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Blasting is a process in which the chemical energy of explosives is rapidly released, causing strong impact on the rock mass and achieving efficient rock fragmentation. The blasting stress wave and blasting gas generated by explosive detonation are the main driving forces for rock fragmentation. The high peak value and short duration of blasting stress wave are generally referred to as “dynamic action”, the low peak value and long duration of blasting gas are generally referred to as “quasi-static action”. The coupling mechanism between the work done by explosive detonation and the energy consumption of rock fragmentation, as well as the fine control principle of explosive energy release and blast crack propagation, are two key scientific problems that need to be solved in the fundamental theories of rock blasting. The study of “dynamic−static relationship” is one of the important ways to solve key scientific problems. Because of the complexity of the blasting process, traditional blasting design often relies on empirical formulas and field tests, posing problems such as low efficiency, high cost, and uncontrollable safety risk. Traditional designs often only focus on the final crushing effect but ignore the fine control of the blasting process. However, the study of dynamic–static relations has introduced a new approach to solution. This article uses a literature review analysis method to conduct a topic search and analysis of journal papers published in CNKI (China national knowledge infrastructure) and WOS (Science citation index-expanded database) in 2000–2023. The study of the dynamic–static relationship in rock blasting is divided into three different stages: Stages I (before 2006), Ⅱ (2007–2015), and Ⅲ (2016–present). Stage I focuses on the dynamic action of blasting stress waves, revealing the propagation mechanism of blasting stress waves and providing a scientific basis for related engineering applications. Stage Ⅱ studies the crack propagation behavior under the action of blasting stress waves; many studies researched the interaction mechanism between blasting stress waves and cracks and the change in the dynamic stress field at the crack tip, helping relevant researchers to understand the crack propagation and rock fracture process under the action of blasting stress waves. Under a national strategy, Stage Ⅲ has a clear application scenario, and the effect of blasting gas has become the focus of this stage. Through the combination of experiments and numerical simulations, the mechanism of the effect of blasting gas on rock fragmentation, energy transfer, and crack propagation during blasting is deeply discussed. These studies reveal the behavioral characteristics of blasting gas under different conditions and provide theoretical support for practical engineering applications. In addition, authors and research institutions with important contributions to the study of dynamic–static relationships are identified. Keywords and their cooccurrence relationships are revealed, highlighting the focus and trends of the research. Through a systematic review of existing research, combined with the research work and achievements of the author team, the research direction of the dynamic–static relationship in the new era has been clarified, including the distribution and efficient utilization of dynamic–static energy between the blasting stress wave and blasting gas, numerical simulation algorithms considering the real physical processes of rock blasting fracture, and quantitative control of the dynamic–static rock breaking effect of blasting stress waves and blasting gas. The in-depth study of the dynamic–static relationship will promote blasting engineering, from experience-led to theoretical guidance and from extensive to fine.

In the dynamic field of the internet in modern life, networks are increasingly vulnerable to a diverse range of cyberattacks. Conventional intrusion detection systems based on machine learning techniques require a large number of samples for training. However, in some scenarios, only a limited number of malicious samples can be collected. To address the issue of insufficient training samples and unbalanced sample classes for intrusion detection system in real network environments, this paper proposes an intrusion detection method named DeepInsight–transfer learning–convolutional neural network (DI–TL–CNN), which is based on DI and TL. First, the DI method is used to convert the intrusion dataset into an image form suitable for CNN model input. The DI method can transform text while maintaining the semantic relationships between data points, thereby providing high-quality images. In this step, we map the 1D feature vector representation of the input data onto the 2D image representation using T-SNE and construct 2D grayscale images. In the second step, we train and optimize the VGG16 model through TL and fine-tuning, enhancing the model’s adaptability and performance. We propose six TL schemes by freezing and fine-tuning the parameters of different modules in the CNN model to enhance intrusion detection performance. In the TL process, the VGG16 model, pretrained on the ImageNet dataset, demonstrates promising results for generic image classification tasks. The bottom layers of CNN models often learn basic feature patterns that are applicable to various tasks, while the features acquired by the top layers of the model are specific to the target domain intrusion dataset. Fine-tuning allows the model to adjust the pretrained architecture’s higher-order features to better match the targeted dataset. During the training process, the bottom layers of the pretrained architecture are frozen, whereas the top layers are unfrozen for fine-tuning. The optimal intrusion detection model is determined through a comparison of the performance of the six TL schemes. Finally, the correctness and effectiveness of the proposed DI–TL–CNN method are validated on a dataset with insufficient training samples, using metrics such as accuracy, precision, recall, and F1-score. In the experiments, compared with existing state-of-the-art models for intrusion detection, the proposed method considerably enhances accuracy in the detection of network traffic data. The experimental results show that the DI–TL–CNN method is suitable for intrusion detection with small samples and unbalanced data, demonstrating the good application prospects of the method in complex networks.

In this paper, we explore the decentralized control problem through the lens of adaptive dynamic programming for continuous-time nonlinear systems, particularly those with unknown mismatched interconnections and asymmetric input constraints. First, the unknown interconnection term is addressed by approximating it with a radial basis function neural network. This approximation relies on the local states of isolated subsystems and the reference states of coupled subsystems, thus sidestepping the common assumption that interconnections are matched and upper bounded. Following this, the challenge of designing a decentralized optimal controller design is reframed as a series of local optimal controller design problems. This reframing is facilitated by adaptive critic networks and considers the asymmetric constraints of subsystems. The application of the Lyapunov stability theorem demonstrates that controllers, even with asymmetric input constraints, can rapidly stabilize the large-scale system. More importantly, we conclude that the control laws designed here serve as the decentralized control strategies for large-scale nonlinear systems. Our methodology employs the radial basis function neural network and the critic neural network. The former approximates interconnection terms, while the latter deals with cost functions, enabling to derive optimal decentralized control strategies under asymmetric constraints. The uniform ultimate boundedness of observation error and weight approximation error are assured by using the Lyapunov theorem. This is further supported by the introduction of a state observer to estimate the state of the interconnected subsystems and the use of the critic neural network to approximate an improved cost function. This approach allows for an approximate solution to the Hamilton–Jacobi–Bellman equation, resulting in optimal decentralized control strategies satisfying the asymmetric input constraints. At the same time, based on the weight updating rules of the critic neural network, we can guarantee that weight approximation errors are uniformly ultimately bounded by selecting the suitable Lyapunov function. The selection of neural networks in this study is driven by considerations of convergence speed and computational burden, leading to the choice of two specific types of networks. The effectiveness of the developed control method is then rigorously tested through simulation and comparative experiments implemented in a MATLAB environment. Comparative experiments underscore the advancements made by the algorithm developed in this paper, especially under asymmetric control constraints. Contrasting our approach with unimproved cost functions and strategies lacking control constraints, we showcase significant improvements. The simulation results are shown in Figs. 1–9, which fully verify the effectiveness of our established scheme. We can derive that the developed control method significantly enhances stabilization speed and performance.

With continuously evolving wireless communication technologies, the technological revolution marked by advances in artificial intelligence, the Internet of Things, and the metaverse is fundamentally reshaping our society, making it more convenient, intelligent, and information-centric. However, owing to the booming development and popularization of 5G and mankind’s over-reliance on a variety of smart devices, electromagnetic waves have permeated every aspect of people’s lives. This has led to an alarming increase in the density of electromagnetic radiation and electromagnetic pollution. Electromagnetic absorption materials are functional materials that efficiently absorb incident electromagnetic waves and convert microwave energy into Joule heat to process external electromagnetic waves, thereby regulating them. Over the last few decades, electromagnetic wave-absorbing materials have made significant strides and are playing increasingly crucial roles in radiation protection and antiradar detection, owing to their effective attenuation of incident electromagnetic waves. With the vigorous development of nanotechnology, the design of high-performance electromagnetic wave-absorbing materials has relied on the intrinsic properties of single-component media and has focused on the synergistic effect of different components, resulting in rich loss mechanisms. In recent years, carbide-based composites have received increasing attention in the field of electromagnetic absorption. Among the various candidate materials, carbides are typically characterized by chemical stability, low density, tunable dielectric properties, and diverse morphologies/microstructures. Therefore, exploring and designing carbide-based composites is a feasible approach for the development of novel electromagnetic wave-absorbing materials with promising prospects for practical applications. A summary of the status of the development of carbide-based composites as a new generation of electromagnetic wave-absorbing materials would be helpful for understanding and furthering their advancement. In this review, we introduce the electromagnetic loss mechanisms associated with dielectric composites and discuss the recent advances in the use of various types of carbide-based composites as high-performance electromagnetic wave-absorbing materials. These composites include covalent carbides, interstitial carbides, less common carbide-based composites, and multicomponent composites, such as MXene and high-entropy MAX phase carbides. Key information on composition optimization, structural engineering, performance enhancement, and structure–function relationships are discussed. Additionally, the properties of representative composites are compared, and the challenges and prospects associated with the development of carbide-based composites are presented.

Nickel-based superalloys exhibit excellent high strength and thermal fatigue resistance at 650 ℃, making them widely used for manufacturing elevated-temperature components such as turbine blades for aero-engines. Laser-powder bed fusion (L-PBF) is a rapidly developing metal additive manufacturing technology that is increasingly important for producing nickel-based superalloy products. The design and service life of aero-engine turbine blades typically require more than 107 load cycles. Therefore, it is crucial to investigate the very-high-cycle fatigue characteristics of L-PBF nickel-based superalloys at increased temperatures. Internal failure is a common elevated-temperature fatigue mode of L-PBF nickel-based superalloys that is currently not well understood. To address this issue, first, axial fatigue tests with stress ratios of −1 and 0.1 are conducted at 650 ℃. Specifically, partial typical internal failure fractures at a stress ratio of 0.1 are selected as the focus of this study. Second, scanning electron microscopy and ultra-depth field microscopy are employed to observe the 2D and 3D morphology of the fatigue fracture surfaces and analyze the crack nucleation areas and growth paths. The results show that irrespective of the presence of defects, the emergence and aggregation of numerous facets occur in the “facetted cracking area (FCA),” a typical internal failure characteristic of L-PBF nickel-based superalloys. Measurements indicate that the size of these facets leading to cracking is comparable to that of large grains and correlates with variations in grain orientation. Therefore, internal failures are categorized into two modes of cracking: “defect-assisted faceted cracking” and “non-defect-assisted faceted cracking.” Third, the FCA exhibiting typical internal failure fractures is sectioned and subjected to electron backscatter diffraction analysis to observe surface and subsurface crystallographic features related to crack nucleation and growth behavior. The analysis reveals that microcracks mainly originate from large grains with softer orientations, propagate through slips, expand along the direction of maximum shear stress, and ultimately form a perforated fracture pattern. Fourth, subsurface microcrack features beneath the FCA are examined via focused ion beam milling and imaging. Transmission electron microscopy is then employed to observe slip bands and dislocation structures near the microcracks. The results confirm that the fatigue deformation mechanism of facets at 650 ℃ is mainly controlled by a combination of anti-phase boundary shearing, precipitate bypassing, and stacking fault shearing. This mechanism is especially evident under stress concentration effects induced by cracks or defects. Finally, according to the definition of the crack tip stress intensity factor, a crack nucleation life prediction method that accounts for the characteristics of faceted cracks is proposed. The predicted results align well with the experimental findings.

