Hasil untuk "Environmental effects of industries and plants"

Sumegha Garg, Jabari Hastings, Chirag Pabbaraju et al.

We present a unified framework for proving memory lower bounds for multi-pass streaming algorithms that detect planted structures. Planted structures -- such as cliques or bicliques in graphs, and sparse signals in high-dimensional data -- arise in numerous applications, and our framework yields multi-pass memory lower bounds for many such fundamental settings. We show memory lower bounds for the planted $k$-biclique detection problem in random bipartite graphs and for detecting sparse Gaussian means. We also show the first memory-sample tradeoffs for the sparse principal component analysis (PCA) problem in the spiked covariance model. For all these problems to which we apply our unified framework, we obtain bounds which are nearly tight in the low, $O(\log n)$ memory regime. We also leverage our bounds to establish new multi-pass streaming lower bounds, in the vertex arrival model, for two well-studied graph streaming problems: approximating the size of the largest biclique and approximating the maximum density of bounded-size subgraphs. To show these bounds, we study a general distinguishing problem over matrices, where the goal is to distinguish a null distribution from one that plants an outlier distribution over a random submatrix. Our analysis builds on a new distributed data processing inequality that provides sufficient conditions for memory hardness in terms of the likelihood ratio between the averaged planted and null distributions. This result generalizes the inequality of [Braverman et al., STOC 2016] and may be of independent interest. The inequality enables us to measure information cost under the null distribution -- a key step for applying subsequent direct-sum-type arguments and incorporating the multi-pass information cost framework of [Braverman et al., STOC 2024].

Nannan Zhang, Stewart Ledgard, Shelley Falconer et al.

New Zealand (NZ) is an important global exporter of various livestock products, however its potential for production is being constrained by environmental restrictions. The aim of this study was to explore future pathways for NZ livestock (dairy, beef, and sheep) production from grazed pastures, which includes changes in land use, GHG mitigations, increased dairy-beef and net carbon neutrality. Life cycle assessment methodology was used to determine national level environmental impacts of livestock production in NZ. Carbon, reactive nitrogen (Nr) and Eutrophication Potential (EP) footprints on a total production basis at national level from livestock systems could be decreased by 39%, 36% and 30%, respectively. Achieving net carbon neutrality of all livestock production with a multiple mitigation scenario gave corresponding emission reductions of 41% for Nr and 36% for EP, requiring afforestation of 0.9 Mha of sheep and beef land. However, transitioning to carbon neutrality for all production reduced total livestock protein production by 7% for current systems and by 21% for the mitigation scenario compared to no carbon neutrality changes. In contrast, increased integration of dairy-derived beef increased national livestock protein production by 3%. Optimized livestock production through greater dairy beef integration and use of multiple mitigations offers the most promising pathway for low environmental-impact livestock production in NZ. However, this should aim at minimizing effects on livestock production due to the large impact on global food exports, while higher value or premiums for low environmental-impact products will be important to encourage changes to meet the environmental constraints.

Kasturima Das, Bikramjit Goswami and Girija T. R.

The global construction industry faces significant challenges related to environmental sustainability and resource scarcity. Researchers are increasingly exploring innovative approaches to repurpose waste materials, aiming to mitigate environmental pollution while producing value-added construction materials. This paper reviews the sustainability of current methodologies for synthesizing construction materials from pollutants, considering industrial by-products, post-consumer waste, and pollutants as potential feedstocks. The evaluation focuses on various recycling, upcycling, and bioconversion techniques, assessing their environmental and technical feasibility. The paper also discusses case studies of successful implementations and emerging trends in the field to highlight practical applications and future research directions. Ultimately, the paper advocates for sustainable practices in the construction sector by promoting a circular economy model, where waste is transformed into valuable resources, fostering wealth development.

Jubaedah Jubaedah, Iskandar Iskandar, Dwi Putro Tejo Baskoro et al.

