Environmental Processes Research Articles

Enhanced synergistic catalysis of bisphenol A in river water using an anti-aging photocatalytic membrane

Bisphenol A (BPA), as an endocrine disruptor, poses a potential threat to ecosystems and human health in aquatic environments. Membrane catalytic systems can accelerate the degradation of BPA and facilitate its conversion into harmless compounds. Nevertheless, the complex nature of the water environments and the limited stability of catalysts often result in challenges such as catalyst aging and deactivation. Herein, an anti-aging multifunctional AgFeO2 catalytic material with electron transfer membrane support was prepared for synergistic catalysis of low-energy LED light (12 W) excitation and peroxydisulfate (PDS) activation. The anti-aging photocatalytic membrane completely degraded 10 ppm of BPA within 30 min, and did not show significant aging after the long-term synergistic catalytic process. In addition, actual river water was employed to assess the aging process and catalytic efficiency in a practical environment. A 60.79 cm2 photocatalytic membrane completely purified 10 L of BPA polluted river water, while the total organic carbon content decreased by 50 %. This was mainly due to the synergistic catalytic effect of the membrane, which boosted photoelectron transfer through electron transfer shortcuts, thereby enhancing persulfate activation. Overall, the multifunctional membrane provides an effective strategy for achieving a long-lasting catalytic effect and controlling photocatalyst aging in practice.

A Binned Multilevel Regression Fitting Method for Monitoring Geladandong Glacier Elevation Changes from ICESat-2 Data

ABSTRACT Studying glacier changes is essential for understanding global glacier mass balance, patterns in climate change, and the potential consequences they may have. The glaciers of Geladandong Mountain serve as a crucial water source for the Yangtze River and its downstream regions. Any changes in the glacier at the river source will directly impact the ecological environment and water recycling process downstream. This study presents an approach for monitoring Geladandong Mountain glacier elevation changes using ICESat-2 data combined with a Binned Multilevel Regression Fitting Method (BMRFM). We analysed multi-year ICESat-2 data, considering various topographic features, to understand glaciers’ annual and seasonal elevation changes. Our research reveals significant spatial heterogeneity in glacier elevation changes, influenced by altitude, slope, and aspect factors. It demonstrates the effectiveness of ICESat-2 in glacier monitoring, offering insights into the impacts of climate change on glacier dynamics. Moreover, the results indicate a predominant trend of ongoing melting at lower altitudes, with a small amount of accretion at higher elevations. Over the last two decades, the overall elevation change rate is −0.23 ± 0.12 metres per year. Seasonal analysis shows substantial glacier thickening from April to August, primarily due to increased precipitation offsetting elevated temperatures. This study offers a robust method for monitoring glaciers using satellite data, thereby advancing the precision and reliability of glacier research.

A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment

Despite the significant advancements in Artificial Intelligence and its applications, traditional control techniques still dominate industrial and manufacturing processes. These techniques often rely on static setups and default values, which make them resistant to environmental changes. However, in dynamic and uncertain contexts, real-time process adaptation offers significant advantages in decision-making and control. In this research, we present a novel approach utilising Deep Reinforcement Learning for real-time dynamic parameter adaptation and autonomous control of processes in unpredictable environments. The effectiveness of our proposal is illustrated through the development and training of a Deep Q-Learning control system tailored for optimising a hot stamping process. The control model aims to minimise defect rates and enhance efficiency by reducing forming times during batch productions. Comparative analysis against conventional industry practices, characterised by static and non-adaptive time settings, validates the efficacy of our solution, with improvements of over 20% in the times, depending on the scenario considered. Experimental evaluation in a semi-industrial pilot setting not only demonstrates the adaptability of our approach but also highlights its potential scalability for diverse industrial applications.

Effect of wildfires on mobility of metribuzine herbicide in agricultural soil

Metribuzine has been used extensively for controlling wide range of broadleaf weeds and grasses, which can lead to severe contamination of soil and groundwater. Wildfires are an important process in environment, which can be severe for many areas and could have a significant impact on the properties of the soil. The objective of this study was to investigate the effect of wildfires for the fate and transport of atrazine for agricultural soil. Miscible displacement experiments were used for the objectives. Results showed that the average distribution coefficient (Kd) obtained from metribuzine was 0.28 and 0.13 L/Kg for soils prior to and after wildfire, respectively. Results indicate that organic matter content of soil has most likely played major role on sorption of metribuzine and exhibited rate-limited sorption-desorption. In addition, sorption of metribuzine onto soil after wildfire was significantly lower which could lead to more contamination risk of groundwater resources. The results from the present study would also help in designing of effective herbicide management strategies for contaminated sites.