At present, cracking is the main form of damage to ultra-high performance concrete (UHPC), and increasing flexural toughness is the primary technical approach to addressing this issue. Currently, fiber, polymer, or nanomaterial modification is commonly used to improve UHPC toughness. Emulsified asphalt has also demonstrated the potential to toughen and resist cracking. To investigate the toughening effect of emulsified asphalt in cementitious materials, this study employed a method in which emulsified asphalt and water are added simultaneously during wet mixing to prepare UHPC specimens. The effects of different types and dosages of emulsified asphalt on the workability, mechanical strength, and flexural toughness of UHPC were investigated through extensibility tests, flexural toughness tests, compressive strength tests, flexural strength tests, and tensile strength tests. The results showed that the workability of UHPC mixes with two types of emulsified asphalt gradually decreased as the emulsified asphalt dosage increased. When the dosage of either type of emulsified asphalt exceeded 3%, the extension did not meet the specification requirements. Furthermore, as the dosage of the two types of emulsified asphalt increased, the mechanical strength of the UHPC slightly decreased. Compared with the same dosage of cationic emulsified asphalt, anionic emulsified asphalt had a smaller impact on the mechanical strength of UHPC because anionic emulsified asphalt and cement hydration products both carried the same charge, causing them to repel each other. This mutual repulsion reduced the influence of emulsified asphalt on the cement hydration reaction process, which was conducive to the generation of cement hydration products. Consequently, the negative impact on the formation of UHPC strength was lessened, and the uniformity of the steel fiber distribution within the UHPC was ensured. In terms of flexural toughness, both types of emulsified asphalt enhanced the flexural toughness of the UHPC, with anionic emulsified asphalt providing better results than cationic emulsified asphalt. Compared with cationic emulsified asphalt, anionic emulsified asphalt resulted in better workability and mechanical strength in the UHPC, and its toughening effect was more significant. Considering both mechanical properties and overall performance, the use of anionic emulsified asphalt in UHPC is recommended, with an optimal dosage of 3%. The microstructure of UHPC specimens without emulsified asphalt and those mixed with 3% anionic emulsified asphalt was observed using a scanning electron microscope. The observations revealed that emulsified asphalt could fill the original microcracks in UHPC, improve the internal structure of the matrix, increase the effective contact area between steel fibers and the matrix to a certain extent, and enhance the toughening effect of steel fibers. Additionally, the viscoelastic properties of emulsified asphalt increased the energy required for crack propagation, effectively reducing the generation and development of microcracks and serving as a buffer for the destabilization damage of hydration products.

The three-level converter has become the mainstream converter topology because of its good output power quality and high power factor. The three-level dual pulse width modulation frequency speed control system with rectifier–inverter structure has become a research hotspot in academic circles because of its advantages of bidirectional energy flow, high power quality, and controllable intermediate direct current (DC) voltage. Aiming at the high-performance and high-efficiency control of the three-level neutral point clamped (NPC) rectifier–inverter drive system of an induction motor, this study builds a prediction and loss model of the three-level rectifier–inverter system, constructs a cost function including the midpoint voltage balance and loss optimization, and proposes a model predictive control without weighting factors based on a sequential parallel structure. With the development of the field of power electronics, the control performance and efficiency of the converter have gradually improved, and the model predictive control applied to the converter is no longer limited to the traditional control objectives. The proposed strategy introduces the DC bus midpoint voltage and converter switching frequency control to the traditional sequential model predictive control and constructs a unified cost function with multiple control objectives. According to the actual requirements of the multiple control objectives in the operation of the rectifier–inverter system, the control objectives in the cost function are divided into primary and secondary control objectives and classified into two sequence optimization sets, and different sequence sets are sequentially optimized. In the same sequence set, adaptive parallel optimization is used to select the optimal switching state, which ensures the synchronous optimization of each control object in the sequence, thus avoiding the need for weighting factors. The parallel structure can rank multiple control targets, reduce the number of sequences to increase the number of optional voltage vectors between each sequence and increase the control effect of nonprimary control targets. Moreover, synchronous optimization of control targets of the same importance is realized at the same level, which solves the problem that targets of similar importance must be sequentially optimized in conventional sequential model predictive control, solves the problem that the priority of different targets is difficult to adjust, and has stronger applicability for the complex topology structure with multiple control requirements. The simulation and experimental results showed that the proposed algorithm can improve the steady-state and dynamic performance of the system, reduce the midpoint voltage bias, reduce the switching frequency of the rectifier and inverter, reduce the total harmonic bias, and adjust the midpoint voltage unbalance.

Overdependence on manual experience frequently leads to the irrational selection of orepass. Therefore, a scheduling model needs to be established to make sound decisions on orepass selection, increase the efficiency of underground rail transport, and improve production efficiency in metal mines. In this study, the −495 level of the Baixiangshan iron mine in Anhui Province is used as a research object. Orepass information, tunnel information, and historical mining, loading, and transporting data are collected. The data are then preprocessed to obtain a 130-order matrix that can describe the rail transit topology. Several vectors containing road/orepass basic information, road/orepass trajectory information, and orepass chronological material-level information are used for model training and validation. Time-series transformer graph convolutional network, which is denoted as T-TransGCN, is a temporal graph neural network that integrates orepass features, road features, and rail topology information. T-TransGCN is proposed to determine the optimal orepass selection. It enhances performance through splitting temporal features, fine-tuning the pooling layer architecture, and embedding edge features. Validated results show that (1) the T-TransGCN model is better than the Time-series multi-layer perceptron (T-MLP) and the Time-series graph convolutional network (T-GCN). The label accuracy, F1 score, and Top-3 accuracy of T-TransGCN improve by 7.33%, 17.00%, and 14.26% compared with those of T-MLP, which indicates that T-TransGCN can effectively integrate node attributes and topology information. Moreover, T-TransGCN has a relatively higher number of model parameters, more complex model structure, greater stability, and stronger fitting capability than T-GCN. (2) The addition of chronological material-level features to T-TransGCN increases its F1 score and Top-3 accuracy by 11.75% and 17.02%, while the addition of trajectory features improves them by 11.83% and 10.01%. Both new data preprocessing methods are effective in enhancing the generalization ability of T-TransGCN. The chronological material-level features help T-TransGCN understand the recent state of orepass, while the trajectory features reflect the importance of different orepasses dynamically. The trajectory features help the model understand structural information, such as the similarity of adjacent nodes or the similarity of forked nodes. (3) The addition of edge features further distinguishes orepass nodes from road nodes. The optimization of the outputs of the pooling layer helps avoid the distraction of unimportant information. When chronological features are split, the F1 score and Top-3 accuracy of T-TransGCN improve by 15.94% and 12.34%. This increment enhances the focus of the model on the chronological material-level information. The integration of the abovementioned model improvements further increases the fitting capability, generalization ability, and stability of T-TransGCN.

As one of the important parameters of flow characteristics, flow velocity occupies an important position in the study of vertical pipeline lifting efficiency. To more accurately measure flow velocity and reveal the flow dynamics of vertical pipeline conveying systems, we focus on the solid–liquid two-phase flow in pipelines. In this paper, we study the method of pipeline velocity measurement and reveal the flow characteristics of the pipeline system. First, we use a high-speed camera to transform the flow velocity measurement into a computer vision problem, and combine the computer vision problem with deep learning technology to propose an A-RAFT (attention-based recurrent all-pairs field transforms) neural network model based on the attention mechanism. The model uses a convolutional layer to extract feature information and reduces the computational load through a pooling layer. Additionally, we introduce a correlation layer to perform inter-correlation operations on the feature information and calculate pixel displacement. In this process, the attention mechanism focuses on regions with flow velocity changes, enhancing the ability of the network to estimate velocity field variations. This helps the model better select and focus on key features in the input data, providing more accurate feature information for matching. Consequently, the estimation accuracy of the model is improved, particularly for the boundary regions of solid particles in solid–liquid two-phase flow. The model also effectively estimates flow rates for particles of varying shapes and sizes, with enhanced overall performance and accuracy. In addition, this paper constructs a combined real and virtual dataset for training the neural network model. The dataset is based on nine types of classical single-phase flow field data, and real particle texture information is fused into the dataset through real experiments to enhance data diversity. This dataset effectively simulates the optical flow changes of the pixels in the front and back frames in real experiments. The proposed model is evaluated with this dataset, and the results show that the model achieves high-precision velocity field computation on synthetic images, and the estimation error is 15.6% lower than those of other existing models. In the simulation experiments of solid particle transportation in vertical pipelines, the proposed model demonstrates accurate estimation performance on the collected real flow field data, with relative errors of lower than 5% for the measurement of particle velocities. These errors are derived from comparisons with the true values. The results validate the method in terms of both estimation accuracy and the generalization ability of the model. This study can provide new insights for solid–liquid two-phase flow characterization in energy extraction, tunneling, wastewater treatment, and long-distance pipeline transportation.

Mg/Al bimetallic layered composites are in great demand for lightweight and high-performance manufacturing applications owing to the advantageous combination of the low density of magnesium alloys and the corrosion resistance of aluminum alloys. The bimetallic solid-phase composite fabrication process, in which the contact surfaces of metal materials are directly combined in the solid state, offers significant advantages in bimetallic composite technology. This process avoids the detrimental effects of oxidation, inclusions, and other defects that can impact the performance of composite materials formed through liquid–liquid or liquid–solid composite processes. Temperature, strain rate, and strain are critical parameters in many joining and forming processes of Al/Mg alloy hybrid structures/components, but the relationship between these parameters and interfacial bonding strength remains to be quantified. In this study, hot compression composite experiments were conducted to elucidate the influence of heat deformation conditions on the performance of the Mg/Al bimetallic composite interface. The experiments were performed at deformation temperatures of 300–430 ℃, strain rates of 5×10−3–1 s−1, and strains of 20%–40%. A scanning electron microscope with energy dispersive spectroscopy (SEM–EDS) and a Vickers hardness tester were used to analyze the microstructure, element distribution, and hardness distribution of the composite interface. The results showed that the bonding interface was not effectively formed owing to the presence of micro-gaps at a strain of 0.2 or a temperature of 300 ℃. Furthermore, the strain rate mainly affected the shape of the bonding interface, indicating that strain and temperature were the critical factors influencing metallurgical bonding in the bimetallic compounding process. As the strain rate decreased and deformation and temperature increased, the element diffusion time increased, and diffusion ability improved. This resulted in a thicker transition region and the formation of high-hardness intermetallic compounds (IMCs) composed of Mg17Al12 and Al3Mg2 phases. According to this, an evolution model of the intermetallic compound layer thickness in the transition region, parameterized by the elemental diffusion activation energy, was established. Through the incorporation of the critical strain required for the bimetal to achieve metallurgical bonding, a diagram illustrating the evolution of the Mg/Al bimetallic composite interface under various heat deformation conditions was constructed. Metallurgical bonding was achieved through the complete diffusion of metal atoms at the interface; however, the hardness and brittleness of the resulting intermetallic compound layer were not conducive to the quality of the Mg/Al bimetallic interface. Therefore, considering metallurgical bonding and the characteristics of the intermetallic compound layer is essential. Controlling the extent of elemental diffusion allowed for minimizing the thickness of the intermetallic compound layer while ensuring effective interfacial metallurgical bonding. The calculation results indicated that deformation conditions of higher temperature (>400 ℃) and higher strain rate (～1 s−1) could inhibit the formation and growth of the intermetallic compound layer while ensuring metallurgical bonding, thus contributing to a high-quality composite interface. The combination of high strain rates and high temperatures enabled the formation of a fully bonded interface with a minimal intermetallic compound layer thickness, maximizing bonding strength. The research findings and developed models can guide the optimization of parameters associated with the Mg/Al bimetallic joining or forming process via plastic deformation.

