Search Results (1,258)

The introduction of the ”dual carbon” target has increased the need for products that can accurately measure carbon dioxide levels, reflecting the rising demand. Due to challenges in achieving the required spatiotemporal resolution, accuracy, and spatial continuity with current carbon dioxide concentration products, it is essential to explore methods for obtaining carbon dioxide concentration products with completeness in space and time. Based on the 2018 OCO-2 carbon dioxide products and environmental variables such as vegetation coverage (FVC, LAI), net primary productivity (NPP), relative humidity (RH), evapotranspiration (ET), temperature (T) and wind (U, V), this study constructed a multiple regression model to obtain the spatial continuous carbon dioxide concentration products in the provinces of Huai River Basin. Using indicators such as correlation coefficient, root mean square error (RMSE), local variance, and percentage of valid pixels, the performance of model was validated. The validation results are shown as follows: (1) Among the selected environmental variables, the primary factors affecting the spatiotemporal distribution of carbon dioxide concentration are ET, LAI, FVC, NPP, T, U, and RH. (2) Compared with the OCO-2 carbon dioxide products, the percentage of valid pixels of the reconstructed carbon dioxide concentration data increased from less than 1% to over 90%. (3) The local variance in reconstructed data was significantly larger than that of original OCO-2 ${\mathrm{C}\mathrm{O}}_2$ products. (4) The average monthly RMSE is 2.69. Therefore, according to the model developed in this study, we can obtain a carbon dioxide concentration dataset that is spatially complete, meets precision requirements, and is rich in local detail information, which can better reflect the spatial pattern of carbon dioxide concentration and can be used to examine the carbon cycle between the terrestrial environment, biosphere, and atmosphere. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

College student military training is an organized, high-intensity, short-term militarized activity in China; this study aims to explore the differences in thermal perception between different intensities of military training. Questionnaires and microclimate measurements were conducted during summer military training in cold regions, including the Protective and Rescue Training and Assessment (PRTA), Formation Training (FT), the Shooting and Tactical Training and Assessment (STTA), the Route March (RM), and Dagger Practice (DP). The results indicated that (1) there was no significant correlation between the intensity of the activity and votes on thermal perception. The strongest thermal sensations, the lowest comfort, and the lowest thermal acceptability were experienced during FT, with a lower activity intensity. (2) Air temperature (T_a), globe temperature (T_g), relative humidity (RH), mean radiant temperature (T_mrt), and solar radiation (G) had significant effects on the TSV. (3) FT involved the lowest neutral temperatures (NUTCI/NPET), while DP and RM training had the highest NUTCI and NPET values, respectively. The neutral temperature range during military training was narrower compared to that in other aerobic activities. This study reveals, for the first time, the non-traditional correlation between military training intensity and thermal perception, confirming the specificity of thermal sensations in mandatory training and providing a theoretical basis for optimizing military training arrangements and developing thermal protection strategies. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity (RH) in estimating soil evaporation—a method that significantly outperforms the original PT-JPL formulation in Mediterranean semi-arid irrigated areas. This remote sensing framework enabled us to quantify spatial and temporal variations in water use across both natural and agricultural systems within the UNESCO World Heritage site of Doñana. Our analysis revealed an increasing evapotranspiration (ET) trend in intensified agricultural areas and rice fields surrounding the National Park (R = 0.3), contrasted by a strong negative ET trend in wetlands (R < −0.5). These opposing patterns suggest a growing diversion of water toward irrigation at the expense of natural ecosystems. The impact was especially marked during droughts, such as the 2011–2016 period, when precipitation declined by 16%. In wetlands, ET was significantly correlated with precipitation (R > 0.4), highlighting their vulnerability to reduced water inputs. These findings offer crucial insights to support sustainable water management strategies that balance agricultural productivity with the preservation of ecologically valuable systems under mounting climatic and anthropogenic pressures typical of semi-arid Mediterranean environments. Full article

To investigate the corrosion resistance of 60Si2MnA spring steel coated with Zn-Al in a domestic atmospheric environment containing harmful salts, the corrosion environmental factors (temperature, humidity, deposited salts, and pH) were obtained through field research. The deliquescence and weathering behavior of harmful salts were studied using impedance methods to establish their characteristic curves. Additionally, a self-designed salt deposition test apparatus was employed to conduct accelerated atmospheric corrosion tests under constant salt deposition (10 g/m²) and controlled temperature and humidity conditions (20 °C/75% RH and 40 °C/75% RH) over different corrosion periods. The results show that noticeable red rust appeared on the samples after one month of corrosion. As the temperature increased, the consumption of the coating accelerated. XRD and Raman analyses reveal that the main corrosion products of the coating materials were ZnO, Zn(OH)₂, and Zn₅(CO₃)₂(OH)₆, while the red rust primarily consisted of iron oxides and hydroxides. In the early stages of corrosion, the self-corrosion current density was relatively low due to the protective effects of the coating and the corrosion product layer, indicating good corrosion resistance. However, in the later stages, the integrity of the coating and the corrosion product layer deteriorated, leading to a significant increase in the self-corrosion current density and a decline in corrosion resistance. This study provides a data foundation for understanding the corrosion behavior of Zn-Al-coated spring steel in atmospheric environments and offers theoretical insights for developing more corrosion-resistant coatings and optimizing anti-corrosion measures. Full article

