Search Results (173)

Creating a comfortable microclimate in the premises of buildings is currently becoming one of the priorities in the field of architecture, construction and engineering systems. The increased attention from the scientific community to this topic is due not only to the desire to ensure healthy and favorable conditions for human life but also to the need for the rational use of energy resources. This area is becoming particularly relevant in the context of global challenges related to climate change, rising energy costs and increased environmental requirements. Practice shows that any technical solutions to ensure comfortable temperature, humidity and air exchange in rooms should be closely linked to the concept of energy efficiency. This allows one not only to reduce operating costs but also to significantly reduce greenhouse gas emissions, thereby contributing to sustainable development and environmental safety. In this connection, this study presents a parametric assessment of the influence of climatic and geometric factors on the aerodynamic characteristics of the air cavity, which affect the heat exchange process in the ventilated layer of curtain wall systems. The assessment was carried out using a combined analytical calculation method that provides averaged thermophysical parameters, such as mean air velocity ($V_s$), average internal surface temperature ($t_{in.s}^{av}$), and convective heat transfer coefficient (${\alpha }_s$) within the air cavity. This study resulted in empirical average values, demonstrating that the air velocity within the cavity significantly depends on atmospheric pressure and façade height difference. For instance, a 10-fold increase in façade height leads to a 4.4-fold increase in air velocity. Furthermore, a three-fold variation in local resistance coefficients results in up to a two-fold change in airflow velocity. The cavity thickness, depending on atmospheric pressure, was also found to affect airflow velocity by up to 25%. Similar patterns were observed under ambient temperatures of +20 °C, +30 °C, and +40 °C. The analysis confirmed that airflow velocity is directly affected by cavity height, while the impact of solar radiation is negligible. However, based on the outcomes of the analytical model, it was concluded that the method does not adequately account for the effects of solar radiation and vertical temperature gradients on airflow within ventilated façades. This highlights the need for further full-scale experimental investigations under hot climate conditions in South Kazakhstan. The findings are expected to be applicable internationally to regions with comparable climatic characteristics. Ultimately, a correct understanding of thermophysical processes in such structures will support the advancement of trends such as Lightweight Design, Functionally Graded Design, and Value Engineering in the development of curtain wall systems, through the optimized selection of façade configurations, accounting for temperature loads under specific climatic and design conditions. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

Ningxia has emerged as a strategic hub for China’s photovoltaic (PV) industry by leveraging abundant solar energy resources and geoclimatic advantages. This study analyzed the spatiotemporal expansion trends and microclimatic impacts of PV installations (2015–2024) using Gaofen-1 (GF-1) and Landsat8 satellite imagery with deep learning algorithms and multidimensional environmental metrics. Among semantic segmentation models, DeepLabV3+ had the best performance in PV extraction, and the Mean Intersection over Union, precision, and F1-score were 91.97%, 89.02%, 89.2%, and 89.11%, respectively, with accuracies close to 100% after manual correction. Subsequent land surface temperature inversion and spatial buffer analysis quantified the thermal environmental effects of PV installation. Localized cooling patterns may be influenced by albedo and vegetation dynamics, though further validation is needed. The total PV site area in Ningxia expanded from 59.62 km² to 410.06 km² between 2015 and 2024. Yinchuan and Wuzhong cities were primary growth hubs; Yinchuan alone added 99.98 km² (2022–2023) through localized policy incentives. PV installations induced significant daytime cooling effects within 0–100 m buffers, reducing ambient temperatures by 0.19–1.35 °C on average. The most pronounced cooling occurred in western desert regions during winter (maximum temperature differential = 1.97 °C). Agricultural zones in central Ningxia exhibited weaker thermal modulation due to coupled vegetation–PV interactions. Policy-driven land use optimization was the dominant catalyst for PV proliferation. This study validates “remote sensing + deep learning” framework efficacy in renewable energy monitoring and provides empirical insights into eco-environmental impacts under “PV + ecological restoration” paradigms, offering critical data support for energy–ecology synergy planning in arid regions. Full article