Editorial: Mathematical thinking, practices, and processes in non-formal learning environments

Editorial: Mathematical thinking, practices, and processes in non-formal learning environments

Southern Bug River: water security and climate changes perspectives for post-war city of Mykolaiv, Ukraine

This article focuses on water security in Mykolaiv, a city of 0.5 million inhabitants in southern Ukraine, in the situation of scarcity of usable water resources caused by climate change and military operations. This problem arose after the Dnipro-Mykolaiv water pipeline was destroyed in April 2022 as a result of military operations and the supply of drinking water to the city was cut off. To ensure that the city’s population has constant access to sufficient water of acceptable quality, a search for alternative water sources and a climate risk assessment were carried out for the new municipal water supply system from the Southern Bug River. The possible change in flow and its intra-annual distribution under the influence of climate change was modeled using the WaterGAP2 hydrological model and climate projections under the SSP1-2.6 and SSP5-P8.5 scenarios. It was found that under the SSP1-2.6 scenario, the reduction in river flow will be insignificant (up to a maximum of 14% in the far future) and there will be no restrictions on the city’s water supply from this section of the river in the near (2021-2050) and far (2051-2080) period. The maximum water withdrawal for municipal water supply and the minimum environmental flow will reach their maximum value only in August (56% of the projected flow), which is not critical. Under the SSP5-8.5 scenario, in the long-term perspective of 2051-2080, the largest decrease in runoff will occur from May to October, and the water withdrawal will increase to 40-79% of the projected flow. The use of the research results not only in water management, but also in municipal administration, and their dissemination in territorial communities will contribute to the successful adaptation of socio-economic and environmental processes in the region and can bring successful benefits not only to the economy, but also to communities.

Unraveling a novel biphasic CO2 capture process through rigorous modeling

Modification of the chemical absorbent is a crucial aspect in the field of CO2 capture. Nevertheless, the consequences arising from the utilization of modified solvents have not been thoroughly examined on a process scale. This study investigates a biphasic chemical absorption process for CO2 capture using a blended solvent composed of 2-ethylamino ethanol (EAE), diethylene glycol diethyl ether (DEGDEE), and water. The design was based on rigorous consideration of the physical properties of the pure components and the solution, as well as chemical equilibrium. Our results indicate that the proposed biphasic process reduces specific regeneration energy by 6.45 % compared to the conventional method using monoethanolamine (MEA) solution as a solvent (biphasic: 3.41 GJ/Ton; conventional: 3.63 GJ/Ton). Furthermore, it presents the advantage of utilizing low-temperature waste heat for solvent stripping, resulting in a notable 56 % decrease in indirect emissions (biphasic process: 0.119 Ton/Ton, conventional: 0.274 Ton/Ton). When processing flue gases that are more diluted in CO2, the CO2-e of the biphasic process was only slightly increased. Finally, the techno-economic analysis demonstrated that the biphasic process exhibits minimum required treatment prices (MRTPs) comparable to those of conventional methods for treating flue gases with CO2 concentrations ranging from 10 to 19 mol% (biphasic: 0.755 to 1.452 USD/kg, conventional: 0.756 to 1.455 USD/kg). This finding highlights the economic potential of the biphasic process. Overall, this investigation sheds light on the potential of intensifying the CO2 capture process in a biphasic environment.

QCD predictions for vector boson plus hadron production at the LHC

The identification of a hadron in the final state of hadron-collider events that feature a leptonically decaying vector boson can provide essential information on the parton content of the colliding protons. Moreover, the study of hadrons inside jets can provide deeper insights into the fragmentation dynamics. We provide theoretical predictions for specific observables involving either the production of a Z boson in association with light charged hadrons inside a jet or the production of a W boson together with a charmed hadron. We present results for various fragmentation functions and compare our predictions with measurements by LHCb and ATLAS at s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{s} $$\\end{document} = 13 TeV. Our predictions are obtained using the antenna subtraction formalism which has been extended to cope with infrared singularities associated to the fragmentation processes in a hadron-collider environment at NLO accuracy.