Carrier phase measurement plays a crucial role in achieving rapid and high-precision positioning within a global navigation satellite system (Global navigation satellite system, GNSS). A pivotal aspect of this process is the accurate resolution of the integer ambiguity. Although the particle swarm optimization algorithm (Particle swarm optimization, PSO) demonstrates quick convergence, it tends to become trapped in local optima, showing a relatively weak ability to fix ambiguity. Conversely, the artificial fish school algorithm (Artificial fish swarm algorithm, AFSA) excels the global optimization performance. However, its natural selection mode, which operates without a “leader,” renders the integer ambiguity resolution process more time-consuming. By integrating the strengths of PSA and AFSA, we propose an improved hybrid algorithm, termed the particle swarm and artificial fish swarm (PSOAF) algorithms, to efficiently search for integer ambiguity solutions in GNSS. The process begins by solving the floating-point solution and its corresponding covariance matrix using the carrier phase double-difference equation. Then, to address the correlation issue, the inverse integer Cholesky algorithm is used to effectively decorrelate them. Recognizing the limitations inherent in integer least squares estimation, we further refine the effectiveness of the PSOAF algorithm by optimizing the fitness function. This optimization significantly enhances the convergence speed and search performance of the algorithm, resulting in a precise resolution of the integer ambiguity. In the initial stage of integer ambiguity search, the PSO’s characteristic of rapid convergence facilitates a coarse search, yielding a suboptimal solution. This solution serves as foundational data for the AFSA, guiding the fine search required for integer ambiguity resolution. To verify the PSOAF algorithm’s effectiveness and practicality, we conducted both three-dimensional and twelve-dimensional simulation analyses based on a classical example. The results demonstrate that the PSOAF algorithm not only converges to the optimal solution at an unprecedented rate but also exhibits markedly superior search efficiency compared to single algorithms. Further validation of the PSOAF algorithm’s real-world applicability and effectiveness was sought through experiments utilizing actual Beidou data. The results from these experiments were promising, showing that the baseline resolution error mainly remained within a 10-mm range. This finding confirms the correctness of the double-difference integer ambiguity search conducted using the PSOAF algorithm. The applicability and effectiveness of the PSOAF algorithm in real scenarios are verified. In conclusion, this study underscores the PSOAF algorithm’s significant potential for practical applications, particularly in scenarios involving short baselines.

China’s iron ore deposits are generally characterized by substantial thickness and low grade, and this presents challenges for traditional mining methods to satisfy market demands economically and efficiently. To maintain a competitive edge in the market while complying with environmental and low-carbon development principles, large-scale, low-cost, environmentally friendly, and low-carbon mining models that can handle tens of millions of tons annually must be adopted. Such models not only considerably lower the unit cost of ore extraction but also minimize the environmental effect, lower the carbon emissions, and attain sustainable resource development and utilization. This paper studies the new mining models, advanced safety technologies, green requirements, and intelligent trends in the efficient, green exploitation of large-scale deep mines. This study aims to address three crucial issues: the principles and methods for the continuous large-scale mining of deep thick ore bodies, the mechanisms and mechanical behaviors of catastrophic events triggered by strong cyclic disturbances in high-stress rock masses, and the adaptive intelligent energy-saving ventilation mechanisms for deep hot mines with multiple fans. This paper aims to advance five key technologies: spatiotemporal collaborative continuous mining techniques for deep inclined thick ore bodies, efficient sensing devices for large-scale mining dynamic environments and disaster prevention and control technologies, high-flow continuous steady-state preparation and transportation technologies for tailings paste, multistage dynamic intelligent ventilation and direct cooling technologies for thermal hazard prevention in deep hot mines, and intelligent decision-making and collaborative operation control technologies for the equipment chain in stope mining operations. These innovations aim to attain the efficient continuous mining of thick ore bodies with large parameters; the effective disaster prevention and control for rock mechanics and dynamic disasters in large-scale mining; the efficient utilization of mining and processing of solid waste through backfilling, intelligent energy-saving ventilation, and thermal hazard prevention; and the efficient collaboration of intelligent stope mining operation chains. The eventual objective is to develop new theories, technologies, methods, and equipment for the green, intelligent mining of metallic mines at depths surpassing one kilometer, to create a demonstrative mine, and to form a technological system for the efficient, green mining of large-scale deep mines that can be widely applied. This paper aspires to improve the capacity for large-scale green development of deep strategic mineral resources in China comprehensively and to provide a theoretical foundation and technical support for the large-scale green exploitation of deep mineral resources. By addressing these key issues and developing the associated technologies, this paper aims to improve the efficiency, safety, and sustainability of mining operations in deep iron ore deposits remarkably, ultimately contributing to the long-term viability and competitiveness of the mining industry in China.

Investigating the effect of steel fiber blending schemes on the dynamic mechanical properties and damage evolution of concrete is crucial for deep-shaft construction projects. Dynamic impact tests were conducted on steel fiber concrete specimens with varying mixing schemes and grades, according to the settings of the ultra-deep shaft-lining support project of a mine in Yunnan Province. The evolution of strain fields on the specimen surfaces under impact loads was analyzed using digital image correlation techniques. The steel fiber mixing schemes were as follows: single mixing, in which 55 kg of end-hook long fibers were added per cubic meter of concrete; double mixing, which involved the addition of 15 kg of end-hook long fibers and 40 kg of short copper-plated straight fibers per cubic meter of concrete; and triple mixing, which involved the addition of 40 kg of end-hook long fibers, 5 kg of copper-plated straight medium-length fibers, and 10 kg of short copper-plated straight fibers per cubic meter of concrete. The test results indicated that the dynamic compressive strength and dissipated energy ratio of the triple-mixed steel fiber concrete specimens were greater than those of the single- and double-mixed steel fiber concrete specimens. The smaller dynamic strength of plain concrete, the more pronounced the enhancement in the dynamic strength observed in concrete specimens with a steel fiber admixture. A high-speed camera was used to record the complete failure process of the steel fiber concrete specimens. The camera revealed that the failure mode was influenced by the concrete grade and steel fiber mixing scheme. The observed failure modes included shear, splitting, and shear-splitting failures. Compared with plain concrete specimens, steel fiber concrete specimens exhibited fewer cracks, a lower percentage of reflected energy, and higher percentages of transmitted and dissipated energy under impact loading. This indicates that the steel fibers effectively inhibited crack initiation and propagation, thereby enhancing the stability of the shaft wall concrete. Non-destructive impact tests on steel fiber concrete specimens showed that the triple-mixed steel fiber scheme inhibited concrete damage under impact loading to the maximum extent. It is recommended that the lining support for the deep shaft of the mine be constructed using grade C50 concrete with steel fibers mixed with 40 kg of end-hooked long fibers, 5 kg of copper-plated straight medium-length fibers, and 10 kg of short copper-plated straight fibers per cubic meter of concrete.

To address the problems of strong noise interference, long detection times, uneven fuselage surfaces, lack of visual information in two-dimensional images, and difficulty in automatic detection, an automatic detection algorithm for aircraft skin pit damage based on a multiscale surface model was designed. First, an automatic acquisition platform system was constructed using an unmanned vehicle, a lifting pole, and a depth camera. The point-cloud data of the aircraft skin was obtained using this acquisition platform system. The point-cloud data were then preprocessed using the radius filter algorithm, voxel grid filter algorithm, and moving least squares algorithm. Then, the preprocessed point-cloud data were divided into multiscale regions and split into multiple local skin mesh regions to obtain multiple local grid area data. For each local grid region data, the surface models of each local grid region and regional spatial adjacency were obtained by constructing and optimizing the estimation of the local quadric surface based on the random sampling consensus algorithm. The spatial adjacency, surface model, and its index together form the region tree. The local surface models at different scales were aggregated by storing information and normal vector angles in the region tree to identify the damaged and nondamaged regions. Finally, surface features, such as curvature and normal vector, were used to cluster the pit points in the damaged area, and the pit point-cloud data were aggregated to obtain the final pit damage results. The proposed algorithm was compared with existing traditional algorithms, such as the point-cloud block method and the point feature region growth method based on normal vector and curvature. Experimental results showed that the accuracy, recall, and F-value of the point-cloud block method were 4.00%, 20.00%, and 6.67%, respectively, with an average detection time of 20 s. For the point feature region growth method based on normal vector and curvature, the accuracy, recall, and F-value were 30.77%, 26.67%, and 28.79%, respectively, with an average detection time of 25 s. The accuracy, recall, F-value, and average detection time were significantly improved, with mean values of 92.86%, 86.67%, 89.92%, and 6 s, respectively. Additionally, the detection results of the three algorithms on the aircraft skin engine, fuselage, and wing were compared, and the influence of curvature in different regions on the algorithm was analyzed. The detection results of the proposed algorithm were significantly better than those of existing traditional algorithms, such as the point-cloud block method and the point feature area growth method based on normal vector and curvature. The proposed algorithm achieved the goal of automatically detecting pit damage in aircraft skin scenes.

The production splitting coefficient is a key parameter for predicting gas production and reserves, evaluating gas recovery efficiency, and guiding the optimization of well patterns in multilayer commingled gas reservoirs. Therefore, developing a reliable splitting method to calculate this coefficient is critical for the development of multilayered commingled gas reservoirs. The gas drainage radius emerges as a key parameter in determining the production splitting coefficient grounded in the physical concept of production splitting. This coefficient is influenced by several factors—such as the wellbore radius, production layer thickness, porosity, initial gas saturation, gas deviation coefficient, reservoir temperature, gas drainage radius, and average formation pressure. Usually, calculating the gas drainage radius and average formation pressure cannot be performed in actual applications. Combining the gas well deliverability and material balance equations forms a closed-loop model for calculating the production splitting coefficient. This model features three equations with three unknowns—gas drainage radius, average formation pressure, and the production splitting coefficient. It introduces a double-cycle calculation method to solve the model. Specifically, the internal cycle computes the gas drainage radius, whereas the external cycle determines the production splitting coefficient, each offering unique solutions and employing alternating forward recursion and reverse recursion rules. The establishment of a double-cycle calculation process—defined by specific cycle step sizes and an error threshold—ensures that the external cycle progresses only upon the convergence of the internal cycle. Moreover, it determines whether the internal cycle can converge. Numerical simulations demonstrate that the production splitting coefficient exhibits different patterns across different production stage layers, highlighting the accuracy and mechanism soundness of the double-cycle method compared with direct numerical simulations. Moreover, it has good feasibility in field application because it can calculate the production splitting coefficient even without known values for gas drainage radius. Comparing gas-producing profiles indicates that the dual-cycle calculation method has reliable accuracy in field applications when the gas drainage radius and average formation pressure are unknown. Ultimately, the double-cycle calculation method offers a comprehensive solution for a three-element equation system, enabling simultaneous estimations of the production splitting coefficient, gas drainage radius, and average formation pressure.

