Fog Conditions Research Articles

Automatic Traffic Sign Detection and Classification

Abstract: Automatic detection and recognition of road traffic signs is an essential task for regulating the traffic, guiding, and warning the drivers and pedestrians. M a n y challenges like cluttered background, foreground scenery, various geographic, metrological, weather conditions namely cloudy day, rain, snow, fog, changeable and uncontrollable lighting conditions depending on the time of the day exhibits the detection of road signs. The traffic sign may have different sizes and colors. Our goal is to detect the traffic signs with red, blue, yellow colors with any of the available shapes. Then the detected signs, combined with the color and shape information are classified. The recognition of Traffic sign involves two stages: detection stage, it finds the region consisting of traffic signs from the image and then the classification stage where the detected signs are categorized into different classes like information, warning, prohibition and so on. The image is acquired and is processed in CSR block, in which the base features from the image are extricated. The base features extricated from the image are color, shape, and Region of Interest (ROI) position. This pre-processing is very quick, because no special transformation is required. RGB and HSV color space is chosen for color segmentation. By extracting the color, CSR block creates 3 binary maps (red, blue, and yellow). From these binary maps, the shape of the traffic sign is detected. From the image the ROI is segmented, which is the input to the shape detecting block. After the shape is detected, traffic sign is classified according to its detected color and shape.

Ultraviolet communication with a large scattering angle via artificial agglomerate fog.

Considering strong signal attenuation of the large-angle non-line-of-sight (NLOS) link achieved due to the ultraviolet (UV) scattering properties, we propose to increase the UV communication link gain under a large scattering angle via generating agglomerate fog within a certain range as a secondary light source. In this study, a channel model with locally strong scatterers from agglomerate fog is proposed based on Monte Carlo ray-tracing approaches. Mie theory is adopted to calculate the atmospheric channel parameters, to further evaluate the link gain of a channel under non-uniform atmosphere. The performance of scattering system in the presence of fog conditions depends on the relative positions of the light source to the fog and to the receiver. The link gain results reveal the transmission capabilities for different scattering channel geometries, and give the optimal spray point location within a 10 m communication range. We further establish a foggy NLOS system using an agglomerate fog generator to verify our research in the real environment. The results show that the received signal strength of the NLOS link can be effectively enhanced by constructing scatterers in the atmospheric channel, which significantly decreases the bit-error rate (BER).

Multi-band polarization imaging and image processing in sea fog environment

Considering the advancement in marine research in recent years, studies on the identification of offshore scenery are becoming popular globally. In this study, multiband polarimetric imaging is presented to address the deficiencies of the previous single-band study. Polarization imaging experiments of sea fog and non-sea fog in an indoor simulated environment are carried out and compared and analyzed by establishing an artificial simulation system to characterize the sea fog concentration by optical thickness with different concentrations of sea fog environment as the medium. The polarization information of each waveband converted by Stokes parametric is then brought into the two-dimensional discrete wavelet algorithm for image fusion processing. The findings indicate that when the optical thickness of sea fog increases, the polarized light in the chaotic medium recedes and the effect of the image blurs. Finally, after the image fusion process, the contrast of the image is improved and the detail of the target contour is obvious, which proves that the method has good robustness under the low signal-to-noise ratio of the sea fog environment. This provides a solid platform for targeted surveys and civic operations under dense marine fog conditions.