Review of Innovative Cavity Designs in Metal–Insulator-Metal Waveguide-Based Plasmonic Sensors

AbstractPlasmonic sensors utilizing metal–insulator-metal (MIM) waveguides represent a significant advancement in sensing technology due to their high sensitivity and versatility. These sensors leverage surface plasmon polaritons to detect minute changes in the surrounding environment, making them highly effective for a range of applications. For instance, they can precisely measure variations in the Refractive Index, which is crucial for monitoring chemical concentrations and biological interactions. Additionally, MIM waveguides can be adapted to sense temperature fluctuations, pressure changes, and the presence of specific gases, providing valuable insights in fields such as environmental surveillance, medical diagnostics, and industrial processes. In recent years, a variety of sensor cavity shapes have been proposed to enhance sensor performance. This review examines how these innovative geometries optimize sensor cavities to achieve unprecedented levels of resolution and sensitivity, underscoring their transformative potential across a broad spectrum of scientific and practical applications.

Electrochemical Detection of H2O2 Using Bi2O3/Bi2O2Se Nanocomposites.

The development of high-performance hydrogen peroxide (H2O2) sensors is critical for various applications, including environmental monitoring, industrial processes, and biomedical diagnostics. This study explores the development of efficient and selective H2O2 sensors based on bismuth oxide/bismuth oxyselenide (Bi2O3/Bi2O2Se) nanocomposites. The Bi2O3/Bi2O2Se nanocomposites were synthesized using a simple solution-processing method at room temperature, resulting in a unique heterostructure with remarkable electrochemical characteristics for H2O2 detection. Characterization techniques, including powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM), confirmed the successful formation of the nanocomposites and their structural integrity. The synthesis time was varied to obtain the composites with different Se contents. The end goal was to obtain phase pure Bi2O2Se. Electrochemical measurements revealed that the Bi2O3/Bi2O2Se composite formed under optimal synthesis conditions displayed high sensitivity (75.7 µA µM-1 cm-2) and excellent selectivity towards H2O2 detection, along with a wide linear detection range (0-15 µM). The superior performance is attributed to the synergistic effect between Bi2O3 and Bi2O2Se, enhancing electron transfer and creating more active sites for H2O2 oxidation. These findings suggest that Bi2O3/Bi2O2Se nanocomposites hold great potential as advanced H2O2 sensors for practical applications.

Adaptive strategies of Listeria monocytogenes: An in-depth analysis of the virulent strain involved in an outbreak in Italy through quantitative proteomics

Despite the general classification of L. monocytogenes strains as equally virulent by global safety authorities, molecular epidemiology reveals diverse subtypes in food, processing environments, and clinical cases. This study focuses on a highly virulent strain associated with a listeriosis outbreak in Italy in 2022, providing insights through comprehensive foodomics approaches, with a specific emphasis on quantitative proteomics. In particular, the ST155 strain of L. monocytogenes strain was subjected in vitro to growth stress conditions (NaCl 2.4 %, pH 6.2, T 12 °C), mimicking the conditions present in the frankfurter, its original source. Then, the protein expression patterns were compared with those obtained in optimal growth conditions.Through quantitative proteomic analysis and bioinformatic assessment, different proteins associated with virulence during the exponential growth phase were identified. This study unveils unique proteins specific to each environment, providing insights into how L. monocytogenes adapts to conditions that are similar to those encountered in frankfurters. This investigation contributes valuable insights into the adaptive strategies of L. monocytogenes under stressful conditions, with implications for enhancing food safety practices.

An innovative deep reinforcement learning-driven cutting parameters adaptive optimization method taking tool wear into account

Tool wear is critically important for the optimization of cutting parameters. However, the increasing nature of tool wear presents challenges to traditional meta-heuristic cutting parameter optimization methods. To address this issue, we propose an innovative deep reinforcement learning-driven cutting parameters adaptive optimization method taking tool wear into account. More specifically, we use the Markov Decision Process to simulate the optimization process of cutting parameters. Firstly, an innovative deep transfer learning algorithm is used for monitoring tool wear. With the progress of tool wear, the proximal policy optimization method of the transformer with multi-head attention mechanism interacts with the processing environment through a process of trial and error, and accumulates a wealth of experience in selecting cutting parameters through the reward function. The deep reinforcement learning model has quickly discern the best cutting parameters, relying on real-time tool wear value. The experimental results show that the proposed method outperforms other algorithms.