Studies have shown that coal fly ash (CFA) can improve soil physical properties (such as bulk density) and increase available water content. However, its pozzolanic properties may also contribute to soil compaction. The overall impact of these contrasting effects on soil behavior remains insufficiently understood. This study investigated the effects of CFA amendment on soil resistance, water retention, and corn root growth in sandy soil. Using a completely randomized design, the research was conducted at the Taman Bogo Agricultural Station in East Lampung for two planting seasons (October 2022 - June 2023). Four CFA rates (0%, 3%, 6%, and 12% w/w) were applied in lysimeter plots with five replications. The results demonstrated that CFA application reduced soil bulk density (BD) at 6% and 12% rates due to CFA’s lower BD than the soil. However, higher CFA doses increased soil penetration resistance (PR), likely due to surface crust formation rather than pozzolanic reactions. Plant available water capacity (PAWC) significantly increased by 6% and 12% CFA, thus improving soil water retention. Improving physical properties in the second season promoted root growth at the 12% CFA rate. The use of CFA in sandy soils leads to improvement in selected physical properties and enhances water retention. Therefore, while CFA enhances water retention, its effects on soil compaction and root growth must be carefully managed to ensure optimal results.

Sigit Sutikno, Muhamad Yusa, Andy Hendri et al.

Peatlands play a crucial role in the global carbon cycle, water regulation, biodiversity conservation, research, education, and recreation. Peat thickness and groundwater level (GWL) are key parameters for optimizing these peatland functions; therefore, mapping peat thickness and GWL quickly, accurately, and cost-effectively is essential. This study applied a geophysical survey using a portable electromagnetic instrument to estimate peat thickness and GWL. The instrument, which is simple to operate and wirelessly connected to a mobile phone, enables rapid measurement and visualization of subsurface resistivity. A research site in Indragiri Hilir Regency, Riau Province, Indonesia, was picked up as an experiment site to test the instrument. Three transects with measurement path lengths of 100 m each and a distance of about 1.4 km each were designed for the experiment. To validate the resistivity data against subsurface stratigraphy, core sampling was conducted at three points along each transect. The results demonstrated that the electromagnetic method effectively identified the interface between peat soil and the underlying marine clay. Analysis revealed that the resistivity values for unsaturated peat, saturated peat, and saturated clay were 68-81 ohm m, 75-96 ohm m, and 82-115 ohm m, respectively. These findings suggest that GWL mapping and peat stratigraphy characterization can be accurately achieved using this method.

López-Sintas Jordi, Giuseppe Lamberti, Negin Abedini

This study investigates the intricate relationship between cognitive sociology and sustainable eating practices, specifically focusing on organic food purchase behaviours. We aim to uncover the complex social drivers influencing these behaviours by bridging social practice theory with sustainable consumption patterns. We extend traditional models that primarily focus on individual drivers of eco-food purchase behaviours by examining how the broader social context shapes and modulates the strength of individual factors. Using data for a representative sample of the Spanish population, we developed a comprehensive model that reveals how social practices contribute to explaining heterogeneity in the effects of various drivers. Our analysis identified four distinct social mechanisms that shape organic food purchase practices. These mechanisms differ significantly in how they influence the sequential determinants of behaviour. Notably, we found that age and family lifecycle stage were more influential in explaining these differences than social position. The results of our study underscore the need for nuanced policy interventions sensitive to the heterogeneous social determinants of organic food purchase behaviours. We advocate for targeted approaches based on a deeper understanding of these social mechanisms, as this would lead to more effective and inclusive sustainable consumption policies. Our research contributes to the growing body of literature on sustainable consumption by highlighting the critical role of cognitive processes shaped by social food purchase behaviours in understanding and promoting sustainable nutrition. Our findings have significant implications for policymakers, marketers, and researchers seeking to encourage more sustainable food choices in diverse populations.

Ji-Yan Wu, Zheng Yong Poh, Anoop C. Patil et al.

Accurate detection of nutrient deficiency in plant leaves is essential for precision agriculture, enabling early intervention in fertilization, disease, and stress management. This study presents a deep learning framework for leaf anomaly segmentation using multispectral imaging and an enhanced YOLOv5 model with a transformer-based attention head. The model is tailored for processing nine-channel multispectral input and uses self-attention mechanisms to better capture subtle, spatially-distributed symptoms. The plants in the experiments were grown under controlled nutrient stress conditions for evaluation. We carry out extensive experiments to benchmark the proposed model against the baseline YOLOv5. Extensive experiments show that the proposed model significantly outperforms the baseline YOLOv5, with an average Dice score and IoU (Intersection over Union) improvement of about 12%. In particular, this model is effective in detecting challenging symptoms like chlorosis and pigment accumulation. These results highlight the promise of combining multi-spectral imaging with spectral-spatial feature learning for advancing plant phenotyping and precision agriculture.

