Hasil untuk "Environmental Science"

M. Herrero, J. Mendiola, A. Cifuentes et al.

Among the different extraction techniques used at analytical and preparative scale, supercritical fluid extraction (SFE) is one of the most used. This review covers the most recent developments of SFE in different fields, such as food science, natural products, by-product recovery, pharmaceutical and environmental sciences, during the period 2007-2009. The revision is focused on the most recent advances and applications in the different areas; among them, it is remarkable the strong impact of SFE to extract high value compounds from food and natural products but also its increasing importance in areas such as heavy metals recovery, enantiomeric resolution or drug delivery systems.

R. Triebskorn, T. Braunbeck, T. Grummt et al.

Abstract The current paper critically reviews the state-of-the-science on (1) microplastics (MP) types and particle concentrations in freshwater ecosystems, (2) MP and nanoplastics (NP) uptake and tissue translocation, (3) MP/NP-induced effects in freshwater organisms, and (4) capabilities of MP/NP to modulate the toxicity of environmental chemicals. The reviewed literature as well as new data on MP and NP concentrations in the river Elbe and on particle uptake into human cells indicate an environmental relevance of small particles in the low nano- and micrometer range higher than that of larger MP.

J. Fergusson

V. Pawlowsky-Glahn, A. Buccianti

M. Hansen, Hongtao Li, R. Svarverud

Abstract Ecological civilization (shengtai wenming ) has been written into China’s constitution as the ideological framework for the country’s environmental policies, laws and education. It is also increasingly presented not only as a response to environmental degradation in China, but as a vision for our global future. In this article, scholars from the disciplines of media science, anthropology and sinology analyse media representations of eco-civilization in order to explore which values and visions this highly profiled state project actually entails. The article argues that eco-civilization is best understood as a sociotechnical imaginary in which cultural and moral virtues constitute key components that are inseparable from the more well-known technological, judicial, and political goals. The imaginary of eco-civilization seeks to construct a sense of cultural and national continuity, and to place China at the center of the world by invoking its civilization’s more than 2000 years of traditional philosophical heritage as a part of the solution for the planet’s future. It is constructed as a new kind of Communist Party led utopia in which market economy and consumption continue to grow, and where technology and science have solved the basic problems of pollution and environmental degradation. Download : Download high-res image (6KB) Download : Download full-size image

H. Gupta, Thorsten Wagener, Yuqiong Liu

A. Clark

D. Demeritt

T. M. Crisp, E. Clegg, R. Cooper et al.

This report is an overview of the current state of the science relative to environmental endocrine disruption in humans, laboratory testing, and wildlife species. Background information is presented on the field of endocrinology, the nature of hormones, and potential sites for endocrine disruption, with specific examples of chemicals affecting these sites. An attempt is made to present objectively the issue of endocrine disruption, consider working hypotheses, offer opposing viewpoints, analyze the available information, and provide a reasonable assessment of the problem. Emphasis is placed on disruption of central nervous system--pituitary integration of hormonal and sexual behavioral activity, female and male reproductive system development and function, and thyroid function. In addition, the potential role of environmental endocrine disruption in the induction of breast, testicular, and prostate cancers, as well as endometriosis, is evaluated. The interrelationship of the endocrine and immune system is documented. With respect to endocrine-related ecological effects, specific case examples from the peer-reviewed literature of marine invertebrates and representatives of the five classes of vertebrates are presented and discussed. The report identifies some data gaps in our understanding of the environmental endocrine disruption issue and recommends a few research needs. Finally, the report states the U.S. Environmental Protection Agency Science Policy Council's interim position on endocrine disruption and lists some of the ongoing activities to deal with this matter.

G. Pierzynski, G. Vance, T. Sims

L. Firth, A. Knights, D. Bridger et al.

Yupeng LI, Yaning CHEN, Fei WANG et al.

