuracy, multi-objective optimization is applied to modify the parameters of BP. Moreover, an interval forecasting architecture with several statistical tests is constructed to address forecasting uncertainties. Short-term load data from Victoria in Australia is selected as a case study. Results demonstrate that the proposed method can significantly boost forecasting stability and accuracy, and help strategy making in the field of energy and electricity system management and planning. (c) 2021 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). Show less
Zhang, C.; Hu, M.; Sprecher, B.; Yang, X.; Zhong, X.; Li, C.; Tukker, A. 2021
Around 35% of the buildings in Europe are over 50 years old and almost 75% of the building stock is energy-inefficient. A European project VEEP is developing an innovative prefabricated concrete... Show moreAround 35% of the buildings in Europe are over 50 years old and almost 75% of the building stock is energy-inefficient. A European project VEEP is developing an innovative prefabricated concrete element (PCE) system to improve the thermal performance of new buildings (PCE1) and old buildings (PCE2). This study focused on retrofitting of old buildings via over-cladding of the building envelope with PCE2. This study aims to from a building owner/consumer’s perspective to explore the life cycle economic performance of the PCE2 system at an early stage and associated cost optimization strategies under the European context. This study tries to answer four questions: 1) whether the use of the PCE2 leads to an economic advantage over a specific life cycle of an existing building. 2) what is the biggest cost stressor in the life cycle of a PCE2? 3) the potential route for further cost optimization. and 4) how would the discount rate affect the life cycle costs, especially when Europe has entered a negative rate age? A typical apartment building in the Netherlands is selected as the case study for dynamic thermal simulation, in which the heating and cooling energy demands before and after refurbishment with PCE2 will be evaluated. By employing environmental life cycle costing (LCC), the life cycle costs over 40 years and associated strategy for cost optimization will be investigated. This research not only unveils meaningful financial implications on resource-efficient building energy retrofitting in Europe but also provides insight on methodological dilemmas within the application of LCC. Show less
The transmission of antibiotic resistance in surface water has attracted much attention due to its increasing threat to human health. The role of sunlight irradiation and the effect of dissolved... Show moreThe transmission of antibiotic resistance in surface water has attracted much attention due to its increasing threat to human health. The role of sunlight irradiation and the effect of dissolved organic matter (DOM) on the transmission of antibiotic resistance are still unclear. In this study, photo-inactivation of antibiotic resistant bacteria (ARB) was investigated using antibiotic resistant E. coli (AR E. coli) that contained the tetracycline resistance gene (Tc-ARG) as a representative. The results showed that AR E. coli underwent significant photo-inactivation due to the membrane damage induced by direct irradiation and by the generated reactive oxygen species. Simulated sunlight irradiation specifically suppressed the expression of tetracycline resistance, which is attributed to the destruction of tetracycline-specific efflux pump. Tetracycline inhibited the photo-inactivation of AR E. coli due to its selective pressure on tetracycline resistant E. coli and competitive light absorption effect. Suwannee River fulvic acid (SRFA), a representative DOM, promoted the inactivation of AR E. coli and further inhibited the expression of tetracycline resistance gene due to the generation of its excited triplet state, singlet oxygen, and hydroxyl radical. The extracellular Tc-ARG also underwent fast photodegradation under light irradiation and in the presence of SRFA, which leads to the decrease of its transformation efficiency. This study provided insight into the sunlight-induced inactivation of ARB, which is of significance for understanding the transmission of tetracycline resistance in surface water. Show less
The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the... Show moreThe transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermittency of RE, it is challenging to design an accurate and reliable short-term load forecasting model. Recently, machine learning (ML) based forecasting models have been applied for short-term load forecasting whereas most of them ignore the importance of characteristics mining, parameters fine-tuning, and forecasting stability. To dissolve the above issues, a short-term load forecasting model is proposed that incorporates thorough data mining and multi-step rolling forecasting. To alleviate the chaos of short-term load, a de-noising method based on decomposition and reconstruction is used. Then, a phase space reconstruction (PSR) method is employed to dynamically determine the train-test ratios and neurons settings of the artificial neural network (ANN). Further, a multi-objective grasshopper optimization algorithm (MOGOA) is applied to optimize the parameters of ANNs. Case studies are conducted in the urban smart grid systems of Victoria and New South Wales in Australia. Simulation results show that the proposed model can forecast short-term load well with various measurement metrics. Multiple criterion and statistical evaluation also show the good performance of the proposed forecasting model in terms of accuracy and stability. To conclude, the proposed model achieves high accuracy and robustness, which will provide references to RE transitions and smart grid optimization, and offer guidance to sustainable city development. Show less
