Search Results (56)

Water vapor plays a crucial role in maintaining global energy balance and water cycle, and it is closely linked to various meteorological disasters. Precipitable water vapor (PWV), as an indicator of variations in atmospheric water vapor content, has become a key parameter for meteorological and climate monitoring. However, due to limitations in observation costs and technology, traditional atmospheric monitoring techniques often struggle to accurately capture the distribution and variations in space–time water vapor. With the continuous advancement of Global Navigation Satellite System (GNSS) technology, ground-based GNSS monitoring technology has shown rapid development momentum in the field of meteorology and is considered an emerging monitoring tool with great potential. Hence, based on the GNSS observation data from July 2023, this study retrieves PWV using the Global Pressure and Temperature 3 (GPT3) model and evaluates its application performance in the “7·31” extremely torrential rain event in Beijing in 2023. Research has found the following: (1) Tropospheric parameters, including the PWV, zenith tropospheric delay (ZTD), and zenith wet delay (ZWD), exhibit high consistency and are significantly affected by weather conditions, particularly exhibiting an increasing-then-decreasing trend during rainfall events. (2) Through comparisons with the PWV values through the integration based on fifth-generation European Centre for Medium-Range Weather Forecasts (ERA-5) reanalysis data, it was found that results obtained using the GPT3 model exhibit high accuracy, with GNSS PWV achieving a standard deviation (STD) of 0.795 mm and a root mean square error (RMSE) of 3.886 mm. (3) During the rainfall period, GNSS PWV remains at a high level (>50 mm), and a strong correlation exists between GNSS PWV and peak hourly precipitation. Furthermore, PWV demonstrates the highest relative contribution in predicting extreme precipitation, highlighting its potential value for monitoring and predicting rainfall events. Full article

Ammonia (NH₃) detection in liquids and biological fluids is essential for monitoring environmental contamination and industrial processes, ensuring food safety, and diagnosing health conditions. Existing detection techniques are often unsuitable for point-of-care (POC) use due to limitations including complex sample handling, lack of portability, and poor compatibility with miniaturized systems. This study introduces a proof-of-concept for a compact, portable device tailored for POC detection of gaseous ammonia released from liquid samples. The device combines a polyaniline (PANI)-based chemoresistive sensor with interdigitated electrodes and a resistance readout circuit, enclosed in a gas-permeable hydrophobic membrane that permits ammonia in the vapor phase only to reach the sensing layer, ensuring selectivity and protection from liquid interference. The ink formulation was optimized. PANI nanoparticle suspension exhibited a monomodal, narrow particle size distribution with an average size of 120 nm and no evidence of larger aggregates. A key advancement of this device is its ability to limit the impact of water vapor, a known source of interference in PANI-based sensors, while maintaining a simple sensor design. A tailored signal processing strategy was implemented, extracting the slope of resistance variation over time as a robust metric for ammonia quantification. The sensor demonstrated reliable performance across a concentration range of 1.7 to 170 ppm with strong logarithmic correlation (R² = 0.99), and very good linear correlations in low (R² = 0.96) and high (R² = 0.97) subranges. These findings validate the feasibility of this POC platform for sensitive, selective, and practical ammonia detection in clinical and environmental applications. Full article

This work presents the recent development of a fiber-coupled multipass near-infrared (NIR) gas sensor used to monitor water vapor desorption of small material coupons. The gas sensor design employs a White cell topology to maximize the optical path length over a compact, hand-size footprint. Water vapor concentrations are quantified over a large dynamic range by simultaneously applying wavelength modulation and tunable diode laser absorption spectroscopy techniques. A custom headspace optimized for material desorption experiments is assembled using commercially available vacuum chamber components. We provide in situ measurements of water vapor desorption from two geometries of the industrially important silicone elastomer Sylgard-184 as a case study for sensor viability. To corroborate the results, the gas sensor data are compared to numerical simulations based on a triple-mode diffusion–sorption model, consisting of Henry, Langmuir, and Pooling modes. Full article

