Environmental Humidity Changes Research Articles

Study on the impact of humidity on dust dispersion in open-pit mining under different environmental temperatures

To investigate the impact of changes in environmental temperature and humidity on the dust migration patterns during the open-pit mining and loading process, a 1:1 simulation model of open-pit mining and loading was established using SCDM software, based on actual working conditions. Fluent software was used to study the airflow velocity field and dust migration patterns under different temperature and humidity conditions. The simulation results were validated by on-site measurements of dust concentration, with the error values at selected measurement points all within 20%. The results indicate that as relative humidity increases, the dust dispersion capacity decreases, with a more significant reduction in high-temperature environments. Dust concentration is higher in the plane at heights of 3 m to 6 m, and dust tends to accumulate within a range of approximately 10–15 m around the ore transport vehicle. As relative humidity increases from 20% to 80%, the dust concentration within the height range of 3–6 m gradually decreases. At lower environmental temperatures, dust is less likely to settle and tends to move upwards above 6 m. As the environmental temperature increases, dust tends to settle near the ore transport vehicle.

Design of low-cost humidity sensor based on chipless RFID

It is of great significance to design a low-cost chipless radio frequency identification(RFID) sensor for environmental humidity monitoring. To this end, polyvinyl alcohol (PVA) film is used as a humidity sensitive material, and the overall size of the rectangular substrate is 18 mm × 18 mm ×0.5 mm. Through the humidity sensing principle and simulation analysis, the change of environmental humidity causes the change of the dielectric constant of the humidity sensitive material PVA, which in turn affects the resonant frequency offset of the entire sensor. The simulation results show that the relative humidity working range of the designed humidity sensor is 21.9% ∼ 52.5%, the corresponding sensor resonant frequency range is 2.76 ∼ 2.51GHz, the total offset reaches 250 MHz, and the average sensitivity at the maximum relative humidity is 23.08MHz/% . The designed humidity sensor has the advantages of miniaturization, simple structure and low cost, and can be used for humidity monitoring in various target environments. Keywords.low cost; chipless RFID humidity sensor; polyvinyl alcohol; humidity monitoring; humidity sensitive material.

Diurnal humidity cycle driven selective ion transport across clustered polycation membrane

The ability to manipulate the flux of ions across membranes is a key aspect of diverse sectors including water desalination, blood ion monitoring, purification, electrochemical energy conversion and storage. Here we illustrate the potential of using daily changes in environmental humidity as a continuous driving force for generating selective ion flux. Specifically, self-assembled membranes featuring channels composed of polycation clusters are sandwiched between two layers of ionic liquids. One ionic liquid layer is kept isolated from the ambient air, whereas the other is exposed directly to the environment. When in contact with ambient air, the device showcases its capacity to spontaneously produce ion current, with promising power density. This result stems from the moisture content difference of ionic liquid layers across the membrane caused by the ongoing process of moisture absorption/desorption, which instigates selective transmembrane ion flux. Cation flux across the polycation clusters is greatly inhibited because of intensified charge repulsion. However, anions transport across polycation clusters is amplified. Our research underscores the potential of daily cycling humidity as a reliable energy source to trigger ion current and convert it into electrical current.

Managing Green and Sustainable Technologies: Climate-Informed Corrosion Prediction for Steel Structures

The unpredictability of atmospheric circumstances is one of the major elements that contribute to the capability to anticipate the corrosion growth in metal structures over time accurately. Climate shifts can potentially modify the long-term attributes of these factors throughout the operational life of metal structures, both those currently in existence and those newly developed. The impact of climate irregularity on the probabilistic nature of atmospheric variables, which significantly impact corrosion situations, can add intricacy to corrosion predictions in these constructions. This project presents an incorporated framework to quantify the impact of climate alteration on the corrosion rates of steel structures in Jordan. It considers the changes in environmental conditions, specifically temperature, relative humidity, and wind speed, and their impacts on atmospheric corrosion. Global Climate Models are employed to assess the long-term effects of climate transformation on these environmental circumstances. An analytical model for anticipating corrosion rate is integrated with climate transformation models to predict modifications in the corrosion rates of steel parts relative to historical situations. This project also examines the impact of climate transformation on the fluctuations of these climatic parameters and offers a contrast between historical data and projected conditions across the country. The findings indicate a significant increase in corrosion rates across Jordan, which calls for localized green building codes and standards to ensure that future infrastructure is sustainable and capable of withstanding the new climatic norms. This approach addresses the immediate challenges posed by climate change and contributes to the broader goals of sustainable urban development and managing green technology adoption in Jordan. Doi: 10.28991/CEJ-2024-010-08-016 Full Text: PDF

