Search Results (4,335)

Nitrogen dioxide (NO₂) drives ozone and secondary aerosol formation and harms human health. Chongqing, a mountainous megacity of 32 million people, lacks a fine-scale satellite assessment of its NO₂ evolution. We analyzed tropospheric NO₂ vertical column density (VCD) over Chongqing for 2019–2024. The analysis used Sentinel-5P TROPOMI observations. We computed monthly, seasonal, and annual composites at 5.5 km resolution. Trends were quantified with the Theil–Sen slope and, at the pixel level, the Seasonal Mann–Kendall (SMK) test applied to the full 72-month series. A MODIS land-cover mask separated urban built-up from non-urban pixels. NO₂ concentrated in the central districts and along the Yangtze valley. The core exceeded the mountainous counties by a factor of 3 to 4. TROPOMI resolved the Wanzhou and Yongchuan–Jiangjin hotspots as separate features. The record was divided into three phases. The 2020 lockdown produced the minimum, 31% below the prior February. Rebound emissions produced the 2021 maximum of 5.6 × 10¹⁵ molecules cm⁻² under near-normal dispersion conditions, with ERA5 (the European Centre of Medium-range Weather Forecasts Reanalysis v.5) showing the January 2021 boundary layer 4.3% deeper than its climatological norm. Thereafter the regional mean stabilized: the area-weighted SMK slope was +0.042 × 10¹⁵ molecules cm⁻² yr⁻¹ and not significant (p = 0.14), because emission controls in the core and rising county emissions canceled in the average. Trends diverged sharply in space. The nine core districts declined (median urban Sen slope −0.11 × 10¹⁵ molecules cm⁻² yr⁻¹), whereas 41% of peripheral pixels rose significantly (p < 0.05). The urban-to-rural ratio narrowed from 3.0 in 2019 to 2.3 in 2024 (annual means). This convergence was robust to the built-up threshold (30–50%). Industrial relocation and county urbanization explain the peripheral rise. The results support extending vehicle and industrial emission standards from the core to the receiving counties. Full article

Environmental noise is a major environmental and public health concern in rapidly urbanizing cities, yet integrated studies combining field measurements, GIS-based spatial analysis, and predictive modelling remain limited in Central Asia. This study assessed the spatial distribution of urban noise pollution in Samarkand (Uzbekistan) and explored its statistical association with selected public health indicators, forecasting future trends. Measurements were conducted at 50 georeferenced sites covering more than 300 streets. The measured data were processed and mapped using ArcGIS 10.5 (Esri, Redlands, CA, USA) to produce the spatial distribution of environmental noise across the study area. Official data on registered vehicles, industrial enterprises, and disease incidence (2014–2024) were analysed using Pearson correlation, Autoregressive Integrated Moving Average (ARIMA), and its extension incorporating exogenous variables (ARIMAX) models. Results revealed pronounced spatial heterogeneity in noise levels, highest along transport corridors and industrial zones. Industrial enterprises showed the strongest correlations with disease incidence; vehicle registrations were excluded from final models owing to collinearity with industrial activity. ARIMA projected continued industrial growth through 2030, while ARIMAX models identified significant associations between industrial activity and diseases of the ear and mastoid process and of the nervous system (MAPE 28.55% and 19.78%). As an ecological, exploratory study using infrastructural proxies rather than measured noise exposure, findings should be interpreted as associations rather than causation. The framework offers a transferable approach for environmental risk assessment and sustainable urban planning. Full article