Tunable diode laser absorption spectroscopy (TDLAS) of water vapor transitions has been used to effectively measure temperature under high temperature and pressure conditions. However, due to the weak variation in transmittance and low signal-to-noise ratio, applying the same technique to measure temperature in near-ambient environments is difficult. This study reports the rapid and accurate measurement of near-ambient temperature through monitoring water vapor transitions with a three-point measurement method based on TDLAS. The transmission spectra of two selected water vibrational transitions at 1389.01 and 1389.89 nm are investigated, and the monotonic variations in the dip area are validated both theoretically and experimentally. The results show that by using the proper regression parameter (Ratio_dipA/Ratio_dipB)², the temperature measurement time can be reduced to 40 s, with an uncertainty as low as 0.39 °C and a p-value as small as 1.98 × 10⁻¹³. This work contributes to rapid and accurate non-invasive temperature measurement in near-ambient complex environments. Full article

Ensuring secure and efficient water level monitoring is critical for the intelligent management of hydropower plants, especially in challenging indoor environments. Existing methods, which are tailored for open areas with optimal conditions (adequate lighting, absence of debris interference, etc.), frequently falter in scenarios characterized by poor lighting, water vapor, and confined spaces. To address this challenge, this study introduces a robust indoor water level monitoring framework specifically for hydropower plants. This framework integrates a temporal super-resolution technique with an improved Yolov5 model. Specifically, to enhance the quality of indoor monitoring images, we propose a temporal super-resolution enhancement module. This module processes low-resolution water-level images to generate high-resolution outputs, thereby enabling reliable detection even in suboptimal conditions. Furthermore, unlike existing complex model-based approaches, our enhanced, lightweight Yolov5 model, featuring a small-scale feature mapping branch, ensures real-time monitoring and accurate detection across a variety of conditions, including daytime, nighttime, misty conditions, and wet surfaces. Experimental evaluations demonstrate the framework’s high accuracy, reliability, and operational efficiency, with recognition speeds reaching O(n). This approach is suitable for deployment in emerging intelligent systems, such as HT-for-Web analysis software 0.2.3 and warning platforms, providing vital support for hydropower plant security and emergency management. Full article

The monitoring of acetone in exhaled breath is expected to provide a noninvasive and painless method for dynamic monitoring of summarized physiological metabolic status during obesity treatment. Although the commonly used Mass Spectrometry (MS) technology has high accuracy, the long detection time and large equipment size limit the application of daily bedside detection. As for the real-time and accurate detection of acetone, the gas sensor has become the best choice of gas detection technology, but it is easy to be disturbed by water vapor in breath gas. An integrated breath gas detection system based on cavity ring-down spectroscopy (CRDS) is reported in this paper, which is a laser absorption spectroscopy technique with high-sensitivity detection and absolute quantitative analysis. The system uses a 266 nm single-wavelength ultraviolet laser combined with a breath gas pretreatment unit to effectively remove the influence of water vapor. The ring-down time of this system was 1.068 μs, the detection sensitivity was 1 ppb, and the stability of the system was 0.13%. The detection principle of the integrated breath gas detection system follows Lambert–Beer’s law, which is an absolute measurement with very high detection accuracy, and was further validated by Gas Chromatography–Mass Spectrometer (GC-MS) testing. Significant differences in the response of the integrated breath gas detection system to simulated gases containing different concentrations of acetone indicate the potential of the system for the detection of trace amounts of acetone. Meanwhile, the monitoring of acetone during obesity treatment also signifies the feasibility of this system in the dynamic monitoring of physiological indicators, which is not only important for the optimization of the obesity treatment process but also promises to shed further light on the interaction between obesity treatment and physiological metabolism in medicine. Full article

Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction. Full article

Sunflower husk (SFH) contributes 45–60% of the total sunflower seed weight and is a by-product of the sunflower oil industry. Among other elements, SFH ash contains K, Na, Ca and Mg. These elements cause rapid growth of ash deposits on convective heating surfaces of the boiler, resulting in reduced efficiency. The aim of this paper is to examine the possibility of producing quality fuel from SFH by its pretreatment with the technique of torrefaction in a fluidized bed in superheated water vapor. Continuous monitoring of the innovative SFH torrefaction process allowed for the determination of optimal process durations. SFH could be converted into a biofuel, having high calorific value and suitable characteristics for co-combustion with coal. Furthermore, the torrefaction in a fluidized bed of superheated water vapor allowed for a 6-fold reduction in the required process duration in comparison with data reported from the literature for the process of torrefaction in a dense bed, along with a 3-fold reduction in the chlorine content in SFH ash. These effects are beneficial to resolve the problem of corrosion on convective heating surfaces of boilers. However, torrefaction in superheated water vapor did not significantly reduce the content of alkaline and alkaline-earth elements in SFH ash. Still, this issue may be alleviated by significantly increasing the duration of SFH pretreatment. Full article

