Hasil untuk "Environmental sciences"

H. Simon

This excerpt from the first chapter of The Sciences of the Artificial (1969; 1996) by Herbert A. Simon establishes the epistemological foundations for distinguishing natural sciences from the “sciences of the artificial.” While natural sciences seek hidden patterns to explain how things are, the sciences of the artificial deal with objects synthesised by human beings, characterised by functions, goals, and normative imperatives—addressing how things “ought” to be. Simon introduces the crucial concept of the artefact as an “interface” between an “inner” environment (the substance and organisation of the object itself) and an “outer” environment (the context in which it operates); the artefact’s effectiveness depends on the successful adaptation of these two environments to one another. The text further explores the role of simulation as a source of new knowledge that can reveal the hidden implications of known premises. It defines both computers and the human mind as “physical symbol systems.” According to Simon, intelligence is fundamentally the work of these systems, which can encode information, manipulate structures, and adapt to their environment.The re-proposal of this classic text within the contemporary context of urban studies and Artificial Intelligence (PlanAIr) is driven by three fundamental reasons. First, Simon provides a critical ontological definition, reminding us that the world we inhabit is predominantly man-made. In this view, the city is the artefact par excellence: not a natural phenomenon to be passively observed, but a complex, designed system that must answer to human purposes, thus legitimising urban planning as a rigorous science of the artificial. Second, the vision of the artefact as a “meeting point” between inner and outer environments offers a powerful metaphor for Urban AI. Intelligent technologies in the city act as an interface between physical infrastructure and citizens’ social or environmental dynamics, requiring mutual adaptation to function effectively. Finally, as Simon’s work is foundational to symbolic Artificial Intelligence, revisiting it today allows us to grasp the theoretical roots of rule-based and logical AI. This historical perspective is crucial for distinguishing and potentially integrating symbolic approaches with the currently dominant data-driven paradigms, thereby recovering the capacity to reason about goals, meanings, and design imperatives rather than relying solely on raw data processing.In this article, we examine these ontological issues, discuss existing frameworks that aim to unify fragmented information, and explore the practical implications for urban AI applications. The thesis is that ontologies—structured and formal representations of knowledge—offer a powerful tool to address the challenges outlined above, while serving as a blueprint for defining, categorizing, and interrelating the entities present in urban environments and putting them to work in urban planning.

H. Ali, Ezzat Khan, I. Ilahi

Heavy metals are well-known environmental pollutants due to their toxicity, persistence in the environment, and bioaccumulative nature. Their natural sources include weathering of metal-bearing rocks and volcanic eruptions, while anthropogenic sources include mining and various industrial and agricultural activities. Mining and industrial processing for extraction of mineral resources and their subsequent applications for industrial, agricultural, and economic development has led to an increase in the mobilization of these elements in the environment and disturbance of their biogeochemical cycles. Contamination of aquatic and terrestrial ecosystems with toxic heavy metals is an environmental problem of public health concern. Being persistent pollutants, heavy metals accumulate in the environment and consequently contaminate the food chains. Accumulation of potentially toxic heavy metals in biota causes a potential health threat to their consumers including humans. This article comprehensively reviews the different aspects of heavy metals as hazardous materials with special focus on their environmental persistence, toxicity for living organisms, and bioaccumulative potential. The bioaccumulation of these elements and its implications for human health are discussed with a special coverage on fish, rice, and tobacco. The article will serve as a valuable educational resource for both undergraduate and graduate students and for researchers in environmental sciences. Environmentally relevant most hazardous heavy metals and metalloids include Cr, Ni, Cu, Zn, Cd, Pb, Hg, and As. The trophic transfer of these elements in aquatic and terrestrial food chains/webs has important implications for wildlife and human health. It is very important to assess and monitor the concentrations of potentially toxic heavy metals and metalloids in different environmental segments and in the resident biota. A comprehensive study of the environmental chemistry and ecotoxicology of hazardous heavy metals and metalloids shows that steps should be taken to minimize the impact of these elements on human health and the environment.