Vincent Gbouna Zakka, Minhyun Lee

Understanding the energy consumption pattern in the built environment is invaluable for the evaluation of the sources of energy wastage and the development of strategies for efficient energy management. An integrated monitoring system that can provide high granularity energy consumption and indoor environmental quality (IEQ) data is essential to enable intelligent, customized, and user-friendly energy management systems for end users and help improve building system performance. This paper, therefore, presents an integrated design of an internet of things (IoT)-based embedded, non-invasive, and user-friendly monitoring system for efficient building energy and IEQ management. The hardware unit of the system is comprised of a wireless microcontroller unit, current and voltage sensor, IEQ sensors, and power management unit that enables the acquisition, processing, and telemetering of energy and IEQ data. The software unit is made up of embedded software and a web-based information provision unit that handles real-time data analysis, transmission, and visualization on the web and provides relevant notifications to the end users about energy consumption patterns. The proposed system provides a promising solution for real-time information exchange regarding energy consumption and IEQ for both end-users and managers that will enhance effective building energy and environmental management.

Alexandra Sasha Luccioni, Emma Strubell, Kate Crawford

As the climate crisis deepens, artificial intelligence (AI) has emerged as a contested force: some champion its potential to advance renewable energy, materials discovery, and large-scale emissions monitoring, while others underscore its growing carbon footprint, water consumption, and material resource demands. Much of this debate has concentrated on direct impacts -- energy and water usage in data centers, e-waste from frequent hardware upgrades -- without addressing the significant indirect effects. This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption. We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses. Rebound effects undermine the assumption that improved technical efficiency alone will ensure net reductions in environmental harm. Instead, the trajectory of AI's impact also hinges on business incentives and market logics, governance and policymaking, and broader social and cultural norms. We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions. We conclude with recommendations that address rebound effects and challenge the market-driven imperatives fueling uncontrolled AI growth. By broadening the analysis to include both direct and indirect consequences, we aim to inform a more comprehensive, evidence-based dialogue on AI's role in the climate crisis.

Dharampal Bajaj and Pratiksha D. Khurpade

Corrosion of metals and alloys is one of the most frequent problems encountered in chemical and process industries. Inefficient corrosion control measures typically lead to an increased risk of unplanned downtime, huge economic loss, environmental damage, and health and safety hazards. Hence, it is essential to develop environment-friendly and cost-effective corrosion inhibitors over existing toxic anticorrosive agents. The main objective of this work is to examine the efficacy of eco-friendly ethanolic extract of Mangifera indica leaves (MIL) in different concentrations as a green corrosion inhibitor for stainless steel (SS-316L) under an acidic environment. The inhibition efficiency of Mangifera indica leaves extract in 1 M hydrochloric acid (HCl) was evaluated by conventional weight loss method along with adsorption isotherm analysis. Chemical compounds present in leaf extract and changes in surface morphology of SS-316L samples were assessed using Fourier Transform Infrared spectroscopy (FTIR) and Field Emission Scanning Electron Microscopy (FE-SEM) provided with elemental analysis. The results of the weight loss method revealed that the inhibition efficiency increases with increasing MIL extract concentration due to higher surface coverage. The highest inhibition efficiency of almost 63.43% in 14 days and minimum corrosion rate of 0.433 mm per year was obtained for SS-316 L in 1.0 M HCl with 1000 ppm concentration. The adsorption of MIL extract on SS-316L surface followed Freundlich adsorption isotherm, and the obtained value of free Energy of adsorption (ΔG˚ads = – 9.20 kJ.mol-1) indicates the physical adsorption mechanism. The developed regression-based models can predict the corrosion rate as a function of inhibitor concentration and exposure time with good accuracy (>80%). Thus, the present findings demonstrate that Mangifera indica L. leaves extract can suitably be applied as an inexpensive, non-toxic, biodegradable, efficient green corrosion inhibitor for the protection of stainless steel in acidic media.