Grinding process monitoring is key to assessing the intelligence of grinding processing and ensuring manufacturing quality of the intended product. The grinding force in the grinding process is an important characteristic, and the grinding force signal has a close relationship with the degree of wheel wear, grinding heat, grinding wheel and workpiece contact state, and other entities. Therefore, the analysis of the grinding force signal using the time domain and frequency domain analysis methods can effectively reveal the grinding characteristics and wheel wear evolution mechanism in the gear grinding process. In this study, the microcrystalline corundum grinding wheel was used to conduct the profile grinding test on 20CrMnTi steel gears installed in vertical machining centers. A high-performance general-purpose dynamometer was used to collect the discrete time series data of the grinding force in real time; a confocal laser microscope and scanning electron microscope were used to observe the wear characteristics of the microcrystalline wheel surface; finally, a micrometer was used to measure the thickness of the wheel’s end-top surface to assess the degree of wheel wear in a quick and simple way. To improve the precision, automation, and intelligence level of the gear grinding process considering the microcrystalline corundum grinding wheel, online monitoring of the grinding process and wear mechanism of the grinding wheel was performed. The intrinsic correlation between the time-frequency domain signals of the grinding force, the contact status of the grinding wheel and workpiece, and the wear degree of the grinding wheel was established. Thus, in this study, a new online monitoring method for monitoring the grinding process of the grinding wheel was proposed based on real-time force signals. Meanwhile, the wear mechanism underlying the morphology of microcrystalline corundum grinding wheels was characterized and analyzed using laser confocal microscopy and scanning electron microscopy. The results showed that the distribution pattern of waveform characteristic indicators such as kurtosis, waveform index, peak index, and pulse index in the time domain spectrum of the grinding force showed a distinct trend of varying cutting depth grinding state ≥ steady-state grinding state of a significantly worn grinding wheel ≥ steady-state grinding state of a lightly worn grinding wheel > noncontact state. However, the variation of the two waveform characteristic values of the grinding force, amplitude spectrum and pulse indicator under different grinding states is completely opposite to that of the time domain signals. After assessing the three-dimensional roughness of the working surface of the grinding wheel, it is observed that there is a slight decrease and a subsequent rapid increase in the roughness of the working surface of the wheel with the evolution of wheel wear. The polycrystalline structure of the microcrystalline corundum abrasive particles gives them the ability to update and sharpen along the microcrystalline interface, showing layer-by-layer cleavage peeling.

The paper proposes a method based on kernel principal component analysis (KPCA) and multi-scale temporal convolution network (MTCN) for identifying faults in lithium-ion batteries, which is crucial for ensuring the safe and stable operation of energy-storage systems. Lithium-ion batteries are the primary component of energy storage units. The method involves the following steps: First, fault data are normalized, and KPCA is used for dimensionality reduction and single fault detection to reduce computational complexity and improve data reliability. According to the different types of overcharge faults and unknown faults, KPCA is used to reduce the data from the original dimension to 2 or 4 dimensions. The KPCA model is trained using the normal data corresponding to the two groups of fault data, and the fault data are inputted as the test data. The results show that the SPE statistic and the T2 statistic considerably exceed the control limit, verifying the reliability of the data. Then, the data are labeled according to the fault type: the overcharge data are labeled as D0 (normal) and D1 (fault), and the unknown fault data as F0 (normal) and F1 (fault). The labeled data are divided into training and test sets according to a specific proportion. Afterward, the MTCN model is trained with the training dataset, and its hyperparameters are optimized with the frost algorithm to improve model accuracy. Finally, the trained MTCN model is used to classify the test dataset. The method is validated on two groups of data: overcharge fault data and unknown fault data. The results show that the frost algorithm can optimize the hyperparameters after about 20 iterations. Compared with LSTM and CNN, which are also optimized by the frost algorithm, MTCN achieves a higher classification accuracy, reaching 99.265% and 99.688%, on the overcharge fault dataset and unknown fault dataset, respectively, while maintaining comparable performance to Xception and ResNet50. Additionally, to verify the influence of the training data amount, the training and test sets are divided according to different proportions, and the three algorithms are tested. KPCA verifies the reliability of charging fault and unknown fault data, and the results show that MTCN has the highest classification accuracy, especially on the overcharge fault dataset. Owing to the low dimensionality of the original data set, LSTM and CNN exhibit poor classification performance. In contrast, MTCN can extract more temporal information, achieving high classification accuracy. These results demonstrate the effectiveness and superiority of the method in fault diagnosis of lithium-ion batteries.

Abstract This article consists of two main parts, both in relation to Serbia’s accession to the European Union - EU in relation to its energy sector: (1) Political and policy issues, and (2) Energy production, consumption and pricing. Each is heavily influenced by the Russia-Ukraine War. Political issues are primarily related to Kosovo, which unilaterally declared independence from Serbia. Regarding supply, Serbia’s energy sector is affected unevenly: while electricity production remains self-sufficient, shortages in oil and gas necessitate imports. Natural gas imports, primarily from Russia, now bypass Ukraine, aided by the new Turk Stream pipeline via Black Sea and also new EU-funded interconnector with Bulgaria. Newly introduced EU sanctions restrict Russian crude oil imports especially via maritime routes, but Russian influence still dominates domestic refineries and hydrocarbon extraction. Serbia’s heavy reliance on environmentally unfriendly lignite conflicts with EU renewable goals, but on the other hand protests against small hydropower projects for capturing rivers in pipes are frequent nowadays. Recent issues in the largest thermal power plant led to costly temporary imports. Despite challenges, Serbia navigates a complex energy landscape, balancing geopolitical realities with domestic and EU objectives while addressing environmental concerns, energy security and its national interest.

We introduce a novel method to retrieve the cloud fraction and the optical thickness of liquid clouds over a water surface based on polarimetry. The approach is well suited for satellite observations providing multi-angle polarization measurements, in particular those of the Hyper-Angular Rainbow Polarimeter #2 (HARP2). Unlike commonly used methods to derive cloud fractions, our method does not depend on the spatial resolution of observations, and it does not require any threshold values for cloud detection. Based on radiative transfer simulations, we show that the cloud fraction and the cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region. In the cloudbow, the degree of polarization mainly depends on the cloud optical thickness. Conversely, for a viewing direction in the sun glint region, the degree of polarization depends on the clear fraction of the pixel, because here the radiation scattered by cloud droplets is almost unpolarized, whereas radiation reflected by the surface is highly polarized. Utilizing these dependencies, we developed a retrieval using a simple lookup table approach. Based on sensitivity studies, we show that prior information about wind speed and aerosol optical thickness improves the accuracy of the cloud fraction retrieval. Prior information about the cloud droplet size distribution can reduce the uncertainty of the cloud optical thickness retrieval. The prior information should be obtained by combining our method with already existing aerosol and cloud retrieval algorithms. We performed 3D radiative transfer simulations and found that the cloud optical thickness is generally underestimated due to neglect of 3D scattering effects. The cloud fraction is overestimated in cloud shadows and underestimated in in-scattering regions. As a demonstration, we apply the methodology to airborne observations from polarization cameras of the Munich Aerosol Cloud Scanner (specMACS) instrument. The high-spatial-resolution data (10–20 m) have been averaged to a spatial resolution of approximately 2.5 km to mimic satellite observations. From the average linear polarization at scattering angles of 140 and 110°, we derive continuous cloud fraction values and the corresponding cloud optical thicknesses. Comparison for cases including low, medium, and high cloud fractions shows that the retrieval, using only reflected polarized radiances at two scattering angles, provides accurate estimates of the cloud fraction for observations with coarse spatial resolution.

This study developed an algorithm for the Level 2 (L2) atmospheric radiation flux and heating rate product by a Japanese team for Earth Cloud, Aerosol and Radiation Explorer (EarthCARE). This product offers vertical profiles of downward and upward longwave (LW) and shortwave (SW) radiative fluxes and their atmospheric heating rates. This paper describes the algorithm developed for generating products, including the atmospheric radiative transfer model and input datasets, and its validation against measurement data of radiative fluxes. In the testing phase before the EarthCARE launch, we utilized A-Train data that provided input and output variables analogous to EarthCARE, so that the developed algorithm could be directly applied to EarthCARE after its launch. The results include comparisons of radiative fluxes between radiative transfer simulations and satellite and ground-based observations that quantify errors in computed radiative fluxes at the top of the atmosphere against Clouds and the Earth's Radiant Energy System (CERES) observations and their dependence on cloud type with varying thermodynamic phases. For SW fluxes, the bias was 24.4 W m−2, and the root mean square error (RMSE) was 36.3 W m−2 relative to the CERES observations at spatial and temporal scales of 5° and 1 month, respectively. On the other hand, LW exhibits a bias of −10.7 W m−2 and an RMSE of 14.2 W m−2. When considering different cloud phases, the SW water cloud exhibited a bias of −11.7 W m−2 and an RMSE of 46.2 W m−2, while the LW showed a bias of 0.8 W m−2 and an RMSE of 6.0 W m−2. When ice clouds were included, the SW bias ranged from 58.7 to 81.5 W m−2 and the RMSE from 72.8 to 91.6 W m−2 depending on the ice-containing cloud types, while the corresponding LW bias ranged from −8.8 to −28.4 W m−2 and the RMSE from 25.9 to 31.8 W m−2, indicating that the primary source of error was ice-containing clouds. The comparisons were further extended to various spatiotemporal scales to investigate the scale dependency of the flux errors. The SW component of this product exhibited an RMSE of approximately 30 W m−2 at spatial and temporal scales of 40° and 40 d, respectively, whereas the LW component did not show a significant decrease in RMSE with increasing spatiotemporal scale. Radiative transfer simulations were also compared with ground-based observations of the surface downward SW and LW radiative fluxes at selected locations. The results show that the bias and RMSE for SW are −17.6 and 172.0 W m−2, respectively, which are larger than those for LW that are −5.6 and 19.0 W m−2, respectively.

Objectives Globally, the mass proliferation of phytoplankton driven by climate change has emerged as a significant societal issue. This study analyzes over two decades of long-term hydraulic, hydrological, and water quality data collected from the lower Nakdong River, along with changes in phytoplankton community biomass. The aim is to evaluate long-term trends in water quality and the ecological changes occurring in this region. Methods The monitoring site in the lower Nakdong River is Mulgeum, where samples were collected weekly from January 2000 to December 2021 for the analysis of physicochemical water quality characteristics, as well as phytoplankton abundance and species composition. The hydrological status was assessed using flow rate data from the Jin-dong (Haman) station of the Nakdong River, along with rainfall data from the Korea Meteorological Administration for eight regions influencing the lower Nakdong River. Results and Discussion Analysis of the annual average concentration changes of water quality parameters at the Mulgeum intake site in the lower Nakdong River found that water quality has generally improved since the construction period (2009-2012) of the weir. This improvement is attributed to the strengthening of T-P water quality standards for sewage treatment plant discharges in 2012, which resulted in enhanced phosphorus treatment at wastewater facilities in the river's middle and upper regions. Consequently, significant reductions were observed in the annual average concentrations of BOD, NO3-N, T-N, and T-P. Evaluating long-term changes in flow rate and rainfall, it was found that both annual average rainfall and flow rate decreased after the weir was installed, particularly from May to September when temperatures rise. Rainfall decreased by approximately 8% to 38%, while flow rate decreased by 46% to 62%. Long-term temperature changes indicated that summer temperatures increased by 0.9oC to 1.4oC, and winter temperatures rose by 1.6oC to 2.0oC, resulting in an overall annual average increase of about 1.3oC. An analysis of long-term changes in phytoplankton biomass and community composition revealed an increase in average biomass from 3,639 cells/mL before the weir was installed to 4,034 cells/mL afterward, representing an increase of about 11%. In winter, the dominance period and biomass of diatoms decreased, while in summer, the biomass and dominance period of cyanobacteria increased. Notably, in August, biomass increased approximately 7.5 times, rising from 2,009 cells/mL before the dam to 15,059 cells/mL afterward. This significant increase was identified as a key factor in the overall rise in phytoplankton biomass following the weir's installation. Conclusion Recent climate change impacts, such as rising average temperatures and shifting rainfall patterns, have become increasingly evident in South Korea. This study utilizes over 20 years of long-term data to demonstrate that, in the lower Nakdong River, there has been a gradual decline in both rainfall and flow rates, accompanied by an increase in average temperatures. Consequently, the dominance period and biomass of diatoms have decreased. In contrast, cyanobacteria, which thrive in warmer conditions, have experienced an extended dominance period into early spring and late autumn as a result of rising temperatures, leading to an overall increase in cyanobacterial biomass.

