Fog Events Research Articles

Datasets for Publication "Regional Atmospheric Circulation Patterns Favouring Consecutive Fog Events in the United Arab Emirates"

Datasets for Publication "Regional Atmospheric Circulation Patterns Favouring Consecutive Fog Events in the United Arab Emirates"

Datasets for Publication "Regional Atmospheric Circulation Patterns Favouring Consecutive Fog Events in the United Arab Emirates"

Datasets for Publication "Regional Atmospheric Circulation Patterns Favouring Consecutive Fog Events in the United Arab Emirates"

Datasets for Publication "Regional Atmospheric Circulation Patterns Favouring Consecutive Fog Events in the United Arab Emirates"

Datasets for Publication "Regional Atmospheric Circulation Patterns Favouring Consecutive Fog Events in the United Arab Emirates"

Correction to: WRF sensitivity simulations of a dense advection fog event in Istanbul

Correction to: WRF sensitivity simulations of a dense advection fog event in Istanbul

Primary Emissions and Secondary Aerosol Processing During Wintertime in Rural Area of North China Plain

AbstractThe vast rural areas often experience more severe haze pollution than megacities during wintertime in North China Plain (NCP), yet the sources and evolution processes of aerosol particles, particularly organic aerosol (OA) remain poorly understood. Here we conducted real‐time measurements of submicron aerosol (PM1) species using a high‐resolution aerosol mass spectrometer at a representative rural site in NCP in winter 2019. Our results showed the dominance of organics (36%) and nitrate in PM1 (22%) in 2019. Positive matrix factorization analysis illustrated similarly important primary sources from traffic emissions, coal combustion, and biomass burning, yet secondary OA (SOA) from photochemical and aqueous‐phase related processing exceeded primary OA (53% vs. 47%). Substantial decreases in primary species and considerable increases in nitrate and sulphate were observed since winter 2018, demonstrating enhanced secondary formation in winter 2019. OA composition changed significantly from clean period to fog events with the contribution of aqueous phase‐related oxygenated OA increasing from 6% to 44%, while the photochemical SOA decreased correspondingly from 51% to 19%. The size distributions of aerosol species also changed by shifting toward large sizes during fog events. Elemental analysis of OA and the Van Krevelen diagram (H/C vs. O/C) illustrated the different roles of photochemical and aqueous‐phase processing during daytime and nighttime, respectively, and aqueous‐phase processing is subject to the formation of organic compounds with high H/C and O/C ratios. The large differences in fog processing of submicron aerosol species between 2018 and 2019 due to different temperatures were also elucidated.

Characterization of chemical and physical changes in atmospheric aerosols during fog processing at Baengnyeong Island, South Korea

In situ interactions between aerosols and atmospheric fog droplets were explored using aerosol physical and chemical measurements and fog samples taken at the Baengnyeong Island Intensive Air Quality Monitoring Station, South Korea during June 16th to July 21st, 2014. To investigate wet scavenging by fog, aerosols were characterized by aerosol mass spectrometer before, during, and after five fog events. Total non-refractory PM1 mass decreased by an average of 22.8% with the onset of fog. The scavenging efficiency (η) of organics was 7–34%, where lower η was associated with lower organic oxidation and hygroscopicity. The η was 16–68% for NH4+, -1–35% for SO4, and 17–86% for NO3−, consistent with previous studies and the relatively high NO3− hygroscopicity. The variation in scavenging was size dependent for both organics and NO3−. Particles with lower NO3−η on 7/18 were smaller (mode = 346 nm), while those with higher NO3−η were larger (593 nm). Differences in mass were also explored before and after fog events. Three episodes showed overall decreases in all the components after the fog event, while two episodes showed overall increases, most notably in organics. Both organic mass and SO2+/H2SO4+ ratio were used to indicate the prevalence of hydroxymethanesulfonate (HMS), an aqueous secondary organic aerosol (aqSOA) formation marker, which increased after the 7/18 fog episode. The aerosol carbon oxidation state decreased toward the end of all fog episodes suggesting possible molecular fragmentation and loss of highly oxidized functional groups. On 7/18, the organic content developed lower O:C and higher H:C moving “up” a slope of −1 in a van Krevelen diagram over the course of the fog episode, indicating carboxylic acid or hydroxycarbonyl loss. This study explores relatively rare time-resolved aerosol fog processing measurements. The results confirm previously established relationships between scavenging and particle size, demonstrate overall increases and decreases in particle mass after fog processing based on dominant local chemistry and wet deposition, and show predominant decreases in organic oxidation after fog processing, thereby, contributing to our understanding of particle processing and organic cycling in the atmosphere.