M. V. Shah, N. M. Rathod, D. N. Prajapati, P. J. Mehta, R. R. Panchal and Vijay Upadhye

Soil stabilization is a very important method of science and engineering for improving the properties of soil. This paper aims to stabilize expansive black cotton soil through a biological approach involving plant extracts, plant waste materials, and microorganisms. While chemical methods exist, i.e., lime stabilization, geotextiles, etc., they are not economically feasible for large-scale applications. The primary issue with black cotton soil is due to the presence of montmorillonite clay mineral, which makes it unsuitable for the construction of roads and airfields. The cation exchange capacity (CEC) can be defined as the ability of soil to absorb and exchange positively charged ions; thus, if free positively charged ions are not available, the soil will not exchange them with others. The CEC of the soil is diminished, and ultimately, the soil is stabilized to some extent. This paper explores the preparation of plant extract, which contains a high number of anions, and directly inoculates it with soil, which nullifies the positive charge of the soil and diminishes the CEC. The use of cellulose and lignin-degrading microorganisms as an energy source and other minerals that are needed for their growth will be utilized from the soil to reduce CEC, i.e., Mg required for DNA replication and Ca required for their growth and maintenance. Another approach to diminishing the CEC is to use the microorganisms that produce EPS, which require Ca and Mg as adhesions for the formation of biofilm, i.e., Pseudomonas aeruginosa, Bacillus subtilis, and Escherichia coli. The use of microorganisms that have specific enzymes is also used in the diminishing soil CEC, i.e., by using ureolytic enzyme-producing bacteria like Sporosarcina pasteurii, Bacillus paramycoides, Citrobacter sedlakii, and Enterobacter bugadensis.

Yingkai Fang, Frank Asche, Jinghua Xie

Seasonality and within day variation are important characteristics of electricity supply/demand and for emissions from electricity generation. This paper investigates the social costs of using electricity at different times of the day, allowing for seasonal patterns and differences between weekdays and weekends in Sacramento, California in 2013 and 2019. The social costs of electricity use refer to a combination of the emission costs and the electricity price. Using simulations for the social costs of CO2, results show that the highest costs of using electricity occur at different hours across seasons on weekdays and weekends. As CO2 price increases, the social costs of electricity use become more volatile, but it becomes more pronounced that the low costs hours shift to daytime hours for most seasons in both years, except summer and spring 2019. This pattern helps evaluate the social impacts of using electricity and providing policy implications for electricity demand changes.

Ayi Yustiati, Alifia Ajmala Palsa, Titin Herawati, Roffi Grandiosa, Ibnu Bangkit Bioshina Suryadi and Ichsan Nurul Bari

This study aims to determine the maximum concentration and the long-term effects after exposure to a bio-insecticide with active ingredients eugenol and azadirachtin on the survival rate, immunity, and growth of Nile tilapia. The method used in this study was experimental, using a completely randomized design (CRD) with six treatments and three replications. Fishes were exposed to eugenol and azadirachtin at concentrations 10, 20, 30, 40, and 50% of LC50 value for 14 days, followed by 14 days of maintenance to see the effect on growth. The results showed that 66 mg.L-1 treatment was a concentration that did not interfere with the survival rate of Nile tilapia, which was 86.7%. The number of leukocytes increased on the third day by the highest increase in 66 mg.L-1 treatment at 12.01 × 104 cells.mm-3. Meanwhile, erythrocytes decreased, with the highest decrease in 66 mg.L-1 treatment at 1.13 × 106 cells.mm-3. The average growth rate in fish slowed down with increasing concentrations of exposure, with the lowest average growth in length and absolute weight in the 66 mg.L-1 treatment was 0.57 cm and 1.68 g.

Rounak Meyur, Hung Phan, Koby Hayashi et al.

The National Environment Policy Act (NEPA) stands as a foundational piece of environmental legislation in the United States, requiring federal agencies to consider the environmental impacts of their proposed actions. The primary mechanism for achieving this is through the preparation of Environmental Assessments (EAs) and, for significant impacts, comprehensive Environmental Impact Statements (EIS). Large Language Model (LLM)s' effectiveness in specialized domains like NEPA remains untested for adoption in federal decision-making processes. To address this gap, we present NEPA Question and Answering Dataset (NEPAQuAD), the first comprehensive benchmark derived from EIS documents, along with a modular and transparent evaluation pipeline, MAPLE, to assess LLM performance on NEPA-focused regulatory reasoning tasks. Our benchmark leverages actual EIS documents to create diverse question types, ranging from factual to complex problem-solving ones. We built a modular and transparent evaluation pipeline to test both closed- and open-source models in zero-shot or context-driven QA benchmarks. We evaluate five state-of-the-art LLMs using our framework to assess both their prior knowledge and their ability to process NEPA-specific information. The experimental results reveal that all the models consistently achieve their highest performance when provided with the gold passage as context. While comparing the other context-driven approaches for each model, Retrieval Augmented Generation (RAG)-based approaches substantially outperform PDF document contexts, indicating that neither model is well suited for long-context question-answering tasks. Our analysis suggests that NEPA-focused regulatory reasoning tasks pose a significant challenge for LLMs, particularly in terms of understanding the complex semantics and effectively processing the lengthy regulatory documents.