Objectives The study aims to address the gap in research on governance strategies for managing used electric vehicle (EV) batteries in Korea. The goal is to explore strategies that enhance governance and promote a sustainable ecosystem for used batteries, positioning them as a circular resource to aid in reducing greenhouse gas emissions in the transportation sector. Methods An expert survey involving 37 respondents was conducted to gather insights into the governance models for EV battery management. The survey sought to identify key actors and preferred governance models necessary to establish a comprehensive ecosystem for used batteries. Results and Discussion The survey results revealed that companies (54%) were identified as the primary actors needing adjustments and expanded authority in EV battery governance, followed by the central government (40%) and local governments (5%). Regarding preferred governance models, the Business-to-Government (B2G) model was the most favored (40%), followed by Business-to-Central Government (B2CG) (37%), Business-to-Local Government (B2LG) (16%), and Central Government-to-Local Government (CG2LG) (5%). These findings emphasize the importance of cooperative governance in fostering the growth of the EV battery industry. Conclusion The study provides valuable insights for developing a sustainable ecosystem for used batteries as a circular resource. The findings offer critical evidence to inform policy formulation, underscoring the need for a collaborative governance framework to support industry growth and sustainability.

Objectives This study aimed to evaluate the removal efficiency of trace organic contaminants (TrOCs) in UVbased advanced oxidation processes (AOPs) as an alternative to the post-ozonation process in a drinking water treatment plant (DWTP) in the downstream of Nakdong River. The results are expected to be utilized as basic data for modernizing aging water treatment facilities. Methods Eight organophosphorus flame retardants (OPFRs), known for their persistence and resistance to degradation, were selected as the target TrOCs for evaluating the efficiency of ozonation and UV-based AOPs. Experiments were conducted using sand-filtered water from a DWTP in the downstream of Nakdong River, with the OPFRs spiked into the sample matrix. Lab-scale experiments of ozonation and UV-based AOPs (UV, UV/H2O2, and UV/Cl2) were performed, and the concentrations of the OPFRs were analyzed using stir bar sorptive extraction (SBSE) system, followed by GC-MS/MS. Results and Discussion The second-order rate constants (kOH) for the reaction of OPFRs with OH radicals were estimated using the group contribution method and ranged from 1.4(±0.6)×109 to 1.8(±0.1)×1010 M⁻1 s⁻1, depending on their structural characteristics. In the ozonation process, the removal of Cl-containing aliphatic OPFRs was not effective, likely due to the electron-withdrawing effects of the -Cl groups, which inhibited their reactivity with both ozone and OH radicals. However, for non-Cl-containing aliphatic and aromatic OPFRs, more than 90% removal efficiency was achieved at a specific ozone dose of 1.5 mgO3/mgDOC. In the UV-based AOPs, the aliphatic OPFRs showed poor removal efficiency of 5~35% at a UV fluence of 1,000 mJ/cm2 under direct UV photolysis. However, in the presence of oxidants (H2O2 or free available chlorine), the UV/H2O2 process showed up to an 89% increase in removal efficiency, and the UV/Cl2 process exhibited up to a 49% increase. The difference in removal patterns between the UV/H2O2 and UV/Cl2 processes is likely attributed to the difference in reactivity between reactive chlorine species and aliphatic OPFRs. For aromatic OPFRs, direct UV photolysis achieved high enough removal efficiencies of 90~98% at a UV fluence of 1,000 mJ/cm2. These results demonstrate the potential of UV/H2O2 and UV/Cl2 processes for the effective removal of OPFRs, particularly for Cl-containing aliphatic OPFRs, which exhibited low removal efficiency in the ozonation process. Conclusion The comparative evaluation of removal efficiencies of OPFRs in ozone and UV-based AOPs confirmed the potential of UV processes for efficient removal of OPFRs. The UV/H2O2 and UV/Cl2 processes showed promise as alternative treatments to ozonation for the removal of OPFRs during drinking water treatment.

Objectives This study aims to evaluate the removal rates of nine cyanotoxins produced by cyanobacteria using a laboratory-scale simulated drinking water treatment process (DWTP), providing useful data for DWTP operations during algal blooms. Methods A lab-scale simulated DWTP was used to evaluate the removal rates of nine cyanotoxins under typical operating conditions, including specific chemical dosages and contact times. The study employed chlorine and ozone, as well as powdered activated carbon (PAC) and biological activated carbon (BAC). Results and Discussion According to the experimental results of removal efficiency for chlorination and ozonation, microcystin-LR (MC-LR), MC-RR, MC-LA, MC-LF, MC-LY, MC-YR, cylindrospermopsin (CYN), and nodularin (NOD) were effectively removed within the typical ranges of pre- and post-chlorine and pre- and post-ozone concentrations in the DWTP. However, anatoxin-a (ANA) exhibited a significantly lower removal efficiency. The evaluation of removal efficiency for PAC treatment indicated that MC-LA, MC-LF, and MC-LY had low removal rates. In contrast, the other six cyanotoxins achieved over 50% removal when PAC concentrations were above 25 mg/L and contact times exceeded 30 minutes. The evaluation of removal rate for BAC treatment showed that under conditions with an empty bed contact time (EBCT) of more than 5 minutes, over 70% of the nine cyanotoxins were removed. When the EBCT exceeded 2 minutes, removal rates reached between 95% and 100%. In the BAC process, the removal of MC-RR was primarily facilitated by the biodegradation, while the removal of CYN was mainly achieved through adsorption. Conclusion Due to climate change, the bloom periods of various cyanobacteria in domestic water sources are gradually increasing, resulting in a rising trend in both the frequency and concentration of detected cyanotoxins. This study evaluated the removal efficiency of various cyanotoxins in DWTPs, focusing on chlorine/ozone treatment (oxidation), PAC treatment (adsorption), and BAC (adsorption and biodegradation). The different DWTPs at the facility act as multiple barriers, effectively removing cyanotoxins upon their introduction.

Purpose This study aims to provide basic data for the design and operation of adsorption towers to effectively remove hydrogen sulfide (H2S) from biogas. Methods Various adsorbents, such as activated carbon, zeolite, and iron sulfate-treated zeolite (Zeolite-Fe7), were used to evaluate the efficiency of H2S removal. The experiments were conducted under different operating conditions, including reactor length, gas flow rate, humidity, and the combined application of absorption and adsorption methods. Key factors influencing the performance of each adsorbent and hydrogen sulfide removal were analyzed. Results and Discussion Activated carbon recorded the highest H2S removal efficiency at approximately 97.8%, while zeolite and Zeolite-Fe7 showed relatively lower efficiency. The gas flow rate significantly impacted the removal performance of the adsorbents, and reactor length and humidity had varying effects depending on the adsorbent. Additionally, when absorption and adsorption methods were applied simultaneously, a removal rate of about 95.6% was achieved. Conclusion Activated carbon demonstrated higher efficiency and resistance to humidity compared to other adsorbents, making it the most suitable material for H2S removal from biogas. This study provides basic data for optimizing the design and operation conditions of adsorption towers and is expected to contribute to the future design of pre-treatment processes for H2S removal in biogas.

Objectives Due to climate change, countries around the world, including Korea, are making various efforts to achieve carbon neutrality. Among the various reduction methods for achieving carbon neutrality, the utilization of green space through carbon storage and herbaceous plants, which are the only functions, is increasing. However, due to spatial limitations in everyday life, continuous target sites for green space are needed. Accordingly, this study conducted a study on carbon reduction through rooftop greening to activate covered bus stops. Methods After analyzing the growth of herbaceous plants, a feasible target group was selected and when herbaceous plants were planted on the rooftops of covered bus stops, the carbon reduction figures and construction costs were compared with the same conditions as solar power facilities. The carbon storage amount was calculated on a nationwide basis and the carbon reduction cost per m2 was analyzed in comparison with the carbon reduction according to solar power facilities. Results and Discussion The research results showed that the reduction cost of herbaceous plants per m2 was 53 KRW, and the carbon reduction cost due to solar power installation was about 116 KRW. It was confirmed that herbaceous plants were about twice as cheap as carbon reduction through solar power generation. In addition, the environmental and economic benefits of herbaceous plants were also confirmed to have additional benefits among local communities or surrounding area. Conclusion Starting with this study, it is expected that it can be used as basic data to realize green space more efficiently in environmental and landscape aspects by increasing the planting ratio of herbaceous plants in order to increase the amount of CO2 stored (absorbed) in daily life.

Objectives The fouling and wetting of membranes in membrane distillation processes can significantly reduce membrane performance, thereby affecting overall process efficiency. The purpose of this study is to develop methods for restoring the performance of membranes used in a membrane distillation pilot plant operated in the Middle East by testing and optimizing various cleaning methods. This aims to maintain the long-term performance and improve the operational efficiency of the pilot plant through the reuse of restored membranes. Methods To analyze the main fouling substances on the hollow fiber membranes collected from a module operated in a pilot plant, FTIR, XRD, and SEM analysis techniques were employed. A lab-scale module was fabricated using the collected membranes, and cleaning methods were applied. Citric acid was used for cleaning to restore membrane performance, which was evaluated by measuring water permeability and conductivity before and after cleaning. Results and Discussion FTIR and XRD analyses revealed that the main fouling substances on the membranes used in the pilot plant were NaCl, CaCO3, and CaSO4. SEM and ICP analyses confirmed that the membranes near the feed inlet of the module experienced severer fouling compared to those near the outlet. The cleaning method involving pump circulation after inducing artificial wetting was found to be the most effective for restoring membrane performance. Notably, when a high concentration of citric acid was used, almost all fouling substances on the membrane surface were removed, restoring membrane performance to nearly its original state. Conclusion The cleaning method involving artificial wetting followed by pump circulation was determined to be the optimal method for successfully restoring the performance of membranes fouled and wetted in membrane distillation processes.

