Visible Variables Research Articles

Current challenges in the visibility improvement of urban Chongqing in Southwest China: From the perspective of PM2.5-bound water uptake property over 2015–2021

The visibility (VIS) improvement remains challenging such as in Southwest China, although the aerosol loading was reduced markedly. More in-depth understanding of interactions between visibility and aerosol physicochemical properties is required. This study systematically investigated mechanisms behind the interannual variation of low visibility at urban Chongqing from the aspect of PM2.5-bound optical enhancements over a relatively large timescale of 2015–2021. Results show that VIS <10 km frequently exceeded 50% in 2021 despite remarkable emission reductions since 2015, with the most severe challenge in visibility improvement faced in winter. Generally, the annual mean aerosol hygroscopicity and light extinction capacity were promoted during 2015–2021, as demonstrated by a higher optical enhancement factor (f(RH)), aerosol liquid water content per unit PM2.5 mass (i.e., ALWC/PM2.5), and dry mass extinction efficiency (MEEdry) observed for 2021 in comparison to previous years. A pronounced ascending trend existed in the annual mean fraction of nitrate (fNO3−), which even surpassed organics and sulfate to be the predominant composition in PM2.5. A similar evolution of fNO3− was observed during the visibility degradation specifically when VIS <10 km, highlighting the importance of highly hydrophilic inorganics in the haze formation. Given that the higher level of fNO3− normally introduced an elevated ALWC/PM2.5 which likely enhanced secondary aerosol formation (e.g., sulfate, nitrate, and secondary organics) via multiphase reactions, the combined effects could further exacerbate the visibility degradation particularly under highly humid and polluted conditions. Stricter controls such as on NOx and volatile organic precursors would be beneficial for the visibility improvement in China.

Study on Elevated Interval Evacuation of Metro Train considering the Influence of Fire, Railings, and Track Bed

Elevated metro is a typical type of urban rail transit that operates on viaducts. Due to its fast construction speed and low cost, it plays an important role in transportation systems with dense concentrations of people. Once a fire accident occurs in rush hour, it will cause high casualty and enormous injuries. In this regard, studying the characteristics of fire smoke spread and passengers’ evacuation is of profound significance. In this paper, Pyrosim and Pathfinder software was utilized to simulate metro fire and evacuation process. The variation of temperature, CO concentration, and visibility inside carriage and outside walkaway were studied to explore the characteristic of fire and smoke. Evacuation time, speed, density, and path were analyzed for six cases considering the influence of railing settings on evacuation walkaway and the use of track bed. The impact of smoke on movement speed reduction and temperature on the available door numbers were taken into consideration. Results revealed that the temperature of accident carriage rose to tenability limit after 50 s and the visibility decreased to 5 m within 150 s. CO concentration was below safety criteria due to sufficient oxygen. Temperature was the most important factor that leads to the risk of evacuation, especially inside the accident carriage. The length of evacuation walkaway could highly reduce evacuation time and density. Railings had a negative impact on evacuation while the usage of track bed had a positive effect. Besides, the impact of railings was not as significant as the usage of the track bed. Research results can provide decision‐making reference for fire protection and evacuation design to alleviate congestion and improve safety management.

Characterization of road surfaces using three near-infrared diode lasers

Rapid road network expansion has heightened the importance of surface condition information for traffic accident prevention and route optimization. This article introduces a laser diode-based sensor that identifies seven surface conditions and accurately measure ice, water, and snow film thicknesses on roads. An optical module was developed to detect weak optical signals based on the characteristic absorption spectrum of the target surface. The module used three laser diodes (1310, 1440, and 1550 nm wavelengths) as light sources. Additionally, a road classification algorithm that is adaptable to foggy weather was developed using a multi-wavelength processing protocol. The sensor was subjected to numerous calibration and performance verification experiments. During thick foggy measurements, the processed spectra displayed a maximum variation of 2.372% across a 600 to 25,000 m visibility range with a relative standard deviation of only 0.328%. This demonstrated effective weakening of the effects of visibility variations. During winter field testing, the sensor classified road conditions effectively and accurately measured ice, snow, and water film thicknesses, with a correlation coefficient of 0.97444. The accuracy of the measurements was less than 0.5 mm. The sensor’s effectiveness for long-term field-based road testing has been verified.