Cellulose nanocrystals-based optical organohydrogel fiber with customizable iridescent colors for strain and humidity response

Stimuli-responsive optical hydrogels are widely used in various fields including environmental sensing, optical encryption, and intelligent display manufacturing. However, these hydrogels are susceptible to water losses when exposed to air, leading to structural damage, significantly shortened service lives, and compromised durability. This study presents mechanically robust, environmentally stable, and multi-stimuli responsive optical organohydrogel fibers with customizable iridescent colors. These fibers are fabricated by incorporating tunicate cellulose nanocrystals, alginate, and acrylamide in a glycerol-water binary system. The synthesized fibers exhibit high strength (1.38 MPa), moisture retention capabilities, and elastic properties. Furthermore, a sensor based on these fibers demonstrates high- and low-temperature resistance along with stimuli-responsive characteristics, effectively detecting changes in environmental humidity and strains. Moreover, the fiber sensor demonstrates continuous, repeatable, and quantitatively predictable moisture discoloration responses across a humidity range of 11 % and 98 %. During strain sensing, the optical-organohydrogel-based sensor demonstrates a large working strain (50 %) and excellent cycling stability, underscoring its potential for effectively monitoring a wide range of intricate human motions. Overall, the synthesized fibers and their simple fabrication method can elicit new avenues for numerous related applications including the large-scale implementation of advanced wearable technology.

Simultaneous measurement of temperature and humidity using a dual-parameter sensor based on SPR and no-core fiber technology

In this article, we present a surface plasmon resonance (SPR) sensor based on a no-core fiber (NCF) structure for simultaneous measurement of temperature and humidity. The sensor is simulated by depositing a silver film on the exterior of the NCF by magnetron sputtering, followed by the application of a composite thin film of polyvinyl alcohol (PVA) and polydimethylsiloxane (PDMS). This configuration induces SPR resonance phenomena at two distinct wavelengths, resulting in the splitting of the resonance peak into two distinct peaks, enabling simultaneous measurement of temperature and humidity. To achieve optimal sensor performance, the thickness of the PDMS-PVA composite film, the proportion of sensitive materials, the thickness of the silver film, and the structural parameters of the fiber were optimized. Simulation results show that the sensor exhibits a humidity sensitivity of 8.60 nm/%RH over a relative humidity (RH) range of 50%–100%. The highest temperature detection sensitivity achieved is 7.40 nm °C−1. This sensor holds great potential for applications in monitoring changes in environmental temperature and humidity.

Multi-factors-regulated memristor based on Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 for artificial neural network

The memristor, characterized by its resistive switching (RS) behavior, has garnered significant interest within the scientific community, particularly because of its vast potential applications in the fields of artificial intelligence (AI) and information storage. This is attributed to its unique properties, which align well with the requirements of advanced computational and memory systems. Ferroelectric memristors are currently a thriving area of research, and this study uses Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 (Sm-PMN-PT) and polyvinylidene difluoride (PVDF) as the functional layer. A multi-factor responsive memristor based on a Ag/Sm-PMN-PT:PVDF/ITO sandwich structure is fabricated, for which the RS behavior of the memristor can be adjusted by multi-factors such as voltage scanning rate, bias voltage amplitude, temperature and environmental humidity. Specifically, this device is sensitive to changes in environmental humidity and exhibits the properties of an artificial neural synapse. These advantageous characteristics endow this device with great potential for use in environmental sensors and artificial neural network (ANN) systems.