Objectives This study aimed to explore the potential of halloysite and magnetite powders to enhance anaerobic digestion efficiency and to compare their effects. Methods Anaerobic digestion experiments were conducted in a total of four consecutive batch processes using reactors with halloysite and magnetite powders added at concentrations of 1 g/L, 10 g/L, and 50 g/L, as well as a control reactor without any powder addition. Cumulative methane production was measured in each batch, and the Gompertz model was applied to calculate kinetic parameters, which were then compared with the control. Additionally, microbial community analysis was performed using 16S rRNA amplicon sequencing, and correlations between the powder concentrations, Gompertz kinetic parameters, and microbial groups were evaluated. Results and Discussion Both powders demonstrated an effect on improving methane production rates. When halloysite was added at a concentration of 50 g/L, an increase in methane production rate was observed in all batches. Compared to the control, the lag phase (λ) was reduced by up to 31.4%, and the maximum methane production rate (Rm) increased by 17.6%. While the effects of halloysite at concentrations of 1 g/L and 10 g/L were less significant compared to 50 g/L, trends of reduced lag phase and increased Rm were noted in at least two batches. For magnetite powder, the most effective concentration in the first and second batches was 1 g/L, but in the third and fourth batches, lag phase (λ) was reduced by up to 39.3%, and the maximum methane production rate (Rm) increased by 13.2% at concentrations of 10 g/L and 50 g/L. Microbial community analysis revealed an increased dominance of Geobacter and Methanosarcina associated with the DIET mechanism as the amount of magnetite increased. Conclusion This study confirms the potential of halloysite and magnetite powders to enhance methane production rates in anaerobic digestion processes. These findings suggest the feasibility of using powdered additives to improve the efficiency of anaerobic digestion, providing foundational data for future related research.

Purpose ESG management, which enhances corporate sustainability, is being highlighted as an essential management strategy to respond to social and environmental changes. Especially with the global emphasis on climate change, the importance of reducing and offsetting carbon emissions in the environmental (E) pillar of ESG management is increasing. This study aims to analyze Apple's approach to achieving carbon neutrality through forestation, demonstrating how corporate forest activities can serve as practical strategies in ESG management. Methods The study analyzes how Apple acquired certified emission reductions through forestation activities and offset its own carbon emissions. It evaluates the connection between forest activities and major ESG indicators and issues, assessing their impact on enhancing a company's ESG performance. Results The analysis demonstrates that Apple's forestation efforts effectively contributed to achieving carbon neutrality. The study highlights the linkage between corporate forest activities and ESG indicators, offering practical solutions for companies in the environmental (E) pillar and climate change response. Conclusion Based on these findings, this research suggests the potential for developing carbon reduction and neutrality strategies across various industries through forest activities, offering both academic and practical implications for future ESG management.

The primary source of measurement error from widely used particulate matter (PM) PurpleAir sensors is ambient relative humidity (RH). Recently, the US EPA developed a national correction model for PM2.5 concentrations measured by PurpleAir sensors (Barkjohn model). However, their study included few sites in the southeastern US, the most humid region of the country. To provide high-quality spatial and temporal data and inform community exposure risks in this area, our study developed and evaluated PurpleAir correction models for use in the warm–humid climate zones of the US. We used hourly PurpleAir data and hourly reference-grade PM2.5 data from the EPA Air Quality System database from January 2021 to August 2023. Compared with the Barkjohn model, we found improved performance metrics, with error metrics decreasing by 16 %–23 % when applying a multilinear regression model with RH and temperature as predictive variables. We also tested a novel semi-supervised clustering method and found that a nonlinear effect between PM2.5 and RH emerges around RH of 50 %, with slightly greater accuracy. Therefore, our results suggested that a clustering approach might be more accurate in high humidity conditions to capture the nonlinearity associated with PM particle hygroscopic growth.

The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun (FY)-4A geostationary satellite has provided high-spatiotemporal-resolution visible reflectance data since 12 March 2018. Data assimilation experiments under the framework of observing system simulation experiments have shown the great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To assimilate the AGRI visible reflectance in real-world cases, it is important to evaluate the quality and to quantify the observation errors in these data. In this study, the FY-4A AGRI channel 2 (0.55–0.75 µm) reflectance data (O) were compared with the equivalents (B) derived from the short-term forecasts of the China Meteorological Administration Mesoscale (CMA-MESO) model using the Radiative Transfer for the Television Infrared Observation Satellite Operational Vertical Sounder (RTTOV, v12.3). It is shown that the O–B biases could be used to reveal the abrupt change related to the measurement calibration processes. In general, the O–B departure was positively biased in most cases. Potential causes include the deficiencies of the NWP model, the forward-operator errors, and the unresolved aerosol processes. The relative biases of O–B computed from cloud-free and cloudy pixels were used to correct the systematic biases for the corresponding scenarios over land and sea surfaces separately. In general, the method effectively reduced the O–B biases. Moreover, the bias-correction method based on an ensemble forecast is more robust than a deterministic forecast due to the advantages of the former in dealing with uncertainties in cloud simulations. The findings demonstrate that analyzing the O–B biases has a potential to monitor the performance of the FY-4A AGRI visible instrument and to correct the systematic biases in the observations, which will facilitate the assimilation of these data in conventional data assimilation applications.

A new retrieval approach for obtaining vertical profiles of the aerosol extinction coefficient from measurements of scattered solar light in the limb-viewing geometry made by the Ozone Mapper and Profiler Suite Limb Profiler (OMPS-LP) instrument is presented. In contrast to many other published limb-scatter retrievals, our new algorithm does not employ normalization by a limb measurement at an upper tangent height. Instead, the measured limb radiances are normalized to solar irradiance. The main advantage of this approach is an almost complete elimination of the dependence of the retrieval results on the prior aerosol extinction profile used in the retrieval. This makes the retrieval better suited for the analysis of observation scenes with highly elevated aerosol plumes, such as those that occurred after the Hunga Tonga–Hunga Ha′apai volcanic eruption in January 2022. The results from the new approach were compared to the vertical profiles of the aerosol extinction coefficients retrieved from the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) and the Optical Spectrograph and InfraRed Imaging System (OSIRIS). In general, agreement within 25 % between the different data products was observed in the 18–23 km altitude range, although larger differences were seen after very strong volcanic eruptions and wildfires. In comparison with OSIRIS, larger differences are seen at high southern latitudes (above 60° S). The new data product was used to investigate the evolution of the aerosol plume after the Hunga Tonga–Hunga Ha′apai volcanic eruption.

Cloud fraction as a vital component of meteorological satellite products plays an essential role in environmental monitoring, disaster detection, climate analysis and other research areas. Random forest (RF) and multilayer perceptron (MLP) algorithms were used in this paper to retrieve the cloud fraction of AGRI (Advanced Geosynchronous Radiation Imager) on board the Fengyun-4A (FY-4A) satellite based on its full-disk level-1 radiance observation. Corrections have been made subsequently to the retrieved cloud fraction in areas where solar glint occurs using a correction curve fitted with sunglint angle as weight. The algorithm includes two steps: the cloud detection is conducted firstly for each AGRI field of view to identify whether it is clear sky, partly cloudy or overcast within the observation field. Then, the cloud fraction is retrieved for the scene identified as partly cloudy. The 2B-CLDCLASS-lidar cloud fraction product from the CloudSat and CALIPSO active remote sensing satellite is employed as the truth to assess the accuracy of the retrieval algorithm. Comparison with the operational AGRI level-2 cloud fraction product is also conducted at the same time. The results indicate that both the RF and MLP cloud detection models achieved high accuracy, surpassing that of operational products. However, both algorithms demonstrated weaker discrimination capabilities for partly cloudy conditions compared to clear-sky and overcast situations. Specifically, they tended to misclassify fields of view with low cloud fractions (e.g., cloud fraction = 0.16) as clear sky and those with higher cloud fractions (e.g., cloud fraction = 0.83) as overcast. Between the two models, RF exhibited higher overall accuracy. Both RF and MLP models performed well in cloud fraction retrieval, showing lower mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) compared to operational products. The ME for both RF and MLP cloud fraction retrieval models was close to zero, while RF had slightly lower MAE and RMSE than MLP. During daytime, the high reflectance in sunglint areas led to larger retrieval errors for both RF and MLP algorithms. However, after correction, the retrieval accuracy in these regions improved significantly. At night, the absence of visible light observations from the AGRI instrument resulted in lower classification accuracy compared to daytime, leading to higher cloud fraction retrieval errors during nighttime.

Radar has consistently been proven to be the most reliable source of information for the remote detection of hail within storms in real time. Currently, existing hail detection techniques have limited ability to clearly distinguish storms that produce severe hail from those that do not. This often results in a prohibitive number of false alarms that hamper real-time decision-making. This study utilises convolutional neural network (CNN) models trained on dual-polarisation radar data to detect severe-hail occurrence on the ground. The morphology of the storms is studied by leveraging the capabilities of a CNN. Two datasets of images of 60 km × 60 km containing 19 different radar-derived features are built. The first is created from severe-hail cases (≥2 cm), and the second is obtained from rain or small-hail cases (rain or hail cm) selected with the help of a cell identification algorithm above densely populated areas with no hail reports. After a tuning phase on the CNN architecture and its input size, the CNN is trained to output one probability of severe hail on the ground per image of 30 km × 30 km. A test set of 1396 images between 2018 and 2023 demonstrates that the CNN method outperforms state-of-the-art methods according to various metrics. A feature importance study indicates that existing radar-based hail proxies as input features are beneficial to the CNN, particularly the maximum estimated size of hail (MESH). The study demonstrates that many of the existing hail proxies can be adjusted using a threshold value and a threshold area to achieve better performance. Finally, the output of 10 fitted CNN models in inference mode on a hail event is shown.

Abstract The impact of carbon emission and other pollution generated by the construction industry on the natural environment and ecosystems is immeasurable. Wood, as a renewable building material with excellent performance, is a practical solution for future low-carbon buildings. Current screwed wood connections have good ductility, but the initial stiffness is relatively low. In this paper, composite wood connections with screw and adhesive were proposed and investigated. Static loading tests were conducted to screwed connections and composite connection specimens. The failure modes of different connection types, as well as the effects of adhesive type and adhesive connection area on the joint bearing capacity are discussed. The results show that the yield load and ultimate load of the composite joint made with the existing adhesive increased by up to 169% and 222%, respectively, compared with the screw connection. At the same time, the influence of curing time and bonding should also be taken into consideration. This study provides a foundation for wood joint design and promotes the sustainable development of the building industry.

Abstract Electric three-wheelers consume a great deal of power causing load shedding in industrial and residential areas. This research investigates the feasibility of a solar-assisted electric three-wheeler for deployment in Bangladesh, integrating solar photovoltaic system and pedaling generator. This research focused on enhancing the energy efficiency and sustainability of electric three-wheelers using renewable energy reducing dependency on the national grid. The rooftop space of the vehicle is insufficient for the solar PV system, necessitating the whole surface area of the vehicle with a 600 W solar panel. Simulations for Dhaka, Cox’s Bazar, and Rangpur assessed the solar energy potential across Bangladesh. A 48 V solar PV array and a Maximum Power Point Tracking (MPPT) charge controller were designed to optimize battery charging, achieving converter efficiencies between 97%–99.2%. Partial shading significantly impacted performance, while tilt angle optimization suggested a 0 $$ ^{\circ }$$ ∘ tilt for moving vehicles and a 23 $$ ^{\circ }$$ ∘ tilt during idle periods from October to March for optimal energy capture. A pedaling DC generator was introduced to charge the battery during idle times without grid reliance. The simulations for energy harvesting estimated an annual energy production of approximately 820 kWh. The integration of these systems increased drag, reducing maximum speed and requiring additional power. 172.28 kWh annual additional energy consumption was calculated for the solar PV system and 200.75 kWh for the pedaling charger. The study found the solar-assisted system with enhanced efficiency and sustainability but the limited economic viability of the pedaling generator. In future, the system can be developed for other countries with different vehicles.