Mine ventilation systems are critical for ensuring operational safety, yet air leakage remains a pervasive challenge, leading to energy inefficiency and heightened safety risks. Traditional tracer gas methods, while effective in simple networks, exhibit significant errors in complex multi-entry systems due to static empirical parameters and environmental interference. This study proposes an integrated methodology that combines multi-path airflow analysis with dynamic longitudinal dispersion coefficient correction to enhance the accuracy of air leakage detection. Utilizing sulfur hexafluoride (SF6) as the tracer gas, a phased release protocol with temporal isolation was implemented across five strategic points in a coal mine ventilation network. High-precision detectors (Bruel & Kiaer 1302) and the MIVENA system enabled synchronized data acquisition and 3D network modeling. Theoretical models were dynamically calibrated using field-measured airflow velocities and dispersion coefficients. The results revealed three deviation patterns between simulated and measured tracer peaks: Class A deviation showed 98.5% alignment in single-path scenarios, Class B deviation highlighted localized velocity anomalies from Venturi effects, and Class C deviation identified recirculation vortices due to abrupt cross-sectional changes. Simulation accuracy improved from 70% to over 95% after introducing wind speed and dispersion adjustment coefficients, resolving concealed leakage pathways between critical nodes and key nodes. The study demonstrates that the dynamic correction of dispersion coefficients and multi-path decomposition effectively mitigates errors caused by turbulence and geometric irregularities. This approach provides a robust framework for optimizing ventilation systems, reducing invalid airflow losses, and advancing intelligent ventilation management through real-time monitoring integration. Full article

In the field of autonomous driving, the accurate identification of drivable areas on roads is the key to ensuring the safe driving of vehicles. However, unstructured roads lack clear lane lines and regular road structures, and they have fuzzy edges and rutting marks, which greatly increase the difficulty of identifying drivable areas. To address the above challenges, this paper proposes a drivable area detection method for unstructured roads based on the MRNet model. To address the problem that unstructured roads lack clear lane lines and regular structures, the model dynamically captures local and global context information based on the self-attention mechanism of a Transformer, and it combines the input of image and LiDAR data to enhance the overall understanding of complex road scenes; to address the problem that detailed features such as fuzzy edges and rutting are difficult to identify, a multi-scale dilated convolution module (MSDM) is proposed to capture detailed information at different scales through multi-scale feature extraction; to address the gradient vanishing problem in feature fusion, a residual upsampling module (ResUp Block) is designed to optimize the spatial resolution recovery process of the feature map, correct errors, and further improve the robustness of the model. Experiments on the ORFD dataset containing unstructured road data show that MRNet outperforms other common methods in the drivable area detection task and achieves good performance in segmentation accuracy and model robustness. In summary, MRNet provides an effective solution for drivable area detection in unstructured road environments, supporting the environmental perception module of autonomous driving systems. Full article

Current substation inspection robots mainly use Lidar as a sensor for localization and map building. However, laser SLAM has the problem of localization error in scenes with similar and missing environmental structural features, and environmental maps built by laser SLAM provide more single-road information for inspection robot navigation, which is not conducive to the judgment of the road scene. For this reason, in this paper, 3D Lidar information and visual information are fused to create a SLAM algorithm applicable to substation inspection robots to solve the above laser SLAM localization error problem and improve the algorithm’s localization accuracy. First, in order to recover the scalability of monocular visual localization, the algorithm in this paper utilizes 3D Lidar information and visual information to calculate the true position of image feature points in space. Second, the laser position and visual position are utilized with interpolation to correct the point cloud distortion caused by the motion of the Lidar. Then, a position-adaptive selection algorithm is designed to use visual position instead of laser inter-frame position in some special regions to improve the robustness of the algorithm. Finally, a color laser point cloud map of the substation is constructed to provide more road environment information for the navigation of the inspection robot. The experimental results show that the localization accuracy and map-building effect of the VO-Lidar SLAM algorithm designed in this paper are better than the current laser SLAM algorithm and verify the applicability of the color laser point cloud map constructed by this algorithm in substation environments. Full article