A Study on the Effectiveness of SCD Seeding Fog Dissipation Mechanism Using LiDAR Sensor

Fog interferes with traffic flow and causes major accidents. In foggy conditions, traffic accident death rates are higher than in other weather conditions. Research on fog dissipation technology is needed to reduce the incidence of accidents caused by fog. There are various artificial methods to remove fog. In this study, two methods of natural dissipation by gravity sedimentation and a solid carbon dioxide seeding fog dissipation mechanism were compared and analyzed in cold fog conditions. Solid carbon dioxide was selected as the fog dissipation particle. In this experiment, solid carbon dioxide seeding with three different values of weight (500 g, 1000 g, and 1500 g) was considered. This is because fog particles can be supercooled and fog can be removed. A light detection and ranging (LiDAR) sensor were used to quantitatively check the effect of improving visibility when solid carbon dioxide was seeded in the fog. The LiDAR sensor detects the surrounding environment through distance measurements by emitting lasers and processing the laser responses. A camera was used to visually observe the phenomenon occurring inside the calorimetric chamber. As a result, the fog dissipation mechanism using solid carbon dioxide seeding under cold fog conditions was proven to be effective in improving the visible distance compared with natural dissipation.

Study on Risk Prediction Model of Expressway Agglomerate Fog-Related Accidents

Based on meteorological observations, traffic flow data and information of traffic accidents caused by fog or agglomerate fog along the expressways in Jiangsu Province and Anhui Province in China from 2012 to 2021, key impact factors including meteorological conditions, road hidden dangers and traffic flow conditions are integrated to establish the prediction model for risk levels of expressway agglomerate fog-related accidents. This model takes the discrimination of the occurrence conditions of agglomerate fog as the starting term, and determines the hazard levels of agglomerate fog-related accidents by introducing the probability prediction value of meteorological conditions for fog-related accident as the disaster-causing factor. On this basis, the hourly road traffic flow and the location of road sections with a hidden danger of agglomerate fog are taken as traffic and road factors to construct the correction scheme for the hazard levels, and the final predicted risk level of agglomerate fog-related accident is obtained. The results show that for the criteria of disaster-causing factor classification threshold, 72.3% of fog-related accidents correspond to a hazard of a medium level or above, and 86.2% of the road traffic flow conditions are consistent with the levels of the traffic factor defined based on parametric indexes. For risk level prediction, six out of the seven agglomerate fog-related accidents correspond to the level of higher risk or above, which can help provide meteorological support for traffic safety under severe weather conditions. Moreover, the model takes into account the impacts of traffic flow and the road environment, which is conducive to further improving the reliability of the risk assessment results.

Fog Intermittency and Critical Behavior

The intermittency of fog occurrence (the switching between fog and no-fog) is a key stochastic feature that plays a role in its duration and the amount of moisture available. Here, fog intermittency is studied by using the visibility time series collected during the month of July 2022 on Sable Island, Canada. In addition to the visibility, time series of air relative humidity and turbulent kinetic energy, putative variables akin to the formation and breakup conditions of fog, respectively, are also analyzed in the same framework to establish links between fog intermittency and the underlying atmospheric variables. Intermittency in the time series is quantified with their binary telegraph approximations to isolate clustering behavior from amplitude variations. It is shown that relative humidity and turbulent kinetic energy bound many stochastic features of visibility, including its spectral exponent, clustering exponent, and the growth of its block entropy slope. Although not diagnostic, the visibility time series displays features consistent with Pomeau–Manneville Type-III intermittency in its quiescent phase duration PDF scaling (−3/2), power spectrum scaling (−1/2), and signal amplitude PDF scaling (−2). The binary fog time series exhibits properties of self-organized criticality in the relation between its power spectrum scaling and quiescent phase duration distribution.

Applicability of the low-cost OPC-N3 optical particle counter for microphysical measurements of fog