Cloud radar Doppler spectra are of particular interest for investigating cloud microphysical processes, such as ice formation, riming and ice multiplication. When hydrometeor types within a cloud radar observation volume have different terminal fall velocities, they can produce individual Doppler spectrum peaks. The peaks of different particle types can overlap and be further broadened and blended by turbulence and other dynamical effects. If these (sub-)peaks can be separated, properties of the underlying hydrometeor populations can potentially be estimated, such as their fall velocity, number, size and to some extent their shape. However, this task is complex and dependent on the operation settings of the specific cloud radar, as well as atmospheric dynamics and hydrometeor characteristics. As a consequence, there is a need for adjustable tools that are able to detect peaks in cloud radar Doppler spectra to extract the valuable information contained in them. This paper presents the synergistic use of two algorithms used for analyzing the peaks in Doppler spectra: PEAKO and peakTree. PEAKO is a supervised machine learning tool that can be trained to obtain the optimal parameters for detecting peaks in Doppler spectra for specific cloud radar instrument settings. The learned parameters can then be applied by peakTree, which is used to detect, organize and interpret Doppler spectrum peaks. The application of the improved PEAKO–peakTree toolkit is demonstrated in two case studies. The interpretation is supported by forward-simulated cloud radar Doppler spectra by the Passive and Active Microwave TRAnsfer tool (PAMTRA), which are also used to explore the limitations of the algorithm toolkit posed by turbulence and the number of spectral averages chosen in the radar settings. From the PAMTRA simulations, we can conclude that a minimum number of n = 20–40 spectral averages is desirable for Doppler spectrum peak discrimination. Furthermore, small liquid peaks can only be reliably separated for eddy dissipation rate values up to approximately 0.0002 m2 s−3 in the simulation setup which we tested here. The first case study demonstrates that the methods work for different radar systems and settings by comparing the results for two cloud radar systems which were operated simultaneously at a site in Punta Arenas, Chile. Detected peaks which can be attributed to liquid droplets agree well between the two systems, as well as with an independent liquid-predicting neural network. The second case study compares PEAKO–peakTree-detected cloud radar Doppler spectrum peaks to in situ observations collected by a balloon-based holographic imager during a campaign in Ny-Ålesund, Svalbard. This case demonstrates the algorithm toolkit's ability to identify different hydrometeor types but also reveals its limitations posed by strong turbulence and a low n. Despite these challenges, the algorithm toolkit offers a powerful means of extracting comprehensive information from cloud radar observations. In the future, we envision PEAKO–peakTree applications on the one hand for interpreting cloud microphysics in case studies. The identification of liquid cloud peaks emerges as a valuable asset, e.g., in studies on cloud radiative effects, in seeder–feeder processes, or for tracing vertical air motions. Furthermore, the computation of the moments for each subpeak enables the tracking of hydrometeor populations and the observation of growth processes along fallstreaks. On the other hand, PEAKO–peakTree applications could be extended to statistical evaluations of longer data sets. Both algorithms are openly available on GitHub, offering accessibility for the scientific community.

Thermodynamic profiles in the atmospheric boundary layer can be retrieved from ground-based passive remote sensing instruments like infrared spectrometers and microwave radiometers with optimal-estimation physical retrievals. With a high temporal resolution on the order of minutes, these thermodynamic profiles are a powerful tool to study the evolution of the boundary layer and to evaluate numerical models. In this study, we describe three recent modifications to the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) retrieval framework, which improve the availability of valid solutions for different atmospheric conditions and increase the temporal consistency of the retrieved profiles. We present methods to enhance the availability of valid solutions retrieved from infrared spectrometers by preventing overfitting and by adding information from an additional spectral band in high-moisture environments. We show that the characterization of the uncertainty of the input and the choice of spectral infrared bands are crucial for retrieval performance. Since each profile is retrieved independently from the previous one, the time series of the thermodynamic variables contain random uncorrelated noise, which may hinder the study of diurnal cycles and temporal tendencies. By including a previous retrieved profile as input to the retrieval, we increase the temporal consistency between subsequent profiles without suppressing real mesoscale atmospheric variability. We demonstrate that these modifications work well at midlatitudes, polar and tropical sites, and for retrievals based on infrared spectrometer and microwave radiometer measurements.

Greenhouse gas monitoring is important to ensure climate goals are being achieved. This study unveils the potential of using atmospheric tall towers in direct flux measurements, bridging the gap between atmospheric and ecosystem monitoring networks. The ICOS Cities (PAUL) project aims to monitor CO2 emissions in urban areas, where concentrated emissions make them key targets for climate change mitigation. This study explores the synergy between ICOS atmospheric and ecosystem networks by utilizing slow-response analysers (∼ 3 s) on tall atmospheric towers for ecosystem studies using the eddy covariance method. A standard setup with an ultrasonic anemometer and an infrared (IR) fast-response CO2 analyser was installed and compared with measurements from an existing cavity ring-down spectroscopy (CRDS) analyser measuring CO2, CO, and CH4. Deployed on the 100 m Saclay tower near Paris, covering a 43.9 km2 80 % footprint with heavy traffic roads, a nearby heating plant, and a forest, the setup addressed technical challenges and height-induced complexities. Corrections for flux attenuation by high-frequency losses were limited to < 20 % on average for all stabilities and around 11 % for unstable conditions. Elevated mean fluxes for CO2 (10 µmolm-2s-1) and CH4 (200 µmolm-2s-1) were observed from the heating plant wind direction during December and January. Conversely, the forest direction exhibited the strongest sink among all wind directions, with −4 µmolm-2s-1 during July and August. Storage and vertical advection were estimated using the routine three-level profile measurements done in ICOS atmospheric towers. Storage term was of the same magnitude as turbulent flux, increasing at night and de-stocking during the first half of the day. Vertical advection averaged zero on a monthly basis. These results demonstrate the feasibility and versatility of utilizing atmospheric towers for urban emission monitoring, offering valuable insights for emission monitoring strategies worldwide.

Atmospheric dioxygen (O2) concentration and isotopic composition are closely linked to the carbon cycle through anthropic carbon dioxide (CO2) emissions and biological processes such as photosynthesis and respiration. The measurement of the isotopic ratio of O2, trapped in ice core bubbles, brings information about past variation in the hydrological cycle at low latitudes, as well as past productivity. Currently, the interpretation of those variations could be drastically improved with a better (i.e., quantitative) knowledge of the oxygen isotopic fractionation that occurs during photosynthesis and respiration processes. This could be achieved, for example, during experiments using closed biological chambers. In order to estimate the isotopic fractionation coefficient with good precision, one of the principal limitations is the need for high-frequency online measurements of isotopic composition of O2, expressed as δ18O of O2 (δ18O(O2)) and O2 concentration. To address this issue, we developed a new instrument, based on the optical-feedback cavity-enhanced absorption spectroscopy (OF-CEAS) technique, enabling high-temporal-resolution and continuous measurements of O2 concentration as well as δ18O(O2), both simultaneously. The minimum Allan deviation occurred between 10 and 20 min, while precision reached 0.002 % for the O2 concentration and 0.06 ‰ for δ18O(O2), which correspond to the optimal integration time and analytical precision before instrumental drift started degrading the measurements. Instrument accuracy was in good agreement with dual-inlet isotope ratio mass spectrometry (IRMS). Measured values were slightly affected by humidity, and we decided to measure δ18O(O2) and O2 concentration after drying the gas. On the other hand, a 1 % increase in O2 concentration increased the δ18O(O2) by 0.53 ‰. To ensure the good quality of O2 concentration and δ18O(O2) measurements we eventually proposed to measure the calibration standard every 20 min.

Ground-based lidar data have proven extremely useful for profiling the convective boundary layer (CBL). Many groups have derived higher-order moments (e.g., variance, skewness, fluxes) from high-temporal-resolution lidar data using an autocovariance approach. However, these analyses are highly uncertain near the CBL top when the depth of the CBL (zi) is changing during the analysis period. This is because the autocovariance approach is usually applied to constant height levels and the character of the eddies is changing on either side of the changing CBL top. Here, a new approach is presented wherein the autocovariance analysis is performed on a normalized height grid, with a temporally smoothed zi. Output from a large eddy simulation model demonstrates that deriving higher-order moments from time series on a normalized height grid has better agreement with the slab-averaged quantities than the moments derived from the original height grid.

Abstract Recycled aggregate concrete (RAC) is recognized as an environmentally friendly construction material derived from reclaimed concrete components. This paper aims to conduct a comprehensive scientometric analysis of RAC research published between 2000 and 2023 in the Web of Science core database. The study includes analyses of publication trends over time, contributions and collaborations among authors, productivity of institutions and countries, co-citation networks, and keyword co-occurrence patterns. Additionally, the research identifies emerging frontiers in RAC studies. The results are visually presented to provide a holistic overview of the current state of RAC research and future developmental trajectories. The study analyzes publication trends over time, with over 80% of the papers published after 2017, reflecting the growing interest in sustainable construction. Key trends identified include the increasing focus on improving the mechanical properties and durability of RAC, microstructural analysis, and innovative manufacturing techniques. While the field has advanced significantly, challenges remain in areas such as the integration of nanoparticles, biomineralization techniques, carbon capture and utilization, and 3D printing technologies. These challenges underscore the need for continued innovation and exploration. With these advancements, RAC has the potential to play a pivotal role in promoting sustainable construction practices in the future.

Atmospheric deposition is a major nutrient influx in ecosystems, while high anthropogenic deposition may disrupt ecosystem functioning. Quantification of the deposition flux is required to understand the impact of such anthropogenic pollution. However, current methods to measure nutrient deposition are costly, labor-intensive and potentially inaccurate. Ion exchange resin (IER) appears to be a promising cost- and labor-effective method. The IER method is potentially suited for deposition measurements on coarse timescales and for areas with little rainfall and/or low elemental concentrations. The accuracy of the IER method is, however, hardly classified beyond nitrogen. We tested the IER method for bulk deposition and throughfall measurements of macro- and micro-elements, assessing resin adsorption capacity, recovery efficiency and field behavior. We show that IER is able to adsorb 100 % of Ca, Cu, Fe, K, Mg, Mn, P, S, Zn and NO3- and > 96 % of P and Na. Loading the resin beyond its capacity resulted mainly in losses of Na, P and NH4+, while losses of Ca, Cu, Fe, Mg, Mn and Zn were hardly detected. Heat (40 °C), drought and frost (−15 °C) reduced the adsorption of P by 25 %. Recovery was close to 100 % for NH4+ and NO3- using KCl solution (1 or 2 M), while high (83 %–93 %) recoveries of Ca, Cu, Fe, K, Mg, Mn and S were found using HCl as an extractant (2–4 M). We found good agreement between the conventional method and the IER method for field conditions. Overall, IER is a powerful tool for the measurement of atmospheric deposition of a broad range of elements as the measurements showed high accuracy. The IER method therefore has the potential to expand current monitoring networks and increase the number of sampling sites.