Environmental monitoring of Nebraska ready-to-eat meat processing establishments resulted in the isolation of Listeria alongside Pseudomonas highly resistant to quaternary ammonia sanitizer

Robust environmental monitoring for Listeria monocytogenes often may not be feasible for small and very small meat processors in the United States due to limitations in finances, staffing, or expertise. Three small/very small processors in Nebraska were sampled using sponge applicators in non-food contact surface areas to determine if biofilm and sanitizer resistance behaviors of Pseudomonas could relate to the prevalence of L. monocytogenes and Listeria spp. in ready-to-eat meat processing environments. Samples were 3.3% (3/90) positive for L. monocytogenes, and 12.2% (11/90) of samples were positive for Listeria spp. Pseudomonas spp. were also isolated. When Listeria spp. and Pseudomonas spp. were assayed for biofilm production and resistance to a quaternary ammonia sanitizer, multiple isolates belonging to both genera capable of forming biofilms were identified. Four Pseudomonas spp. isolates resisted the 200 ppm manufacturer recommended sanitizer concentration for food contact surface sanitation, and one Pseudomonas spp. isolated from a drain sample that was also positive for L. monocytogenes demonstrated a sanitizer minimum bactericidal concentration of 1000 ppm. These findings further support the need for monitoring of small and very small meat processors for L. monocytogenes as well as highlight the need to identify other bacteria in these processing environments, like Pseudomonas, that are resistant to environmental stressors.

Long-Term Elasticity of Environmental Demand: Environmental Protection Expenditures in Sweden, 1972–2020

This study uses reconstructions of historical Environmental Protection Expenditure Accounts (EPEA) in Sweden from 1970–2020 to present a time series approach that analyses the demand for environmental services. The paper’s aim is to analyse environmental adaptation as a historical, economic and environmental process and to explore structural shifts suggestive of an overall periodisation of environmental demand. The article contributes to a deeper understanding of demand-side factors in environmental adaptation as part of contemporary economic history. The investigation also provides evidence that the income elasticity of environmental demand was slightly higher than 1.0 and largely stable over time, and even though we were able to detect trend breaks, there is insufficient evidence for major structural breaks suggesting sharp shifts in societal environmental preferences. Policies requiring sharply rising environmental expenditures should consider this level of historical stability. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

A Break in the Size–Stellar Mass Relation: Evidence for Quenching and Feedback in Dwarf Galaxies

Mapping stars and gas in nearby galaxies is fundamental for understanding their growth and the impact of their environment. This issue is addressed by comparing the stellar “edges” of galaxies D stellar, defined as the outermost diameter where in situ star formation significantly drops, with the gaseous distribution parameterized by the neutral atomic hydrogen diameter measured at 1 M ⊙ pc−2, D HI. By sampling a broad H i mass range 105 M ⊙ < M HI < 1011 M ⊙, we find several dwarf galaxies with M HI < 109 M ⊙ from the field and Fornax Cluster that are distinguished by D stellar ≫ D HI. For the cluster dwarfs, the average H i surface density near D stellar is ∼0.3 M ⊙ pc−2, reflecting the impact of quenching and outside-in gas removal from ram pressure and tidal interactions. In comparison, D stellar/D HI ranges between 0.5 and 2 in dwarf field galaxies, consistent with the expectations from stellar feedback. Only more massive disk galaxies in the field can thus be characterized by the common assumption that D stellar ≲ D HI. We discover a break in the D stellar–M ⋆ relation at m break ∼ 4 × 108 M ⊙ that potentially differentiates the low-mass regime, where the influence of stellar feedback and environmental processes more prominently regulates the sizes of nearby galaxies. Our results highlight the importance of combining deep optical and H i imaging for understanding galaxy evolution.

Investigation of mixing performance and safety characteristics of polymer-based energetic materials simulant via screw-pressing blending extrusion charges

The present study introduces a screw-pressing charging method to tackle deficiencies in automation and charge uniformity during the melt-casting of polymer-based energetic materials. To ensure the safety of the experiments, this study used inert materials with similar physical properties to partially substitute for the actual energetic components in the preparation of simulant materials. By thoroughly analyzing slurry physical properties, a simulation framework and an extensive performance evaluation method were developed. Such tools guide the design of the structure and configuration of process parameters. Results demonstrate that employing the Pin element significantly enhances radial mixing within the screw, minimizes temperature variations in the slurry, and improves both efficiency and safety in the mixing process. Further, adjustments such as widening the cone angle of the barrel, modifying the solid content of the slurry, and varying the speed of the screw can optimize the mechanical and thermal coupling in the flow field. These adjustments promote higher-quality slurry and create a safer production environment for the extrusion process.

Research of Converter Station Intelligent Inspection System Algorithm

Abstract This article mainly reveals a converter station equipment defect detection system based on the YOLOv5 algorithm. The research encompasses the establishment of a simulated environment for converter station inspection tasks, problem formulation, algorithm design, training process, and experimental evaluation. Key contributions include the enhancement of the YOLOv5 algorithm through improved clustering techniques, dataset preparation, and multi-object detection model training. This highlights the importance of appropriate anchor box initialization in object detection tasks, especially when dealing with small-sized images and varied defect types. In order to verify the correctness of this research, a comparative simulation analysis with mainstream detection algorithms further validates the superiority of the proposed method. The findings of this research contribute valuable insights into the development of AI-driven defect detection systems for power equipment inspection.

