Visibility Reduction Research Articles

Development of an Integrated Modeling Framework for Visibility and Air Quality Forecasting in Delhi

Abstract Rapid urbanization has subjected the megacities of developing countries to various environmental stresses. Delhi, a major Indian megacity, faces increasing urban stress leading to reduction in air quality and visibility. These challenges necessitate an integrated modeling framework to mitigate adverse environmental impacts on public health. Therefore, we have developed an advanced version of the high-resolution Delhi Model with Chemistry and aerosol framework (DM-Chem) at the National Centre for Medium Range Weather Forecasting (NCMRWF) under the Weather and Climate Science for Service Partnership India (WCSSP India) project. This collaborative initiative between India and the United Kingdom aims to provide real-time forecasts of visibility and fine particulate matter (PM2.5) for Delhi and neighboring regions during the winter season. The DM-Chem framework is unique due to its detailed urban canopy scheme and realistic aerosol representation, making it well suited for city-scale forecasts. It is designed to predict extreme fog and pollution events in the winter season. Here, we discuss the major physical parameterization improvements for the model, along with its skill and deficiencies in predicting extreme events. Notably, irrigation effects on surrounding agricultural areas have significantly improved fog and visibility forecasts, but have degraded the wind forecasts. We demonstrate the applicability of this modeling framework to study aerosol–radiation interaction during fog holes and discuss its potential to be applied or adapted to other megacities worldwide.

A hybrid design based on alternating layered fluids for the cloaking of elastic cylinders.

With the combination of two critical features from Cummer-Schurig, acoustic cloaking and the scattering cancellation technique, this study reports a hybrid design for the cloaking of elastic cylinders using alternating layered fluids (the effective density to be anisotropic), which are achieved by alternately immersing HGM (a syntactic foam, light solid material with high sound velocity) and Pb (lead, a heavy solid material) in the background fluid medium. The cloaking performance of the proposed design is investigated both by the numerical simulation and by experimental measurement. For a lead cylinder of radius 50 mm, the measured visibility reduction below -5 dB is obtained in the frequency range from 18 kHz to 23 kHz. Compared with the scattering cancellation by the thin elastic shell, the proposed cloaking can be obtained at shorter wavelengths due to the suppression of more higher-order scattering. In addition, the performance of cloaking has no dependence on the incident angles, which has an advantage over the scattering cancellation using scatters distributed unevenly. This is the first experiment using layered fluids to obtain the cloaking of an elastic cylinder, which has potential application in underwater acoustic stealth.

Field-based investigation of snow-drift flux increases in blowing snow and application to mapping of short-term visibility reduction using mesoscale meteorological simulation

Field-based investigation of snow-drift flux increases in blowing snow and application to mapping of short-term visibility reduction using mesoscale meteorological simulation

Representation of Blowing Snow and Associated Visibility Reduction in an Operational High-Resolution Weather Model

Abstract Blowing snow is a hazard for motorists because it may rapidly reduce visibility. Numerical weather prediction models in the United States do not capture the movement of snow once it reaches the ground, but visibility reductions due to blowing snow can be diagnosed based on model-predicted land surface and environmental conditions that correlate with blowing snow occurrence. A recently developed diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction is applied to High-Resolution Rapid Refresh (HRRR) and Rapid Refresh Forecast System (RRFS) model output including surface snow conditions to predict surface visibility reduction due to blowing snow. Twelve blowing snow events around Wyoming from 2018 to 2023 are examined. The analysis shows that visibility reductions due to blowing snow tend to be overpredicted, caused by the initial assumption of full driftability of the snowpack. This study refines the aging of the blowing snow reservoir with two methods. The first method estimates driftability based on time-varying snow density from the Rapid Update Cycle land surface model (RUC LSM) used in the HRRR and experimental RRFS models and is evaluated in a real-time context with the RRFS model. The second, complementary method diagnoses snowpack driftability using a process-based approach that requires data for recent snowfall, wind speed, and skin temperature. Compared to the full driftability assumption, this method shows limited improvements in forecasting skill. To improve model-based diagnosis of visibility reduction due to blowing snow, empirical work is needed to determine the relation between snowpack driftability and the recent history of snowfall and other weather conditions. Significance Statement Blowing snow presents a significant hazard to motorists and airport operations through sometimes very rapid and intense reductions in visibility, yet little predictive guidance exists for blowing snow. This study aims to improve the prediction of blowing snow occurrence and associated surface visibility reduction using diagnostics from an operational high-resolution weather model. One key challenge regards the question of driftability of the snowpack. This study evaluates two approaches to quantify driftability in terms of visibility reduction due to blowing snow and acknowledges that more measurements are needed to improve the representation of blowing snow physics in NWP models.