The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. Autonomous underwater vehicles (AUVs) rely on simultaneous localization and mapping (SLAM) for real-time navigation and mapping in these complex environments. However, traditional SLAM techniques face significant obstacles, including poor visibility, dynamic lighting conditions, sensor noise, and water-induced distortions, all of which degrade the accuracy and robustness of underwater navigation systems. Recent advances in deep learning (DL) have introduced powerful solutions to overcome these challenges. DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. This survey provides a comprehensive analysis of the latest developments in DL-enhanced SLAM for underwater applications, categorizing approaches based on their methodologies, sensor dependencies, and integration with deep learning models. We critically evaluate the benefits and limitations of existing techniques, highlighting key innovations and unresolved challenges. In addition, we introduce a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs). UWSNs offer a collaborative framework that enhances localization, mapping, and real-time data sharing among AUVs by leveraging acoustic communication and distributed sensing. Our proposed taxonomy provides new insights into how communication-aware SLAM methodologies can improve navigation accuracy and operational efficiency in underwater environments. Furthermore, we discuss emerging research trends, including the use of transformer-based architectures, multi-modal sensor fusion, lightweight neural networks for real-time deployment, and self-supervised learning techniques. By identifying gaps in current research and outlining potential directions for future work, this survey serves as a valuable reference for researchers and engineers striving to develop robust and adaptive underwater SLAM solutions. Our findings aim to inspire further advancements in autonomous underwater exploration, supporting critical applications in marine science, deep-sea resource management, and environmental conservation. Full article

The photochemical production of tropospheric ozone (O₃) is very closely linked to seasonal cycles and peaks in solar radiation occurring during warm seasons. In the Mediterranean Basin, which is a hotspot for climate and air mass transport mechanisms, boreal warm seasons cause a notable increase in tropospheric O₃, which unlike stratospheric O₃ is not beneficial for the environment. At the Lamezia Terme (code: LMT) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) station located in Calabria, Southern Italy, peaks of tropospheric O₃ were observed during boreal summer and spring seasons, and were consequently linked to specific wind patterns compatible with increased photochemical activity in the Tyrrhenian Sea. The finding resulted in the introduction of a correction factor for O₃ in the O₃/NO_x (ozone to nitrogen oxides) ratio “Proximity” methodology for the assessment of air mass aging. However, some of the mechanisms driving O₃ patterns and their correlation with other parameters at the LMT site remain unknown, despite the environmental and health hazards posed by tropospheric O₃ in the area. In general, the issue of ozone photochemical pollution in the region of Calabria, Italy, is understudied. In this study, the behavior of O₃ at the site is assessed with remarkable detail using nine years (2015–2023) of data and correlations with surface temperature and solar radiation. The evaluations demonstrate non-negligible correlations between environmental factors, such as temperature and solar radiation, and O₃ concentrations, driven by peculiar patterns in local wind circulation. The northeastern sector of LMT, partly neglected in previous works, yielded higher statistical correlations with O₃ than expected. The findings of this study also indicate, for central Calabria, the possibility of heterogeneities in O₃ exposure due to local geomorphology and wind patterns. A case study of very high O₃ concentrations reported during the 2015 summer season is also reported by analyzing the tendencies observed during the period with additional methodologies and highlighting drivers of photochemical pollution on larger scales, also demonstrating that near-surface concentrations result from specific combinations of multiple factors. Full article

Microplastic (MP) occurrence is a growing concern in environmental research, with significant attention focused on its presence in various ecosystems worldwide. While much research has centered on large lakes and water bodies, remote alpine lakes remain relatively unexplored in terms of microplastic occurrence. Studying microplastic occurrence in remote alpine lakes is important to understand the global spread of pollution, assess its impact on pristine ecosystems, and inform conservation efforts in these vulnerable environments. This study investigates microplastic presence in the sediment of Lake Cadagno, a remote alpine lake situated in the Piora Valley of southern central Switzerland. The lake has no effluents, and its meromictic nature means that the water on the bottom is not mixed with the water above, which can potentially lead to an enhanced accumulation of microplastics in the sediments that perpetuate in the lake system. Through sediment core sampling and analysis, we aim to identify the sources and deposition trends of microplastics in this pristine alpine environment. Our findings reveal the presence of microplastic within Lake Cadagno: in total, 186 MP particles were extracted from 756 cm³ of processed sediment (0.24 MP/cm³) with an average of 19.5 MP/sample (SD ± 11.8 MP/sample). Our results suggest that microplastics are predominantly attributable to localized sources associated with nearby human activities. The absence of synthetic fibers and the limited polymer types detected suggest a minimal contribution from atmospheric deposition, reinforcing the significance of local anthropogenic influences. Spatial clustering of microplastic particles near potential sources underscores the impact of surrounding land use activities on microplastic distribution. Overall, this study highlights the importance of addressing microplastic contamination even in remote and relatively unmodified ecosystems like Lake Cadagno, to elucidate the need for strict adherence to waste management and correct disposal actions to reduce the impacts of microplastic contamination. Full article