Machine learning regression and classification methods for fog events prediction

Atmospheric low-visibility events are usually associated with fog formation. Extreme low-visibility events deeply affect the air and ground transportation, airports and motor-road facilities causing accidents and traffic problems every year. Machine Learning (ML) algorithms have been successfully applied to many fog formation and low-visibility prediction problems. The associated problem can be formulated either as a regression or as a classification task, which has an impact on the type of ML approach to be used and on the quality of the predictions obtained. In this paper we carry out a complete analysis of low-visibility events prediction problems, formulated as both regression and classification problems. We discuss the performance of a large number of ML approaches in each type of problem, and evaluate their performance under a common comparison framework. According to the obtained results, we will provide indications on what the most efficient formulation is to tackle low-visibility predictions and the best performing ML approaches for low-visibility events prediction.

Particle number size distribution of PM1 and PM10 in fogs and implications on fog droplet evolutions

Particle microphysical and chemical properties, including particle number size distribution (PNSD) and chemical composition of both ambient PM1 and PM10 particles were measured in the North China Plain (NCP) during the Multiphase chemistry experiment in Fogs and Aerosols in the North China Plain (McFAN) campaign in November 2018. Focused on the differences of measured PNSD between PM1 and PM10, the variations of aerosol microphysical and chemical properties in fog were analyzed and their corresponding influence on fog droplets evolution was evaluated. Two nighttime fog events were observed on the 12th and 13th November, respectively. During fog events, PNSDs of particles with diameter larger than ∼200 nm sampled from the PM10 inlet were similar to those after fogs. But in particle size range larger than 200 nm, particles number concentration sampled from the PM1 inlet were very few and much lower than those after fogs, indicating that some particles larger than 200 nm have grown into droplets with diameter larger than 1 μm and thus were not detected under the PM1 inlet. No significant decrease in particle number concentration larger than ∼200 nm was detected under the PM10 inlet after the dissipation of these fog events, suggesting that fog droplets were generally smaller than 10 μm and could be sampled by the PM10 inlet. The additional particles sampled in PM10 in fogs demonstrated a normal distribution, which indicates that they belonged to an individual particle group externally mixed with those sampled under the PM1 impactor and could outgrow 1 μm either through hygroscopic growth or through activation. This can lead to uncertainties in droplet number concentration estimations within fog from microphysical and chemical properties measurements of the aerosol with assumption of fully internal mixture. We find that PM1 hygroscopicity derived from particle chemical composition measurements was overestimated, which may result in significant bias in the estimated droplets number concentration in the North China Plain. These results and approaches in this study can improve the understanding of aerosol microphysical properties in fogs and the corresponding influence of aerosol particles on fog microphysics.

The importance of hydroxymethanesulfonate (HMS) in winter haze episodes in North China Plain

Hydroxymethanesulfonate (HMS), a key marker species of aqueous-phase processing, plays a significant role in sulfur budget in atmosphere. Here we have a comprehensive characterization of HMS at urban and rural sites in North China Plain (NCP) by using the real-time measurements from a high-resolution aerosol mass spectrometer (AMS) and a single-particle AMS together with offline filter analysis. Our results showed much higher winter concentration of HMS at the rural site (average±1σ: 2.58 ± 2.56 μg m−3) than that (1.70 ± 2.68 μg m−3) in Beijing due to the more frequent fog events, low particle acidity and high concentration of precursors. The HMS on average contributed 6.3% and 5.2% to organic aerosol (OA), and 16% and 12% to the total particulate sulfur, at the rural and urban sites, respectively. HMS was highly correlated with aqueous-phase secondary OA and sulfate, and its contribution to the total particulate sulfur increased significantly as a function of relative humidity demonstrating the effective HMS production from aqueous-phase processing. Single-particle analysis showed that HMS-containing particles were mainly mixed with amine-related compounds. In addition, we found that organosulfur compounds (OS) estimated from sulfur-containing fragments of AMS correlated well with HMS at both urban and rural sites. While OS at the rural site was dominated by HMS, other types of OS were also important in urban area. The high HMS also affected the estimation of particle acidity using the AMS measured and predicted ammonium, particularly during severe haze episodes. Overall, our results demonstrated the importance of HMS in winter in NCP, and it could be more important in total particulate sulfur budget as the continuous decrease in sulfate in the future.