Снижение числа несчастных случаев и аварий при управлении поездом в автоматическом режиме

Railroad transport is the main element of economic development of Russian regions; it ensures reliable communication between industrial enterprises and raw material supply bases in Russia. Transportation of enterprise employees and freights is an integral part of the successful functioning of any industrial company. Ensuring safe conditions of train traffic is a matter of high priority for all participants in the transportation process. The article provides external and internal parameters affecting the safety of rolling stock. The impact of external factors, such as weather conditions, optical visibility, illumination, and air transparency on the operation of technical vision systems when driving a train in automatic mode have been analyzed. Taking the impact of these factors into account is necessary to limit the speed of rolling stock in the sectors of low visibility and sharp reduction of visibility range of obstacles, which is crucial to ensure safe train movement when calculating the braking curve. Such a dependence requires the introduction of correcting coefficients into algorithms of the train movement model and considering the correction when calibrating and configuring computers and devices of the technical vision system. In order to determine the internal parameters of rolling stock, an algorithm for the calculation of the coordinates of a technical object has been proposed. For a moving object, the coordinates are calculated on the orthodromic trajectory using the existing dependence between the current longitude and latitude of the object and the speed projections measurements of the rolling stock on the axis of the geographic coordinate system. The proposed options to determine the rolling stock parameters improve the quality and accuracy of determining speed and coordinates and reduce computing and hardware costs aboard a locomotive, which, in turn, affects the time of decision-making to ensure optimal control of rolling stock and prevention of accidents and emergencies.

Improving visibility forecasting during haze-fog processes in shanghai and eastern China: The significance of aerosol and hydrometeor extinction

Improving visibility forecasting during haze-fog processes in shanghai and eastern China: The significance of aerosol and hydrometeor extinction

The Landscape of User-centered Misinformation Interventions - A Systematic Literature Review

Misinformation is one of the key challenges facing society today. User-centered misinformation interventions as digital countermeasures that exert a direct influence on users represent a promising means to deal with the large amounts of information available. While an extensive body of research on this topic exists, researchers are confronted with a diverse research landscape spanning multiple disciplines. This review systematizes the landscape of user-centered misinformation interventions to facilitate knowledge transfer, identify trends, and enable informed decision-making. Over 6,000 scholarly publications were screened, and a systematic literature review ( N=172 ) was conducted. A taxonomy was derived regarding intervention design (e.g., labels, showing indicators of misinformation, corrections, removal, or visibility reduction of content), user interaction (active or passive), and timing (e.g., pre or post exposure to misinformation or on request of the user). We provide a structured overview of approaches across multiple disciplines and derive six overarching challenges for future research regarding transferability of approaches to (1) novel platforms and (2) emerging video- and image-based misinformation, the sensible combination of automated mechanisms with (3) human experts and (4) user-centered feedback to facilitate comprehensibility, (5) encouraging media literacy without misinformation exposure, and (6) adequately addressing particularly vulnerable users such as older people or adolescents.