Saccadic eye movement speed is related to variations in phantom array effect visibility

The phantom array effect is one of the temporal light artefacts that can decrease performance and increase fatigue. The phantom array effect visibility shows large individual differences; however, the dominant factors that can explain these individual differences remain unclear. We investigated the relationship between saccadic eye movement speed and phantom array visibility at two different angles and four different directions of saccadic eye movement. The peak speed of saccadic eye movement and the phantom array effect visibility were measured at different modulation frequencies of the light source. Our results show that phantom array visibility increased as eye movement speed increased; the phantom array visibility was higher at a wide viewing angle with fast eye movement speed than at a narrow viewing angle. Moreover, when clustered into subgroups according to individual eye movement speed, the mean speed of the saccadic eye movement of each subgroup is related to the variations in the visibility of the phantom array effect of the subgroup. Therefore, saccadic eye movement speed is related to variations in phantom array effect visibility.

Time-Frequency Domain Variation Analysis and LSTM Forecasting of Regional Visibility in the China Region Based on GSOD Station Data

Atmospheric visibility is an important indicator that reflects the transparency of the atmosphere and characterizes the air quality, so it is of great significance to study the long-term change in visibility. This paper is based on the global surface summary of day data (GSOD) site dataset and other relevant data, using the Mann–Kendall (MK) mutation point test, wavelet transform, and seasonal autoregressive integrated moving average (SARIMA) model forecasting. The time-frequency domain variation characteristics and related influencing factors of regional visibility in China were studied in detail, and the visibility was predicted; the results of the study showed the following: (1) the overall interannual variation of regional visibility in China has a decreasing trend, and the four-season variation has a decreasing trend, except for the rising trend in summer, with abrupt change points in both the overall interannual variation and the four-season variation. (2) There are main cycles of visibility in the Chinese region with time scales of 180 months and 18 months. Under the time scale of 180 months for the main cycle, the variation period of visibility is about 123 months, experiencing two high to low variations; under the time scale of 18 months for the main cycle, the variation period of visibility is 12 months, experiencing 21 high to low variations. (3) The development of the economy indirectly affects changes in visibility. Cities with high economies are densely populated, with concentrations of various particulate emissions and high concentrations of particulate matter, which can directly reduce visibility. (4) Two prediction models, SARIMA and long and the short-term memory (LSTM) neural network, were used to predict the visibility in China, both of which achieved good evaluation indexes, and the visibility in China may show an increasing trend in the future.

Combined effect of surface PM2.5 assimilation and aerosol-radiation interaction on winter severe haze prediction in central and eastern China

In this study, the effects of aerosol-radiation interaction (ARI) and data assimilation (DA) during a haze episode in December 2016 are evaluated by four sets of parallel experiments using the atmospheric chemical model GRAPES_Meso5.1/CUACE. The results show that although the BASE experiment without ARI and DA generally captures the variations of particulate matter (PM) and visibility (VIS), it dramatically underestimates the high PM concentrations and overestimates the low VIS on severe haze days. Moreover, the ARI effect is not significant when the aerosol concentration is relatively low (PM2.5 <150 μgm−3). Assimilation of surface PM2.5 corrects the model chemical initial conditions (ICs) and increases the aerosol concentration in severe haze areas. Focusing on the severe pollution periods, the combined effect of ARI and DA significantly improves forecast accuracy and prolongs this improvement, which reduces the average negative biases of PM2.5 and PM10 by 33% and 35%, and increases the average correlation coefficients by 14% and 12%. Moreover, this combined effect also changes the vertical distribution of the atmosphere, especially at a low level, leading to a 2.6 K decrease in potential temperature (PT) and a 7.6% increase in relative humidity (RH) below 300 m. Due to the contributions of PM and RH, the low VIS prediction is significantly improved, with the root mean square error (RMSE) reduced by 30%. The study results show the combined effect of ARI and DA on aerosols and meteorology and suggest the importance of considering ARI and DA simultaneously in the atmospheric chemical model.