Researchers are depending more than ever on remote sensing techniques to monitor and assess the agricultural water status, as well as to estimate crop water usage or crop actual evapotranspiration. In the current work, normal and stressed baselines for irrigated wheat genotypes were developed in an arid part of the Sohag governorate, Egypt, using infrared thermometry in conjunction with weather parameters. The experiment was carried out in a randomized complete block design in the normal and drought stress conditions based on three replicates using ten bread wheat genotypes (G1–G10), including five accessions, under drought stress. A standard Class-A-Pan in the experimental field provided the daily evaporation measurements (mm/day), which was multiplied by a pan factor of 0.8 and 0.4 for normal and stressed conditions, respectively. The relationship between the vapor pressure deficit (VPD) and canopy-air temperature differences (Tc − Ta) was plotted under upper (fully stressed) and lower baseline (normal) equations. Accordingly, the crop water stress indexes (CWSIs) for the stressed and normal baselines for wheat genotypes were developed. Additionally, the intercept (b) and the slope (a) of the lower baseline equation were computed for different genotypes. The results indicate that, before applying irrigation water, the CWSI values were high in both growing seasons and under all irrigation regimes. After that, the CWSI values declined. G10 underwent stress treatment, which produced the greatest CWSI (0.975). Conversely, the G6 condition that received well-watered irrigation yielded the lowest result (−0.007). When compared to a well-watered one, the CWSI values indicated a trend toward rising stress. There existed an inverse link between the CWSI and grain yield (GY); that is, a lower CWSI resulted in better plant water conditions and a higher GY. Under standard conditions, the wheat’s highest GY was recorded in G2, 8.36 Ton/ha and a WCSI of 0.481. In contrast, the CWSI result for the stress treatment was 0.883, indicating a minimum GY of 5.25 Ton/ha. The Water Use Efficiency (WUE) results demonstrated that the stress irrigation regime produced a greater WUE value than the usual one. This study makes a significant contribution by investigating the techniques that would allow CWSI to be used to estimate irrigation requirements, in addition to determining the irrigation time. Full article

The carbon source/sink nature and the water balance of a drip-irrigated and mulched watermelon cultivated under a semi-arid climate were investigated. Biodegradable films, plants and some fruits were left on the soil as green manure. The study spanned from watermelon planting to the subsequent crop (June–November 2023). The eddy covariance technique was employed to monitor water vapor (H₂O) and carbon dioxide (CO₂) fluxes, which were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method.This method utilizesthe Monin–Obukhov similarity theory to separate stomatal (photosynthesis and transpiration) from non-stomatal (respiration and evaporation) processes. The results indicate that mulching films contribute to carbon sequestration in the soil (+19.3 g C m⁻²). However, the mulched watermelon crop presented in this study functions as a net carbon source, with a net biome exchange, representing the net rate of C accumulation in or loss from ecosystems, equal to +230 g C m⁻². This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 g C m⁻²). These findings recommend further research to enhance the sustainability of this crop in terms of both water and carbon balances. Full article