Sustainable Dynamic Wrinkle Efficacy: Non-Invasive Peptides as the Future of Botox Alternatives

Dynamic wrinkle reduction continues to challenge aesthetic dermatology, predominantly addressed through Botulinumtoxin (Botox) injections. Despite Botox’s robust efficacy with up to an 80% reduction in wrinkle visibility within just one week, its invasive administration and specific mechanism of soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex inhibition prompt the exploration of safer, non-invasive alternatives. This review critically assesses recent innovations in non-invasive effects, with a focus on peptides and botanical extracts that exhibit a diverse array of mechanisms including SNARE complex inhibition, modulation of calcium and sodium channels, and interactions with acetylcholine receptors, contributing to their effectiveness in muscle relaxation on dynamic wrinkle approaches. Noteworthy peptides such as Argireline and SYN-Ake replicate the neuromodulatory effects of Botox, achieving up to a 52% reduction in wrinkles within four weeks without injections. Moreover, botanical extracts meet the rising demand for clean beauty solutions by enhancing skin elasticity and health through gentle yet potent mechanisms. However, the main concern with peptides is their low absorption rate, with only six clinical validations regarding Botox-like peptide anti-wrinkle efficacy available. These advancements not only deepen our understanding of cosmetic dermatology but also significantly influence market dynamics and consumer behavior, underscoring their pivotal role in redefining the future landscape of anti-aging effects.

Determining infrared radiation intensity characteristics for the exhaust manifold of gas turbine engine ТВ3-117 in МІ-8МSB-B helicopter

The object of this study is the screen-exhaust device in the TV3-117 engine of the Mi-8MSB-B helicopter. To reduce visibility in the thermal range, a system of mixing hot engine exhaust gases with ambient air is used; this technique makes it possible to reduce the infrared radiation of engines. For this purpose, a new sample of screen-exhaust device was designed for testing. A thermal imaging survey of the helicopter was conducted. Three variants of thermal images were acquired: a helicopter without installation of a thermal visibility reduction system, a helicopter with standard exhaust shields installed, and a helicopter with newly developed shield exhaust devices installed. Based on the obtained experimental results, the characteristics of the intensity of infrared radiation were determined for three variants of research in the range of thermal waves of 3–5 μm. The study uses a comprehensive approach to solving the tasks, which includes a statistical analysis of known and promising ways to protect a helicopter from guided missiles with infrared homing heads based on reduced radiation forces and a theoretical method for calculating flow and temperature fields. The advantages of placing the section of the exhaust channel of the designed screen-exhaust device in the horizontal plane for complete shielding of infrared radiation in the lower hemisphere have been experimentally proven. The benefits of directing the flow of exhaust gases from the screen-exhaust device into the space above the helicopter propeller and dividing this flow into four separate flows were shown. The results of experimental research could be used to design new or improve existing screen-exhaust devices by the developers of military aviation

A mechanism for coastal fog genesis at evening transition

AbstractTransitional changes in the atmospheric boundary layer (ABL) are known to facilitate the onset of terrestrial fog, which is defined as a condition with near‐surface visibility <1 km due to airborne water droplets. In particular, the evening transition from a daytime convective ABL to a night‐time stable ABL provides favorable conditions for fog. This article describes a local fog event observed during the evening transition at a Canadian islet in the north Atlantic known as Sable Island during the “Fog and Turbulence Interactions in the Marine Atmosphere (Fatima)” field campaign. The comprehensive set of data collected using a myriad of instruments covering a wide range of scales allowed identification of a novel mechanism underlying this fog event. Therein an ocean–land discontinuity created a flow regime consisting of several stacked boundary layers, interplay of which produced a thin low‐level cloud that then diffused downward to the surface, causing visibility reduction. This mechanism offers useful insights on the role of boundary layers, stratification, and turbulence in fog genesis over oceanic islands.