Abstract. Low-cost devices for particulate matter measurements are characterised by small dimensions and a light weight. This advantage makes them ideal for UAV measurements, where those parameters are crucial. However, they also have some issues. The values of particulate matter from low-cost optical particle counters can be biased by high ambient humidity. In this article, we evaluate the low-cost Alphasense OPC-N3 optical particle counter for measuring the microphysical properties of fog. This study aimed to show that OPC-N3 not only registers aerosols or humidified aerosols but also registers fog droplets. The study was carried out on the rooftop of the Institute of Geophysics, University of Warsaw, Poland, during autumn–winter 2021. To validate the results, the data from OPC-N3 were compared with the data obtained from the reference instrument, the Oxford Lasers VisiSize D30. VisiSize D30 is a shadowgraph device able to register photos of individual droplets. Considering the effective radius of droplets, it is possible to differentiate low-visibility situations between fog conditions (which are not hazardous for people) from haze events, when highly polluted air can cause health risks to people. The compared microphysical properties were liquid water content (LWC), number concentration (Nc), effective radius reff and statistical moments of radius. The Pearson correlation coefficient between both devices for LWC was 0.92, Nc was 0.95 and reff was 0.63. Overall, these results suggest good compliance between instruments. However, the OPC-N3 has to be corrected regarding professional equipment.

Bayesian inference of visibility in fog and smoke artificial conditions from 3D-LiDAR point clouds

3D-LiDARs are heavily impacted by a degraded visual environment (DVE) like rain, fog and smoke which limits their use for perception algorithms. The capacity to retrieve information about the environmental conditions from an embedded sensor can be an asset to improve autonomous driving performances. False positive artifacts in the point clouds caused by aerosols and hydrometeors particles tend to cause perception issues and thus need filtered out. However, those artifacts can also be used as valuable information to infer weather properties and maybe improve filters. This article proposes a Bayesian inference model which can classify discrete values of visibility using 3D-LiDAR point clouds. Gamma and Log-normal distributions are used to model the distance distributions of the noise points and the model is extended using the Random Finite Set (RFS) formalism with the Poisson and Binomial RFS models. Experiments in artificial fog and smoke conditions are presented and the classification model is trained and tested independently for each experiment. The used point clouds are extracted from specific parts of the field-of-view that can be used to generalize the proposed method to any outdoor scenario. The model shows good classification results with increased performances when the RFS extension is used.

Are two naïve and distributed heads better than one? Factors influencing the performance of teams in a challenging real-time task.

Collective decisions in dynamic tasks can be influenced by multiple factors, including the operational conditions, quality and quantity of communication, and individual differences. These factors may influence whether two heads perform better than one. This study examined the "two heads are better than one" effect (2HBT1) in distributed two-person driver-navigator teams with asymmetrical roles performing a challenging simulated driving task. We also examined the influence of communication quality and quantity on team performance under different operational conditions. In addition to traditional measures of communication volume (duration and speaking turns), patterns of communication quality (optimality of timing and accuracy of instructions) were captured. Participants completed a simulated driving task under two operational conditions (normal and fog) either as individual drivers (N = 134; 87 females, mean age = 19.80, SD = 3.35) or two-person teams (driver and navigator; N = 80; 109 females, mean age = 19.70, SD = 4.69). The normal condition was characterized by high visibility for both driver and navigator. The fog condition was characterized by reduced visibility for the driver but not for the navigator. Participants were also measured on a range of cognitive and personality constructs. Teams had fewer collisions than individuals during normal conditions but not during fog conditions when teams had an informational advantage over individuals. Furthermore, teams drove slower than individuals during fog conditions but not during normal conditions. Communication that was poorly timed and/or inaccurate was a positive predictor of accuracy (i.e., collisions) during the normal condition and communication that was well timed and accurate was a negative predictor of speed during the fog condition. Our novel measure of communication quality (i.e., content of communication) was a stronger predictor of accuracy, but volume of communication was a stronger predictor of time (i.e., speed). Results indicate when team performance thrives and succumbs compared with individual performance and informs theory about the 2HBT1 effect and team communication.