Effects of environmental humidity on the gap of mortise-tenon joints: Experimental and numerical investigations

Mortise-tenon (MT) joints play a vital role in the lateral deformability and seismic performance of traditional timber structures. However, moisture exposure can cause shrinkage-induced gaps and then affect the durability of MT joints. Limited knowledge exists on the humidity response of MT joints. To investigate the effect of environmental humidity on the gaps between mortise and tenon, multiple measurements of moisture content (MC) and shrinkage deformation were conducted on straight MT joints that were subjected to humidity variations. Different ambient conditions and exposure levels were taken into account. Three-dimensional numerical models were established to simulate the humidity response of MT joints. Model validation was then performed by comparing the simulation and experimental results in terms of moisture distribution and gaps. On this basis, quantitative analysis was made to reveal the development mechanism and main influence factors of moisture-induced gaps. Results show that straight MT joints are vulnerable to environmental humidity changes. The observed gap is estimated to cause a decrease of up to 23.1% in the initial elastic stiffness. Although only contributed by the shrinkage deformation of tenons, the vertical gap is theoretically larger than the horizontal one which is attributed to a combination of both mortise and tenon. A clear linear relationship is found between the vertical gaps and the MC decrease, with the product of the radial shrinkage coefficient and tenon height as the theoretical ratio. Controlling the moisture exposure is considered the most effective way to reduce moisture-induced gaps in MT joints.

Strain- and humidity-insensitive, stretchable hydrogel-based oxygen sensor with corrosion-free electrodes for wireless oxygen detection

As the public puts higher and higher demands on wearing comfort, wearable gas sensors rapidly advance toward intrinsic flexibility and stretchability. However, many challenges arise during the development of intrinsically stretchable sensors. The deformation of the sensor following the movement of the wearer and uncertain changes in environmental humidity can cause additional interference signals. In addition, severe electrode corrosion also challenges the long-term continuous monitoring of sensors. Here, a stretchable strain- and humidity-insensitive O2 sensor is proposed by adopting a serpentine hydrogel fiber structure and hydrophobic elastomer encapsulation strategy. Besides, the introduction of the Pt/C electrode and salt immersion strategy effectively eliminate electrode corrosion and electrolyte consumption, significantly prolonging hydrogel sensor's lifespan. Importantly, we propose, for the first time, an oxygen pump mechanism to elucidate positive current transferring and electrode corrosion-free in hydrogel oxygen sensors, which is further validated through well-designed experiments. Optimized sensor exhibits a wide detection range (30 ppm-100%), linear sensitivity (0.03%/ppm), exceptional repeatability and remarkable tolerance towards environmental variations. Integrating oxygen sensor with Bluetooth circuit further enables wireless monitoring of oxygen while confirming its practicality. This research provides insights into improving the interference immunity and longevity of flexible oxygen sensors while showcasing their potential in wearable applications.

Biological, physical and morphological factors for the programming of a novel microbial hygromorphic material

The urgency for energy efficient, responsive architectures has propelled smart material development to the forefront of scientific and architectural research. This paper explores biological, physical, and morphological factors influencing the programming of a novel microbial-based smart hybrid material which is responsive to changes in environmental humidity. Hygromorphs respond passively, without energy input, by expanding in high humidity and contracting in low humidity. Bacillus subtilis develops environmentally robust, hygromorphic spores which may be harnessed within a bilayer to generate a deflection response with potential for programmability. The bacterial spore-based hygromorph biocomposites (HBCs) were developed and aggregated to enable them to open and close apertures and demonstrate programmable responses to changes in environmental humidity. This study spans many fields including microbiology, materials science, design, fabrication and architectural technology, working at multiple scales from single cells to ‘bench-top’ prototype.Exploration of biological factors at cellular and ultracellular levels enabled optimisation of growth and sporulation conditions to biologically preprogramme optimum spore hygromorphic response and yield. Material explorations revealed physical factors influencing biomechanics, preprogramming shape and response complexity through fabrication and inert substrate interactions, to produce a palette of HBCs. Morphological aggregation was designed to harness and scale-up the HBC palette into programmable humidity responsive aperture openings. This culminated in pilot performance testing of a humidity-responsive ventilation panel fabricated with aggregated Bacillus HBCs as a bench-top prototype and suggests potential for this novel biotechnology to be further developed.