The whole pavement life cycle is divided into five phases: raw material production, construction, service use, maintenance and rehabilitation, and end-of-life (EOL). Distinct system boundary definitions (cradle to gate, cradle to site, cradle to grave) are clearly distinguished, and two categories of vehicle-related emissions are strictly differentiated: baseline vehicle operation emissions (excluded) and pavement-induced incremental emissions (included only for full cradle-to-grave accounting). According to cited highway pavement inventory data (functional unit: 1 m² full cross-section composite pavement, cradle-to-gate material-only boundary), cement-related materials account for merely 4.7% of total structural material mass yet contribute over 84.5% of material-phase carbon emissions, while asphalt mixture construction emissions generally make up less than 10% of mixing-stage outputs. In the use phase, pavement deformation, rolling resistance elevation and surface texture loss trigger extra vehicle fuel consumption and associated greenhouse gas increments. Maintenance-stage emissions stem from repair material manufacturing, on-site machinery operation and traffic congestion delays during lane closure; milling, transportation and recycling dominate EOL carbon outputs. This review further classifies all emissions into direct engineering emissions and pavement-derived indirect emissions, compares carbon performance and service-life extension effects of eight mainstream maintenance strategies, and thoroughly decomposes milling, stockpiling, haulage and recycling links of waste asphalt, alongside multiple environmental burden allocation methods for reclaimed asphalt pavement (RAP). A full spectrum of green low-carbon technologies is summarized, including biochar bio-materials, RAP, crumb rubber, industrial byproducts, warm-mix asphalt (WMA), cold recycling and CCUS negative-carbon materials. We also balance their emission reduction benefits against potential deterioration risks to rutting resistance, fatigue life and moisture stability. Combined with a life-cycle cost assessment (LCCA), this study analyzes cost-emission trade-offs of all technical routes, and deeply discusses multi-source uncertainty, sensitive input parameters and universal methodological limitations of pavement LCA. Core takeaways indicate that raw material production and long-term service use are the two dominant carbon emission stages; a medium RAP-WMA combination and cold in-place recycling represent the most economically and environmentally balanced mitigation solutions. Major research gaps and targeted future research directions are proposed, providing standardized theoretical support and dual environmental–economic decision references for low-carbon asphalt pavement design and full-life carbon accounting. Full article

Missing bolts compromise the structural integrity of bolted connections in steel bridges and industrial infrastructure. Manual visual inspection remains labour-intensive, subjective, and hazardous in hard-to-reach locations. This study presents a comparative benchmarking framework for unmanned aerial vehicles (UAVs) missing-bolt patch classification using convolutional neural network (CNN), focusing on balancing accuracy and computational efficiency. A UAV-acquired dataset of bolt-centric image patches was developed to evaluate four systematic experimental schemes: (i) a custom lightweight CNN trained from scratch, (ii) the lightweight CNN integrated with Squeeze-and-Excitation (SE) attention blocks across multiple positions, (iii) nine fine-tuned state-of-the-art (SOTA) pretrained CNN backbones, and (iv) SE-enhanced versions of these pretrained models. All architectures were evaluated under a standardised experimental protocol. Results show that the proposed lightweight CNN achieves classification performance comparable to heavyweight pretrained models while requiring significantly lower computational resources. Integrating SE blocks did not improve classification performance for this localised task and, in several configurations, reduced accuracy and training stability. Pretrained transfer learning models achieved high accuracy overall, but their computational complexity limits direct deployment on edge devices and UAV platforms. Grad-CAM visual explanations confirmed that the lightweight CNN consistently focuses on relevant bolt and hole regions. The findings demonstrate that a task-specific lightweight CNN offers a practical balance between inspection reliability and deployment efficiency for automated structural monitoring. Full article

Existing reviews on AI in tea production are either agriculture-generic or limited to isolated tasks. This review thoroughly compares vision technologies (RGB, hyperspectral, near-infrared, thermal, Light Detection and Ranging (LiDAR), Unmanned Aerial Vehicle (UAV)) and establishes a task-oriented algorithm selection framework for the tea industry. For small-sample or near-linear problems, traditional machine learning (ML) (support vector machine (SVM); partial least squares regression (PLSR)) remains effective. For unstructured field tasks, deep learning achieves superior performance: pest detection accuracy exceeds 97%, tea bud detection reaches 96.8% with RGB images, and hyperspectral imaging predicts nitrogen content with R² > 0.90 and tea polyphenols with R² up to 0.925. Algorithm choice further differentiates by task granularity: lightweight convolutional neural networks (CNNs) balance speed and accuracy for edge deployment at 16 fps; You Only Look Once (YOLO) series detectors enable real-time localization on mobile platforms at 93.1% accuracy, 24 ms per target. No single algorithm dominates all tea tasks; selection is a trade-off among accuracy, speed, data availability, and computational constraints. These findings outline a structured analysis of the challenges and pathways for transitioning computer vision (CV) from laboratory research toward field-deployable tools. Full article