Nitrous oxide (N2O) is the third most important greenhouse gas modified by human activities after carbon dioxide and methane. This study examines the feasibility of airborne differential absorption lidar to measure N2O concentration enhancements over agricultural, fossil fuel combustion, industrial, and biomass burning sources. The mid-infrared spectral region, where suitably strong N2O absorption lines exist, challenges passive remote sensing by means of spectroscopy due to both low solar radiation and thermal emission. Lidar remote sensing is principally possible thanks to the laser as an independent radiation source but has not yet been realized due to technological challenges. Mid-infrared N2O absorption bands suitable for remote sensing are investigated. Simulations show that a spectral trough position between two strong N2O lines in the 4.5 µm band is the favored option. A second option exists in the 3.9 µm band at the cost of higher laser frequency stability constraints and less measurement sensitivity. Both options fulfill the N2O measurement requirements for agricultural areal or point-source emission quantification (0.5 % measurement precision, 500 m spatial resolution) with technically realizable and affordable transmitter (100 mW average laser power) and receiver (20 cm telescope) characteristics for integrated-path differential absorption lidar that measures the column concentration beneath the aircraft. The development of an airborne N2O lidar is feasible yet would benefit from progress in infrared laser transmitter and low-noise-detection technology. It will also serve as a precursor to space versions, which are still out of reach due to the lack of space technology.

This paper introduces the Ocean Derived Column Optical Depth (ODCOD) algorithm. ODCOD is now being used to retrieve full-column optical depths from the 532 nm measurements acquired by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft. ODCOD uses the lidar integrated attenuated backscatter from the ocean surface, together with collocated wind speed estimates from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to estimate the full-column optical depths of particulates (i.e., clouds and aerosols) in the Earth's atmosphere. Unlike CALIOP's standard retrievals, which estimate optical depths only when particulate layers are detected, ODCOD retrievals deliver a comprehensive estimate that accounts for attenuation by all particulates present within the lidar profiles. This paper describes the ODCOD algorithm, develops random uncertainty estimates, and characterizes the systematic differences between ODCOD optical depths and those reported by previously validated data sets. This paper presents performance assessments of ODCOD cloud-free profiles to compare the ODCOD aerosol optical depth (AOD) retrievals to collocated measurements made by the airborne High Spectral Resolution Lidar (HSRL) instruments flown by NASA Langley Research Center (LaRC), to daytime estimates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), and to daytime and nighttime retrievals using the Synergized Optical Depth of Aerosols (SODA) algorithm. ODCOD AODs are biased high relative to LaRC HSRL AODs by 0.009 ± 0.043 (median ± median absolute deviation), with a correlation coefficient of 0.724, and biased low relative to MODIS by 0.009 ± 0.041, with a correlation coefficient of 0.834. Relative to SODA, which derives AOD from a combination of CALIOP and CloudSat ocean surface measurements, ODCOD is biased high in the daytime by 0.004 ± 0.035 and higher at night by 0.027 ± 0.034, with correlation coefficients of 0.887 and 0.891, respectively. Because ODCOD estimates are independent from the standard CALIOP optical depth retrievals, they offer potential for future advances in the CALIPSO data record, both in validating CALIOP's standard estimates and as a potential total column constraint to improve extinction coefficient retrievals.

We introduce NitroNet, a deep learning model for the prediction of tropospheric NO2 profiles from satellite column measurements. NitroNet is a neural network trained on synthetic NO2 profiles from the regional chemistry and transport model WRF-Chem, which was operated on a European domain for the month of May 2019. This WRF-Chem simulation was constrained by in situ and satellite measurements, which were used to optimize important simulation parameters (e.g. the boundary layer scheme). The NitroNet model receives NO2 vertical column densities (VCDs) from the TROPOspheric Monitoring Instrument (TROPOMI) and ancillary variables (meteorology, emissions, etc.) as input, from which it reproduces NO2 concentration profiles. Training of the neural network is conducted on a filtered dataset, meaning that NO2 profiles showing strong disagreement (>20 %) with colocated TROPOMI column measurements are discarded. We present a first evaluation of NitroNet over a variety of geographical and temporal domains (Europe, the US West Coast, India, and China) and different seasons. For this purpose, we validate the NO2 profiles predicted by NitroNet against satellite, in situ, and MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) measurements. The training data were previously validated against the same datasets. During summertime, NitroNet shows small biases and strong correlations with all three datasets: a bias of +6.7 % and R=0.95 for TROPOMI NO2 VCDs, a bias of −10.5 % and R=0.75 for AirBase surface concentrations, and a bias of −34.3 % to +99.6 % with R=0.83–0.99 for MAX-DOAS measurements. In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. During wintertime considerable low biases arise because the summertime/late-spring training data are not fully representative of all atmospheric wintertime characteristics (e.g. longer NO2 lifetimes). Nonetheless, the wintertime performance of NitroNet is surprisingly good and comparable to that of classic regional chemistry and transport models. NitroNet can demonstrably be used outside the geographic and temporal domain of the training data with only slight performance reductions. What makes NitroNet unique when compared to similar existing deep learning models is the inclusion of synthetic model data, which offers important benefits: due to the lack of NO2 profile measurements, models trained on empirical datasets are limited to the prediction of surface concentrations learned from in situ measurements. NitroNet, however, can predict full tropospheric NO2 profiles. Furthermore, in situ measurements of NO2 are known to suffer from biases, often larger than +20 %, due to cross-sensitivities to photooxidants, which other models trained on empirical data inevitably reproduce.

We present the CHORA (Cloud Height Ozone Reference Algorithm) for retrieving tropospheric-ozone columns from S5P-TROPOMI (Sentinel-5 Precursor–TROPOspheric Monitoring Instrument). The method uses a local-cloud reference sector (CLC – CHORA Local Cloud) to determine the stratospheric (above-cloud) column, which is subtracted from the total column in clear-sky scenes in the same zonal band to retrieve the tropospheric column. The standard CCD (convective cloud differential) approach uses cloud data from the Pacific region (CPC – CHORA Pacific Cloud) instead. An important assumption for the standard method is the zonal invariance of stratospheric ozone. The local-cloud approach is the first step to diminish this constraint in order to extend the CCD method to mid-latitudes, where stratospheric-ozone variability is larger. An iterative approach has been developed for the automatic selection of an optimal local-cloud reference sector around each retrieval grid box varying latitudinally by ± 1° and longitudinally between ± 5 and ± 50°. The optimised CLCT (CHORA Local Cloud Theil–Sen) algorithm, a follow-up from the CLC, employs a homogeneity criterion for total ozone from the cloud reference sector in order to overcome the inhomogeneities in stratospheric ozone. It directly estimates the above-cloud column ozone for a common reference altitude of 270 hPa using the Theil–Sen regression. The latter allows for the combination of the CCD method with the cloud-slicing algorithm that retrieves upper-tropospheric ozone volume mixing ratios. Monthly averaged tropospheric-column ozone (TCO) using the Pacific cloud reference sector (CPC) and the local-cloud reference sector (CLC, CLCT) has been determined over the tropics and subtropics (26° S–22° N) using TROPOMI for the time period from 2018 to 2022. The accuracy of the various methods was investigated by means of comparisons with spatially collocated NASA/GSFC SHADOZ (Southern Hemisphere Additional Ozonesondes) measurements and the ESA TROPOMI level-2 tropospheric-ozone product. At eight out of nine tropical stations, tropospheric-ozone columns using the CLCT yield better agreement with ozonesondes than the CPC. In the tropical region (20° S–20° N), the CLCT shows a significantly lower overall mean bias and dispersion of 1 ± 7 %, outperforming both the CPC (12 ± 10 %) and CCD-ESA (22 ± 10 %). The CLCT surpasses the ESA operational product, providing more accurate tropospheric-ozone retrievals at eight out of nine stations in the tropics. For the Hilo station, with a larger stratospheric-ozone variability due to its proximity to the subtropics, the bias of +30 % (CPC) is effectively reduced to −5 % (CLCT). Similarly, in the subtropics (Reunion, Irene, Hanoi, and King's Park), the CLCT algorithm provides an overall bias and scatter of −11 ± 9 % with respect to sondes. The CLCT effectively reduces the impact of stratospheric-ozone inhomogeneity, typically at higher latitudes. These results demonstrate the advantage of the local-cloud reference sector in the subtropics. The algorithm is therefore an important basis for subsequent systematic applications in current and future missions of geostationary satellites, like GEMS (Geostationary Environment Monitoring Spectrometer, Korea), ESA Sentinel-4, and NASA TEMPO (Tropospheric Emissions: Monitoring of POllution), predominantly covering the middle latitudes.

We reviewed 60 sensor networks and 17 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. This study is not exhaustive of every gas-phase sensor network on the globe but rather exists to categorize types of sensor networks by their key characteristics and explore general trends. This also exposes gaps in monitoring efforts to date, especially regarding the availability of gas-phase measurements compared to particulate matter (PM) and geographic coverage gaps (the Global South, rural areas). We categorize ground-based networks that measure gas-phase air pollutants into two main subsets based on their deployment type – quasi-permanent (long term) and campaign (short to medium term) – and explore commonplace practices, strengths, and weaknesses of stationary monitoring networks. We conclude with a summary of cross-network unification and quality control efforts. This work aims to help scientists looking to build a sensor network explore best practices and common pathways and aid end users in finding low-cost sensor datasets that meet their needs.

Abstract Decarbonizing heat-intensive industries by reusing the waste heat for power or combined heat and power systems is becoming increasingly important to address global warming. The Organic Rankine Cycle has shown a high level of feasibility and performed efficiently for utilizing medium-to-low-grade heat from renewable resources and heat-intensive industries for direct power generation. This study contributes to the field by conducting a techno-economic investigation of various Organic Rankine Cycle configurations to enhance energy conversion when real-life transient waste heat sources are available. These configurations were optimized to maximize energy output along with economic benefits. The non-linear programming by quadratic Lagrangian, a computational unintensive yet accurate optimization algorithm, was utilized for the multi-objective optimization. The optimized cycle configurations showed a 12.57% enhancement of turbine efficiency. Combining regeneration and recuperation enhanced the superheating by 32%, and the optimized air preheater cycle improved the overall objective by 64.2% compared to the pre-optimized conventional cycle, leading to a feasible 1.72-year payback period.

Direct analysis in real time mass spectrometry (DART-MS) has recently emerged as a promising approach for measuring semi-volatile organic compounds (SVOCs) on indoor surface films. However, its broader application in indoor environments is limited by low measurement repeatability and no separation of isomers. Herein we developed a sampling suite of indoor surface films for DART-MS analysis, optimized settings of DART to obtain higher analytical performance, and demonstrated the possibility of separating isomeric compounds using tandem mass spectrometry (MS/MS). Two pairs of isomeric phthalate esters, including di(2-ethylhexyl) phthalate (DEHP) and di-n-octyl phthalate (DnOP) and diisobutyl phthalate (DiBP) and di-n-butyl phthalate (DnBP), were used as examples for method optimization and validation. Under optimized conditions, the instrument responses for all four compounds exhibited good linearity (r>0.992) and acceptable repeatability (intraday relative standard deviation (RSD) < 11.0 %). The limits of quantification for the four phthalate esters ranged from 0.042 to 0.24 ng cm−2. The uncertainty in the separation of isomeric components using MS/MS was < 11.4 %, which is acceptable for real sample analysis. To further assess the developed method, we analyzed 10 film samples collected side by side in an occupied office. DnOP was not detected. The RSD among samples was 6.1 % for DEHP, 4.6 % for DnBP, and 10.4 % for DiBP, indicating the overall good repeatability of the collection and measurement method developed. With improved performance, the developed method increases the feasibility of the DART-MS technique for monitoring the dynamics of chemical composition of indoor surface films.