Deterioration of steel structures due to corrosion considering the global effects of climate change

The randomness of atmospheric conditions is among the key contributing factors that affect the ability to accurately predict the corrosion growth in steel structures. Climate change has the potential to alter the long-term characteristics of these factors over the lifespan of steel structures, both those already existing and those newly built. The impact of climate variability on the stochastic nature of atmospheric variables, which greatly influence corrosion conditions, can add complexity to corrosion predictions in these structures. This paper introduces an integrated framework to quantify the impact of climate change on corrosion rates of steel structures worldwide. It considers the changes in environmental conditions, specifically temperature, relative humidity and wind speed and their effects on atmospheric corrosion. Global Climate Models are employed to evaluate the long-term impacts of climate change on these environmental conditions. An analytical model for predicting corrosion rate is integrated with climate change models to predict alterations in the corrosion rates of steel components relative to historical conditions. This paper also discusses the impact of climate change on the variations of these climatic parameters and offers a comparison between historical data and projected conditions across the globe. The results indicate that the effect of climate change on the corrosion rate depends on the considered region. While regions such as Australia, North America and Europe see an increase, others such as Asia and Africa observe a decline. Notably, all oceans, especially the Antarctic and Arctic, experience a significant increase in corrosion by the end of the century.

Moisture actuated cobalt alginate discoloration artificial muscle

Artificial muscles have the ability to sense changes in the external environment and generate rapid and significant contraction deformation or rotational motion. However, most artificial muscles are limited to a single form of deformation, and there are still great difficulties in achieving complex dual response modes. Herein, a novel artificial muscle made of cobalt alginate fiber with both rotational/contraction deformation and discoloration functions was successfully prepared by a simple wet spinning and directional twisting methods. The wet-sensitive color change performance is achieved by the formation of different hydrates of cobalt ions in varying humidity conditions, while rotational motion is induced by radial untwisting resulting from fiber swelling upon moisture absorption. In addition, the artificial muscle fiber exhibited a transition from blue to purple to red as the environmental humidity changes from 35 % to 85 %. When the fibers were exposed to water, the twisted fiber with a twist count of 2500 truns provided a rotational speed of over 2000 rpm, and the actuating and discoloration performance did not significantly decrease in 50 repeated cycle tests. This stable shape and color dual response artificial muscle can simulate traditional windmill devices, while also providing a new design idea for smart textiles and humidity visualization devices.

Edible Mushroom Greenhouse Environment Prediction Model Based on Attention CNN-LSTM

The large-scale production of edible mushrooms typically requires the use of greenhouses, as the greenhouse environment significantly affects the growth of edible mushrooms. It is crucial to effectively predict the temperature, humidity, and carbon dioxide fluctuations within the mushroom greenhouse for determining the environmental stress and pre-regulation of edible mushrooms. To address the nonlinearity, temporal dynamics, and strong coupling of the edible mushroom greenhouse environment, a temperature, humidity, and carbon dioxide prediction model based on the combination of the attention mechanism, the convolutional neural network, and the long short-term memory neural network (A-CNN-LSTM) is proposed. Experimental data were collected from both the inside and outside of the greenhouse, including environmental data and the on–off data of environmental control devices. After completing missing data using linear interpolation, denoising with Kalman filtering, and normalization, the recurrent neural network (RNN) model, long short-term memory (LSTM) model, and A-CNN-LSTM model were trained and tested on the time series data. These models were used to predict the environmental changes in temperature, humidity, and carbon dioxide inside the greenhouse. The results indicate that the A-CNN-LSTM model outperforms the other two models in terms of denoising, non-denoising, and different prediction time steps. The proposed method accurately predicts temperature, humidity, and carbon dioxide levels with errors of 0.17 °C (R2 = 0.974), 2.06% (R2 = 0.804), and 8.367 ppm (R2 = 0.993), respectively. These results indicate improved prediction accuracy for temperature, humidity, and carbon dioxide values inside the edible mushroom greenhouse. The findings provide a decision basis for the precise control of the greenhouse environment.