Enhanced aerial vehicle system techniques for detection and tracking in fog, sandstorm, and snow conditions

Unmanned aerial vehicles are rapidly being utilized in surveillance and traffic monitoring because of their great mobility and capacity to cover regions at various elevations and positions. It is a challenging task to detect vehicles due to their various shapes, textures, and colors. One of the most difficult challenges is correctly detecting and counting aerial view vehicles in real time for traffic monitoring objectives using aerial images and videos. In this research, strategies are presented for improving the detection ability of self-driving vehicles in tough conditions, also for traffic monitoring, vehicle surveillance. We make classification, tracking trajectories, and movement calculation where fog, sandstorm (dust), and snow conditions are challenging. Initially, image enhancement methods are implemented to improve unclear images of roads. The improved images are then subjected to an object detection and classification algorithm to detect vehicles. Finally, new methods were evaluated (Corrected Optical flow/Corrected Kalman filter) to get the least error of trajectories. Also features like vehicle count, type, tracking trajectories by (Optical flow, Kalman Filter, Euclidean Distance) and relative movement calculation are extracted from the coordinates of the observed objects. These techniques aim to improve vehicle detection, tracking, and movement over aerial views of roads especially in bad weather. As a result, for aerial view vehicles in bad weather, our proposed method has an error of less than 5 pixels from the actual value and give the best results. This improves detection and tracking performance for aerial view vehicles in bad weather conditions.

Carbon fluxes associated with fog in an elfin cloud forest in Anaga (Tenerife, Canary Islands)

The role of fog in the relict laurel forests of the Macaronesia is still not fully understood and it is even more relevant in the Canary Islands, where precipitations are rather scarce and summers are dry. Eddy covariance CO2 exchange (FCO2) measurements in a “brezal de tejo” elfin cloud forest in Tenerife (Canary Islands), over a four-weeks period during the wet season, showed that the vegetation can sustain a carbon assimilation rate in an environment of fog, leading to higher median water use efficiency values (19.5 μmol mg−1 ≤ WUE ≤ 68.3 μmol mg−1) than during no-fog periods (WUE = 1.8 μmol mg−1), being highest under thick fog (WUE = 68.3 μmol mg−1). Median FCO2 values across the day were slightly greater under foggy versus no-fog conditions during the study period. These increments were more prominent under a low light environment, especially in the morning, thus highlighting the important difference between cloud immersion versus high clouds. A greater proportion of light diffusivity due to fog may enhance carbon assimilation, although additional concomitant factors may intervene during thick fog conditions. Also, net carbon assimilation was initiated earlier under foggy conditions. During cloud immersion and depleted light, the maximal CO2 exchange, FCO2_max, diurnal values were higher than those observed when fog was absent. Additionally, FCO2_max remained high (FCO2_max ≈ 13 μmol m−2 s−1) for large vapour pressure deficit conditions (VPD > 0.6 kPa). All this is indicative of a profligate water use strategy of the vegetation, presumably due to low stomatal control. Fog was present during 36.9 % of the time and, as a consequence, the estimated mean daily settling flux of water droplets was 1.186 mg m−2 s−1. For the entire period measured, the cumulative settling flux was 1.30 mm compared to the 97.6 mm of rainfall and the 36.6 mm of throughfall, and the canopy was wet at least 33.3 % of the time. The results suggest that such an anisohydric behaviour of the vegetation permits a continuous growth with maximal water use efficiency, while the frequent presence of fog and wetting of the canopy would minimize the risk of cavitation associated with such non-conservative water use response.

MERRA-2 reanalysis-aided sea fog detection based on CALIOP observation over North Pacific