Analysis of temporal and spatial variation of visibility in Beijing, China, from 2015 to 2020

This study analyzes the visibility characteristics in Beijing from 2015 to 2020 using the 10-min average horizonal visibility hourly data from 20 national meteorological stations (NMSs). We examine the visibility trends on different time and space scales, including year, month, day. Our findings reveal that the visibility of the Beijing area shows a noticeable change trend year by year. Overall, the number of days with high visibility (visibility ≥10 ​km) has increased, and the number of hours with low visibility (visibility < 1 ​km) has decreased. Low visibility in Beijing mainly occurs during winter, whereas high visibility occurs throughout the year. On a daily scale, low visibility mainly occurs around 6 a.m. and 8 p.m. Furthermore, the spatial distribution of high and low visibility in Beijing correlates with the topographic boundary between plains and mountains. We observed that mountainous areas have more days with increased visibility and fewer hours with low visibility, while plain areas have fewer days with high visibility and more hours with high visibility. These findings have implications for understanding the environmental hazards caused by poor visibility in Beijing, such as impaired air quality, increased traffic accidents, and reduced human mobility. By identifying the temporal and spatial patterns of visibility, this study provides valuable information that can be used to improve hazard mitigation strategies and promote public safety.

New insights into submicron particles impact on visibility.

The aim of the study was to analyze the impact of very fine atmospheric particles (submicron particulate matter; PM1) on visibility deterioration. Taking into consideration not only their entirely different physio-chemical properties in comparison to a well-recognized PM10 but also the origin and a growing environmental awareness of PM1, the main research problem has been solved in few steps. At first, the chemical composition of PM1 was determined in two selected urban areas in Poland. Measurements of meteorological parameters, i.e., air temperature and humidity, precipitation, atmospheric pressure, wind speed, and visibility, were also conducted. The next step of the work was the analysis of (1) seasonal changes of the concentration of PM1 and its main components, (2) the influence of chemical components of PM1 on light extinction, and (3) the influence of PM1 and humidity on visibility. Hierarchical cluster analysis, correlation matrixes and a heat map, and classification and regression tree analysis were used. The light extinction coefficient is influenced mainly by coarse mass of PM, and PM1-bound ammonium nitrate, organic matter, and by Rayleigh scattering. The less important in the light extinction coefficient shaping has PM1-bound ammonium sulfate, elemental carbon, and soil. In this way, the secondary origin PM1 components were proved to most significantly influence the visibility. The obtained results confirmed the possibility of the use of statistical agglomeration techniques to identify ranges of variation of visibility, including independent variables adopted to analyses (meteorological conditions, chemical composition of PM1, etc.).

Decoration and Density Increase of Dislocations in PVT-Grown SiC Boules with Post-Growth Thermal Processing

Post-growth thermal processing at higher temperature generates more BPDs (basal plane dislocations). It is observed that dislocation visibility in surface inspection tool images varies significantly even at comparable dislocation densities. Combination of dislocation decoration and light absorbance from SiC matrix by point defects or dopants has been proposed as a working hypothesis to explain dislocation visibility variations.

Baseline of Surface and Column-Integrated Aerosol Loadings in the Pearl River Delta Region, China