Joseph Poore, T. Nemecek

The global impacts of food production Food is produced and processed by millions of farmers and intermediaries globally, with substantial associated environmental costs. Given the heterogeneity of producers, what is the best way to reduce food's environmental impacts? Poore and Nemecek consolidated data on the multiple environmental impacts of ∼38,000 farms producing 40 different agricultural goods around the world in a meta-analysis comparing various types of food production systems. The environmental cost of producing the same goods can be highly variable. However, this heterogeneity creates opportunities to target the small numbers of producers that have the most impact. Science, this issue p. 987 Food producer heterogeneity on a global level creates mitigation opportunities with respect to environmental damage caused by food production. Food’s environmental impacts are created by millions of diverse producers. To identify solutions that are effective under this heterogeneity, we consolidated data covering five environmental indicators; 38,700 farms; and 1600 processors, packaging types, and retailers. Impact can vary 50-fold among producers of the same product, creating substantial mitigation opportunities. However, mitigation is complicated by trade-offs, multiple ways for producers to achieve low impacts, and interactions throughout the supply chain. Producers have limits on how far they can reduce impacts. Most strikingly, impacts of the lowest-impact animal products typically exceed those of vegetable substitutes, providing new evidence for the importance of dietary change. Cumulatively, our findings support an approach where producers monitor their own impacts, flexibly meet environmental targets by choosing from multiple practices, and communicate their impacts to consumers.

S. M. Popescu, Sheikh Mansoor, O. A. Wani et al.

Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.

Faustine Cantalloube, Camille Noûs, A. Jolly et al.

In 2025, the French Society for Astronomy \& Astrophysics (SF2A), gave the environmental transition commission the opportunity to share their considerations during a plenary session at the annual SF2A conference. This year, the presentation focused on some of the main results obtained from the survey entitled 'French astronomy and astrophysics research activities in the face of the environmental crisis, from 2019 to 2024'. The survey was initiated in 2019 by the group 'Environnement-Transition' (coordinated by P. Martin) at IRAP, whose results were presented during the SF2A annual conference 2019 in Nice. The survey was updated in 2024 by the CNRS INSU-AA prospective working group 'Climate and ecological challenge' (coordinated by S. Bontemp). The SF2A environmental transition commission took on the survey to the French institutes, sorted the answers and extracted the preliminary results. The full results will be published at the end of 2025 in the final CNRS INSU-AA 2024 prospective document. This publication presents a selection of pieces from the full survey, along with a few of the main discussions it triggers.

Hachem Saddiki, Joshua L. Warren, Corina Lesseur et al.

Environmental mixture approaches do not accommodate compositional outcomes, consisting of vectors constrained onto the unit simplex. This limitation poses challenges in effectively evaluating the associations between multiple concurrent environmental exposures and their respective impacts on this type of outcomes. As a result, there is a pressing need for the development of analytical methods that can more accurately assess the complexity of these relationships. Here, we extend the Bayesian weighted quantile sum regression (BWQS) framework for jointly modeling compositional outcomes and environmental mixtures using a Dirichlet distribution with a multinomial logit link function. The proposed approach, named Dirichlet-BWQS (DBWQS), allows for the simultaneous estimation of mixture weights associated with each exposure mixture component as well as the association between the overall exposure mixture index and each outcome proportion. We assess the performance of DBWQS regression on extensive simulated data and a real scenario where we investigate the associations between environmental chemical mixtures and DNA methylation-derived placental cell composition, using publicly available data (GSE75248). We also compare our findings with results considering environmental mixtures and each outcome component. Finally, we developed an R package "xbwqs" where we made our proposed method publicly available (https://github.com/hasdk/xbwqs).