Sea fog can lead to a dangerous meteorological hazard over oceans and coastal areas. Cloud-aerosol Lidar with Orthogonal Polarization (CALIOP) is an effective tool to detect sea fog, however it is ineffective for dense fog detection because the lidar signal has been fully attenuated before reaching the layer base. To address this issue by improving dense fog detection, a reanalysis-aided sea fog detection method using CALIOP lidar measurements and MERRA-2 reanalysis dataset is proposed taking the Northwestern Pacific (NP) as study area. In this method, CALIOP lidar measurements are used to locate the ceiling and base of the hydrometeor layers, and extract the candidate fog layers in the Marine Atmospheric Boundary Layer (MABL). Reanalysis dataset are used to identify real fog from the candidate fog layers based on the near-surface meteorological difference under different weather conditions (sea fog, stratus and stratus precipitation). Method validation using in-situ weather records from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) and five climate stations, shows the detection accuracy and false alarm rate of 89.4% and 2.0%, respectively. Most importantly, about 87.4% of dense fogs are successfully discriminated with the aid of reanalysis data. Model application to CALIOP data from 2007 to 2019 shows pronounced spatial and seasonal characteristics of sea fog distribution over NP, with the highest frequency (>40%) found over the Kuril Islands, Sea of Okhotsk and Bering Sea from June to August. The example demonstrates that by using time series CALIOP observations and the proposed method, one may expect spatio-temporally complete sea fog information, which is important especially for remote regions with scarce in-situ measurements (e.g. polar regions). Our research proposes and proves a novel idea of sea fog detection from space by using satellite lidar and reanalysis data, which may benefit the sea fog forecasting and dynamics analysis, by providing unprecedented large-scale high-quality observations, especially over dense fog conditions.

An Improved Wavelength Diversity Based Free Space Optical Link: Effects of Fog and Atmospheric Turbulence

Free-space optical (FSO) communication systems have received a lot of attention from academia and industries due to their low cost, high security and data rates for various wireless communication applications. However, factors such as fog and atmospheric turbulence degrade the performance of the FSO communication system. Therefore, this study presents an improved wavelength diversity based free space optical link using a single channel approach. The authors employed both Kim and Al-Naboulsi’s models to model the attenuation-induced FSO link. Furthermore, the Gamma-Gamma turbulent model was utilized to explain the turbulent nature of the FSO communication link. Experimental results showed improved performance in terms of Bit Error Rate (BER) and Quality factor of the FSO link up to a distance of 7km compared to the multiple channel approach that gives a signal output for distances less than 3km only. Hence, the impact of attenuation on the FSO link was reduced and the performance of the system was enhanced under fog and atmospheric turbulence conditions.

A 40-Gbps fiber-FSO convergent transmission system employing OFCL-based WDM and external modulation technique

We have proposed and demonstrated a 40Gbps fiber-free space optics (FSO) convergent system based on external modulation and wavelength division multiplexing (WDM) technique. In this experiment, 4010×4 Gbps signal is successfully transmitted employing WDM scheme over 50 km single mode fiber plus 2.5 km FSO distance. Optical frequency comb lines (OFCL) are generated using a continuous wave laser diode, a radio frequency signal generator, a dual drive Mach Zehnder modulator, and an electrical attenuator. OFC lines, having a side mode suppression ratio (SMSR) value of ∼40 dB and flatness of ∼0.4 dB, are employed as broadband laser source in the presented convergent system. The performance of the fiber-FSO system is investigated theoretically for different weather conditions. Very low power penalty with low bit error rate (BER), clear eye diagrams, and standard Q-factor are achieved in the proposed system. The power penalty is restricted within 2.5 dB and 5.8 dB at a BER of 10−9 for 50 km SMF plus 2.5 km FSO distance in clear weather and 50-km SMF plus 0.4-km FSO distance in heavy fog condition respectively. Thus, the proposed WDM-convergent system is efficient enough for high data rate transmission along with low BER value of ∼10−9 and high Q-factor for different environmental scenarios.

Experimental Analysis of Ceramic Insulators with Different Profiles Under Very High Pollution Zone by Solid Layer Method

The outdoor insulators in service are exposed to marine, agricultural waste and industrial pollution. These pollutants get deposited on the insulator surface and lead to flashovers under fog conditions. There has been an abnormal increase in pollution levels due to rapid industrialization in the past two decades. This exponential increase in pollution levels causes frequent flashovers of insulators, leading to the tripping of transmission lines consecutively may result in blackouts, which severely affects the reliability of the power systems. The ESDD and NSDD measurement on the insulator surface are critical parameters for determining the pollution severity levels. This paper discusses ESDD and NSDD measurement procedures. In this paper, field pollution severity has been simulated in the Artificial Pollution Lab. In addition, the Non-Soluble Deposit Density (NSDD) component of pollutants for designing the creepage distance of ceramic insulators under heavy pollution conditions has been emphasized.