What can ethnography offer the gastronomic sciences?

In this article, we explore how ethnography—a social-scientific methodology of immersion in specific social or cultural groups to gain insight—can contribute to the gastronomic sciences.1 Our aim is to illustrate the benefits of using ethnography in gastronomic settings for both social scientists and gastronomy practitioners including chefs, and to discuss strategies for addressing practical challenges that may arise. Based on ethnographic research conducted in a restaurant research and development (R&D) kitchen, we identify three key questions relevant for the gastronomic sciences that ethnography is well-positioned to address, and discuss some strategies for doing so. The three questions are: (1) how does the creative process in culinary environments actually work?; (2) how do chefs, producers, and other culinary professionals become able to ‘taste together’ despite different backgrounds, bodies, and preferences?; and (3) how can different media, perspectives, kinds of knowledge, and disciplinary approaches be made legible to each other and brought together to facilitate the gastronomic sciences? With this paper we contribute to the transdisciplinary agenda and practice of the gastronomic sciences by showing chefs and natural scientists how ethnography can contribute to their work, and showing social scientists some ways ethnographic expertise can help develop the field.

Study on evaluating and optimizing surface property mechanisms of polyalloy for enhanced OER catalyses via electrodeposited technique coupled with molecular dynamics simulations

Polyalloy catalysts composed of non-precious metals exhibit considerable potentials for facilitating overall water splitting processes in alkaline environments. However, achieving evaluations and optimizations of surface property mechanisms of polyalloy for enhanced OER catalyses is the key scientific issue that urgently need to be solved. Electrodeposited technique coupled with molecular dynamics simulations (MDS) are the key to breaking through aforementioned bottlenecks. Moreover, accurately predicting selections of non-precious metal elements in these catalysts through molecular dynamics simulations poses a challenging task. Here, three distinct non-precious metal catalysts (i.e. Ni, NiCo, and NiCoFe) were successfully synthesized through a facile one-step electrodeposition approach under a three-electrode system, which were deposited onto carbon sheets. For the sake of evaluating catalytic properties, MDS were utilized to calculate contact angles on ideal surfaces, which were verified by experimental measurements. Furthermore, ternary alloy catalysts (i.e. NiCoFe) exhibited an overpotential of 254 mV at a current density of 10 mA cm2 and exhibited remarkable stability over a prolonged 150-hour testing period. Finally, main findings can further underscore potentials of utilizing electrodeposition to fabricate efficient non-precious metal catalytic electrodes for overall water splitting, as well as the feasibility of indirectly predicting electrocatalytic performances of such cost-effective catalysts via MDS.

Revealing various change characteristics and drivers of ecological vulnerability in the mountains of southwest China

Ecological vulnerability (EV) assessment is an important means of reflecting the evolutionary process of the local ecological environment, which is crucial for ecological security. The current EV assessment still faces challenges in comprehensively revealing its spatio-temporal change characteristics due to diversity, multi-scale complexity and non-linearity of ecosystems. In this study, we comprehensively revealed the spatio-temporal change features and driver mechanisms of EV in Yunnan Province (YP) using methods such as breaks for additive seasonal and trend (BFAST) and Geodetector. According to the findings, (1) the ecological vulnerability index (EVI) in YP decreased by 0.0265 on the raster scale between 2000 and 2018, and the ecological vulnerability level (EVL) was mainly dominated by level III. At the city scale, EVL in YP was dominated by level IV. At the basin scale, the average EV of the Jinsha River basin was much greater other basins. (2) The linear trend of EV in the YP mainly showed an insignificant decrease, mostly concentrated in the YP’s eastern region. The non-linear trend mainly showed a monotonous decrease, with the mutation time concentrated in 2009. The future persistence trend of EV under different coupling modes was dominated by anti-persistence decrease (Sen-MK + Hurst) and anti-persistence monotonous decrease (BFAST + Hurst), with an area percentage of 40.19 % and 25.13 %, respectively. (3) Gross primary productivity was the priority factor influencing YP’s EV (q = 0.4137). This study not only enriches the cases of EV assessment studies in the high mountain valley area but also bridges the gap of analysing the multi-spatial scale characteristics and change trends of regional EV.