This study presents a hybrid data analysis approach to optimize the growing conditions for beetroot and tarragon microgreens cultivated in hydroponic systems. Maintaining precise microclimate control is essential, as even minor deviations can significantly affect the yield and product quality, but traditional monitoring methods fail to adapt promptly to changing conditions. To overcome this limitation, an automated monitoring system integrating machine learning methods XGBoost 3.0.0, principal component analysis (PCA), and fuzzy logic was developed. The model continuously identifies the deviations in environmental parameters and recommends corrective actions to stabilize the growth conditions. Experimental evaluation demonstrated superior predictive performance by using XGBoost, achieving an accuracy and F1-score of 97.88%, ROC-AUC of 99.99%, and computational efficiency (training completed in 2.3 s), outperforming RandomForest and GradientBoosting algorithms. Real-time data collection was facilitated through IoT sensors transmitting readings via Wi-Fi every 5 s to a local server, accumulating approximately 17,280 records per day. The analysis highlighted air humidity, solution humidity, and temperature as critical influencing factors. This research confirms the developed system’s effectiveness in intelligent hydroponic monitoring, with future work aimed at integrating IoT and IIoT technologies for scalable management across diverse crops. Full article

The international trade in organic food has obviously increased potential in the past decade. The present study was conducted to assess and compare the first global warming impact of fluid milk production in Thailand associated to two systems, namely organic and conventional farms, by applying LCA for a case study approach. The assessment was based on a cradle-to-farm gate, with 1 kg of fat- and protein-corrected milk (FPCM) as a functional unit (FU). The environmental impact was evaluated according to International Dairy Federation or biological allocation. The results showed that the global warming values of organic farms (2.366–2.783 kg CO₂ equivalent/kg FPCM) were 47% moderately higher than those of conventional farms (1.253–1.474 kg CO₂ equivalent/kg FPCM). The main contributors to the global warming impact were feed consumption and CH₄ emission, accounting for 33.41% and 33.19%, respectively. The highest global warming impact was found in the stages of lactating cow. Another interesting finding was lack of local organic feed with a relatively high impact on transportation stage. Based on biological allocation, the global warming impact was increased over 12.652–13.107% by the mass allocation method, whereas economic allocation exhibited no effect on the global warming impact. A sensitivity analysis result indicated that the organic farm was economically feasible as an alternative to conventional farm. The substitution of conventional farming with organic farming of 10–50% led to an annual global warming impact in Thailand of 8–30% higher than that of conventional farms. Full article

Enhancing the visibility of outdoor images under sandstorm conditions remains a significant challenge in computer vision due to the complex atmospheric interference caused by dust particles. While existing image enhancement algorithms perform well in mild sandstorm scenarios, they often struggle to produce satisfactory results in more severe conditions, where residual color casts and chromatic artifacts become pronounced. These limitations highlight the need for a more robust and adaptable restoration method. In this study, we propose an advanced algorithm designed to restore sand-dust images under varying sandstorm intensities, effectively addressing the aforementioned challenges. The approach begins with a color correction step, achieved through channel compensation and color transfer techniques, which leverage the unique statistical properties of sand-dust images. To further refine the restoration, we improve the boundary constraints of the saturation line prior (SLP) by adjusting the local illumination in the atmospheric light map, enhancing the model’s robustness to environmental variations. Finally, the atmospheric scattering model is employed for comprehensive image restoration, ensuring that color correction and dust removal are optimized. Extensive experiments on real-world sandstorm images demonstrate that the proposed method performs on par with state-of-the-art (SOTA) techniques in weaker sandstorm scenarios, showing marked improvements in more severe conditions. These results highlight the potential of our approach for practical applications in outdoor image enhancement under challenging environmental conditions. Full article

