Search Results (52)

As an important means to monitor atmospheric vertical temperature and humidity, the ground-based microwave radiometer has been widely used in environmental monitoring, climate prediction, and other fields, but its application in desert areas is particularly limited. At Minfeng Station on the southern edge of the Taklimakan Desert, Global Telecommunications System (GTS) detection technology was used to evaluate the microwave radiometer observations under different weather conditions and at different altitudes. The planetary boundary layer height (PBLH) was calculated using the potential temperature gradient method, and the planetary boundary layer results were calculated by analyzing dust and rainfall events. The results show that the determination coefficients (R²) of the overall observed temperature (T), specific humidity (q), and water vapor density (${\rho }_v$) of the microwave radiometer are all above 0.8 under different weather conditions. When the relative humidity is 0–10%, the temperature is the best, and the R² is 0.9819. When the relative humidity is 70–80%, the R² of q and ${\rho }_v$ is the best, and the R² is 0.9630 and 0.9777, respectively. This is in good agreement with the temperature observed by the FY–4A satellite; the observation effect is the best in May, and its R² is 0.9142. Under the conditions of clear sky, precipitation day, and dusty weather, the R² of the atmospheric boundary layer height calculated by the microwave radiometer is greater than 0.7 compared to the GTS sounding calculation results. These results demonstrate the reliability of microwave radiometry in extremely arid environments, providing valuable insights for boundary layer studies in desert regions. Full article

The impact of PM_2.5 on the environment and human health has garnered significant attention. While research on PM_2.5 composition is increasing, fewer studies have focused on how dusty conditions in a special region affect the PM_2.5 composition. This region’s unique environmental conditions, characterized by frequent dust events, complicate air quality management. The study investigates the seasonal distribution of inorganic elements in the PM_2.5 under both dusty and non-dusty conditions through systematic sampling. Selective screening methods identified key pollutant elements, and a respiratory system model was developed to examine their diffusion and deposition patterns in the upper respiratory tract. Key findings reveal that inorganic element concentrations in the PM_2.5 follow consistent seasonal trends, with significantly higher levels during dust events compared to non-dusty periods. Crustal elements are dominated in the PM_2.5, but non-metallic elements (Cl, S) and metallic/quasi-metallic elements (Mn, Cd, Cr, As, Hg) are also prevalent, likely influenced by anthropogenic activities and industrial emissions. By PCA with human health assessments, six characteristic pollutants were identified: As, Co, Cd, Cr, V, and Mn. Simulations using COMSOL Multiphysics 6.2 software demonstrated distinct behaviors: As tends to concentrate in the posterior regions of the respiratory tract, while Co and Cd exhibit relatively uniform distributions, primarily affecting areas where airflow slows upstream. Cr, V, and Mn show dispersed and uniform patterns. Notably, even during dusty conditions, the concentration of the six pollutants remains relatively low in the different parts of the upper respiratory tract, suggesting minimal immediate health impacts. Our study provides valuable insights into the behavior of inorganic elements in the PM_2.5 and their potential health implications, highlighting the need for further research on the effects of dusty conditions on air quality and public health. Full article

Safe and reliable lunar landings are crucial for future exploration of the Moon. The regolith ejected by a lander’s rocket exhaust plume represents a significant obstacle in achieving this goal. It prevents spacecraft from reliably utilizing their navigation sensors to monitor their trajectory and spot emerging surface hazards as they near the surface. As part of NASA’s 2024 Human Lander Challenge (HuLC), the team at the University of Michigan developed an innovative concept to help mitigate this issue. We developed and implemented a machine learning (ML)-based sensor fusion system, ARC-LIGHT, that integrates sensor data from the cameras, lidars, or radars that landers already carry but disable during the final landing phase. Using these data streams, ARC-LIGHT will remove erroneous signals and recover a useful detection of the surface features to then be used by the spacecraft to correct its descent profile. It also offers a layer of redundancy for other key sensors, like inertial measurement units. The feasibility of this technology was validated through development of a prototype algorithm, which was trained on data from a purpose-built testbed that simulates imaging through a dusty environment. Based on these findings, a development timeline, risk analysis, and budget for ARC-LIGHT to be deployed on a lunar landing was created. Full article

