Search Results (241)

This study introduces a novel approach for classifying flight trajectory curvature, focusing on early-stage flight characteristics to detect anomalies and deviations. The method intentionally avoids direct coordinate data and instead leverages a combination of trajectory-derived and meteorological features. This research analysed 9849 departure flight trajectories originating from 14 different airports. Two distinct trajectory classes were established through manual visual inspection, differentiated by curvature patterns. This categorisation formed the ground truth for evaluating trained machine learning (ML) classifiers from different families. The comparative analysis demonstrates that the Random Forest (RF) algorithm provides the most effective classification model. RF excels at summarising complex trajectory information and identifying non-linear relationships within the early-flight data. A key contribution of this work is the validation of specific predictors. The theoretical definitions of direction change (using vector values to capture dynamic movement) and diffusion distance (using scalar values to represent static displacement) proved highly effective. Their selection as primary predictors is supported by their ability to represent the essential static and dynamic properties of the trajectory, enabling the model to accurately classify flight paths and potential deviations before the flight is complete. This approach offers significant potential for enhancing real-time air traffic monitoring and safety systems. Full article

The canopy temperature of winter jujube serves as a direct indicator of plant water status and transpiration efficiency, making its accurate prediction a critical prerequisite for effective water management and optimized growth conditions in greenhouse environments. This study developed a data-driven model to forecast canopy temperature. The model serially integrates a Long Short-Term Memory (LSTM) network and a Random Forest (RF) algorithm, leveraging their complementary strengths in capturing temporal dependencies and robust nonlinear fitting. A three-stage framework comprising temporal feature extraction, multi-source feature fusion, and direct prediction was implemented to enable reliable nowcasting. Data acquisition and preprocessing were tailored to the greenhouse environment, involving multi-sensor data and thermal imagery processed with Robust Principal Component Analysis (RPCA) for dimensionality reduction. Key environmental variables were selected through Spearman correlation analysis. Experimental results demonstrated that the proposed LSTM–RF model achieved superior performance, with a determination coefficient (R²) of 0.974, mean absolute error (MAE) of 0.844 °C, and root mean square error (RMSE) of 1.155 °C, outperforming benchmark models including standalone LSTM, RF, Transformer, and TimesNet. SHAP (SHapley Additive exPlanations)-based interpretability analysis further quantified the influence of key factors, including the “thermodynamic state of air” driver group and latent temporal features, offering actionable insights for irrigation management. The model establishes a reliable, interpretable foundation for real-time water stress monitoring and precision irrigation control in protected winter jujube production systems. Full article

Due to its abundance, bird cherry–oat aphid is the most important vector in Poland of the complex of viruses causing barley yellow dwarf virus (BYDV). These viruses infect all cereals. During the growing season, cereal plants are exposed to many species of agrophages, which can limit their growth, development and yield. As observed for many years, global warming contributes to changes in the development of many organisms. Aphids (Aphidoidea), which are among the most important pests of agricultural crops, respond very dynamically to these changes. Under favorable conditions, their populations can increase several-fold within a few days. The bird cherry–oat aphid (Rhopalosiphum padi L.) is a dioecious species that undergoes a seasonal host shift during its life cycle. Its primary hosts are trees and shrubs (Prunus padus L.), while secondary hosts include cereals and various grass species. R. padi feeds directly on bird cherry tree, reducing its ornamental value, and on cereals, where it contributes to yields losses. The species can also damage plants indirectly by transmitting harmful viruses. Indirect damage is generally more serious than direct feeding injury. Monitoring aphid flights with a Johnson suction trap (JST) is useful for plant protection, which enables early detection of their presence in the air and then on cereal crops. To provide early detection of R. padi migrations and to study the dynamics of abundance, flights were monitored in 2020–2024 with Johnson suction traps at two localities: Winna Góra (Greater Poland Province) and Sośnicowice (Silesia Province). The aim of the research conducted in 2020–2024 was to study the dynamics of the bird cherry–oat aphid (Rhopalosiphum padi L.) population in relation to meteorological conditions as recorded by a Johnson suction trap. Over five years of research, a total of 129,638 R. padi individuals were captured using a Johnson suction trap at two locations (60,426 in Winna Góra and 69,212 in Sośnicowice). In Winna Góra, the annual counts were as follows: 5766 in 2020, 6498 in 2021, 36,452 in 2022, 5598 in 2023, and 6112 in 2024. In Sośnicowice, the numbers were as follows: 6954 in 2020, 9159 in 2021, 49,120 in 2022, 3855 in 2023, and 124 in 2024. The year 2022 was particularly notable for the exceptionally high abundance of R. padi, especially in the autumn. Monitoring crops for the presence of pests is the basis of integrated plant protection. Climate change, modern cultivation technologies, and increasing restrictions on chemical control are the main factors contributing to the development and spread of aphids. Therefore, measures based on monitoring the level of threat and searching for control solutions are necessary. Full article