Wei Jia, Roman Jandarov

This manuscript presents a novel Bayesian varying coefficient quantile regression (BVCQR) model designed to assess the longitudinal effects of chemical exposure mixtures on children's neurodevelopment. Recognizing the complexity and high-dimensionality of environmental exposures, the proposed approach addresses critical gaps in existing research by offering a method that can manage the sparsity of data and provide interpretable results. The proposed BVCQR model estimates the effects of mixtures on neurodevelopmental outcomes at specific ages, leveraging a horseshoe prior for sparsity and utilizing a Bayesian method for uncertainty quantification. Our simulations demonstrate the model's robustness and effectiveness in handling high-dimensional data, offering significant improvements over traditional models. The model's application to the Health Outcomes and Measures of the Environment (HOME) Study further illustrates its utility in identifying significant chemical exposures affecting children's growth and development. The findings underscore the potential of BVCQR in environmental health research, providing a sophisticated tool for analyzing the longitudinal impact of complex chemical mixtures, with implications for future studies aimed at understanding and mitigating environmental risks to child health.

Jared Popowski, Lucas Warma, Alicia Abarca Cifuentes et al.

Trichomes, specialized hair-like structures on the surfaces of many plants, play a crucial role in defense against herbivorous insects. We investigated the biomechanics of type VI glandular trichomes in cultivated tomato (Solanum lycopersicum) and its wild relative (Solanum habrochaites). Using micropipette force sensors and high-speed imaging, we uncovered the rupture mechanics underlying gland bursting, highlighting the small forces and short time-scales involved in this process. Additionally, we observed larvae of the Western flower thrips (Frankliniella occidentalis), a major pest in tomato cultivation, inadvertently triggering trichome rupture and accumulating glandular secretions on their bodies. These findings demonstrate how rapid gland bursting and the fluid dynamics of glandular secretions act as an efficient and swift plant defense mechanism against insect herbivory.

Youndo Do, Marc Zebrowitz, Jackson Stahl et al.

Robotics has gained attention in the nuclear industry due to its precision and ability to automate tasks. However, there is a critical need for advanced simulation and control methods to predict robot behavior and optimize plant performance, motivating the use of digital twins. Most existing digital twins do not offer a total design of a nuclear power plant. Moreover, they are designed for specific algorithms or tasks, making them unsuitable for broader research applications. In response, this work proposes a comprehensive nuclear power plant digital twin designed to improve real-time monitoring, operational efficiency, and predictive maintenance. A full nuclear power plant is modeled in Unreal Engine 5 and integrated with a high-fidelity Generic Pressurized Water Reactor Simulator to create a realistic model of a nuclear power plant and a real-time updated virtual environment. The virtual environment provides various features for researchers to easily test custom robot algorithms and frameworks.

Clément Morand, Anne-Laure Ligozat, Aurélie Névéol

The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consumption and environmental impacts associated with AI, possibly shifting impacts from the use phase to the manufacturing phase in the life-cycle of hardware. This paper investigates the evolution of individual graphics cards production impacts and of the environmental impacts associated with training Machine Learning (ML) models over time. We collect information on graphics cards used to train ML models and released between 2013 and 2023. We assess the environmental impacts associated with the production of each card to visualize the trends on the same period. Then, using information on notable AI systems from the Epoch AI dataset we assess the environmental impacts associated with training each system. The environmental impacts of graphics cards production have increased continuously. The energy consumption and environmental impacts associated with training models have increased exponentially, even when considering reduction strategies such as location shifting to places with less carbon intensive electricity mixes. These results suggest that current impact reduction strategies cannot curb the growth in the environmental impacts of AI. This is consistent with rebound effect, where the efficiency increases fuel the creation of even larger models thereby cancelling the potential impact reduction. Furthermore, these results highlight the importance of considering the impacts of hardware over the entire life-cycle rather than the sole usage phase in order to avoid impact shifting. The environmental impact of AI cannot be reduced without reducing AI activities as well as increasing efficiency.

Jaana Rahko

Longqin Li, Zhibo Du, Jiaan Liang et al.