Much attention has been paid to the rapid variation of aerosol loading in the urban areas of the Pearl River Delta (PRD) region. The baseline of aerosol loading in this rapidly developing region is critical in evaluating how and why the aerosol level has evolved, which absolutely requires long-term observations. Based on long-term observations of aerosol optical depth (AOD), visibility, and particulate matter (PM) mass concentrations at Xichong (114.56°E, 22.49°N), a background site in the PRD region, the variabilities of aerosol loading at multiple temporal scales are revealed. The means (±σ) of AOD, visibility, PM10, PM2.5, and PM1 are 0.38 ± 0.07, 12.6 ± 2.3 km, 23.7 ± 12.6 μg/m3, 19.7 ± 11.0 μg/m3, and 16.1 ± 10.1 μg/m3, respectively, which show that aerosol loading at the Xichong site is much lower than that in urban and suburban sites. Significant decreases in PM10, PM2.5, and PM1 mass concentrations are observed with magnitudes up to −2.13, −1.82, and −1.37 yr−1, respectively, at a 95% confidence level. The decrease in aerosol loadings at Xichong is attributed to the strict environmental regulations for improving air quality. Higher AOD and PM (lower visibility) values are observed during the early spring months as a result of long-range transport of biomass burning from Southeastern Asia. Diurnal variations of PM and visibility are dominantly determined by those of boundary layer height and relative humidity. PM mass concentrations show a generally negative (positive) correlation with visibility (AOD) at Xichong, but the correlations are weak with the R2 of 0.22 and 0.54, respectively. Low visibility and high aerosol loading are generally associated with very weak easterly and southerly winds. Understanding of variability of surface particle concentration and column-integrated aerosol loading at this background site in the PRD region would provide a scientific basis for the adoption of pollution prevention and control measures.

FogAdapt: Self-supervised domain adaptation for semantic segmentation of foggy images

This paper presents FogAdapt, a novel approach for domain adaptation of semantic segmentation for dense foggy scenes. Although significant research has been directed to reduce the domain shift in semantic segmentation, adaptation to scenes with adverse weather conditions remains an open question. Large variations in the visibility of the scene due to weather conditions, such as fog, smog, and haze, exacerbate the domain shift, thus making unsupervised adaptation in such scenarios challenging. We propose a self-entropy and multi-scale information augmented self-supervised domain adaptation method (FogAdapt) to minimize the domain shift in foggy scenes segmentation. Supported by the empirical evidence that an increase in fog density results in high self-entropy for segmentation probabilities, we introduce a self-entropy based loss function to guide the adaptation method. Furthermore, inferences obtained at different image scales are combined and weighted by the uncertainty to generate scale-invariant pseudo-labels for the target domain. These scale-invariant pseudo-labels are robust to visibility and scale variations. We evaluate the proposed model on real clear-weather scenes to real foggy scenes adaptation and synthetic non-foggy images to real foggy scenes adaptation scenarios. Our experiments demonstrate that FogAdapt significantly outperforms the current state-of-the-art in semantic segmentation of foggy images. Specifically, by considering the standard settings compared to state-of-the-art (SOTA) methods, FogAdapt gains 3.8% on Foggy Zurich, 6.0% on Foggy Driving-dense, and 3.6% on Foggy Driving in mIoU when adapted from Cityscapes to Foggy Zurich.

Effects of Different Aerosols on the Air Pollution and Their Relationship With Meteorological Parameters in North China Plain

Located in East Asia, the North China Plain (NCP) has a severe air pollution problem. In this study, variations in visibility, particulate matter (PM), gaseous pollutants, vertical meteorological parameters, and different types of aerosols and their optical properties were evaluated during an air pollution episode that occurred from 10 to 15 January 2019 in Tianjin over the NCP. The visibility was &amp;lt;10 km for approximately 96 h, and the concentrations of PM with an aerodynamic diameter of &amp;lt;2.5 μm (PM2.5) and &amp;lt;10 μm (PM10) increased to 300 and 400 μg/m3, respectively. Because of the conversion of SO2 to sulphate aerosol particles, the sulphur dioxide (SO2) concentration decreased to a minimum of 10 μg/m3. The continual deterioration of visibility was related to the high relative humidity and the boundary layer of &amp;lt;0.1 km. The southwest airflow (3.0–4.0 m/s) transported various pollutants from Hebei Province to the NCP. Higher mixing ratios of sulphate aerosols, organic aerosols, and black carbon aerosols were distributed over a 0.5-km-diameter area, and the maximum concentrations were approximately 90, 250–300, and 20 μg/kg, respectively. Higher mixing ratios of dust aerosols and sea salt aerosols were distributed within 1.5 km and 1.0–2.5 km, respectively, and their maximum concentrations were approximately 15 and 9 μg/kg, respectively. The findings are valuable for analysing the relationship between air quality and pollutant transport in the NCP.