Analysis of Changes in Environmental Temperature and Humidity and Exploration of Protection Strategies for Ancient Building Groups

Analysis of Changes in Environmental Temperature and Humidity and Exploration of Protection Strategies for Ancient Building Groups

Managing genomic diversity in conservation programs of Chinese domestic chickens

BackgroundEffective conservation and utilization of farm animals are fundamental for realizing sustainable increases in food production. In situ and ex situ conservation are the two main strategies that are currently used to protect the genetic integrity of Chinese domestic chicken breeds. However, genomic diversity and population structure have not been compared in these conserved populations.ResultsThree hundred and sixty-one individuals from three Chinese domestic chicken breeds were collected from populations conserved in situ and ex situ and genotyped using genotyping-by-sequencing (GBS). First, we used different parameters based on heterozygosity, genomic inbreeding, and linkage disequilibrium to estimate the genomic diversity of these populations, and applied principal component analysis (PCA), neighbor-joining tree, and ADMIXTURE to analyze population structure. We found that the small ex situ conserved populations, which have been maintained in controlled environments, retained less genetic diversity than the in situ conserved populations. In addition, genetic differentiation was detected between the in situ and ex situ conserved populations of the same breed. Next, we analyzed signatures of selection using three statistical methods (fixation index (FST), nucleotide diversity (Pi), and cross-population extended haplotype homozygosity (XP-EHH) to study the genetic footprints that underlie the differentiation between in situ and ex situ conserved populations. We concluded that, in these small populations, differentiation might be caused by genetic drift or by mutations from the original populations. The differentiation observed in the population of Beijing You chicken probably reflects adaptation to environmental changes in temperature and humidity that the animals faced when they were moved from their place of origin to the new site for ex situ conservation.ConclusionsConservation programs of three Chinese domestic chicken breeds have maintained their genomic diversity to a sustainable degree. The small ex situ conserved populations, which are maintained in controlled environments, retain less genetic diversity than populations conserved in situ. In addition, the transfer of populations from their place of origin to another site for conservation purposes results in genetic differentiation, which may be caused by genetic drift or adaptation. This study provides a basis for further optimization of in situ and ex situ conservation programs for domestic chicken breeds in China.

Harvesting of aerial humidity with natural hygroscopic salt excretions.

Plants and animals that thrive in arid regions utilize the diurnal changes in environmental temperature and humidity to optimize their water budget by combining water-harvesting mechanisms and morphophysiological traits. The Athel tamarisk (Tamarix aphylla) is a halophytic desert shrub that survives in arid, hypersaline conditions by excreting concentrated solutions of ions as droplets on its surface that crystallize into salt crystals and fall off the branches. Here, we describe the crystallization on the surface of the plant and explore the effects of external conditions such as diurnal changes in humidity and temperature. The salt mixtures contain at least ten common minerals, with NaCl and CaSO4·2H2O being the major products, SiO2 and CaCO3 main sand contaminants, and Li2SO4, CaSO4, KCl, K2Ca(SO4)2·H2O, CaMg(CO3)2 and AlNaSi3O8 present in smaller amounts. In natural conditions, the hanging or sitting droplets remain firmly attached to the surface, with an average adhesion force of 275 ± 3.5 µN measured for pure water. Rather than using morphological features of the surface, the droplets adhere by chemical interactions, predominantly by hydrogen bonding. Increasing ion concentration slightly increases the contact angle on the hydrophobic cuticle, thereby lowering surface wettability. Small amounts of lithium sulfate and possibly other hygroscopic salts result in strong hygroscopicity and propensity for deliquescence of the salt mixture overnight. Within a broader context, this natural mechanism for humidity harvesting that uses environmentally benign salts as moisture adsorbents could provide a bioinspired approach that complements the currently available water collection or cloud-seeding technologies.

