Hasil untuk "Environmental Science"

Shifa Zhong, Kai Zhang, M. Bagheri et al.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Wondimagegn Mengist, T. Soromessa, G. Legese

Graphical abstract

M. Mahmoudi, M. Landry, Anna Moore et al.

The protein corona spontaneously develops and evolves on the surface of nanoscale materials when they are exposed to biological environments, altering their physiochemical properties and affecting their subsequent interactions with biosystems. In this Review, we provide an overview of the current state of protein corona research in nanomedicine. We next discuss remaining challenges in the research methodology and characterization of the protein corona that slow the development of nanoparticle therapeutics and diagnostics, and we address how artificial intelligence can advance protein corona research as a complement to experimental research efforts. We then review emerging opportunities provided by the protein corona to address major issues in healthcare and environmental sciences. This Review details how mechanistic insights into nanoparticle protein corona formation can broadly address unmet clinical and environmental needs, as well as enhance the safety and efficacy of nanobiotechnology products. Understanding the protein corona can advance nanomedicinal developments and elucidate how nanomaterials impact the environment. This Review discusses the evolution and challenges in characterizing the protein corona, explores how artificial intelligence can supplement experimental efforts and exposes emerging opportunities in nanomedicine and the environment.

J. Felden, Lars Möller, Uwe Schindler et al.

The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.

Muhammad Rehan Anwar, Lintang Dwi Sakti

The rapid urbanization of modern cities presents significant challenges in sustainable development. To address these challenges, there is a growing integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. This research aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can be employed to improve urban development strategies and enhance sustainability assessments. Employing Spatial Analysis with GIS, this study analyzes data on land use, population density, and environmental indicators across several metropolitan areas. The methodology incorporates machine learning algorithms to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments. These tools facilitate more informed decision-making in urban planning by enabling precise predictions about urban expansion and its environmental impacts. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, thereby setting a benchmark for future studies in the realm of sustainable urban planning. This research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.

E. Turnhout

This article offers a critical analysis of environmental science that develops the argument that science has itself become an obstacle for the transformations that are needed to ensure human-ecological well-being. Due to dominant norms and conceptualizations of what science is, how it should relate to policy and society, and what it is that science should contribute to, environmental science is set to continue to serve vested interests and seems unable to break free from this pattern. This deadlock situation is related to persistent patterns of inequality and marginalization in science that keep these dominant norms and conceptualizations in place and that marginalize alternative forms of knowledge, including critical social sciences and humanities, that are better equipped to support transformation. Inspired by feminist and anti-colonial scholarship, I suggest that transforming environmental science will require explicit refusal of dominant norms of science and conceptualizations of the environment, and a commitment to justice and pluralism.

Yixian Chen, Sofie De Geeter, Jean Poesen et al.

Gully formation is a significant driver of soil erosion and land degradation worldwide and often leads to important downstream impacts. Nonetheless, our understanding of the global patterns and the factors controlling this process remains limited. Here, we present the first global assessment of gully density's spatial patterns. Using mapped observations from over 17,000 representative study sites worldwide, we trained random forest models that simulate both the susceptibility to gullying at a 1 km2 resolution and the corresponding gully head density (GHD). Through an interpretable machine learning framework, we demonstrate that global GHD patterns result from a combination of environmental factors with non-linear interactions, leading to significant regional variations in the dominant factors controlling GHD. We distinguish between gully hotspots driven primarily by natural factors such as topography, geomorphology, tectonics, pedology or climate and those where land use and land cover play a dominant role. Based on these insights, we identified critical global areas of gully erosion, i.e., hotspots where gully occurrence is likely highly sensitive to anthropogenic drivers. These include the Chinese Loess Plateau, the Ethiopian Highlands, and large parts of the Mediterranean and Sahel regions. Also desert regions are often characterized by high GHDs. However, in these cases, their occurrence is mainly driven by natural factors. The insights we provide are valuable to inform land management and targeted erosion mitigation strategies.