Poor Visibility in Winter Due to Synergistic Effect Related to Fine Particulate Matter and Relative Humidity in the Taipei Metropolis, Taiwan

Visibility is important because it influences transportation safety. This study examined the relationships among sea–land breezes, relative humidity (RH), and the urban heat island (UHI) effect. The study also sought to understand how the synergistic effects of fine particulate matter (PM2.5) and RH influence visibility. Hourly meteorological, PM2.5 concentration, and visibility data from 2016 to 2019 were obtained from government-owned stations. This study used quadratic equations, exponential functions, and multi-regression models, along with a comparison test, to analyse the relationships between these variables. While sea breezes alone cannot explain the presence of PM2.5, UHI circulation coupled with sea breezes during winter can promote the accumulation of PM2.5. The synergistic effects of RH, PM2,5, and aerosol hygroscopicity exist in synoptic patterns type I and type III. PM2.5 was negatively correlated with visibility in the winter, when the RH was 67–95% and the continental cold high-pressure (CCHP) system was over the Asian continent (type I), or when the RH was 49–89% and the CCHP had moved eastward, with its centre located beyond 125° E (type III). The synergistic predictor variable PM2.5×RH was more important than PM2.5 and RH individually in explaining the variation in visibility.

Study of Clearness Index of Atmosphere over Eastern City of Nepal, Biratnagar

Solar radiation data are of great significance for energy harvesting. This paper’s main objective is to study clearness index of atmosphere over Biratnagar (latitude: 26.45° N, longitude: 87.27° E, altitude: 72 m a. s. l.), an eastern city of Nepal. Daily global solar radiation data are derived by using CMP6 pyranometer for one year period (2015). The monthly, annual and seasonal variations of global solar radiation, clearness index and visibility were analyzed. The annual average of global solar radiation (GSR), clearness index (KT) and visibility indices are found to be 11.37 ± 3.76 MJ/m2 /day, 0.37 ± 0.15 and 7.06 ± 5.56 km are respectively. In the study period, number of clear days (KT &gt;0.65) and number of cloudy days (KT &lt;0.34) are found to be 26 and 172 respectively. Study of clearness index of atmosphere and its dependence on meteorological parameters (such as maximum temperature, minimum temperature, rainfall, relative humidity, total ozone column and dew point) are used on agriculture, hydrology, and climate change. Finding of GSR and visibility helps for energy harvesting and aviation.

Spatial coherence modulation using plane waves generated with a digital micromirror device

Plane waves generated and alternated using a Digital Micromirror Device (DMD) were evaluated for modulating the spatial coherence of a laser beam. The spatial coherence and its modulation can be represented as a sampling problem in the temporal domain. In this way, the integration time in the detector, the frame rate of the DMD, and the laser coherence time were properly adjusted or chosen to achieve the effect of a beam with a particular state of spatial coherence. Two methods were applied to superpose the plane waves and produce controlled visibility variations in the interferogram of a Young’s experiment. The visibility measurements show the variation of the modulus of the complex degree of spatial coherence, controlled by simple phase modulation, and between a pair of points on the wavefront. This procedure, which uses no mobile parts, could be applied in digital holography denoising, beam shaping, optical communications and optical metrology and imaging.

Multilevel air quality evolution in Shenyang: Impact of elevated point emission reduction

Visibility observed at different altitudes is favorable to understand the causes of air pollution. We conducted 4-years of observations of visibility at 2.8 and 60 m and particulate matter (PM) concentrations from 2015 to 2018 in Shenyang, a provincial city in Northeast China. The results indicated that visibility increased with the increasing height in winter (especially at night), and decreased with height in summer (especially at the daytime). PM concentration exhibited opposite vertical variation to visibility, reflecting that visibility degrades with the increase of aerosol concentration in the air. The radiosonde meteorological data showed that weak turbulence in the planetary boundary layer (PBL) in winter favored aerosols’ accumulation near the surface. Whereas in summer, unstable atmospheric conditions, upper-level moister environment, and regional transport of air pollutants resulted in the deterioration of upper-level visibility. Inter-annual variation in the two-level visibility indicated that the upper-level visibility improved more significantly than low-level visibility, much likely due to the reduction in emission of elevated point sources in Shenyang. Our study suggested that strengthening the control of surface non-point emissions is a promising control strategy to improve Shenyang air quality.

