The Environmental Pollution book series includes current, comprehensive texts on critical national and global environmental issues useful to scientists in academia, industry and government from diverse disciplines. These include water, air, and soil pollution, organic and inorganic pollution, risk assessment, human and environmental health, environmental biotechnology, global ecology, mathematics and computing as related to environmental pollution, environmental modelling, environmental chemistry and physics, biology, toxicology, conservation and biodiversity, agricultural sciences, pesticides, environmental engineering, bioremediation/biorestoration, and environmental economics. Environmental problems and solutions are complex and interrelated. Complex problems often require complex solutions. The linkage of many disciplines can result in new approaches to old and new environmental problems as well as pollution prevention. This knowledge will assist in understanding, maintaining and improving the biosphere in which we live. Proposals for this book series can be sent the Series Editor: Jack Trevors at jtrevors@uoguelph.ca or the Publishing Editor Sherestha Saini at Sherestha.Saini@springer.com
Robert B. Jackson, M. Saunois, Philippe Bousquet
et al.
R B Jackson, M Saunois, P Bousquet, J G Canadell, B Poulter, A R Stavert, P Bergamaschi, Y Niwa, A Segers and A Tsuruta 1 Department of Earth System Science, Woods Institute for the Environment, and Precourt Institute for Energy, , Stanford University, Stanford, CA 94305-2210, United States of America 2 Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Universit́e Paris-Saclay, 91191 Gif-sur-Yvette, France 3 Global Carbon Project, CSIRO Oceans and Atmosphere, Canberra, ACT 2601, Australia 4 NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD 20771, United States of America 5 Global Carbon Project, CSIRO Oceans and Atmosphere, Aspendale, VIC 3195, Australia 6 European Commission Joint Research Centre, 21027 Ispra (Va), Italy 7 Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan 8 Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan 9 TNO, Dept. of Climate Air & Sustainability, NL-3508-TA Utrecht, The Netherlands 10 Finnish Meteorological Institute, FI-00101 Helsinki, Finland
E. Abad-Segura, Mariana-Daniela González-Zamar, J. Infante-Moro
et al.
Digital transformation in the education sector has implied the involvement of sustainable management, in order to adapt to the changes imposed by new technologies. Trends in global research on this topic have been analyzed and studied, during the 1986–2019 period. To achieve this purpose, a bibliometric study of 1590 articles from the Scopus database has been applied. The results provided data on the scientific productivity of authors, journals, institutions, and countries that contribute to the development of this research area. The evidence reveals an exponential trend, with special interest in the last five years. The main categories are Social Sciences and Environmental Science. The most productive journal is Sustainability. The author with more articles is Mulder, from The Hague University of Applied Sciences. The most productive institution is Delft University of Technology. The USA is the country with the most academic publications and international collaborations in its studies. The main keywords used in the articles are “sustainability”, “sustainable development”, “higher education”, “innovation”, “technology”, “environmental technology”, “technological development”, and “environmental management”. Global research has followed a growing trend, with optimal publication levels in recent years.
In this review, we survey the diversity of structures and functions which are encountered in advanced self-assembled nanomaterials. We highlight their flourishing implementations in three active domains of applications: biomedical sciences, information technologies, and environmental sciences. Our main objective is to provide the reader with a concise and straightforward entry to this broad field by selecting the most recent and important research articles, supported by some more comprehensive reviews to introduce each topic. Overall, this compilation illustrates how, based on the rules of supramolecular chemistry, the bottom-up approach to design functional objects at the nanoscale is currently producing highly sophisticated materials oriented towards a growing number of applications with high societal impact.
Background: In 2008, the National Institute of Environmental Health Sciences/National Toxicology Program, the U.S. Environmental Protection Agency’s National Center for Computational Toxicology, and the National Human Genome Research Institute/National Institutes of Health Chemical Genomics Center entered into an agreement on “high throughput screening, toxicity pathway profiling, and biological interpretation of findings.” In 2010, the U.S. Food and Drug Administration (FDA) joined the collaboration, known informally as Tox21. Objectives: The Tox21 partners agreed to develop a vision and devise an implementation strategy to shift the assessment of chemical hazards away from traditional experimental animal toxicology studies to one based on target-specific, mechanism-based, biological observations largely obtained using in vitro assays. Discussion: Here we outline the efforts of the Tox21 partners up to the time the FDA joined the collaboration, describe the approaches taken to develop the science and technologies that are currently being used, assess the current status, and identify problems that could impede further progress as well as suggest approaches to address those problems. Conclusion: Tox21 faces some very difficult issues. However, we are making progress in integrating data from diverse technologies and end points into what is effectively a systems-biology approach to toxicology. This can be accomplished only when comprehensive knowledge is obtained with broad coverage of chemical and biological/toxicological space. The efforts thus far reflect the initial stage of an exceedingly complicated program, one that will likely take decades to fully achieve its goals. However, even at this stage, the information obtained has attracted the attention of the international scientific community, and we believe these efforts foretell the future of toxicology.