This study presents a cutting-edge imaging technique for special unmanned vehicles (UAVs) designed to enhance tunnel inspection capabilities. This technique integrates ghost imaging inspired by the human visual system with lateral inhibition and variable resolution to improve environmental perception in challenging conditions, such as poor lighting and dust. By emulating the high-resolution foveal vision of the human eye, this method significantly enhances the efficiency and quality of image reconstruction for fine targets within the region of interest (ROI). This method utilizes non-uniform speckle patterns coupled with lateral inhibition to augment optical nonlinearity, leading to superior image quality and contrast. Lateral inhibition effectively suppresses background noise, thereby improving the imaging efficiency and substantially increasing the signal-to-noise ratio (SNR) in noisy environments. Extensive indoor experiments and field tests in actual tunnel settings validated the performance of this method. Variable-resolution sampling reduced the number of samples required by 50%, enhancing the reconstruction efficiency without compromising image quality. Field tests demonstrated the system’s ability to successfully image fine targets, such as cables, under dim and dusty conditions, achieving SNRs from 13.5 dB at 10% sampling to 27.7 dB at full sampling. The results underscore the potential of this technique for enhancing environmental perception in special unmanned vehicles, especially in GPS-denied environments with poor lighting and dust. Full article

In smoggy and dusty environments, vision- and laser-based localization methods are not able to be used effectively for controlling the movement of a robot. Autonomous operation of a security robot can be achieved in such environments by using millimeter wave (MMW) radar for the localization system. In this study, an approximate center method under a sparse point cloud is proposed, and a security robot localization system based on millimeter wave radar is constructed. To improve the localization accuracy of the robot, inertial localization of the robot is integrated with MMW radar. Based on the concept of inertial localization, the state equation for the motion principle of the robot is deduced. According to principle of MMW localization, the measurement equation is derived, and a kinematics model of the robot is constructed. Further, by applying the Kalman filtering algorithm, a fusion localization system of the robot based on MMWs and inertial localization is proposed. The experimental results show that with iterations of the filtering algorithm, the gain matrix converges gradually, and the error of the fusion localization system decreases, leading to the stable operation of the robot. Compared to the localization system with only MMW radar, the average localization error is approximately reduced from 11 cm to 8 cm, indicating that the fusion localization system has better localization accuracy. Full article

Dust deposition poses a significant challenge in the implementation of photovoltaic panels (PV) especially in hot and dusty environments, such as the Middle East and North Africa (MENA) region. This issue leads to progressive degradation of PV efficiency and output power. In this context, this research work aims to improve PV performance by developing self-cleaning sprays as a preventative solution. Different concentrations of SnO₂ and TiO₂ nanoceramics were dispersed in isopropyl alcohol solvent to reduce the mixture’s viscosity and facilitate smooth spraying on solar panels, whose efficiency was continually assessed in outdoor conditions. Although less commonly used for this application, the nano-SnO₂ was selected for the purpose of enhancing the surface hydrophobicity, whereas nano-TiO₂ was included for its favorable photocatalytic properties. Polydimethylsiloxane (PDMS) oil, known for its self-cleaning characteristic, was served as the base material in the developed sprays. The described blend of materials represents a novel combination. The results indicated that 2.5% nano-SnO₂ and 2.5% nano-TiO₂ in PDMS oil enhanced efficiency by 5.4% compared to a non-sprayed panel after five weeks of outdoor exposure. This efficiency gain was experimentally justified and attributed to the spray’s ability to achieve a water contact angle (WCA) of 100.6°, forming a hydrophobic surface conducive to self-cleaning. Further characterization results, including photocatalysis and zeta potential have been gathered and analyzed. Full article