Transforming milk supply chains with blockchain: enhancing visibility and cost reduction

PurposeMilk is a perishable food product, one of the primary sources of nutrition. Reports worldwide indicate numerous food frauds and foodborne diseases associated with adulterated milk products. These safety concerns highlight the importance of a visible milk supply chain, which can be achieved by cutting-edge technologies. However, these technologies come with high costs. So, this study aims to propose a framework that integrates blockchain, Internet of Things (IoT) and cloud to enhance visibility with reduced cost in an Australian milk supply chain (AMSC).Design/methodology/approachA design science research methodology is used, where a proof of concept is also developed at the retailer end to show how blockchain, IoT and cloud can improve visibility with reduced cost in an AMSC.FindingsAccording to cost and visibility analysis, blockchain implementation in AMSC would generate a high return on investment (ROI). For the given case, ROI becomes positive for all stakeholders after 750 cycles. Integrating IoT, cloud and blockchain is more profitable than just using blockchain. Additionally, technology implementation may not benefit all stakeholders equally. For example, the retailer needs 10 cycles to benefit, but the transporter needs 50 in the given case.Practical implicationsThe findings of this study assist milk industries in decision-making regarding technology implementation in their supply chain and motivate them to implement these technologies, resulting in improved trust and coordination among entities and consumers.Originality/valueA cost and visibility analysis are performed to evaluate the impact of technology implementation on cost and visibility in an AMSC. A SOAR (Strength Opportunities Aspiration Results) analysis is also performed for the strategic planning framework.

A cross layer graphical neural network based convolutional neural network framework for image dehazing

The current version of imaging equipment cannot quickly and effectively make up for the reduction of visibility triggered by bad weather. Traditional strategies minimize hazy impacts by employing an image depth model and a physical model. Following experts, erroneous depth data reduces the efficacy of the dehazing algorithm. Dehazing methods based on CNN are imperfect to handle region which is bright or similar to atmospheric light and thus leads to oversaturation of pixels. These challenges can be addressed by proposing a novel model that incorporates the idea of a Graphical Neural Network. The amount of light coming from the atmosphere is estimated using normalization where the contrast of the image gets adjusted using Bias Contrast stretch Histogram Equalization. An enhanced Transmission map estimator is used to render the hazy scene. Finally, the cross-layer graphical neural network-based CNN model is applied to produce a haze-free image and eliminate the over-saturation of pixels. Extensive evaluation findings show that the proposed approach can significantly recuperate misty imagery, even if the images have a substantial amount of haze.

Numerical prediction of fog: A novel parameterization for droplet formation

AbstractA novel aerosol activation scheme was developed and implemented into the WRF Thompson–Eidhammer Aerosol‐Aware microphysical module. While in most of the numerical weather prediction (NWP) models the activation is based on updraft velocity, even in the fog, the new parameterization considers the local rate of cooling and water vapor flux in the evaluation of the aerosol activation rate. In addition, diagnostic variables are added to evaluate the visibility reduction due to formation of haze droplets. The impact of changing the activation scheme is demonstrated by a case study of a well‐observed fog event. Data observed during the fog event campaign at Budapest and observed at standard meteorological stations are compared with the output of the numerical model. The results are summarized as follows: (i) the inhomogeneous spatial and temporal distribution of the number concentration of droplets is an inherent characteristic of the new parameterization scheme. (ii) Compared to the prior parameterization based on updraft velocity, the new parameterization scheme increases the number concentration of the droplets significantly, especially at the top of the fog. As a consequence, it reduces the downward short‐wave radiation flux prolonging the lifetime of the fog by about 30–60 min. (iii) Analyses reveal that earlier dissipation of the fog comparing to the observed data cannot be explained only by overestimation of the downward short‐wave radiation flux. (iv) The new method is able to evaluate the reduction of visibility due to haze.

Recurrent Exposure Generation for Low-Light Face Detection

This Project introduces an innovative approach to low-light face detection, Exploiting Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and the Zero-Reference Deep Curve Estimation (Zero-DCE) algorithm. The primary goal is to improve the accuracy and reliability of face detection in challenging low-light conditions. Our method integrates LSTM and CNN networks with real-time exposure control, enabling adaptation to dynamic lighting conditions by capturing multiple frames iteratively with varying exposure levels. The incorporation of Zero-DCE facilitates the enhancement of exposure settings, resulting in improved face visibility and noise reduction. Experimental evaluations validate the efficiency of our approach, demonstrating significant advancements in low-light face detection accuracy compared to traditional methods. This project offers a practical adaptable solution with wide-ranging implications for real- world applications, including surveillance, security, and various other domains. Keywords – Low-light face detection, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Zero- Reference Deep Curve Estimation (Zero-DCE), recurrent exposure generation, real-time exposure control, dynamic lighting conditions, noise reduction, surveillance.