In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) reanalysis, TEMP and precipitation (PRE) from Climate Research Unit (CRU), and soil moisture (SM) and evapotranspiration (ET) from the Global Land Evaporation Amsterdam Model (GLEAM). In addition, four teleconnection indices were considered: El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). This study used the Mann–Kendall test and Sen’s slope estimator to analyze trends, alongside multiple linear regression to investigate the relationships between TEMP, RH, and key climatic variables—namely evapotranspiration (ET), soil moisture (SM), and precipitation (PRE)—as well as large-scale teleconnection indices (e.g., IOD, ENSO, PDO, and NAO) on annual and seasonal scales. The key findings are as follows: (1) mean annual TEMP exceeding 30 °C and RH less than 30% were concentrated in arid regions of the Sahelian–Sudano belt in West Africa (WAF), Central Africa (CAF) and North East Africa (NEAF). Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade⁻¹). Winter (0.27 °C decade⁻¹) and spring (0.20 °C decade⁻¹) exhibited the strongest warming, followed by autumn (0.18 °C decade⁻¹) and summer (0.10 °C decade⁻¹). (4) RH trends showed stronger seasonal decline compared to annual changes, with reduction ranging from 5 to 10% per decade in certain seasons, and about 2% per decade annually. (5) Pearson correlation analysis demonstrated a strong negative relationship between TEMP and RH with a correlation coefficient of r = − 0.60. (6) Significant associations were also observed between TEMP/RH and both climatic variables (ET, SM, PRE) and large scale-teleconnection indices (ENSO, IOD, PDO, NAO), indicating that surface conditions may reflect a combination of local response and remote climate influences. However, further analysis is needed to distinguish the extent to which local variability is independently driven versus being a response to large-scale forcing. Overall, this research highlights the physical mechanism linking TEMP and RH trends and their climatic drivers, offering insights into how these changes may impact different ecological and socio-economic sectors. Full article

Gas sensors can provide early warning of fires by detecting pyrolysis gas components in the sheaths of high-voltage cables. However, air humidity significantly affects the thermal decomposition gas production characteristics of the outer sheath of high-voltage cables, which in turn affects the accuracy of this warning method. In this paper, the thermal decomposition and gas production characteristics of the polyethylene (PE) outer jacket of high-voltage cables under different air humidities (20–100%) are studied, and the corresponding density functional theory (DFT) simulation calculations are performed using Gaussian 09W software. The results show that with the increase in humidity, the thermal decomposition gas yield of the PE outer jacket of high-voltage cables exhibits a decreasing trend. Under high-humidity conditions (≥68.28%RH), the generation of certain thermal decomposition gases is significantly reduced or even ceases. Meanwhile, the influence of moisture on the thermal decomposition characteristics of PE was analyzed at the micro level through simulation, indicating that the H-free radicals generated by moisture promote the initial decomposition of PE, but the subsequent combination of hydroxyl groups with terminal chain C forms a relatively stable alkoxy structure, increasing the activation energy of the reaction (by up to 44.7 kJ/mol) and thus inhibiting the generation of small-molecule gases. An experimental foundation is laid for the final construction of a fire warning method for high-voltage cables based on the information of thermal decomposition gas of the outer sheath. Full article

Nafion, while a benchmark proton exchange membrane (PEM) for fuel cells, suffers from significant performance degradation at elevated temperatures and low humidity due to dehydration and diminished mechanical stability. To address these limitations, this study investigated the development and characterization of Nafion nanocomposite membranes incorporating sulfonated silica layered materials (sSLMs). The inherent lamellar structure, high surface area, and abundant sulfonic acid functionalities of sSLMs were leveraged to synergistically enhance membrane properties. Our results demonstrate that sSLM incorporation significantly improved ion exchange capacity, water uptake, and dimensional stability, leading to superior water retention and self-diffusion at higher temperatures. Critically, the nanocomposite membranes exhibited remarkably enhanced proton conductivity, particularly under demanding conditions of 120 C and low relative humidity (i.e., 20% RH), where filler-free Nafion largely ceases to conduct. Single H₂/O₂ fuel cell tests confirmed these enhancements, with the optimal sSLM-Nafion nanocomposite membrane (N-sSLM₅) achieving a two-fold power density improvement over pristine Nafion at 120 C and 20% RH (340 mW cm⁻² vs. 117 mW cm⁻² for Nafion). These findings underscore the immense potential of sSLM as a functional filler for fabricating robust and high-performance PEMs, paving the way for the next generation of fuel cells capable of operating efficiently under more challenging environmental conditions. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