Global Navigation Satellite System (GNSS) signals experience delays when passing through the atmosphere due to the presence of free electrons in the ionosphere and air density in the non-ionized part of the atmosphere, known as the troposphere. The Precise Point Positioning (PPP) technique demonstrates highly accurate positioning along with Zenith Tropospheric Delay (ZTD) estimation. ZTD estimation is valuable for various applications including climate modelling and determining atmospheric water vapor. Current GNSS network resolutions are not completely sufficient for the scale of a few kilometres that regional climate and weather models are increasingly adopting. The Centipede-RTK network is a low-cost option for increasing the spatial resolution of tropospheric monitoring. This study is motivated by the question of whether low-cost GNSS networks can provide a viable alternative without compromising data quality or precision. This study compares the performance of the low-cost Centipede-RTK network in calculating the Zenith Tropospheric Delay (ZTD) to that of the existing EUREF Permanent Network (EPN), using two alternative software packages, RTKLIB demo5 version and CSRS-PPP version 3, to ensure robustness and software independence in the findings. This investigation indicated that the ZTD estimations from both networks are almost identical when processed by the CSRS-PPP software, with the highest mean difference being less than 3.5 cm, confirming that networks such as Centipede-RTK could be a reliable option for dense precise atmospheric monitoring. Furthermore, this study revealed that the Centipede-RTK network, when processed using CSRS-PPP, provides ZTD estimations that are very similar and consistent with the EUREF ZTD product values. These findings suggest that low-cost GNSS networks like Centipede-RTK are viable for enhancing network density, thus improving the spatial resolution of tropospheric monitoring and potentially enriching climate modelling and weather prediction capabilities, paving the way for broader application and research in GNSS meteorology. Full article

This research aims to delve into the intricacies of combustion processes, specifically focusing on heating oil and a blend of heating oil with Tire Pyrolysis Oil (TPO) in a self-developed evaporative combustion chamber featuring steam injection. The primary objective is to scrutinize the impact of steam injection on the combustion dynamics. Conducting a series of tests, the investigation involved the meticulous manipulation of stoichiometric ratios while introducing ambient air through gravity fuel flow. Subsequent iterations of these tests incorporated the introduction of steam into the ambient air stream. The examination encompassed the combustion of both heating oil and the TPO blend within the combustion chamber. The evaluation criteria comprised an in-depth analysis of flame characteristics, temperature distribution within the combustion chamber, and the quantification of emissions such as particulate matter (PM), nitrogen oxides (NO_x), carbon dioxide (CO₂), carbon monoxide (CO), and water vapor (H₂O). Throughout the experimentation phase, commercially available diesel fuel served as the primary fuel source. To facilitate the tests, the combustion chamber under scrutiny was seamlessly integrated into an AVL engine test bench system. Essential parameters, including fuel consumption, were meticulously gauged using an AVL 735 fuel flow meter, while fuel temperature was monitored using the AVL 745 fuel temperature conditioning system. The intake air, a crucial element in the combustion process, was quantified with precision using an AVL Flowsonix sensor. Emission measurements were conducted meticulously using state-of-the-art equipment, with gaseous emissions analyzed using an AVL FTIR AMA i60 exhaust gas analyzer. Simultaneously, soot emissions were quantified through employment of an AVL Micro Soot sensor. This comprehensive approach not only delves into the fundamental aspects of combustion but also extends its reach to the exploration of innovative techniques, such as steam injection, to enhance combustion efficiency and reduce emissions. The integration of advanced measurement tools ensures a robust and thorough analysis of the combustion process and its environmental implications. Full article

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC–carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings. Full article

Currently, it remains a challenge to effectively monitor areas experiencing intense precipitation and the associated atmospheric conditions on a global scale. This challenge arises due to the limitations on both active and passive remote sensing methods. Apart from the lack of observations in remote areas, the quality of some observations deteriorates when heavy precipitation is present, making it difficult to obtain highly accurate measurements of the thermodynamic parameters driving these weather events. However, there is a promising solution in the form of the Global Navigation Satellite System (GNSS) Polarimetric Radio Occultation (PRO) technique. This approach provides a way to assess the large-scale bulk-hydrometeor characteristics of regions with heavy precipitation and the meteorological conditions associated with them. PRO offers vertical profiles of atmospheric variables, including temperature, pressure, water vapor pressure, and information about hydrometeors, all in a single fine-vertical resolution observation. To continue validating the PRO technique, we make use of polarimetric weather data from Next Generation Weather Radars (NEXRAD), focusing on comparing specific differential phase shift ($K_{dp}$) structures to PRO observable differential phase shift ($\Delta \Phi$). We have seen that PAZ and NEXRAD exhibit a good agreement on the vertical structure of the observable $\Delta \Phi$ and that their combination could be useful for enhancing our understanding of the microphysics underlying heavy precipitation events. Full article

Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R² = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R² = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions. Full article