E. Agathokleous, C. Saitanis, Chao Fang et al.

Artificial intelligence (AI) large language models (LLMs) have emerged as important technologies. Recently, ChatGPT (Generative Pre-trained Transformer) has been released and attracted massive interest from the public, owing to its unique capabilities to simplify many daily tasks of people from diverse backgrounds and social statuses. Here, we discuss how ChatGPT (and similar AI technologies) can impact biology and environmental science, providing examples obtained through interactive sessions with ChatGPT. The benefits that ChatGPT offers are ample and can impact many aspects of biology and environmental science, including education, research, scientific publishing, outreach, and societal translation. Among others, ChatGPT can simplify and expedite highly complex and challenging tasks. As an example to illustrate this, we provide 100 important questions for biology and 100 important questions for environmental science. Although ChatGPT offers a plethora of benefits, there are several risks and potential harms associated with its use, which we analyze herein. Awareness of risks and potential harms should be raised. However, understanding and overcoming the current limitations could lead these recent technological advances to push biology and environmental science to their limits.

Baskar Venkidasamy, Ashok Kumar Balaraman, Muthu Thiruvengadan

Ran Wang, Caibo Tang, Yuge Ma et al.

Building-integrated photovoltaics (BIPVs) represent a pivotal technology for enhancing the utilization of renewable energy in buildings. However, challenges persist, including the lack of integrated design models, limited analytical dimensions, and insufficient consideration of climate change impacts. This study proposes a comprehensive performance assessment framework for BIPV that incorporates global climate change factors. An integrated simulation model is developed using EnergyPlus8.9.0, Optics6, and WINDOW7.7 to evaluate BIPV configurations such as photovoltaic facades, shading systems, and roofs. A multi-criteria evaluation system is established, encompassing global warming potential (GWP), power generation, energy flexibility, and economic cost. Future hourly weather data for the 2050s and 2080s are generated using CCWorldWeatherGen under representative climate scenarios. Monte Carlo simulations are conducted to assess performance across variable combinations, supplemented by sensitivity and uncertainty analyses to identify key influencing factors. Results indicate (1) critical design parameters—including building orientation, wall thermal absorptance, window-to-wall ratios, PV shading angle, glazing optical properties, equipment and lighting power density, and occupancy—significantly affect overall performance. Equipment and lighting densities most influence carbon emissions and flexibility, whereas envelope thermal properties dominate cost impacts. PV shading outperforms other forms in power generation. (2) Under intensified climate change, GWP and life cycle costs increase, while energy flexibility declines, imposing growing pressure on system performance. However, under certain mid-century climate conditions, BIPV power generation potential improves due to altered solar radiation. The study recommends integrating climate-adaptive design strategies with energy systems such as PEDF (photovoltaic, energy storage, direct current, and flexibility), refining policy mechanisms, and advancing BIPV deployment with climate-resilient approaches to support building decarbonization and enhance adaptive capacity.

S. Lou, S. Lou, S. Lou et al.

<p>We used the CAM5 model to examine how different particle-bound polycyclic aromatic hydrocarbon (PAH) degradation approaches affect the spatial distribution of benzo(a)pyrene (BaP). Three approaches were evaluated: NOA (no effect of OA coatings state on BaP), shielded (viscous OA coatings shield BaP from oxidation under cool and dry conditions) and ROI-T (viscous OA coatings slow BaP oxidation in response to temperature and humidity). Results show that BaP concentrations vary seasonally, influenced by emissions, deposition, transport and degradation approach, all of which are influenced by meteorological conditions. All simulations predict higher population-weighted global average (PWGA) fresh BaP concentrations during December–January–February (DJF) compared to June–July–August (JJA), due to increased emissions from household activities and reduced removal processes during colder months. The shielded and ROI-T approaches, which account for OA coatings, result in 2–6 times higher BaP concentrations in DJF compared to NOA. The shielded simulation predicts the highest PWGA fresh BaP concentration (1.3 <span class="inline-formula">ng m⁻³</span>), with 90 % of BaP protected from oxidation. In contrast, the ROI-T approach forecasts lower concentrations in middle to low latitudes, as it assumes less effective OA coatings under warmer, more humid conditions. Evaluations against observed BaP concentrations show the shielded approach performs best, with a normalized mean bias (NMB) within <span class="inline-formula">±</span> 20 %. The combined incremental lifetime cancer risk (ILCR) for both fresh and oxidized PAHs is similar across simulations, emphasizing the importance of considering both forms in health risk assessments. This study highlights the critical role of accurate degradation approaches in PAH modeling.</p>

Kristine Godziuk, Mary Forhan, Flavio T. Vieira et al.