Ketones, which are key biomarkers of fat oxidation, are relevant for metabolic health maintenance and disease development, making continuous monitoring essential. In this study, we introduce a novel colorimetric sensor designed for potential continuous acetone detection in biological fluids. The sensor features a polydimethylsiloxane (PDMS) shell that encapsulates a sensitive and specific liquid-core acetone-sensing probe. The microsphere sensors were characterized by evaluating their size, PDMS shell thickness, colorimetric response, and sensitivity under realistic conditions, including 100% relative humidity (RH) and CO₂ interference. The microsphere size and sensor sensitivity can be controlled by modifying the fabrication parameters. Critically, the sensor showed high selectivity for acetone detection, with negligible interference from CO₂ concentrations up to 4%. In addition, the sensor displayed good reproducibility (CV < 5%) and stability under realistic storage conditions (over two weeks at 4 °C). Finally, the accuracy of the microsphere sensor was validated against a gold standard gas chromatography-mass spectrometry (GC-MS) method using simulated and real breath samples from healthy individuals and type 1 diabetes patients. The correlation between the microsphere sensor and GC-MS produced a linear fit with a slope of 0.948 and an adjusted R-squared value of 0.954. Therefore, the liquid-core microsphere-based sensor is a promising platform for acetone body fluid analysis. Full article

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO₂), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO₂ measurement is activated when the specified T and $RH$ change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and $RH$ signals, the number of CO₂ measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO₂ measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO₂ concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Full article

It is a well-known fact that proper nutrition is essential for human beings to live healthy lives. For thousands of years, it has been considered that dates are one of the best nutrient providers. To have better-quality dates and to enhance the shelf life of dates, it is vital to preserve dates in optimal conditions that contribute to food security. Hence, it is crucial to know the shelf life of different types of dates. In current practice, shelf life assessment is typically based on manual visual inspection, which is subjective, error-prone, and requires considerable expertise, making it difficult to scale across large storage facilities. Traditional cold storage systems, whilst being capable of monitoring temperature and humidity, lack the intelligence to detect spoilage or predict shelf life in real-time. In this study, we present a novel IoT-based shelf life estimation system that integrates multichannel gas sensors and a lightweight machine learning model deployed on an edge device. Unlike prior approaches, our system captures the real-time emissions of spoilage-related gases (methane, nitrogen dioxide, and carbon monoxide) along with environmental data to classify the freshness of date fruits. The model achieved a classification accuracy of 91.9% and an AUC of 0.98 and was successfully deployed on an Arduino Nano 33 BLE Sense board. This solution offers a low-cost, scalable, and objective method for real-time shelf life prediction. This significantly improves reliability and reduces postharvest losses in the date supply chain. Full article

Herein, a novel super-hygroscopic material, steam-exploded modified corn stalk pith (SE-CSP), was developed from corn stalk pith (CSP) via the steam explosion (SE) method, and its hygroscopic properties and mechanisms were evaluated. The results confirmed that SE effectively removed lignin and hemicellulose, disrupted the thin cell walls of natural CSP, and formed an aligned porous structure with capillary channels. SE changed the bonding distribution and surface morphology, and enhanced the crystallinity and thermal stability of CSP. The equilibrium hygroscopic percentage of SE-CSP (62.50%) was higher than that of CSP (44.01%) at 25 °C and 80% relative humidity (RH), indicating significantly greater hygroscopicity. The hygroscopic process of SE-CSP followed a Type III isotherm and fitted the Guggenheim–Anderson–de Boer (GAB), Peleg, and pseudo-first-order kinetic models. This process exhibited multi-layer adsorption with enthalpy-driven, exothermic behavior, primarily through physical adsorption involving hydrogen bonds and van der Waals forces. This work offered a new approach for advancing sorption dehumidification technology. Full article

This article analyses the main effect and interaction of thermal modification, wood moisture content, frequency, and vibration direction on the damping of spruce wood. Samples were thermally modified at three different temperatures (180 °C, 200 °C, and 230 °C) and then equilibrated at four different relative humidities (RHs) (20%, 44%, 76%, and 88%). The specimens were then freely supported and excited with a hammer to vibrate freely. Damping at the frequencies of the first three bending vibration modes for vibrations in the radial (LR plane) and tangential (LT plane) directions was determined using the wavelet transform method, which enables a decoupling of the vibration modes and thus a precise and accurate determination of the damping values. Damping increases with the wood moisture content for different modification levels, whereby the damping in the LR vibration direction plane differs from the damping in the LT vibration direction plane. For an unmodified sample and at frequency at the first vibration mode, damping in the radial plane is greater than in the tangential plane, but the relationships change with RHs, modification levels, and vibration direction planes. The dependence of damping on various factors has a strong influence on the calculation of various acoustic indicators, where damping of the wood is considered for the calculation, since damping for the same sample differs depending on the direction of vibration and the frequencies at different vibration modes. Full article