The effect of fog on atmospheric electric fields

Naturally occurring electric fields exist in the atmosphere as part of the global electric circuit. These fields are produced by global phenomena, but they are strongly modified by local conditions. One such local effect is fog, which is the reduction in visibility by droplets suspended in the air. For over a century, it has been known that fog can often result in larger values of electric field compared to that in clear air conditions. However, the physical processes controlling the electric field and its variability in fog are still not fully understood. More detailed information on this is required to assess whether electric field measurements can be used as a fog prediction method, which was suggested many decades ago but is still an open question. Furthermore, improving the understanding of electrical processes in fog will also help in related areas of research such as cloud electrification processes and the impact of charge on the behaviour of aerosol and droplets.This study aims to fully characterise the electric field in fog using an extensive dataset of over 17 years, measured from the University of Reading Atmospheric Observatory, UK. This encompasses electric field, visibility, and meteorological data from over a hundred fog events, which allows us to examine the behaviour of electric fields in fog conditions statistically, rather than only focusing on individual case studies as is often the case in the literature. The long series of data from the site allows the variability in the electrical data during fog events to be more fully characterised than previously.

Nocturnal Seafaring: the Reduction of Visibility at Night and its Impact on Ancient Mediterranean Seafaring. A Study Based on 8–4th Centuries BC Evidence

AbstractThe practice of nocturnal navigation in the Mediterranean Sea could be inferred from both archaeological and written records. While there is sufficient proof that the ships and their crew were quite familiar with nighttime sailing, current scholarship has not satisfactorily investigated how the reduction of visibility could have affected the nautical practice. For this reason, the aim of this contribution is twofold: (1) to evaluate to what extent visibility was reduced at night, and (2) to understand what kind of strategies (if any) could be put in place to overcome the difficulties of a low level of visibility. Amongst the strategies, we will also assess the impact on visibility of fixed and portable lighting devices, such as torches and pierced amphoras, as documented by the archaeological and literary evidence.

FOG/VISIBILITY FORECAST AND VERIFICATION AT IGI AIRPORT, NEW DELHI DURING THE WINTER SEASONS OF 2020-21 & 2021-22

The reduction in visibility due to fog leads to cancellation of flights, delays and diversions at IGI Airport, New Delhi during the winter season. Accurate prediction of fog/ visibility is required within sufficient lead time to make the flight operations safer, economical and more convenient. This study attempts to improve the fog/visibility forecast at IGI airport for the months of December and January during the winter seasons of 2020-21 and 2021-22. Various meteorological parameters required to predict the fog/visibility are obtained from IMD GFS forecasted meteorological information which are valid for next 24 and 48-hours. Forecast verification was performed using the METAR and synoptic observations. The results for 2020-21 fog/visibility forecast for 24-hours are promising with a POD of 0.92, critical success index as 0.68, and false alarm ratio as 0.28. Moreover, the 2021-22 results for 24 -hours fog /visibility forecast are also observed to be promising with a POD of 0.73, critical success index of 0.54 and false alarm ratio at 0.32. This method gives fairly accurate predictions in point locations and can also be used for a larger spatial area. However, the 48-hours forecast performance needs further improvement. The method also predicted the wind speed and relative humidity which were found to be in agreement with the observed data to a great extent. In the upcoming years, the fog/visibility forecast method will be more robust with better prediction accuracy of meteorological parameters from model outputs and observations.