Addressing nonlinear fluctuations in inbound logistics demand in the intelligent automotive industry, this paper proposes a SARIMA-LSTM-Attention forecasting model incorporating Real-Time Production Progress Feedback (RPF) features. By introducing production progress deviation into a hybrid framework that combines SARIMA-based linear forecasting with attention-enhanced LSTM residual correction, the proposed model effectively captures both linear trends and nonlinear demand disturbances. The model was evaluated using 995 daily observations collected from the inbound logistics system of a large new energy vehicle manufacturer from January 2023 to July 2025, with data from January 2023 to December 2024 used for training and validation and January to July 2025 reserved for testing. Compared with six representative benchmark models, the proposed model achieved the best overall performance, reducing sMAPE from 43.23% to 33.13% relative to the SARIMA baseline, representing an absolute reduction of 10.10 percentage points and a relative improvement of 23.36%. These results demonstrate the effectiveness of integrating real-time production feedback for demand forecasting and provide practical support for lean inventory management and logistics decision-making in automotive supply chains. Full article

To reveal the chemical composition and concentration distribution, spatial distribution characteristics, source composition, and health impacts of volatile organic compounds (VOCs) in the two multi-industry industrial parks in Tianjin, 57 VOC species were determined by using SUMMA canister sampling with preconcentration gas chromatography/mass spectrometry. Based on these measurements, the Positive Matrix Factorization (PMF) model was used for source analysis to achieve quantitative identification and contribution analysis of different source factors. The health risks of VOC concentrations were analyzed based on the assessment framework recommended by the United States Environmental Protection Agency (USEPA). The results showed that the average concentrations of TVOCs in A-1 (Industrial Park and Cluster A, 5 m), A-2 (Industrial Park and Cluster A, 10 m), B-1 (Industrial Park and Cluster B, 5 m), and B-2 (Industrial Park and Cluster B, 10 m) were 59.72 ppbv, 45.35 ppbv, 32.44 ppbv, and 21.65 ppbv, respectively. TVOC concentrations were higher at A-1 and B-1 than at A-2 and B-2, and were generally higher at site A than at site B. The VOC components were mainly alkanes (53.5%–71.4%), followed by aromatic hydrocarbons (15.6%–36.2%), alkenes (4.6%–9.9%), and alkynes (3.1%–7.6%). The proportion of aromatic hydrocarbons in A was higher (22.13% and 36.22%), while alkanes dominated absolutely in B (71.4% and 66.9%). n-Hexane was the key species driving the spatial differences (a typical source of VOCs in solvent usage). The concentration in A-1 was 3.1 times that of A-2, and B-1 was 3.3 times that of B-2. It was mainly controlled by local solvent unorganized emissions. The differences in species between the two points at site A for toluene, xylene, etc., were relatively gentle, while at site B, the concentration of the same aromatic hydrocarbons at B-1 was significantly higher than that at B-2, presenting a clearer spatial characteristic, indicating differences in the spatial distribution of organic solvent-related industrial activities in different sites. The source analysis showed that A-1 was dominated by solvent usage (38.64%) and natural gas/LPG sources (36.96%); A-2 by vehicle exhaust (40.20%) and natural gas/LPG sources (36.15%); B-1 by cleaning-agent usage (38.81%) and fuel combustion (30.33%); and B-2 by plastic and rubber production (36.90%) and fuel combustion (31.84%). The health risk assessment showed that benzene, n-hexane, and xylene dominated the non-carcinogenic risks, but the overall non-carcinogenic (HI < 1) and carcinogenic (CR < 1 × 10⁻⁶) risks were both below the threshold. This study reveals inter-site differences in VOC concentrations, compositions, and source contributions across two mixed industrial parks, providing a basis for site-specific VOC control priorities, source-targeted monitoring strategies, and risk management in mixed industrial areas. Full article