Comparison of aqueous secondary organic aerosol (aqSOA) product distributions from guaiacol oxidation by non-phenolic and phenolic methoxybenzaldehydes as photosensitizers in the absence and presence of ammonium nitrate

Abstract. Aromatic carbonyls (e.g., methoxybenzaldehydes), an important class of photosensitizers, are abundant in the atmosphere. Photosensitization and nitrate-mediated photo-oxidation can occur simultaneously, yet studies about their interactions, particularly for aqueous secondary organic aerosol (aqSOA) formation, remain limited. This study compared non-phenolic (3,4-dimethoxybenzaldehyde, DMB) and phenolic (vanillin, VL) methoxybenzaldehydes as photosensitizers for aqSOA formation via guaiacol (GUA) oxidation in the absence and presence of ammonium nitrate (AN) under atmospherically relevant cloud and fog conditions. GUA oxidation by triplet excited states of DMB (3DMB∗) (GUA + DMB) was ∼ 4 times faster and exhibited greater light absorption than oxidation by 3VL∗ (GUA + VL). Both GUA + DMB and GUA + VL formed aqSOA composed of oligomers, functionalized monomers, oxygenated ring-opening species, and N-containing products in the presence of AN. The observation of N-heterocycles such as imidazoles indicates the participation of ammonium in the reactions. The majority of generated aqSOA comprises potential brown carbon (BrC) chromophores. Oligomerization and functionalization dominated in GUA + DMB and GUA + VL, but functionalization appeared to be more important in GUA + VL due to contributions from VL itself. AN did not significantly affect the oxidation kinetics, but it had distinct effects on the product distributions, likely due to differences in the photosensitizing abilities and structural features of DMB and VL. In particular, the more extensive fragmentation in GUA + DMB than in GUA + VL likely generated more N-containing products in GUA + DMB + AN. In GUA + VL + AN, the increased oligomers may be due to VL-derived phenoxy radicals induced by ⚫OH or ⚫NO2 from nitrate photolysis. Furthermore, increased nitrated products observed in the presence of both DMB or VL and AN than in AN alone imply that photosensitized reactions may promote nitration. This work demonstrates how the structural features of photosensitizers affect aqSOA formation via non-carbonyl phenol oxidation. Potential interactions between photosensitization and AN photolysis were also elucidated. These findings facilitate a better understanding of photosensitized aqSOA formation and highlight the importance of AN photolysis in these reactions.

SWEET: A Realistic Multiwavelength 3D Simulator for Automotive Perceptive Sensors in Foggy Conditions

Improving the reliability of automotive perceptive sensors in degraded weather conditions, including fog, is an important issue for road safety and the development of automated driving. Cerema has designed the PAVIN platform reproducing fog and rain conditions to evaluate optical automotive sensor performance under these conditions. In order to increase the variety of scenarios and technologies under test, the use of digital simulation becomes a major asset. The purpose of this paper is to revive the debate around the realism of the various models underlying the numerical methods. The simulation of the radiative transfer equation by Monte Carlo methods and by simplified noise models is examined. The results of this paper show some gaps in foggy scenes between the ray-tracing method, which is considered to be the most realistic, and simple models for contrast evaluation, which can have a particularly strong impact on obstacle detection algorithms.