Computer aided decision support system for mitral valve diagnosis and classification using depthwise separable convolution neural network

The significance of mitral valve (MV) treatment is increasing recently because of an aging population. The computer vision-based acquisition and quantification of the valve anatomy becomes helpful for surgical and intercessional planning. The right option of common treatment and implantation is pertinent for the most favorable results. Several studies reported that the decision support system (DSS) could offer decisions based on the virtual involvement planning and prediction models. Generally, the segmentation and classification of MV from the computed tomography (CT) images are highly complicated, owing to the variations in appearance and visibility. In this paper, an efficient automated DSS model is introduced using watershed segmentation with Xception model for the MV classification. It incorporates four modules: bilateral filtering (BF) based preprocessing, watershed segmentation, Xception based feature extraction and random forest (RF) classification. A watershed algorithm with channel separation is used to segment the MV images. The Xception model with random forest (RF) model is utilized for training and classifying images. A detailed simulation is performed on the CT images collected from hospitals. The presented WS-X model is tested and a comparative study is made with the relevant works to highlight its superior nature. The obtained results stressed out that the WS-X model is an appropriate model for the MV problem under various aspects.

Prediction of Attenuation using Visibility variations and other Meteorological Parameters in George, Western Cape, South Africa.

In this work, the impact of selected atmospheric parameters on attenuation of terrestrial and satellite signals over George, Western Cape in South Africa for the purpose of planning reliable and resilient free space optical communication links has been presented. The meteorological parameters data (visibility, temperature, rainfall rate, relative humidity, pressure, wind speed and wind direction) were obtained for ten years (2010 – 2019) from the South African Weather Service (SAWS). Total attenuation for the studied location was calculated from attenuation due to aerosol scattering, attenuation due to rain and attenuation due to scintillation. Three different wavelengths namely: 850 nm, 1200 nm and 1500 nm within the optical windows were used in calculating the wavelength dependent functions (attenuation due to aerosol scattering and attenuation due to scintillation). The result shows that attenuation due to rain was observed to account for 58% of the total attenuation and it is wavelength independent. Attenuation due to rain based on subtropics model was found to be about half of the calculated value based on temperate model. Further result shows total attenuation for worst visibility period (summer) throughout the year in the studied location is 7.7 dB/km (aerosol scattering: 0.53dB/km; rain: 4.5 dB/km and scintillation: 2.7 dB/km) at 850 nm. Overall results will be applicable in the area of the design and implementation of a reliable free space optical (FSO) links in the studied location.

Spatial Variability of Lower Tropospheric Visibility and Meteorology of Urban centers in Nigeria

The study analyzed the spatial variability of atmospheric visibility and meteorological parameters over selected cities in Nigeria. This study employed the use of secondary data which includes a 36 years (1982-2017) visibility, rainfall, relative humidity, windspeed and temperature data which was gotten from the weather register of the selected meteorological stations. Descriptive statistics, analysis of variance (ANOVA) and student t-test were employed in the analysis of data. Research findings revealed that revealed that the coastal cities witnessed better visibility than the northern part of the study area especially during the dry season. The Analysis of Variance (ANOVA) revealed statistically significant variation in visibility, temperature, rainfall, relative humidity and windspeed at 95% probability level across the selected locations. The student t-test statistics also revealed that a statistically significant difference in visibility, rainfall, windspeed and relative humidity occurs between the wet and dry seasons in most of the selected cities. The study recommends that awareness campaigns and enlightenment programmes be undertaken by various meteorological authorities and other relevant government agencies to improve the awareness of members of the public on the need to adhere strictly to weather alerts and warning systems.

Visibility variations in Tokyo Urban Area

Visibility variations in Tokyo Urban Area