From the Department of Environmental Health Sciences and Columbia Center for Children’s Environmental Health, Columbia University, New York (F.P.); and the Departments of Medicine, Pediatrics, Otolaryngology, and Epidemiology and Population Health, Stanford University, Stanford, CA (K.N.). Dr. Perera can be contacted at fpp1@ columbia . edu or at the Columbia Center for Children’s Environmental Health, Columbia University, 722 W. 168th St., 12th Fl., New York, NY 10032.
M. Lawrence, Stephen Williams, Patrizia Nanz
et al.
Mark G. Lawrence,1,2,5,* Stephen Williams,1 Patrizia Nanz,1,3 and Ortwin Renn1,4 1Institute for Advanced Sustainability Studies (IASS), Potsdam, Germany 2Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany 3Political and Administrative Sciences, University of Potsdam, Potsdam, Germany 4Institute of Social Sciences, University of Stuttgart, Stuttgart, Germany 5Twitter: @mark_g_lawrence *Correspondence: mark.lawrence@iass-potsdam.de https://doi.org/10.1016/j.oneear.2021.12.010
Recent controversy has led to calls for increased standardization and transparency in the methods used to synthesize climate change research. Though these debates have focused largely on the biophysical dimensions of climate change, human dimensions research is equally in need of improved methodological approaches for research synthesis. Systematic review approaches, and more recently realist review methods, have been used within the health sciences for decades to guide research synthesis. Despite this, penetration of these approaches into the social and environmental sciences has been limited. Here, we present an analysis of approaches for systematic review and research synthesis and examine their applicability in an adaptation context. Customized review frameworks informed by systematic approaches to research synthesis provide a conceptually appropriate and practical opportunity for increasing methodological transparency and rigor in synthesizing and tracking adaptation research. This review highlights innovative applications of systematic approaches, with a focus on the unique challenges of integrating multiple data sources and formats in reviewing climate change adaptation policy and practice. We present guidelines, key considerations, and recommendations for systematic review in the social sciences in general and adaptation research in particular. We conclude by calling for increased conceptual and methodological development of systematic review approaches to address the methodological challenges of synthesizing and tracking adaptation to climate change.
Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China University of Chinese Academy of Sciences, Beijing 100049, China
Sundararaman Gopalakrishnan, Krishna K. Osuri, Dev Niyogi
et al.
ABSTRACT Over the last decade, tropical cyclone (TC) track and intensity predictions have improved by nearly 50% in the Atlantic and Northern Indian Ocean, driven by advancements in ocean‐coupled numerical models, data assimilation techniques, and an expanding network of observations. However, the prediction of severe weather events driven by convection, particularly those associated with heavy precipitation over land, has not kept pace with these improvements in TC forecasting. While 1–2 km horizontal resolutions are crucial for capturing convection over land and ocean, seamless prediction across scales demands an accurate representation of the coupled evolution of ocean, land, and atmospheric states. To address the complex problem of severe weather across a spectrum of atmospheric motions—including TCs over the ocean and severe convective systems over coastal and inland regions—we have developed the Indian Ocean–Land–Atmosphere (IOLA) Coupled Mesoscale Prediction Framework. This Framework integrates the well‐tested nonhydrostatic model (NMM) dynamical core with advanced nesting techniques from the hurricane weather research and forecast (HWRF) system. It further incorporates ocean coupling from HWRF and physics packages adopted from the WRF community model. This represents the first‐ever coupled modeling system explicitly designed to tackle extreme weather events across multiple domains and scales. Extensive testing of this novel modeling framework demonstrates that a high‐resolution (1–2 km) “all‐purpose” severe weather prediction system can effectively address the challenges of forecasting extreme weather over the Indian region. One of the key focuses of this work is the application of 1‐km horizontal resolution moving nests over the monsoon region, where synoptic‐scale interactions play a critical role in modulating severe weather and heavy precipitation events. With this configuration, the model provides a high equitable threat score (ETS) > 0.18 for heavy to extreme rainfall events for 48 h and above lead times. This framework enables a unified approach to simulating severe weather phenomena accurately and flexibly. Also, it sets a new benchmark for seamless prediction of extreme weather, paving the way for improved resilience against coastal hazards and inland severe weather events.