Aerosol liquid water in PM2.5 and its roles in secondary aerosol formation at a regional site of Yangtze River Delta

Aerosol liquid water content (ALWC) plays an important role in secondary aerosol formation. In this study, a whole year field campaign was conducted at Shanxi in north Zhejiang Province during 2021. ALWC estimated by ISORROPIA-II was then investigated to explore its characteristics and relationship with secondary aerosols. ALWC exhibited a highest value in spring (66.38µg/m3), followed by winter (45.08µg/m3), summer (41.64µg/m3), and autumn (35.01µg/m3), respectively. It was supposed that the secondary inorganic aerosols (SIA) were facilitated under higher ALWC conditions (RH > 80%), while the secondary organic species tended to form under lower ALWC levels. Higher RH (> 80%) promoted the NO3- formation via gas-particle partitioning, while SO42- was generated at a relative lower RH (> 50%). The ALWC was more sensitive to NO3- (R=0.94) than SO42- (R=0.90). Thus, the self-amplifying processes between the ALWC and SIA enhanced the particle mass growth. The sensitivity of ALWC and OX (NO2+O3) to secondary organic carbon (SOC) varied in different seasons at Shanxi, more sensitive to aqueous-phase reactions (daytime R=0.84; nighttime R=0.54) than photochemical oxidation (daytime R=0.23; nighttime R=0.41) in wintertime with a high level of OX (daytime: 130-140µg/m3; nighttime: 100-140µg/m3). The self-amplifying process of ALWC and SIA and the aqueous-phase formation of SOC will enhance aerosol formation, contributing to air pollution and reduction of visibility.

Assessment Method Integrating Visibility and Toxic Gas for Road Tunnel Fires Using 2D Maps for Identifying Risks in the Smoke Environment

This study proposes an assessment method to quantify the risks of the smoke environment for road tunnel fire safety based on previous studies. The assessment method integrates visibility and toxic gases to address the hazards of smoke distribution more comprehensively. Considering that the hazards of visibility reduction and toxic gases for tunnel users vary with exposure time and location in a fire event, the smoke environment (SE) levels are defined as a function of longitudinal location and time. The SE levels simplify smoke distribution as calculated from 3D computational fluid dynamics (CFDs). For easily identifying SE risks, SE levels are illustrated on a 2D map to analyze the potential hazard by quantifying specific areas and times of smoke exposure. To demonstrate the applicability of the assessment method of this study, cases are carried out using CFD simulation to investigate the risks associated with tunnel fires with various tunnel cross-section types, longitudinal velocities, and gradients. In the analysis of the SE level in different cross-section types and longitudinal velocities under the condition of no vehicle, a velocity of 0.9–1.1 m/s can maintain a less serious SE level both upstream and downstream in a horizontal rectangular tunnel, and 0.3–0.5 m/s in a horizontal horseshoe-shaped tunnel. Both rectangular and horseshoe-shaped tunnels reveal an obvious rise within 10–15 min. In the case of inclined tunnels, for both rectangular and horseshoe-shaped tunnels, the SE level near the fire source obviously deteriorates. Thus, the longitudinal velocity range for the purpose of maintaining a relatively less serious SE level should be slightly reduced for inclined tunnels compared with horizontal tunnels.

BUILD AN EFFECTIVE DEEP LEARNING MODEL FOR UNDERWATER IMAGE ENHANCEMENT BASED ON EUVP DATA

Images taken underwater often suffer from colour distortion as well as a reduction in visibility because of the light absorption and scattering that occurs. Existing approaches for underwater image enhancement make use of numerous assumptions/constraints to produce decent results. However, these solutions all have the same disadvantage in that the assumptions used may not work for certain scenarios. To solve this issue, this research provides an end-to-end system for underwater image augmentation that includes a CNN-based network dubbed VGG and UNet. There have been several studies in recent years proving the efficiency of deep learning approaches in various application fields. Color correction and haze removal tasks are used to train the VGG and UNet. This combined training technique allows for the simultaneous learning of a robust feature representation for both tasks. A pixel disrupting method is used in the suggested learning framework to better extract the intrinsic characteristics in local patches, which considerably enhances convergence speed and accuracy. We used EUVP dataset training images based on the underwater imaging model to handle VGG and UNet training. The testing findings on several real-world underwater settings show that the suggested strategy yields aesthetically pleasing outcomes. The experimental results for the full-reference measures SSIM, PSNR, RMSE, UCIQE, and UIQM demonstrate the reliability and efficacy of the proposed technique.