Blast furnace tuyeres are the primary dust emission source in ironmaking facilities (accounting for over 30% of total pollutants). High-temperature dust plumes with intense thermal energy are prone to dispersion, while China’s steel industry ultra-low emission standards (particulate matter ≤ 10 mg/m³) impose strict requirements on capture efficiency. Existing technologies often neglect crosswind interference and lack coordinated design between air volume regulation and hood structure, leading to excessive fugitive emissions and non-compliance. This study established a localized numerical model for high-temperature dust capture at blast furnace tuyeres, investigating air volume’s impact on velocity fields and capture efficiency, revealing crosswind interference mechanisms, and proposing optimization strategies (adding hood baffles, adjusting dimensions, installing ejector fans). Results show crosswind significantly reduces efficiency—only 78% at 1.5 m/s crosswind and 400,000 m³/h flow rate. The optimal configuration (2.5 m side flaps plus1.4 m baffles) achieves 99% efficiency, maintaining high performance at lower flow rates: 350,000 m³/h (1.5 m/s crosswind) and 250,000 m³/h (0.9 m/s crosswind). This study provides technical support for blast furnace tuyere dust control and facilitates ultra-low emission compliance in the steel industry. This study supports blast furnace tuyere dust control and aids the steel industry in meeting ultra-low emission standards. Notably, the proposed optimization scheme boasts simple structural adjustments, low retrofitting costs, and good compatibility with existing production lines, enabling direct industrial promotion and notable environmental and economic gains. Full article

The convergence of biometric and environmental sensing represents a transformative advancement in wearable technology, moving beyond single-parameter tracking towards a holistic, context-aware paradigm for health monitoring. This review comprehensively examines the landscape of multi-modal wearable devices that simultaneously capture physiological data, such as electrodermal activity (EDA), electrocardiogram (ECG), heart rate variability (HRV), and body temperature, alongside environmental exposures, including air quality, ambient temperature, and atmospheric pressure. We analyze the fundamental sensing technologies, data fusion methodologies, and the critical importance of contextualizing physiological signals within an individual’s environment to disambiguate health states. A detailed survey of existing commercial and research-grade devices highlights a growing, yet still limited, integration of these domains. As a central case study, we present an integrated prototype, which exemplifies this approach by fusing data from inertial, environmental, and physiological sensors to generate intuitive, composite indices for stress, fitness, and comfort, visualized via a polar graph. Finally, we discuss the significant challenges and future directions for this field, including clinical validation, data security, and power management, underscoring the potential of convergent sensing to revolutionize personalized, predictive healthcare. Full article

This study proposes a novel framework for allocating CO₂ emission rights within the Yangtze River Delta (YRD) urban agglomeration, tackling the inter-city CO₂ transmission dynamics frequently neglected in conventional allocation models. Current emission allocation methods fail to capture the spatial spillover effects of CO₂ emissions driven by atmospheric transport, resulting in potential inequities. Leveraging the WRF model to simulate carbon emissions across 27 cities, we develop a two-stage network Data Envelopment Analysis (DEA) model that integrates both emission generation and governance capacities. Our findings highlight significant inter-city CO₂ transmission, with the wind direction and speed playing a pivotal role in emissions spread. In contrast to traditional models, our approach considers the regional interdependence of emissions, enhancing both fairness and efficiency in the allocation process. The results indicate that cities with stronger governance systems, including green technology investments and effective air quality management, are rewarded with higher carbon allowances. Moreover, our model demonstrates that policies prioritizing environmental governance over raw emission levels can foster long-term sustainability. This work provides a comprehensive methodology for achieving a balanced allocation of emission rights that integrates economic growth, environmental management, and equity considerations within complex urban agglomerations. Full article