Tajikistan, a mountainous country and a vital water tower for Central Asia, is becoming increasingly vulnerable to snow drought under climate change, threatening its snow- and glacier-fed streamflow. Yet, the impacts of snow drought on the regional hydrology remain insufficiently understood. In this study, we integrated multisource data, including the Fifth Generation European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis for Land Applications (ERA5-Land) data and hydrological station data, to systematically assess the snow drought patterns and their impacts on streamflow during 1950–2023. We identified snow drought events based on precipitation and snow fraction anomalies relative to climatological means and classified them into warm snow drought, dry snow drought, and warm&dry snow drought. The results revealed that snow drought was a recurrent phenomenon, occurring in 51.70% of the years during the study period, with warm&dry snow drought accounting for 21.90% of the total events. Both the frequency and severity exhibited pronounced spatial variability, largely governed by the elevation and snowfall fraction. Specifically, the frequency of warm snow drought was negatively correlated with the snowfall fraction, decreasing on average by 0.20 per unit increase in snowfall fraction, whereas the frequency of dry snow drought was positively correlated, increasing by 0.07 per unit increase. The streamflow analysis results demonstrated that snow drought typically reduced the warm-season discharge by 5.00%–18.00% in certain rivers, thereby exacerbating the water stress during the dry season. The results of this study advance our understanding by explicitly linking the types of snow drought to hydrological responses in Central Asia’s high mountains, providing a scientific basis for climate adaptation and sustainable water resource management in Tajikistan.

J.I. Mnyango, B. Hlangothi, B. Nyoni et al.

Natural rubber (NR) is a high-performance elastomer valued for its elasticity, tensile strength, and tear resistance, making it indispensable in diverse industrial applications, including automotive tires, vibration-damping systems, medical devices, and consumer goods. However, conventional synthetic additives used in NR compounding pose environmental and health concerns due to their persistence, toxicity, and non-renewable origin. This review critically examines recent developments in natural-based additives—sourced from plants (e.g., lignin and vegetable oils), animals (e.g., collagen and chitosan), and minerals (e.g., silica and clay)—and their integration into NR compounding and processing. The discussion highlights how these bio-derived alternatives influence rheological, mechanical, thermal, and chemical properties of NR compounds, with comparative insights against conventional formulations. Challenges related to additive availability, processing behavior, cost, and suitability for high-performance applications are highlighted, alongside their potential to reduce reliance on petroleum-based materials. Finally, emerging directions such as smart/nano-enabled additives and AI/ML-driven formulation optimization are considered, underscoring the potential for natural-based additives to advance environmentally sustainable practices while meeting evolving industrial demands.

Ting Hu, Ying Sun

Attribution of historical climate changes is particularly challenging for periods before the mid-20th century due to sparse and low-quality observational data, raising questions about which temporal scales best capture the climate system’s response to external forcings and can robustly constrain future projections. To address these issues, this study performs a multi-scale attribution analysis of temperature extremes over the period 1901–2020 and examines the robustness of attribution-based scaling factors across different time scales. Despite uncertainties in the early data, the newly developed homogenized observations show pronounced warming in both cold and hot extremes, along with a lengthening of the growing season during 1901–2020. These trends intensified markedly after the 1950s, with the magnitude of changes approximately doubling for some extreme indices. Coupled Model Intercomparison Project Phase 6 (CMIP6) models successfully reproduce the overall warming trends in observations, although they underestimate the magnitude of changes, particularly in the pre-1950 period. Using optimal fingerprinting, more than 70% of the observed changes are attributed to greenhouse gas forcing, with aerosols offsetting less than 35% of the greenhouse gas-induced warming. Attribution analysis conducted within a large-ensemble model framework across multiple time scales shows that the ranges of best estimates and confidence intervals (CIs) for scaling factors decrease as the time period lengthens. The century-scale attribution yields the narrowest CIs and most robust best estimates, indicating the most robust detection results. Despite the robustness of century-scale results, scaling factors from 1951–2020 are selected to constrain projections due to more reliable observational constraints. Constrained end-of-century (2081–2100) projections show amplified increases of 20.3%–33.1% for most extremes compared to raw projections, highlighting the critical impact of attribution period selection and providing a transferable framework for regional climate risk assessment.

S. Scholz, S. Fischer, Ulrike Gündel et al.

Wenfu Tang, Christine Wiedinmyer, Louisa K. Emmons et al.

Abstract Structure fires in the wildland–urban interface (WUI) are becoming more frequent and destructive, yet their emissions of air pollutants remain poorly quantified and are not included in national inventories. Here we present a conterminous-scale inventory of WUI-related structure fire emissions in the United States from 2000 to 2020. A small number of highly destructive events dominate structure fire emissions—the 20 most destructive fires account for 68% of total carbon monoxide emissions. Structure fire emissions are more spatially concentrated than vegetation fire emissions, and in several states emissions of specific hazardous air pollutants such as hydrochloric acid exceed those from all anthropogenic sources combined. We show that structure burning in wildfires is strongly influenced by fire-conducive weather, and destructive structure fires are more likely to occur in forested and urbanized landscapes. These results reveal structure fires as a major source of toxic air pollution, with important implications for air quality, public health, and fire management.