Fuel cells (FCs) are critical for decarbonizing the transport industry, with Life Cycle Assessment (LCA) serving as the standard evaluation framework. However, existing literature exhibits severe methodological heterogeneities and divergent system boundaries that introduce deep epistemic uncertainties. This review conducts a systematic analysis to critically harmonize FC environmental performance across the road, aviation, and maritime sectors. Quantitative synthesis reveals global warming potential (GWP) as the dominant metric. For light-duty vehicles, GWP drops to around 30 gCO₂eq/km, matching battery-electric configurations exclusively under deeply decarbonized grids. Manufacturing FC stacks and advanced storage imposes a severe upfront carbon debt, particularly prominent in heavy-duty freight (60–130 tCO₂eq). In aviation, 80–90% in-flight GWP reductions trigger massive burden-shifting, transferring 60–70% of lifecycle damages to ground-based infrastructure. Maritime FCs shrink GWP to 0.06–0.60 kgCO₂eq/kWh, strictly contingent on upstream hydrogen production. Crucially, despite long-term GWP advantages, FC pathways face systematic penalties in acidification, eutrophication, and ecotoxicity, heavily driven by platinum-group catalysts and fluoropolymer membranes. By isolating software-driven biases and database discrepancies, this work delivers an actionable methodological roadmap, establishing a policy-aligned baseline for future FC transportation sustainability frameworks. Full article

Automated Guided Vehicles (AGVs) are a key part of today’s industrial automation, especially for material handling and intralogistics operations. The conventional AGV designs, such as differential-drive vehicles and fixed-steering AGVs, tend to exhibit limited maneuverability in restricted spaces. Such behavior can be attributed to comparatively large turning radii in classic designs, which limit the possibility of efficient movement. Thus, the production of affordable AGVs with high motion flexibility and load stability remains a challenge in AGV development. To resolve this issue, PID-controlled reverse-phase steering method is suggested. Experimental evaluation with 12 trials demonstrated a decreased turning radius for the designed AGV from 1.5 ± 0.08 m (literature-reported value) to 0.84 ± 0.05 m (current study finding), corresponding to an approximately 46.7% reduction. Results demonstrate the proposed AGV’s improved cornering capabilities. In addition, the lateral deviation achieved from the designed AGV stands at an average of 3.1 ± 0.5 cm, while the Root Mean Square Error (RMSE) is 3.5 cm, resulting in an overall accuracy rate of 96% ± 1.2%. Obstacle avoidance tests confirm successful performance within an obstacle range of up to 80 cm. Overall, the developed AGV represents a scalable and economical system for intelligent material handling within the industrial environment. Full article

The mining industry continues to face significant safety challenges, particularly with powered haulage equipment (PHE). PHE incidents account for a substantial percentage of mining-related fatalities, often resulting from vehicle collisions, equipment rollovers, operator errors, and blind-spot hazards. Despite the industry’s efforts to improve safety protocols, fatal accidents involving haulage trucks remain persistent. The mining industry has increasingly adopted automation to enhance operational efficiency and improve safety, particularly in surface mines where haulage truck accidents remain a critical concern. Automation has significantly reduced human exposure to hazardous tasks by removing operators from dangerous environments, thereby mitigating risks associated with human error and fatigue-related accidents. However, achieving zero fatalities in mining operations remains an ongoing challenge, necessitating a deeper evaluation of current technologies and safety interventions. This paper explores the review and integration of advanced safety technologies, such as real-time monitoring, machine learning-based predictive models, and enhanced automation frameworks to improve hazard detection and response time. A structured methodology is employed to review automated systems, accident data analysis, and an assessment of automation technologies in active mining operations. Specific findings highlight the impact of automation on reducing accident rates, the effectiveness of various intervention strategies, and challenges in full-scale implementation. The novelty of this paper lies in its roadmap to achieving zero fatalities through a review of structured integration of automation and predictive safety interventions. It outlines the broader benefits of Automated Haulage Systems, including productivity gains and operational cost reductions, contributing to the ongoing discourse on mining safety by providing a data-driven framework for the successful implementation of automated haulage trucks, ensuring a safer and more efficient mining environment. Full article