A Flexible Triboelectric Nanogenerator for Bio-Mechanical Energy Harvesting and Basketball Self-Powered Sensing

Recently, intelligent wearable flexible sensors for sports monitoring have received extensive attention. Here, we designed a AGO hydrogel-based triboelectric nanogenerator (A-TENG) for bio-mechanical harvesting and basketball posture monitoring. The AGO hydrogel can be rapidly prepared only by a simple mechanical mixing method, and there is no need to heat and add cross-linking agent during the preparation process. Besides, the A-TENG has good environmental adaptability, and the change of environmental humidity will not have irreversible impact on it. Furthermore, according to results, when the external resistance is 9[Formula: see text]M[Formula: see text], the output power density of A-TENG device reaches a maximum of 3.1[Formula: see text]W/m2. The self-powered sensor based on A-TENG device can be used to monitor the basketball posture. In the future, the A-TENG device can have high application value in basketball training, in order to serve intelligent sports monitoring.

The Observed Effects of Cold Pools on Convection Triggering and Organization During DYNAMO/AMIE

AbstractCold pools that form from existing convection can generate new clouds nearby, a process that has been suggested to contribute to cloud organization. However, some disagreement exists on the role of cold pools in cloud organization over tropical oceans, and much remains to be understood on how this role depends on the state of large‐scale disturbances such as the Madden‐Julian Oscillation (MJO). This study addresses this question by examining the intraseasonal variability in the properties of cold pools and their effects on convection triggering using observations. The unique set of surface meteorology data and ground‐based radars deployed during the DYNAMO/AMIE field campaign are used to identify cold pools and the associated spatiotemporal evolution of rainfall. The results suggest that cold pools enhance rainfall at their expected speed of 3–8 m s−1, which includes the Doppler‐shifting effect by the background wind. The cold pools also enhance rainfall mainly within a 20 km radius, especially in the direction of boundary‐layer vertical wind shear. However, gravity waves appear to contribute predominantly to the propagation of convection over a wider range of distances due to their faster propagation speeds. The interface of convection triggering and propagation at different speeds of cold pools and gravity waves seems to contribute to cloud organization. The effectiveness of cold pools and gravity waves on cloud organization also depends highly on the MJO due to the combined effects of changes in environmental humidity and boundary to lower‐tropospheric wind.

The influence of environmental humidity change on the stability of rock caves in Longyou Grottoes

Silty sandstone grottoes are suffered climate changes. The Longyou Grottoes are typical argillaceous siltstone. This paper utilized the coupled humid-mechanical-damage model to investigate the influence of environmental humidity change on the deformation and stability of Cave 4 and Cave 5 in Longyou Grottoes. It is found that the change in environmental humidity significantly influences the stability of caves in Longyou Grottoes. The water saturation of surrounding rock changes with the increase or decrease of ambient humidity. The water swelling and dry shrinkage processes greatly affect the stress state and mechanical properties of the surrounding rock, resulting in micro-cracks which may form fractures that disturb the structural integrity and aggravate the geological hazard of weathering. The cave roof is the most dangerous position during the humidity variation. Pre-existing fissures act as shortcuts for humidity diffusion and stress generation. In turn, the fast diffusion rate and high stress cause further propagation of the fissures. Repairing pre-existing fissures and applying waterproof measurements on the inner wall of caves contributes to the stability and durability of the grottoes. This study improves the understanding of the relationship between the changes in environmental humidity and the weathering of grottoes and provides theoretical support for the subsequent disaster risk management and preservation of Longyou Grottoes.

Extreme low apparent temperature forecasting for early warning of mortality by data-driven deep learning

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low appar-ent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecast-ing methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.