The temperature of the water wall in the furnace chamber is extremely important for the daily operation of a boiler. Considering the high temperature and dusty environment in the furnace, a temperature measurement device mainly composed of four parts (armored temperature sensor, in-furnace heat-collecting block, out-furnace fixing base, and protective cannula) was designed in this study, which could be used to directly obtain the temperature of the in-furnace water-wall. Numerical simulations of temperature measurement devices with different heat-collecting block structures were carried out using the computer fluid dynamics method. After comparing the measurement accuracy and considering the practical application scenarios, the optimized heat-collecting block structure with a specific expansion gap (0.5 mm wide and 4 mm deep) was selected for practical application. Such a temperature measurement device was then applied to a 1000 MW ultra-supercritical coal-fired boiler in China, and the tested in-furnace water-wall temperature data were in good agreement with relevant research. Compared with the conventional temperature measurement device arranged outside the furnace, the in-furnace water-wall temperature-measurement device adopted in this study has a more sensitive response characteristic and can directly reflect the temperature of the water wall inside the furnace. In addition, it can also reflect the local slag formation state of the water wall and has a long service life. Full article

The problem of increasing heat resistance and corrosion and erosion resistance of gas turbine units in compressor stations was solved through the development of new layered materials containing nanostructured grains. The authors carried out a destruction analysis of gas turbine units in compressor stations. It was shown that after 10–30,000 h of operation, the greatest damage occurred when the gas turbine operated in dusty environments at high temperatures (or in air environments with a high salt content). The developed layered composites include the thermal barrier and functional reinforced nanostructured layers consisting of refractory carbides and oxides. This paper describes the destruction mechanism of gas turbine units under the influence of high-temperature aggressive environments. As a result, a new formation technology for reinforced nanostructured layered composites has been developed. The developed composition makes it possible to increase the heat resistance of materials by approximately 10 times. This significantly increases the reliability and durability of gas turbine units in compressor stations. The structural and mechanical parameters of the layered nanostructured heat-resistant composites have been studied. Full article

This paper considers extreme dusty conditions at workplaces in underground coal mine. These extreme conditions stem from various physical factors that affect employees’ performance. The extreme effect of the dust can significantly contribute to permanent health damage or even the death of employees. In this study, we present and discuss the results of measurements of airborne dust and respiratory dust taken during wall cutting in a coal mine and propose effective measures to reduce the burden on the life and health of employees and the environment. Full article

This study presents an analysis of the influence of weather conditions on the performance of a multicrystalline silicon photovoltaic module, which operates under constant resistive load and is situated near a limestone quarry. The quarry is a significant source of dust, and hence the focus of the study is on the weather factors influencing the presence of soiling on the module’s surface. The analysis encompasses a three-week period, during which the global horizontal irradiance and wind speed were recorded at 10-min intervals by an on-site weather station. The current, voltage, and back temperature of the module were also measured. Supplementary weather data were obtained from the Copernicus Atmosphere Monitoring Service and the NASA POWER databases. The primary objective is to assess whether any influence of the observed weather conditions on the presence of soiling can be inferred from the recorded data. The contribution is in part intended to test how different techniques can be used to extract useful information on the weather-related effects from somewhat limited data, assembled from various sources, while dealing with the underlying uncertainties. The analysis indicates a persistent deterioration of the module’s performance because of soiling and its subsequent improvement due to a favourable weather event. Full article

Photovoltaic (PV) modules are often situated in hot and windy environments, such as deserts, where dust accumulation poses a significant problem. The build-up of dust can result in an increase in PV module leakage current, making the modules more vulnerable to potential-induced degradation (PID), ultimately leading to a reduction in the efficiency of PV power generation. In this study, we investigate the impact of dust accumulation on the surface of PV modules on leakage current. A dust model is developed based on the Arrhenius relation, taking into account the impact of temperature and density on dust conductivity. The equation for leakage current due to dust accumulation is derived based on the clean module leakage current equation. We undertake a simulation of natural conditions in a laboratory setting to analyze the impact of dust on the leakage current of photovoltaic modules. The results show the following: At high temperatures, the leakage current will significantly increase due to the elevated conductivity of the dust. The conductivity increased by 27.1%, 48.9%, 64.3%, and 118% for the four groups of dusty PV modules, respectively. Leakage current prediction has a better accuracy when dust is equated to series conductance. Dust can reduce the activation energy of PV modules by up to 3.48%. Full article