Ruixi Huang, David Waxman

Many biological populations exhibit diversity in their strategy for survival and reproduction in a given environment, and microbes are an example. We explore the fate of different strategies under sustained environmental change by considering a mathematical model for a large population of asexual organisms. Fitness is a bimodal function of a quantitative trait, with two local optima, separated by a local minimum, i.e., a mixture of stabilising and disruptive selection. The optima represent two locally `best' trait values. We consider regimes where, when the environment is unchanging, the equilibrium distribution of the trait is bimodal. A bimodal trait distribution generally requires, for its existence, mutational coupling between the two peaks, and it indicates two coexisting clones with distinct survival and reproduction strategies. When subject to persistent environmental change, the population adapts by utilising mutations that allow it to track the changing environment. The faster the rate of change of the environment, the larger the effect of the mutations that are utilised. Under persistent environmental change, the distribution of trait values takes two different forms. At low rates of change, the distribution remains bimodal. At higher rates, the distribution becomes unimodal. This loss of a clone/biodiversity is driven by a novel mechanism where environmental change decouples a class of mutations.

Apoorva Bademi, Miriam Stevens, Isha Sura et al.

There is a projected increase in offshore wind energy generation in the United States over the next three decades, driven by legislative commitments and government funding. Like other renewable technologies, the construction of offshore wind farms has environmental impacts and spillover effects that must be assessed. Developing offshore wind as a reliable domestic energy source requires a multiregional analysis of economic and environmental effects of constructing projects along lakefronts and coastal regions. Although no commercial offshore wind farms currently operate in the United States, seven states have announced capacity commitments exceeding 28 gigawatts by 2035. This study evaluates the spatial economic and environmental impacts of planned projects by linking the National Renewable Energy Laboratory Offshore Renewables Balance-of-system Installation Tool (ORBIT) with a multiregional input-output model of the U.S. economy developed in the Virtual Industrial Ecology Lab. ORBIT provides capital investment requirements for installation, which are combined with the model to estimate economic spillover effects. Environmental impacts are assessed using a newly developed multiregional greenhouse gas emissions dataset for the U.S. to capture supply chain emissions of offshore wind construction. The five projects analyzed require 16.3 billion dollars in capital investment and generate 27.6 billion dollars in direct and indirect economic impacts across the country. Emissions results show that states active in energy generation are most affected, but impacts can be reduced by decarbonizing the grid. A carbon payback analysis indicates the projects offset construction-phase emissions in less than a year. The framework highlights which states experience the greatest spillover effects in terms of emissions and economic activity required to support offshore wind expansion.

William C. Quigley, Mohamed Rahouti, Gary M. Weiss

The maritime industry is governed by stringent environmental regulations, most notably the International Convention for the Prevention of Pollution from Ships (MARPOL). Ensuring compliance with these regulations is difficult due to low inspection rates and the risk of data fabrication. To address these issues, this paper proposes a secure blockchain-assisted framework for real-time maritime environmental compliance monitoring. By integrating IoT and shipboard sensors with blockchain technology, the framework ensures immutable and transparent record-keeping of environmental data. Smart contracts automate compliance verification and notify relevant authorities in case of non-compliance. A proof-of-concept case study on sulfur emissions demonstrates the framework's efficacy in enhancing MARPOL enforcement through real-time data integrity and regulatory adherence. The proposed system leverages the Polygon blockchain for scalability and efficiency, providing a robust solution for maritime environmental protection. The evaluation results demonstrate that the proposed blockchain-enhanced compliance monitoring system effectively and securely ensures real-time regulatory adherence with high scalability, efficiency, and cost-effectiveness, leveraging the robust capabilities of the Polygon blockchain.