The increase in demand for timber and global eucalyptus cultivation has generated controversy regarding its potential impact on water resources, especially in regions with limited water availability, with the myth that “eucalyptus dries out the soil” being spread. In this regard, this review study addresses the factors that influence water consumption by eucalyptus, providing solutions to reduce, mitigate, or even avoid any impact on water resources at a given site. In this manuscript, the authors reviewed 200 works published from 1977 to 2024 to survey all information to confirm if the factual background allows someone to state if eucalyptus can deplete soil water. With a solid scientific basis, many research studies show that eucalyptus’ water demand is comparable to that of native forest species and crops worldwide and that species, age, edaphoclimatic conditions, and forest management practices mainly influence water consumption. On the other hand, it is a hasty conclusion that some eucalyptus species can contribute to reduced soil water. Effectively, without proper management, the environmental impacts of a eucalyptus plantation are the same as those of poorly managed crops. Indeed, if cultivated with proper agroclimatic zoning and correct management practices, the growth of eucalyptus culture is an environmentally correct activity. By adopting measures such as maintaining sufficient native forest cover to ensure ecosystem services, cultivation based on zoning maps, and considering local specificities (e.g., deeper, sandier soils are preferable), selection of species appropriate to the carrying capacity of each region, adoption of lower planting densities, and reduced rotation, eucalyptus cultivation will not negatively affect water resources. Sustainable eucalyptus cultivation has several economic and environmental benefits, in addition to positive social impacts on surrounding communities in terms of employment and family income, and its sustainable management can guarantee its viability, demystifying the idea that eucalyptus trees cause water scarcity. The works reviewed herein demonstrated no solid ground to sustain the eucalyptus’ water depletion myth. Full article

The calibration of Micro-Electro-Mechanical System (MEMS) microphones remains a critical challenge due to their miniaturized geometry and sensitivity to non-uniform acoustic fields. This study presents an advanced calibration methodology that integrates Finite Element Method (FEM) simulations with experimental corrections to improve the accuracy of pressure comparison calibrations using active couplers. A key innovation is the incorporation of asymmetric acoustic field analysis, which systematically quantifies and corrects discrepancies arising from cavity geometry, sensor positioning, and resonance effects peculiar of MEMS microphones. The proposed approach significantly reduces measurement uncertainties, especially in the high-frequency range above 5 kHz, where standard calibration techniques face challenges in taking into account localized pressure variations. Furthermore, the implementation of a measurement set-up, which includes the insert voltage technique, allows for an accurate assessment of the preamplifier gain and minimizes systematic errors. Experimental validation shows that the refined calibration methodology produces highly reliable correction values, ensuring a robust performance over a wide frequency range (20 Hz–20 kHz). These advances establish a rigorous framework for standardizing the calibration of MEMS microphones, strengthening their applicability in acoustic monitoring, sound source localization, and environmental sensing. Full article

Recent national and international emergencies have repeatedly highlighted the role of information, and local information in particular, in synthesising various social and cultural policies proposed by public authorities and providing a correct representation of the living conditions of citizens on the ground, overcoming national media logics that are often based on the speed and spectacularisation of disasters. In fact, citizens have an “innate need” to know what is happening beyond their direct experience, to be aware of events that affect them or that are not happening in front of their eyes. A sociographic approach can be a supportive methodology to remember victims and report on disasters, but also to reconstruct new narratives by socially anticipating future environmental emergencies with the support of the media. Sociography as social narrative weaves together scientific analysis and journalistic storytelling, an old qualitative method that needs to be rediscovered, updated and integrated with new tools and methods. In this study, disaster narratives and analyses are supported by visual journalistic sources. In part, it takes up the gauntlet that Bruno Latour throws down to sociologists in Down to Earth, arguing that the latter should shift the focus of inquiry from theoretical analyses of social problems to descriptions of the existence of problems in experimental contexts, local shared spaces and common practices. This paper considers the description of (and within) the journalistic field as a methodological problem, examines the strengths and limitations of existing descriptive approaches and develops a different way of using a sociographic imagination in an attempt to make sense of changing journalistic practices with reference to specific Italian crisis events. Full article