Unmanned Aerial Vehicles (UAVs) are used in numerous practical applications in industry, science, and ecology; however, the large-scale use of UAVs is hampered by the limited onboard energy capacity. Increasing the energy efficiency of UAVs has thus become one of the most important tasks in UAV research. This review examines approaches to energy optimization of UAVs using algorithms and artificial intelligence (AI). It evaluates and compares algorithms and methods used to optimize energy efficiency of trajectory planning, adaptive speed control, battery management in mission planning, and navigation that accounts for environmental characteristics. It differs from those focusing on hardware solutions by highlighting optimization problems where energy usage is considered the key target for optimization and not a limiting constraint. The analyzed methods have been categorized into five groups, including classical optimization, metaheuristics, machine learning (ML), reinforcement learning (RL), and hybrid approaches. Key approaches such as RL, Model Predictive Control, evolutionary algorithms, and data-driven energy modeling have been outlined and compared with regard to energy-model accuracy, type of validation, scalability, and deployment readiness. Additionally, it emphasizes practical aspects such as the accuracy of energy modeling, real-time capabilities, scalability to multiple UAVs, and robustness to environmental uncertainty. Finally, this review provides directions for future research that will help develop sustainable, intelligent, and energy-efficient UAVs. Full article

There is a large and rapidly growing stock of retired power batteries from new energy vehicles in China. Unregulated informal recycling and improper disposal of these waste batteries trigger serious environmental hazards. Though a batch of regulatory policies on battery recycling have been released in recent years, the power battery recycling sector still faces prominent governance bottlenecks, especially ambiguous responsibility division and poor implementability under the entrusted recycling mode. To fill the existing research gap regarding tripartite interest conflicts and pricing mechanisms in entrusted recycling, this paper constructs a three-party evolutionary game model covering power battery producers, recyclers and government regulators. Two pricing models are further developed to distinguish producer self-operated recycling and third-party entrusted recycling channels. Numerical simulation is adopted to investigate multi-stakeholder interest contradictions, dynamic evolutionary trajectories and equilibrium stability of the recycling system, and the influences of subsidy intensity, supervision intensity and recycling cost on participants’ strategic choices are quantitatively analyzed. The research results demonstrate that inadequate government supervision and insufficient economic returns for formal recyclers serve as the primary obstacles hindering the effective deployment of entrusted recycling. An inherent and reasonable price gap exists between self-operated and entrusted recycling modes. Essentially, the price differential of standardized entrusted recycling represents the profit margin conceded by producers to recyclers instead of direct financial subsidies. To solve existing industry problems, this study proposes targeted recommendations for tripartite collaboration. The government should refine the regulatory framework of Extended Producer Responsibility and adopt differentiated reward and penalty mechanisms. Producers are expected to standardize entrusted recycling management and formulate a scientific pricing range for retired batteries. Recyclers ought to advance recycling technologies and maintain standardized operations. Collective efforts from all stakeholders can facilitate the long-term sustainability of the closed-loop recycling system for retired power batteries. Full article