High concentrations of tropospheric ozone (${\mathrm{O}}_3$) in urban areas pose a significant risk to human health. This study proposes an evaluation framework based on the XGBoost algorithm to predict ${\mathrm{O}}_3$ concentration, assessing the model’s capacity for seasonal extrapolation and spatial transferability. The experiment uses hourly air pollution data (${\mathrm{O}}_3$, NO, ${\mathrm{NO}}_2$, and NOx) and meteorological factors (temperature, relative humidity, barometric pressure, wind speed, and wind direction) from six monitoring stations in the Monterrey Metropolitan Area, Mexico (from 22 September 2022 to 21 September 2023). In the preprocessing phase, the datasets were extended via feature engineering, including cyclic variables, rolling windows, and lag features, to capture temporal dynamics. The prediction models were optimized using a random search, with time-series cross-validation to prevent data leakage. The models were evaluated across a concentration range of 0.001 to 0.122 ppm, demonstrating high predictive accuracy, with a coefficient of determination (${\mathrm{R}}^2$) of up to 0.96 and a root-mean-square error (RMSE) of 0.0034 ppm when predicting summer (${\mathrm{O}}_3$) concentrations without prior knowledge. Spatial generalization was robust in residential areas (${\mathrm{R}}^2$ > 0.90), but performance decreased in the industrial corridor (AQMS-NL03). We identified that this decrease is related to local complexity through the quantification of domain shift (Kolmogorov–Smirnov test) and Shapley additive explanations (SHAP) diagnostics, since the model effectively learns atmospheric inertia in stable areas but struggles with the stochastic effects of NOx titration driven by industrial emissions. These findings position the proposed approach as a reliable tool for “virtual detection” while highlighting the crucial role of environmental topology in model implementation. Full article

With the acceleration of urbanization in China, air pollution is becoming increasingly serious, especially PM_2.5 pollution, which poses a significant threat to public health. The study employed different deep learning models, including recurrent neural network (RNN), artificial neural network (ANN), convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), Transformer, and novel hybrid interpretable CNN–BiLSTM–Transformer architectures for forecasting daily PM_2.5 concentrations on the integrated dataset. The dataset of meteorological factors and atmospheric pollutants in Qingdao City was used as input features for the model. Among the models tested, the hybrid CNN–BiLSTM–Transformer model achieved the highest prediction accuracy by extracting local features, capturing temporal dependencies in both directions, and enhancing global pattern and key information, with low root Mean Square Error (RMSE) (5.4236 μg/m³), low mean absolute error (MAE) (4.0220 μg/m³), low mean absolute percentage error (MAPE) (22.7791%) and high correlation coefficient (R) (0.9743) values. Shapley additive explanations (SHAP) analysis further revealed that PM₁₀, CO, mean atmospheric temperature, O_3, and SO₂ are the key influencing factors of PM_2.5. This study provides a more comprehensive and multidimensional approach for predicting air pollution, and valuable insights for people’s health and policy makers. Full article

Museums open to the public must reconcile heritage preservation requirements with energy-conscious microclimate management and visitors’ environmental experience. In historic buildings, indoor conditions are typically controlled primarily for preventive conservation, while opportunities for detailed assessment of human comfort are often limited by existing monitoring systems and operational constraints. This study investigates visitors’ perceptions of thermal conditions and indoor air quality (IAQ) in two branches of the National Museum in Krakow (NMK) characterized by different microclimate-control strategies: the mechanically ventilated and air-conditioned Cloth Hall and the predominantly passively controlled Bishop Erazm Ciołek Palace. A pilot survey was conducted in spring 2023 to capture subjective assessments of thermal sensation and perceived IAQ. These perceptions were contextualized using long-term air temperature and relative humidity data (2013–2023) routinely monitored for conservation purposes. Environmental data were analyzed to assess the stability of indoor conditions and to provide background for interpreting survey responses, rather than to perform a normative evaluation of thermal comfort. The results indicate that visitors frequently perceived the indoor environment as slightly warm and reported lower air quality in the Palace, where air was often described as stale or stuffy. These perceptions occurred despite relatively small differences in monitored air temperature and relative humidity between the two buildings. The findings suggest that ventilation strategy, air exchange effectiveness, odor accumulation, room configuration, and lighting conditions may influence perceived environmental quality more strongly than temperature or humidity alone. Although limited in scope, this pilot study highlights the value of incorporating visitor perception into discussions of energy-conscious microclimate management in museums and indicates directions for further multidisciplinary research. Full article