Physical Processes in Sea Fog Formation and Characteristics of Turbulent Air-Sea Fluxes at Socheongcho Ocean Research Station in the Yellow Sea

The Yellow Sea is the most fog-prone region of the East Asian marginal seas. Since sea fog is caused due to complex interactions between atmospheric and oceanic environments, direct observations can help understand the physical processes involved in fogging over the oceans. Completed in 2014, the Socheongcho Ocean Research Station (S-ORS) plays a critical role in monitoring air-sea interactions over the Yellow Sea. This study aimed to evaluate the conditions favorable for fog generation and the physical processes underlying it using a suite of observations and turbulent heat flux data from S-ORS. First, we used the visibility data from S-ORS to quantify the frequency of sea fog over the Yellow Sea. From April to June 2016, sea-fog occurred 61 times, with a maximum duration of 135 h (approximately 5.6 days). Next, to understand the origin and characteristics of air mass associated with fog events, we classified the primary airflow paths in the region using a Hybrid Single-Particle Lagrangian Integrated Trajectory model. Among the four clusters identified from the cluster analysis, the third and fourth had distinct physical properties characteristic of cold and warm fog, respectively. The third cluster was characterized by relatively weak or negative heat advection and weak vertical mixing, while the fourth one featured strong positive heat transport and moisture convergence over the Yellow Sea. Finally, based on cluster analysis, we choose the representative cases related to these two clusters observed at S-ORS and compared the characteristics of turbulent air-sea fluxes associated with fog formations.

Terrestrial Free Space Optical Communication Systems Availability based on Meteorological Visibility Data for South Africa

In spite of the numerous advantages of employing free space optical (FSO) communication systems as viable complementary platforms for next-generation networks, the presence of atmospheric disturbances such as fog and scintillations are major sources of signal impairment which degrade system performance. Consequently, it becomes imperative to investigate and contextualize the unique climatic conditions in those locations where FSO links are to be deployed. Statistical evaluation of meteorological visibility data collected for various cities in South Africa is thus hereby employed in estimating the availability performance of FSO links transmitting at both 850 nm and 1550 nm. It is determined that the cities of Mbombela and Cape Town have the lowest performance due to the high occurrence of fog events as compared to other regions in South Africa. During foggy periods, FSO links in Mbombela and Cape Town would have availabilities of ∼99.6% for link distances of 500 and 600 metres, respectively. The bit error rate (BER) estimations of intensity modulation and direct detection (IM/DD) FSO links in the presence of weak atmospheric turbulence were also investigated for the identified locations during foggy weather; with the cities of Mafikeng and Kimberley showing the lowest BER performances because of their high wind velocities, altitudes and refractive index values. In order to obtain a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> , receive signal-to-noise ratio (SNR) values ranging from ~46 to ~51 dB are required for FSO links deployed for data transmission in the various cities investigated in this work.

WRF sensitivity simulations of a dense advection fog event in Istanbul

WRF sensitivity simulations of a dense advection fog event in Istanbul

Multiple Regression Analysis of Low Visibility Focusing on Severe Haze-Fog Pollution in Various Regions of China

Visibility degradation is a pervasive environmental problem in winter in China and its prediction accuracy is therefore important, especially in low visibility conditions. However, current visibility parameterization algorithms tend to overestimate low visibility (&lt;5 km) during haze–fog events. The key point of low visibility calculation and prediction depends on a reasonable understanding of the correlation between visibility, PM2.5 concentration, and relative humidity (RH). Using the observations of PM2.5 concentration and meteorology from December 2016 to February 2017, under different RH levels, the relative contribution differences of PM2.5 concentrations and RH to visibility degradation are investigated in depth. On this basis, new multiple nonlinear regressions for low visibility are developed for eight regions of China. The results show that under relatively low RH conditions (&lt;80% or 85%), PM2.5 concentration plays a leading role in visibility changes in China. With the increase in RH (80–90% or 85–95%), the PM2.5 concentration corresponding to the visibility of 10 and 5 km decreases and the contribution of RH becomes increasingly important. When the RH grows to &gt;95%, a relatively low PM2.5 concentration could also lead to visibility decreasing to &lt;5 km. Within this range, the PM2.5 concentration corresponding to the visibility of 5 km in Central China (CC), Sichuan Basin (SCB), and Yangtze River Delta (YRD) is approximately 50, 50, and 30 μg m−3, and that in Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZP) is approximately 125 μg m−3, respectively. Specifically, based on these contribution differences, new multiple nonlinear regression equations of visibility, PM2.5 concentration, temperature, and dew point temperature of the eight regions (Scheme A) are established respectively after grouping the datasets by setting different RH levels (BTH, GZP, and North Eastern China (NEC): RH &lt; 80%, 80 ≤ RH &lt; 90% and RH ≥ 90%; CC, SCB, YRD, and South China Coastal (SCC): RH &lt; 85%, 85 ≤ RH &lt; 95% and RH ≥ 95%; Xinjiang (XJ): RH &lt; 90% and RH ≥ 90%). According to the previous regression methods, we directly established the multiple regression models between visibility and the same factors as a comparison (Scheme B). Statistical results show that the advantage of Scheme A for 5 and 3 km evaluation is more significant compared with Scheme B. For the five low visibility regions (BTH, GZP, CC, SCB, and YRD), RMSEs of Scheme A under visibility &lt;5 and 3 km are 0.77–1.01 and 0.48–0.95 km, 16–43 and 24–57% lower than those of Scheme B, respectively. Moreover, Scheme A reproduced the winter visibility in BTH, GZP, CC, SCB, YRD, and SCC from 2016 to 2020 well. The MAEs, MBs, and RMSEs under visibility &lt; 5 km are 0.44–1.41, −1.33–1.24, and 0.58–2.36 km, respectively. Overall, Scheme A is confirmed to be reliable and applicable for low visibility prediction in many regions of China. This study provides a new visibility parameterization algorithm for the haze–fog numerical prediction system.