Hao-Neng Luo, Zhi-Xi Wu, Jian-Yue Guan

Bacteriophage-bacteria interactions are central to microbial ecology, influencing evolution, biogeochemical cycles, and pathogen behavior. Most theoretical models assume static environments and passive bacterial hosts, neglecting the joint effects of bacterial traits and environmental fluctuations on coexistence dynamics. This limitation hinders the prediction of microbial persistence in dynamic ecosystems such as soils and oceans.Using a minimal ordinary differential equation framework, we show that the bacterial growth rate and the phage adsorption rate collectively determine three possible ecological outcomes: phage extinction, stable coexistence, or oscillation-induced extinction. Specifically, we demonstrate that environmental fluctuations can suppress destructive oscillations through resonance, promoting coexistence where static models otherwise predict collapse. Counterintuitively, we find that lower bacterial growth rates are helpful in enhancing survival under high infection pressure, elucidating the observed post-infection growth reduction.Our studies reframe bacterial hosts as active builders of ecological dynamics and environmental variation as a potential stabilizing force. Our findings thus bridge a key theory-experiment gap and provide a foundational framework for predicting microbial responses to environmental stress, which might have potential implications for phage therapy, microbiome management, and climate-impacted community resilience.

Cooper Elsworth, Keguo Huang, David Patterson et al.

The transformative power of AI is undeniable - but as user adoption accelerates, so does the need to understand and mitigate the environmental impact of AI serving. However, no studies have measured AI serving environmental metrics in a production environment. This paper addresses this gap by proposing and executing a comprehensive methodology for measuring the energy usage, carbon emissions, and water consumption of AI inference workloads in a large-scale, AI production environment. Our approach accounts for the full stack of AI serving infrastructure - including active AI accelerator power, host system energy, idle machine capacity, and data center energy overhead. Through detailed instrumentation of Google's AI infrastructure for serving the Gemini AI assistant, we find the median Gemini Apps text prompt consumes 0.24 Wh of energy - a figure substantially lower than many public estimates. We also show that Google's software efficiency efforts and clean energy procurement have driven a 33x reduction in energy consumption and a 44x reduction in carbon footprint for the median Gemini Apps text prompt over one year. We identify that the median Gemini Apps text prompt uses less energy than watching nine seconds of television (0.24 Wh) and consumes the equivalent of five drops of water (0.26 mL). While these impacts are low compared to other daily activities, reducing the environmental impact of AI serving continues to warrant important attention. Towards this objective, we propose that a comprehensive measurement of AI serving environmental metrics is critical for accurately comparing models, and to properly incentivize efficiency gains across the full AI serving stack.

Chi Zuo, Martin B. Møller, Pablo Martínez-Nuevo et al.

While being disturbed by environmental noises, the acoustic masking technique is a conventional way to reduce the annoyance in audio engineering that seeks to cover up the noises with other dominant yet less intrusive sounds. However, misalignment between the dominant sound and the noise-such as mismatched downbeats-often requires an excessive volume increase to achieve effective masking. Motivated by recent advances in cross-modal generation, in this work, we introduce an alternative method to acoustic masking, aiming to reduce the noticeability of environmental noises by blending them into personalized music generated based on user-provided text prompts. Following the paradigm of music generation using mel-spectrogram representations, we propose a Blending Noises into Personalized Music (BNMusic) framework with two key stages. The first stage synthesizes a complete piece of music in a mel-spectrogram representation that encapsulates the musical essence of the noise. In the second stage, we adaptively amplify the generated music segment to further reduce noise perception and enhance the blending effectiveness, while preserving auditory quality. Our experiments with comprehensive evaluations on MusicBench, EPIC-SOUNDS, and ESC-50 demonstrate the effectiveness of our framework, highlighting the ability to blend environmental noise with rhythmically aligned, adaptively amplified, and enjoyable music segments, minimizing the noticeability of the noise, thereby improving overall acoustic experiences. Project page: https://d-fas.github.io/BNMusic_page/.