Far-infrared and submillimeter observations have established the fundamental role of dust-obscured star formation in the assembly of stellar mass over the past ∼12 billion years. At z = 2–4, the so-called “cosmic noon”, the bulk of star formation is enshrouded in dust, and dusty star-forming galaxies (DSFGs) contain ∼$50\%$ of the total stellar mass density. Star formation occurs in dense molecular clouds, and is regulated by a complex interplay between all the ISM components that contribute to the energy budget of a galaxy: gas, dust, cosmic rays, interstellar electromagnetic fields, gravitational field, and dark matter. Molecular gas is the actual link between star-forming gas and its complex environment: much of what we know about star formation comes from observations of molecular line emissions. They provide by far the richest information about the star formation process. However, their interpretation requires complex modeling of the astrochemical networks which regulate molecular formation and establish molecular abundances in a cloud, and a modeling of the physical conditions of the gas in which molecular energy levels become populated. This paper critically reviews the main astrochemical parameters needed to obtain predictions about molecular signals in DSFGs. Molecular lines can be very bright compared to the continuum emission, but radiative transfer models are required to properly interpret the observed brightness. We review the current knowledge and the open questions about the interstellar medium of DSFGs, outlining the key role of molecular gas as a tracer and shaper of the star formation process. Full article

Detecting micron-sized particles is an essential task for the analysis of complex plasmas because a large part of the analysis is based on the initially detected positions of the particles. Accordingly, high accuracy in particle detection is desirable. Previous studies have shown that machine learning algorithms have made great progress and outperformed classical approaches. This work presents an approach for tracking micron-sized particles in a dense cloud of particles in a dusty plasma at Plasmakristall-Experiment 4 using a U-Net. The U-net is a convolutional network architecture for the fast and precise segmentation of images that was developed at the Computer Science Department of the University of Freiburg. The U-Net architecture, with its intricate design and skip connections, has been a powerhouse in achieving precise object delineation. However, as experiments are to be conducted in resource-constrained environments, such as parabolic flights, preferably with real-time applications, there is growing interest in exploring less complex U-net architectures that balance efficiency and effectiveness. We compare the full-size neural network, three optimized neural networks, the well-known StarDist and trackpy, in terms of accuracy in artificial data analysis. Finally, we determine which of the compact U-net architectures provides the best balance between efficiency and effectiveness. We also apply the full-size neural network and the the most effective compact network to the data of the PK-4 experiment. The experimental data were generated under laboratory conditions. Full article

This communication investigated the dust effect on microelectromechanical system (MEMS) thermal wind sensors, with an aim to evaluate performance in practical applications. An equivalent circuit was established to analyze the temperature gradient influenced by dust accumulation on the sensor’s surface. The finite element method (FEM) simulation was carried out to verify the proposed model using COMSOL Multiphysics software. In experiments, dust was accumulated on the sensor’s surface by two different methods. The measured results indicated that the output voltage for the sensor with dust on its surface was a little smaller than that of the sensor without dust at the same wind speed, which can degrade the measurement sensitivity and accuracy. Compared to the sensor without dust, the average voltage was reduced by about 1.91% and 3.75% when the dustiness was 0.04 g/mL and 0.12 g/mL, respectively. The results can provide a reference for the actual application of thermal wind sensors in harsh environments. Full article

Workers who are working in dusty environments might be associated with respiratory health problems. In Ethiopia, factories processing wood, textile, coffee, flour, cement and other materials are associated with dust emission. Furthermore, despite the adoption of the International Labor Organization (ILO) convention, the available constitution and labor proclamation, there are a lot of gaps in terms of occupational health and safety measures in Ethiopia. The current review aims to examine the existing evidence, current challenges and future direction regarding dust exposure and respiratory health in selected Ethiopian factories. Searches of peer-reviewed articles with full-length papers were made in online databases such as PubMed, Web of Science, MEDLINE, EMBASE and Google Scholar with a key words “Dust exposure”, “Respiratory health”, “Respiratory symptom”, “Ethiopia” and “Factory workers” from January 2000 to March 2023. The review found that excessive dust exposure is associated with a high prevalence of respiratory health problems. Lack of personal protective equipment and absence of safety and health training were the main occupational health deficits identified in most factories. Actions that focus on these deficiencies are commendable. Interventions focused on safety and health trainings, and the provision of adequate personal protective equipment of the required quality is recommended. In addition, administrative solutions and longitudinal studies on dust exposure and respiratory health are suggested. Full article