This thesis aims to address offshore wind energy (OWE)-related environmental impacts. It includes the future material demand for the manufacturing of OWE turbines and foundations, the cradle-to-the... Show moreThis thesis aims to address offshore wind energy (OWE)-related environmental impacts. It includes the future material demand for the manufacturing of OWE turbines and foundations, the cradle-to-the-grave environmental impacts of global OWE development, the impacts on marine biodiversity, and other impacts on the environment. Show less
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
Pollen classification is considered an important task in palynology. In the Netherlands, two genera of the Urticaceae family, named Parietaria and Urtica, have high morphological similarities but... Show morePollen classification is considered an important task in palynology. In the Netherlands, two genera of the Urticaceae family, named Parietaria and Urtica, have high morphological similarities but induce allergy at a very different level. Therefore, distinction between these two genera is very important. Within this group, the pollen of Urtica membranacea is the only species that can be recognized easily under the micro-scope. For the research presented in this study, we built a dataset from 6472 pollen images and our aim was to find the best possible classifier on this dataset by analysing different classification methods, both machine learning and deep learning-based methods. For machine learning-based methods, we measured both texture and moment features based on images from the pollen grains. Varied feature selection tech-niques, classifiers as well as a hierarchical strategy were implemented for pollen classification. For deep learning-based methods, we compared the performance of six popular Convolutional Neural Networks: AlexNet, VGG16, VGG19, MobileNet V1, MobileNet V2 and ResNet50. Results show that compared with flat classification models, a hierarchical strategy yielded the highest accuracy with 94.5% among machine learning-based methods. Among deep learning-based methods, ResNet50 achieved an accuracy of 99.4%, slightly outperforming the other neural networks investigated. In addition, we investigated the influence on performance by changing the size of image datasets to 1000 and 500 images, respectively. Results demonstrated that on smaller datasets, ResNet50 still achieved the best classification performance. An ablation study was implemented to help understanding why the deep learning-based methods outper-formed the other models investigated. Using Urticaceae pollen as an example, our research provides a strategy of selecting a classification model for pollen datasets with highly similar pollen grains to support palynologists and could potentially be applied to other image classification tasks.(c) 2022 Leiden Institute of Advanced Computer Science, Leiden University. 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
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
Li, C.; Ma, J.; Groenewoud, A.; Ren, J.; Liu, S.; Snaar-Jagalska, B.E.; Dijke, P. ten 2022
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
Ferrara, A.; Sommovigo, L.; Dayal, P.; Pallottini, A.; Bouwens, R.J.; Gonzalez, V.; ... ; Werf, P.P. van der 2022
To identify novel cancer therapies, the tumor microenvironment (TME) has received a lot of attention in recent years in particular with the advent of clinical successes achieved by targeting immune... Show moreTo identify novel cancer therapies, the tumor microenvironment (TME) has received a lot of attention in recent years in particular with the advent of clinical successes achieved by targeting immune checkpoint inhibitors (ICIs). The TME consists of multiple cell types that are embedded in the extracellular matrix (ECM), including immune cells, endothelial cells and cancer associated fibroblasts (CAFs), which communicate with cancer cells and each other during tumor progression. CAFs are a dominant and heterogeneous cell type within the TME with a pivotal role in controlling cancer cell invasion and metastasis, immune evasion, angiogenesis and chemotherapy resistance. CAFs mediate their effects in part by remodeling the ECM and by secreting soluble factors and extracellular vesicles. Exosomes are a subtype of extracellular vesicles (EVs), which contain various biomolecules such as nucleic acids, lipids, and proteins. The biomolecules in exosomes can be transmitted from one to another cell, and thereby affect the behavior of the receiving cell. As exosomes are also present in circulation, their contents can also be explored as biomarkers for the diagnosis and prognosis of cancer patients. In this review, we concentrate on the role of CAFs-derived exosomes in the communication between CAFs and cancer cells and other cells of the TME. First, we introduce the multiple roles of CAFs in tumorigenesis. Thereafter, we discuss the ways CAFs communicate with cancer cells and interplay with other cells of the TME, and focus in particular on the role of exosomes. Then, we elaborate on the mechanisms by which CAFs-derived exosomes contribute to cancer progression, as well as and the clinical impact of exosomes. We conclude by discussing aspects of exosomes that deserve further investigation, including emerging insights into making treatment with immune checkpoint inhibitor blockade more efficient. Show less
A fundamental task in neuroscience is to characterize the brain's developmental course. While replicable group-level models of structural brain development from childhood to adulthood have recently... Show moreA fundamental task in neuroscience is to characterize the brain's developmental course. While replicable group-level models of structural brain development from childhood to adulthood have recently been identified, we have yet to quantify and understand individual differences in structural brain development. The present study examined inter-individual variability and sex differences in changes in brain structure, as assessed by anatomical MRI, across ages 8.0-26.0 years in 269 participants (149 females) with three time points of data (807 scans), drawn from three longitudinal datasets collected in the Netherlands, Norway, and USA. We further investigated the relationship between overall brain size and developmental changes, as well as how females and males differed in change variability across development. There was considerable inter-individual variability in the magnitude of changes observed for all examined brain measures. The majority of individuals demonstrated decreases in total gray matter volume, cortex volume, mean cortical thickness, and white matter surface area in mid-adolescence, with more variability present during the transition into adolescence and the transition into early adulthood. While most individuals demonstrated increases in white matter volume in early adolescence, this shifted to a majority demonstrating stability starting in mid-to-late adolescence. We observed sex differences in these patterns, and also an association between the size of an individual's brain structure and the overall rate of change for the structure. The present study provides new insight as to the amount of individual variance in changes in structural morphometrics from late childhood to early adulthood in order to obtain a more nuanced picture of brain development. The observed individual-and sex-differences in brain changes also highlight the importance of further studying individual variation in developmental patterns in healthy, at-risk, and clinical populations. 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