Ning Xin, Jionglong Su, Md Maruf Hasan

Environmental crisis remains a global challenge that affects public health and environmental quality. Despite extensive research, accurately forecasting environmental change trends to inform targeted policies and assess prediction efficiency remains elusive. Conventional methods for multivariate time series (MTS) analysis often fail to capture the complex dynamics of environmental change. To address this, we introduce an innovative meta-learning MTS model, MMformer with Adaptive Transferable Multi-head Attention (ATMA), which combines self-attention and meta-learning for enhanced MTS forecasting. Specifically, MMformer is used to model and predict the time series of seven air quality indicators across 331 cities in China from January 2018 to June 2021 and the time series of precipitation and temperature at 2415 monitoring sites during the summer (276 days) from 2012 to 2014, validating the network's ability to perform and forecast MTS data successfully. Experimental results demonstrate that in these datasets, the MMformer model reaching SOTA outperforms iTransformer, Transformer, and the widely used traditional time series prediction algorithm SARIMAX in the prediction of MTS, reducing by 50\% in MSE, 20\% in MAE as compared to others in air quality datasets, reducing by 20\% in MAPE except SARIMAX. Compared with Transformer and SARIMAX in the climate datasets, MSE, MAE, and MAPE are decreased by 30\%, and there is an improvement compared to iTransformer. This approach represents a significant advance in our ability to forecast and respond to dynamic environmental quality challenges in diverse urban and rural environments. Its predictive capabilities provide valuable public health and environmental quality information, informing targeted interventions.

Nicolas Page, Alireza Gholami, Qian Zhang

In response to the global challenge of climate change, financial institutions are increasingly called upon to assess and disclose their carbon emissions. Various global carbon quantification and reporting standards were developed, such as the Greenhouse Gas (GHG) Protocol, Task Force on Climate-related Financial Disclosures (TCFD), Partnership for Carbon Accounting Financials (PCAF) and others. Unfortunately, the now diverse landscape of standards increases the complexity for institutions seeking to develop voluntary carbon quantification and reporting. This study addresses the complexity issue by developing a criteria-based tool that summarizes the various components and requirements of the carbon standards. We propose eight criteria that summarize the standards’ key elements, requirements and relevance to the financial industry. We analyze seven major carbon quantification and reporting standards, systematically evaluating them against our tool. By doing so, we provide financial institutions with valuable insights in selecting appropriate standards to inform their emissions quantification and reporting decisions.

Y. Y. Abdullahi, A. S. Nur, A. Sale

Machine learning provides more verbose algorithms capable of accurately predicting, classifying groups as needed. Consequently, the objective of this paper is to assess the benchmarking of Supervised Machine Learning Algorithms of K-Nearest Neighbor, Random Forest, Decision Tree and it variants (ID3, C4.5, C5.0 and CART) based on efficiency and performance metrics using python programming after downloading dataset from Kaggle repository. Dataset to the aforementioned models reveals that, the C4.5 variant of decision tree had the highest prediction accuracy, CART and KNN had the minimal learning and prediction time. If accuracy is the based preference, C4.5 variant of decision tree should be recognized, but when the chief concern is nominal time for training and prediction, then CART and KNN standout.  

Akram Aqil Syahru, Nasrullah, Aven Ghina Salsabila et al.

Environmental degradation driven by negative externalities and fiscal inequality demands a reconfiguration of taxation grounded in the Polluter Pays Principle (PPP). This study aims to develop a normative–comparative framework for a green tax system that internalizes pollution costs while promoting fiscal justice. Using a normative legal research method, the analysis explores the theoretical and institutional foundations of green taxation, drawing from Indonesia’s environmental legislation, the Rio Declaration, and European Union guidelines, while examining fiscal equity and progressive redistribution. A comparative perspective highlights the implementation of PPP across jurisdictions: South Africa’s carbon tax, Portugal’s corporate and VAT-based green tax, and Indonesia’s emerging carbon pricing scheme. The study focuses on legal mechanisms of redistribution, including targeted cash transfers, tax credits, and tax-shift models, as well as the role of fiscal transparency and administrative oversight in mitigating regressive impacts. The findings indicate that a green tax framework rooted in PPP and supported by progressive redistribution and legal transparency enhances ecological accountability, social equity, and policy legitimacy. This paper contributes to environmental fiscal reform discourse by proposing a legally grounded and equitable model for sustainable green tax implementation.