Integration of AI into environmental regulation represents a significant advancement in data management. It offers promising results in both data protection plus algorithmic fairness. This research addresses the critical need for sustainable data protection in the era of ever evolving cyber threats. Traditional encryption methods face limitations in handling the dynamic nature of environmental data. This necessitates the exploration of advanced cryptographic techniques. The objective of this study is to evaluate how AI can enhance these techniques to ensure robust data protection while facilitating fair algorithmic management. The methodology involves a comprehensive review of current advancements in AI-enhanced homomorphic encryption (HE) and multi-party computation (MPC). It is coupled with an analysis of how these techniques can be applied to environmental data regulation. Key findings indicate that AI-driven dynamic key management, adaptive encryption schemes, and optimized computational efficiency in HE, alongside AI-enhanced protocol optimization and fault mitigation in MPC, significantly improve the security of environmental data processing. These findings highlight a crucial research gap in the intersection of AI, cyber laws, and environmental regulation, particularly in terms of addressing algorithmic bias, transparency, and accountability. The implications of this research underscore the need for stricter cyber laws. Also, the development of comprehensive regulations to safeguard sensitive environmental data. Future efforts should focus on refining AI systems to balance security with privacy and ensuring that regulatory frameworks can adapt to technological advancements. This study provides a foundation for future research aimed at achieving secure sustainable environmental data management through AI innovations.
Chelsea M Cary, Taina L Moore, Andrew J Gow
et al.
ABSTRACT Micro and nanoplastic (MNP) detection in human tissues demonstrates that exposure at any life stage is inevitable. We have previously demonstrated that pulmonary exposure to this emerging environmental contaminant impairs endothelial function in the uterine vasculature of nonpregnant and pregnant rats. However, neither the mechanism of this dysfunction nor the role of the endothelial-derived vasodilator, nitric oxide (NO), have been interrogated. Therefore, we assessed uterine macro- and microvascular reactivity in Sprague Dawley rats to determine the mechanistic role of NO signaling in endothelial dysfunction after repeated (gestational day 5-19) MNP inhalation during pregnancy. Results identified that MNP exposure reduced fetal growth and impaired endothelial-dependent dilation in the uterine microcirculation, which control placental perfusion and resource availability to the fetus. Levels of activated endothelial nitric oxide synthase (eNOS), phosphorylated on Ser 1176 , were substantially decreased (<50%) in uterine vessels from exposed rats. This suggests MNP inhalation limited NO production and bioavailability. Endothelial function was partially restored by supplementation of arterial segments with the eNOS cofactor tetrahydrobiopterin (BH 4 ), demonstrating that exposed vessels were BH 4 -deficient. Partial restoration was also achieved by incubation with the reducing agent, DTT, suggesting that exposed vessels contained physiologically relevant levels of reactive oxygen and nitrogen species. Increased 3-nitrotyrosine residues and decreased thioredoxin protein expression further suggest MNP fosters nitrosative and oxidative stress in the uterine vasculature, impairing eNOS and endothelial-dependent dilation. These findings implicate eNOS uncoupling as a mechanistic basis for the vascular toxicity of MNPs and the adverse impact of MNPs on fetal development. NEW & NOTEWORTHY This study reveals that repeated micro and nanoplastic (MNP) inhalation throughout gestation blunts endothelial-dependent dilation in the uterine microcirculation, promoting fetal growth restriction. Exposure impaired endothelial nitric oxide signaling through deactivating endothelial nitric oxide synthase (eNOS), reducing the availability of the eNOS cofactor tetrahydrobiopterin and producing a nitrosative and oxidative environment in uterine vascular tissue. These novel findings highlight the eNOS uncoupling as a key mechanism behind the fetal growth restriction induced by MNP.