Industrial air emissions are major contributors to human exposure to toxic pollutants, posing significant health risks. Life cycle assessment (LCA) is increasingly used to quantify human toxicity impacts from industrial processes. Conventional LCA often overlooks spatial and temporal variability, limiting its ability to capture actual inhaled doses and exposure-driven impacts. To address this, we developed a site-dependent dynamic LCA (SdDLCA) framework that integrates conventional LCA with Enhanced Structural Path Analysis (ESPA) and atmospheric dispersion modeling. Applied to the production of double-glazed PVC windows for a residential project, the framework generates high-resolution, site-specific emission inventories for three key pollutants: nitrogen oxides (NO_x), sulfur dioxide (SO₂), and fine particulate matter (PM_2.5). Local concentration fields are compared with World Health Organization (WHO) air quality thresholds to identify hotspots and periods of elevated exposure. By coupling these fields with the ReCiPe 2016 endpoint methodology and localized demographic and meteorological data, SdDLCA quantifies human health impacts in Disability-Adjusted Life Years (DALYs), providing a direct measure of inhalation toxicity. This approach enhances LCA’s ability to capture exposure-driven effects, identifies populations at greatest risk, and offers a robust, evidence-based tool to guide industrial planning and operations that minimize health hazards from air emissions. Full article

Accurate evaluation of unsaturated shear strength remains a significant challenge in geotechnical engineering because of the nonlinear interaction between matric suction and shear strength. Existing models often assume a linear contribution of suction and are generally restricted to low suction ranges, limiting their predictive capability under highly unsaturated conditions. This study investigated the nonlinear response of unsaturated shear strength through single-stage direct shear tests conducted under constant water content. Two soil types: a high-plasticity clay and a low-plasticity silty clay were examined across a wide suction range extending beyond the air-entry value (AEV). The results revealed a nonlinear behavior expressed as a distinct bi-linear trend, with shear strength increasing with suction up to the optimal moisture condition and then exhibiting a clearly altered rate of increase at higher suction levels. To capture this nonlinear behavior of unsaturated shear strength with suction, an exponential shear strength equation was proposed and validated using eight additional published datasets encompassing different soil classifications and suction magnitudes. The proposed formulation demonstrates that accounting for non-linearity is essential for accurately estimating the unsaturated shear strength of the soil. Moreover, the proposed exponential model outperforms both the well-established linear model of Fredlund and the nonlinear power law model of Abramento and Carvalho, thereby providing a unified framework for capturing the nonlinear interaction of matric suction on unsaturated shear strength. Full article

Direct Air Capture (DAC) technology plays a crucial role in reducing atmospheric CO₂, but large-scale deployment faces challenges such as high energy consumption, operational costs, and slow material development. This study provides a comprehensive review of DAC principles, including chemical and solid adsorption methods, with a focus on emerging technologies like Metal–Organic Frameworks (MOFs) and graphene aerogels. MOFs have achieved adsorption capacities up to 1.5 mmol/g, while modified graphene aerogels reach 1.3 mmol/g. Other advancing approaches include DAC with Methanation (DACM), variable-humidity adsorption, photo-induced swing adsorption, and biosorption. The study also examines global industrialization trends, noting a significant rise in DAC projects since 2020, particularly in the U.S., China, and Europe. The integration of DAC with renewable energy sources, such as photovoltaic/electrochemical regeneration, offers significant cost-reduction potential and can cut reliance on conventional heat by 30%. This study focuses on the integration of Artificial Intelligence (AI) for accelerating material design and system optimization. AI and Machine Learning (ML) are accelerating DAC R&D: high-throughput screening shortens material design cycles by 60%, while AI-driven control systems optimize temperature, humidity, and adsorption dynamics in real time, improving CO₂ capture efficiency by 15–20%. The study emphasizes DAC’s future role in achieving carbon neutrality through enhanced material efficiency, integration with renewable energy, and expanded CO₂ utilization pathways, providing a roadmap for scaling DAC technology in the coming years. Full article