Pilar Ortíz Sandoval, Margarita Aguilera‐Gómez, Anna Kostka et al.

Abstract Anthropogenic activities, such as industrial processes, urban development, intensive agriculture and waste disposal, have significantly contributed to the continuous introduction and accumulation of a wide array of xenobiotic compounds into natural ecosystems. Among them, emerging contaminants (ECs) such as pharmaceuticals, endocrine‐disrupting chemicals (EDCs), and per‐ and polyfluoroalkyl substances (PFAS) are of increasing concern due to their persistence, bioactivity and limited regulation. ECs enter ecosystems through diverse pathways including wastewater discharge, agricultural runoff and atmospheric deposition. Once released, many of these xenobiotics can bioaccumulate in organisms and enter the food chain, posing serious risks to food safety and public health. Traditional physico‐chemical remediation methods are often insufficient or environmentally taxing, prompting a shift toward bio‐based alternatives like bioremediation. These approaches, which rely on the activity of microbial communities to degrade pollutants, offer more sustainable solutions but require further interdisciplinary research to optimise their use. The One Health framework provides an effective model for addressing the complex risks posed by xenobiotics. This research programme aims to harmonise methodologies for cumulative dietary risk assessment across Europe and explore microbial strategies for xenobiotic degradation. By integrating microbiomics, toxicology, environmental science and food safety, this approach supports the development of safer food systems and more effective pollution management in line with the ‘farm to fork’ and One Health principles.

Grace Ra Kim, Hailey Warner, Duncan Eddy et al.

Deep space missions face extreme communication delays and environmental uncertainty that prevent real-time ground operations. To support autonomous science operations in communication-constrained environments, we present a partially observable Markov decision process (POMDP) framework that adaptively sequences spacecraft science instruments. We integrate a Bayesian network into the POMDP observation space to manage the high-dimensional and uncertain measurements typical of astrobiology missions. This network compactly encodes dependencies among measurements and improves the interpretability and computational tractability of science data. Instrument operation policies are computed offline, allowing resource-aware plans to be generated and thoroughly validated prior to launch. We use the Enceladus Orbilander's proposed Life Detection Suite (LDS) as a case study, demonstrating how Bayesian network structure and reward shaping influence system performance. We compare our method against the mission's baseline Concept of Operations (ConOps), evaluating both misclassification rates and performance in off-nominal sample accumulation scenarios. Our approach reduces sample identification errors by nearly 40%

César Barilla, Palaash Bhargava

How does the climatic experience of previous generations affect today's attention to environmental questions? Using self-reported beliefs and environmental themes in folklore, we show empirically that the realized intensity of deviations from typical climate conditions in ancestral generations influences how much descendants care about the environment. The effect exhibits a U-shape where more stable and more unstable ancestral climates lead to higher attention today, with a dip for intermediate realizations. We propose a theoretical framework where the value of costly attention to environmental conditions depends on the perceived stability of the environment, prior beliefs about which are shaped through cultural transmission by the experience of ethnic ancestors. The U-shape is rationalized by a double purpose of learning about the environment: optimal utilization of typical conditions and protection against extreme events.

Yuhao Kang

This entry provides an overview of Human-centered Geospatial Data Science, highlighting the gaps it aims to bridge, its significance, and its key topics and research. Geospatial Data Science, which derives geographic knowledge and insights from large volumes of geospatial big data using advanced Geospatial Artificial Intelligence (GeoAI), has been widely used to tackle a wide range of geographic problems. However, it often overlooks the subjective human experiences that fundamentally influence human-environment interactions, and few strategies have been developed to ensure that these technologies follow ethical guidelines and prioritize human values. Human-centered Geospatial Data Science advocates for two primary focuses. First, it advances our understanding of human-environment interactions by leveraging Geospatial Data Science to measure and analyze human subjective experiences at place including emotion, perception, cognition, and creativity. Second, it advocates for the development of responsible and ethical Geospatial Data Science methods that protect geoprivacy, enhance fairness and reduce bias, and improve the explainability and transparency of geospatial technologies. With these two missions, Human-centered Geospatial Data Sciences brings a fresh perspective to develop and utilize geospatial technologies that positively impact society and benefit human well-being and the humanities.