ABSTRACT Background Treatments aimed at improving physical function and body composition, including reducing fat mass (FM) and increasing muscle mass, may benefit individuals with advanced knee osteoarthritis (OA) and obesity. We investigated the feasibility and efficacy of a multimodal behavioural intervention compared to usual care to enhance physical function and muscle mass in this population. Methods The POMELO (Prevention Of MusclE Loss in Osteoarthritis) study is a two‐arm pilot randomized controlled trial; NCT05026385. Participants aged 40–75 years, with a BMI ≥ 35 kg/m2 and knee OA were randomized 1:1 to either the intervention group (POMELO) or usual care (UC). The 3‐month POMELO intervention incorporated progressive resistance exercise (3 sessions/week), individualized nutrition counselling targeted for OA, and 12 group education sessions on nutrition and arthritis self‐management. The UC group received standard clinical care. After the 3‐month supervised intervention, both groups were followed for 6 months without support. Assessments at baseline, 3 months and 9 months (primary endpoint) included body composition (DXA, measuring FM and appendicular lean soft tissue [ALST]), physical function (chair‐sit‐to‐stands [CSTS], 6‐min walk [6MWT], maximal handgrip strength [HGS]), and health‐related quality of life (Euroqol visual analog scale [EQ‐5D VAS]). Co‐primary outcomes were feasibility (intervention completion ≥ 80% and per‐protocol adherence ≥ 60% [i.e., attendance at 12 education sessions and exercise 3 ×/week]) and acceptability (4‐item Likert‐scale satisfaction survey, and open‐ended questions). Secondary outcomes included changes in physical function and ALST. Results Fifty participants were randomized (POMELO = 25, UC = 25), with 32 completing the study (69% female, mean age 64.9 ± 1.2 years, BMI 42.1 ± 1.0 kg/m2). The POMELO intervention group had 80% completion and 74% adherence, confirming feasibility. Higher satisfaction rates were observed in POMELO compared to UC (3.5 vs. 2.2, p < 0.001) indicating greater acceptability. The POMELO group had improvements in CSTS (mean difference [MD] 3.96, ES 1.2, p < 0.001), 6MWT (MD 31.6 m, ES 0.4, p = 0.039) and EQ‐5D VAS (MD 7.9 points, ES = 0.4, p = 0.01) compared to UC. Both groups experienced FM loss, but only the UC group lost ALST and HGS. Conclusion The POMELO intervention, combining personalized nutrition, resistance exercise and self‐management support, was feasible, acceptable and showed greater efficacy than usual care to improve physical function in patients with knee OA and obesity. Our pilot study of this intervention showed potential benefits on body composition and quality of life without focusing on weight reduction. A larger study is needed to confirm these results, as this approach may offer advantages over usual care, potentially leading to better mobility and health outcomes.

Lisa Dellmuth, Maria-Therese Gustafsson, Suanne Mistel Segovia-Tzompa

Abstract There is a lively debate about the legitimacy of the international climate regime, as represented by the United Nations Framework Convention on Climate Change, and the quality of non-state actor participation in the regime. This commentary examines perceptions of involved non-state actors from 2021–2022 regarding their participation and regime legitimacy. The findings reveal no legitimacy crisis for the adaptation and mitigation regimes, but the surveyed NSAs are divided in their legitimacy beliefs. NSAs also express significant disappointment about their opportunities for participation.