Adverse exposure to noise pollution is increasingly recognized as a significant public health concern. Strong evidence links noise exposure with negative health outcomes such as cardiovascular disease, mental disorders, stress, and sleep disturbance. The presence of noise in parks, which are environmental settings associated with health promotion, recreation, and restoration, presents a paradox that warrants further exploration. The United States offers a distinct context for exploring this paradox, given its vast public park system and a wide array of anthropogenic and environmental noise sources. Our scoping review synthesized findings from fifteen research studies that investigated noise exposure and noise levels in United States public parks. The review examined how studies measured noise, the integration of subjective perceptions with objective assessments, and the role of park characteristics in shaping park visitor noise experiences. Results highlighted varying methodological approaches, with some studies employing sound level meters or modeling techniques, while others also incorporated surveys to capture visitor perceptions. Despite this variety, evidence on the direct health impacts of park noise exposure remains limited, and longitudinal studies are largely absent. Notably, few studies evaluated how noise interacts with other environmental exposures, such as air pollution or greenness, to influence visitor perception and wellness. By synthesizing the current evidence base, this review suggests knowledge gaps and few methodological inconsistencies that limit the field. Findings call for future research mobilizing standardized, multimodal noise assessment methods, and considerations for health outcome measures. Such advancements are important for informing public health interventions and guiding urban planning strategies to improve the acoustic quality and restorative potential of US parks. Full article

The built environment (BE) encompasses an enormous volume and substantial material mass. However, structures within it typically serve single, limited functions. Enhancing these structures with multifunctional capabilities holds significant potential for achieving broader sustainability goals and creating impactful environmental benefits. Among these potential multifunctional applications, carbon capture, reduction, and storage are especially critical, given the current built environment’s substantial contribution of approximately 40% of global energy and CO₂ emissions. Keeping this potential in view, this comprehensive review critically evaluates carbon management strategies for the built environment via three interrelated approaches: carbon capture (via photosynthesis, passive concrete carbonation, and microbial biomineralization), carbon storage (employing carbonation curing, mineral carbonation, and valorization of construction and demolition waste), and carbon reduction (integrating industrial waste, alternative binders, and bio-based materials). The review also evaluates the potential of novel direct air-capture materials, assessing their feasibility for integration into construction processes and existing infrastructure. Key findings highlight significant advancements, quantify CO₂ absorption potentials across various construction materials, and reveal critical knowledge gaps, thereby providing a strategic roadmap for future research direction toward a low-carbon, climate-resilient built environment. Full article

Wildfires are a recurring dry-season hazard in northern Thailand, contributing to severe air pollution and trans-boundary haze. However, the region lacks the ground-based measurements necessary for monitoring Live Fuel Moisture Content (LFMC), a key variable influencing vegetation flammability. This study presents a preliminary framework for near-real-time (NRT) LFMC estimation using Sentinel-2 multispectral imagery. The system integrates normalized vegetation and moisture-related indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and the Moisture Stress Index (MSI) with an NDVI-derived evapotranspiration fraction (ETf) within a heuristic modeling approach. The workflow includes cloud and shadow masking, weekly to biweekly compositing, and pixel-wise normalization to address the persistent cloud cover and heterogeneous land surfaces. Although currently unvalidated, the LFMC estimates capture the relative spatial and temporal variations in vegetation moisture across northern Thailand during the 2024 dry season (January–April). Evergreen forests maintained higher moisture levels, whereas deciduous forests and agricultural landscapes exhibited pronounced drying from January to March. Short-lag responses to rainfall suggest modest moisture recovery following precipitation, although the relationship is influenced by additional climatic and ecological factors not represented in the heuristic model. LFMC-derived moisture classes reflect broad seasonal dryness patterns but should not be interpreted as direct fire danger indicators. This study demonstrates the feasibility of generating regional LFMC indicators in a data-scarce tropical environment and outlines a clear pathway for future calibration and validation, including field sampling, statistical optimization, and benchmarking against global LFMC products. Until validated, the proposed NRT LFMC estimation product should be used to assess relative vegetation dryness and to support the refinement and development of future operational fire management tools, including early warnings, burn-permit regulation, and resource allocation. Full article