The rapid expansion of electric vehicles is generating large volumes of spent lithium iron phosphate (LFP) batteries, yet the environmental performance of closed-loop regeneration under industrial conditions remains insufficiently quantified. Here we develop a life cycle assessment of a closed-loop recycling–regeneration pathway using factory-level inventory data from an integrated plant, benchmarking 1 kg of regenerated LFP cathode-active material (CAM) at the plant gate against virgin LFP CAM (ecoinvent v3.10; ReCiPe 2016 Midpoint). Relative to virgin production, the closed-loop route reduces global warming potential (GWP100) by 7.73% (from 6.59 to 6.08 kg CO₂-eq kg⁻¹ CAM), fossil fuel potential (FFP) by 3.80%, surplus ore potential (SOP) by 97.57%, and carcinogenic human toxicity (HTPc) by 36.72%—a clear but heterogeneous advantage, large for mineral resources and modest for climate. Iron phosphate and lithium carbonate recovery dominate the burdens, with H₂O₂ being the largest single GWP100 contributor (23.7%) and the most sensitive inventory parameter, while the SOP advantage is highly robust. Grid-decarbonization scenarios widen the GWP100 reduction to 22.4% under near-zero-carbon electricity. The carbon competitiveness of closed-loop LFP regeneration is therefore governed by the balance between avoided virgin-material burdens and reagent- and energy-intensive recovery operations. Full article

Timely identification of business-cycle turning points is essential for policymakers, firms and investors, yet traditional macroeconomic indicators are often published with considerable delays. This study examines whether vehicle registrations constitute an effective Leading Economic Indicator (LEI) for the Spanish economy and proposes an integrated framework combining business-cycle analysis, monthly forecasting and daily nowcasting. The cyclical properties of registrations are analyzed using the Christiano–Fitzgerald band-pass filter, cross-correlation analysis and the Bry–Boschan dating algorithm. Monthly forecasting performance is evaluated by comparing TBATS and Prophet through rolling-origin cross-validation, while a novel machine learning-inspired distribution model (MD) is developed to transform monthly forecasts into daily estimates. The results show that passenger-car registrations anticipate industrial production by three to five months, with a ninety per cent bootstrap interval of one to six. TBATS consistently outperforms Prophet in one-step-ahead forecasting. An out-of-sample test confirms that lagged registrations reduce the forecast error of industrial production without recourse to two-sided filtering, although the reduction is modest in magnitude and is not found for quarterly GDP. The proposed MD-shallow model achieves the highest distributional accuracy, surpassing both Prophet and a persistence benchmark. The findings demonstrate that vehicle registrations are a valuable standalone Leading Economic Indicator and that integrating traditional economic indicators with advanced forecasting techniques improves real-time monitoring of automotive demand and business-cycle dynamics. The proposed framework provides a practical tool for policymakers, manufacturers, and researchers seeking earlier and more reliable assessments of economic activity. Full article

Belt conveyors are essential systems for the continuous transport of various materials in many sectors and applications, including heavy industry or mining, which are characterized by demanding environmental and operational conditions. Our research is focused on the development of the system based on magnetic sensors for the detection of ferromagnetic objects. These detection systems are designed to prevent damage to conveyor belts and the downstream vehicles, machines, and processing equipment involved in material transport and processing. By detecting hazardous foreign objects, they help avoid belt damage or tearing, thereby reducing operational disruptions and the associated maintenance and repair costs. Our study confirmed that in addition to the development of the hardware and software solutions, it is also necessary to develop methods for the processing and evaluation of the data recorded by the detection system, as the data represent a valuable source of information not only for the operational workers but also for the managers and are very helpful in the creation of the sustainable transportation system. The article describes an innovative application of the Weibull distribution for the operational enhancement of the system and its comparison to the conventionally used histograms. In addition to that, the utilization possibilities of the obtained statistical data to evaluate the belt conveyor loading for a better planning of the process, to monitor the work of the operational or other employees’ quality of the supported material, or to reveal failures of the detection system or even of the belt conveyor are overviewed and discussed. Full article