Nuiyen Aussanee, Khumin Vinta, Wichai Siriwan

Cadmium contamination found in paddy fields in the Maesot District of Tak Province, Thailand. This area was remediated using 50mg/L of ZVI. The study aimed to isolate and identify soil bacteria in the soil and rice roots and to investigate ZVI’s effect on the isolated bacterial cells. The results indicated no significant difference in soil bacteria content before and after remediation at the 95% confidence level. Twelve isolates of nitrogen-fixing bacteria were obtained. Those isolates could grow at high concentrations of 300 mg/L of ZVI. RH17 had a high tolerance for TSA with 300 mg/L of ZVI at only 10 CFU/ml. The effects of ZVI at 150 mg/L on RH17 cells, a small amount of ZVI was observed adhering to the cells’ surface and forming giant cells, while at 300 mg/L of ZVI, caused a reduction in growth by 81.0%. The nifH gene of RH17 was related to Rhizobium sp. strain 5-1-2. The results demonstrated the cadmium remediation process with 50mg/L of ZVI did not affect the cell count of soil bacteria in the paddy field. However, at 150 mg/L or higher, ZVI damaged the isolated Rhizobium sp. cell membrane. So, the remediation using ZVI must consider the appropriate concentration.

Paulo Roger Lopes Alves, German Andres Estrada-Bonilla, Antonio Marcos Miranda Silva et al.

This study explored how different sugarcane vinasses influence the structure and composition of soil bacterial communities in two tropical Oxisols with contrasting textures. In a controlled microcosm experiment with sugarcane seedlings, two concentrations of three vinasse types were applied, and bacterial communities were monitored over 10, 30, and 60 days using T-RFLP and 16S rRNA gene sequencing. Across all treatments, vinasse application led to clear changes in bacterial community structure in both soils, regardless of the time point. Certain bacterial groups, such as <i>Sphingobacteriia</i>, <i>Alphaproteobacteria</i>, and <i>Gammaproteobacteria</i>, became more abundant—likely responding to increased carbon availability, higher pH, and greater soil moisture. At the same time, other groups declined, possibly due to excess nutrients like potassium and sulfur. Notably, these shifts occurred even when standard biochemical indicators suggested no major impact, highlighting the sensitivity of microbial community-level responses. These findings point to the importance of looking beyond traditional soil quality metrics when assessing the environmental effects of organic residue applications. Incorporating microbial indicators can offer a more nuanced understanding of how practices like vinasse reuse affect soil functioning in tropical agroecosystems.

Katherine Haynes, Ryan Lagerquist, Marie C. McGraw et al.

Neural networks (NN) have become an important tool for prediction tasks – both regression and classification – in environmental science. Since many environmental-science problems involve life-or-death decisions and policy-making, it is crucial to provide not only predictions but also an estimate of the uncertainty in the predictions. Until recently, very few tools were available to provide uncertainty quantification (UQ) for NN predictions. However, in recent years the computer-science field has developed numerous UQ approaches, and several research groups are exploring how to apply these approaches in environmental science. We provide an accessible introduction to six of these UQ approaches, then focus on tools for the next step, namely to answer the question: Once we obtain an uncertainty estimate (using any approach), how do we know whether it is good or bad? To answer this question, we highlight four evaluation graphics and eight evaluation scores that are well suited for evaluating and comparing uncertainty estimates (NN-based or otherwise) for environmental-science applications. We demonstrate the UQ approaches and UQ-evaluation methods for two real-world problems: (1) estimating vertical profiles of atmospheric dewpoint (a regression task) and (2) predicting convection over Taiwan based on Himawari-8 satellite imagery (a classification task). We also provide Jupyter notebooks with Python code for implementing the UQ approaches and UQ-evaluation methods discussed herein. This article provides the environmental-science community with the knowledge and tools to start incorporating the large number of emerging UQ methods into their research.

Wenjia Liu, Jingwen Chen, Haobo Wang et al.

The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.

C. Cvitanovic, R. Shellock, M. Mackay et al.

Jeff Shuford

This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.

C. Magazzino

Deep Learning (DL), a subset of Machine Learning (ML), has emerged as a powerful tool in environmental science, reshaping the landscape of data analysis and interpretation. This study focuses on the remarkable impact of DL on various aspects of environmental science, including remote sensing, climate modelling, biodiversity assessment, pollution monitoring, and environmental health.