Impact of Scintillation Effect on Clear Weather and Fog Conditions using MDM-WDM Modulation Technique

The 5th generation optical wireless communication technologies have unique features of higher data rate, scalability in bandwidth, license free spectrum, cost effectiveness, easy deplorability and secured transmission. The role of free space optical (FSO) communication becomes important as the applications of next generation technologies are evolving in almost every sector. In this work, the performance of the terrestrial FSO MDM-WDM link has been investigated through a DP-QPSK modulation scheme at a data rate of 1000 Gbps. The link attenuation has been analysed with the help of Gamma- Gamma and Lognormal scintillation models considering clear weather and varying fog conditions. The link performance has been evaluated through SNR and received optical power for a varying distance of up-to 20 Km.

Климатические характеристики туманов восточного побережья Каспийского моря и их изменения с 1970 по 2020 г.

In the Caspian Sea, fogs of varying intensity are observed throughout the year. The research aimed to determine the number of days with fog and their duration (in hours) for each month from 1970 (1986) to 2020 at four meteorological stations: Kulaly Island, Fort Shevchenko, Tushchibek, and Aktau. The study included statistical analysis and assessment of climate change in the form of fog indicator longterm series approximation based on two non-stationary average models, including the evaluation of their efficiency and statistical significance. The assessment of climate change revealed the most significant reductions at the Tushchibek and Fort Shevchenko Stations, with decreased fog conditions observed in summer and autumn. At the Kulaly Island Station, the changes were detected in autumn; and were practically absent at the Aktau Station.

Fog-Induced Alteration in Airborne Microbial Community: a Study over Central Indo-Gangetic Plain in India.

Fog supports an increase in airborne microbial loading by providing water with nutrients and protecting it from harmful incoming solar radiation. To improve our present understanding of fog-induced alteration in an atmospheric microbial community, a study was conducted during 1 to 14 January 2021 for continuous investigation of airborne bacteria over a rural site, Arthauli (25.95°N, 85.10°E), in central Indo-Gangetic Plain (IGP) in India. An increase of 36% ± 0.4% in airborne bacterial loading was noticed under fog versus prefog conditions, and a decrease of 48% ± 0.4% was noticed under the postfog condition. Airborne bacterial loading had a strong correlation with RH (R2 = 0.56; P < 0.05), temperature (R2 = -0.55, P < 0.05), and wind speed (R2 = -0.52, P < 0.05). Unique types of bacteria, representing about 29% of the whole community, were detected only under foggy conditions, likely by a continuous supply of nutrients and water from a cold, calm, and humid atmosphere. As a result, no significant diurnal variation of bacterial loading was noticed on a foggy day, with a higher daily mean concentration of about (8.4 ± 1.7) × 105 cells · m-3 than that on a typical winter day [(6.3 ± 3.8) × 105 cells · m-3]. A typical winter day experienced about a 60% decrease in bacterial loading in the afternoon in comparison to that in the morning. A 3-day back-trajectory analysis suggests a slow movement of airmass along with the wind blowing from west to central IGP. Fog pauses wind movement, which reduces continuous transportation of urban sources while increasing airborne bacteria from local sources. The abundances of Gp6 (14.8% ± 8.6%), Anaeromyxobacter (7.1% ± 2.8%), and Gp7 (6.8 ± 2.6%) have been observed to increase due to occurrences of fog over central IGP. IMPORTANCE Fog was investigated in the present study as a cause of alteration in the airborne microbial community. Occurrences of fog were responsible for an increase in airborne microbial loading (36%) over central IGP in India due to the easy availability of nutrients and water in the air and dimming of harmful solar radiation. More than 90% of unique bacteria were detected under fog (64%) and postfog (28%) conditions. A few bacteria, like Gp18 (0.5% ± 0.3%), Alicyclobacillus (0.5% ± 0.1%), Sinomonas (0.4% ± 0.2%), and Phenylobacterium (0.4% ± 0.2%), were detected only under foggy conditions. A strong correlation between meteorological parameters and bacterial loading was found in the current research work. The present study provides additional support toward a new direction in interdisciplinary science for the detailed investigations of the effects of meteorological conditions on airborne bacteria and their implications for society.