Wenqian Zhang, Bo Guo

Abstract Per‐ and poly‐fluoroalkyl substances (PFAS) are interfacially‐active contaminants that adsorb at air‐water interfaces (AWIs). Water‐unsaturated soils have abundant AWIs, which generally consist of two types: one is associated with the pendular rings of water between soil grains (i.e., bulk AWI) and the other arises from the thin water films covering the soil grains. To date, the two types of AWIs have been treated the same when modeling PFAS retention in vadose zones. However, the presence of electrical double layers of soil grain surfaces and the subsequently modified chemical potential of PFAS at the AWI may significantly change the PFAS adsorption at the thin‐water‐film AWI relative to that at the bulk AWI. Given that thin water films contribute to over 90% of AWIs in the vadose zone under many field‐relevant wetting conditions, it is critical to quantify the potential anomalous adsorption of PFAS at the thin‐water‐film AWI. We develop a thermodynamic‐based mathematical model to quantify this anomalous adsorption. The model couples the chemical equilibrium of PFAS with the Poisson‐Boltzmann equation that governs the distribution of electrical potential in a thin water film. Our model analyses suggest that PFAS adsorption at thin‐water‐film AWI can deviate significantly (up to 82%) from that at bulk AWIs. The deviation increases for lower porewater ionic strength, thinner water film, and higher soil grain surface charge. These results highlight the importance of accounting for the anomalous adsorption of PFAS at the thin‐water‐film AWI when modeling PFAS fate and transport in the vadose zone.

Manuela Mendiolar, Jerzy A. Filar, Wen-Hsi Yang et al.

Environmental scientists frequently rely on time series of explanatory variables to explain their impact on an important response variable. However, sometimes, researchers are less interested in raw observations of an explanatory variable than in derived indices induced by episodes embedded in its time series. Often these episodes are intermittent, occur within a specific limited memory, persist for varying durations, at varying levels of intensity, and overlap important periods with respect to the response variable. We develop a generic, parametrised, family of weighted indices extracted from an environmental signal called IMPIT indices. To facilitate their construction and calibration, we developed a user friendly app in Shiny R referred to as IMPIT-a. We construct examples of IMPIT indices extracted from the Southern Oscillation Index and sea surface temperature signals. We illustrate their applications to two fished species in Queensland waters (i.e., snapper and saucer scallop) and wheat yield in New South Wales.

Mariliza Tzes, Vasileios Vasilopoulos, Yiannis Kantaros et al.

In this paper we address mobile manipulation planning problems in the presence of sensing and environmental uncertainty. In particular, we consider mobile sensing manipulators operating in environments with unknown geometry and uncertain movable objects, while being responsible for accomplishing tasks requiring grasping and releasing objects in a logical fashion. Existing algorithms either do not scale well or neglect sensing and/or environmental uncertainty. To face these challenges, we propose a hybrid control architecture, where a symbolic controller generates high-level manipulation commands (e.g., grasp an object) based on environmental feedback, an informative planner designs paths to actively decrease the uncertainty of objects of interest, and a continuous reactive controller tracks the sparse waypoints comprising the informative paths while avoiding a priori unknown obstacles. The overall architecture can handle environmental and sensing uncertainty online, as the robot explores its workspace. Using numerical simulations, we show that the proposed architecture can handle tasks of increased complexity while responding to unanticipated adverse configurations.

J. Peña-Rodríguez, P. A. Salgado-Meza, H. Asorey et al.

This paper describes a collaborative experience to empower organized communities to produce, curate and disseminate environmental data. A particular emphasis is done on the description of open hardware & software architecture and the processes of commissioning of the low cost Arduino-Raspberry-Pi weather station which measures: atmospheric pressure, temperature, humidity, precipitation, cloudiness, and illuminance/irradiance. The idea is to encourage more people to replicate this open-science initiative. We have started this experience training students & teachers from seven mid secondary schools through a syllabus of 12 two-hours lectures with a web-based support which exposes them to basic concepts and practices of Citizen Science and Open Data Science.