Understanding the role of atmospheric circulations and dispersion of air pollution associated with extreme smog events over South Asian megacity.

The winter fog/haze events in northeastern Pakistan and surrounding regions of India are often mixed with pollutants to form smog, and consequently damage human health and hampers daily life in the form of fatalities through road accidents, road blockages, and flight delays. The persistent anti-cyclonic conditions can further trigger the temperature inversion and prolong the smog event from days to weeks. The present study provides characteristics and lasting mechanisms of two persistent winter fog events (2016-2017) in Lahore, Pakistan, by using the fifth generation of European Center for Medium-Range Weather Forecast (ECMWF) ERA5 reanalysis data and National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model simulated with Global Data Assimilation System (GDAS) meteorological data. The results showed the presence of strong low-level anti-cyclonic circulations with wind speed less than 1.5m/s from November to January over Eastern Punjab for two foggy winter seasons. The deep inversion during the fog events was observed that prevented the natural ventilation of air in the upper atmosphere and ultimately the smoke and heavy pollutant accumulated in the lower atmosphere. Furthermore, high relative humidity greater than 83% near the ground indicates a high condensation rate for water vapors to form fog near the ground. The analysis of the NOAA HYSPLIT trajectory model at different vertical heights revealed that smoke from stubble crop burning in the first week of November 2017 in Punjab and Haryana mixed with fog under favorable stable conditions that lead to intense smog over Lahore. This study will help to understand and to develop a forecasting mechanism of fog events by characterizing the meteorological conditions of the study area and to minimize the adverse impacts of smog on public health.

Changes in aerosol particle composition during sea fog formation events in the sea ice regions of the Arctic Ocean

Water soluble ions (WSIs) of aerosol particles is one of the important parameters to study the aerosol-fog interactions. To study the changes of aerosol WSIs during sea fog events, hourly real-time measurements of WSIs (Na+, K+, Mg2+, Ca2+, MSA−, Cl−, NO3−, and SO42−) in total suspended aerosol particles (TSP) were conducted in the atmosphere over Arctic Ocean ice floe regions from August 1 to 12, 2017, when the sea fog events were frequently observed. There were significant differences in Na+, Cl−, Mg2+, methanesulfonic acid (MSA−), sea salt sulfate (ss-SO42) and non-sea salt sulfate (nss-SO42-) ions during sea fog events vs. non-sea fog periods. The mass concentrations of sea salt ions, such as Na+, Mg2+, Cl− and ss-SO42-, clearly increased before the occurrence of sea fog and then decreased substantially with the formation of sea fog; however, nss-SO42- levels did not decrease but remained high during the sea fog processes. These results suggest that sea salt aerosol particles were more likely to serve as condensation nuclei for fog and could be more effectively removed by sea fog than nss-SO42- particles. MSA− only combined with sea salt particles, which were likely to serve as condensation nuclei and be removed by sea fog. Condensation may be the primary factor leading to the reduction of sea salt ions during the sea fog periods, while other factors like the presence of dense sea ice and long-range transport input may also affect the decreasing rate.

Emerging Major Role of Organic Aerosols in Explaining the Occurrence, Frequency, and Magnitude of Haze and Fog Episodes during Wintertime in the Indo Gangetic Plain.

Aerosols are an important part of Earth’s atmosphere. They can absorb, scatter, or reflect the incoming solar radiation, which results in heating or cooling of Earth, thus impacting its climate. It affects the health of exposed human population adversely, reduces visibility, disturbs environmental systems, and causes material damage. This study summarizes the research carried out to understand the role of aerosol load and its physicochemical characteristics on occurrence, frequency, and magnitude of haze and fog events during wintertime within the Indo Gangetic Plain (IGP) in the past decade. For most species, the highest concentration was measured during foggy events at night-time over the winter season. A few species such as water-soluble organic and inorganic carbon (WSOC and WSIC), K+, SO42–, and NO3–, owing to their hygroscopic nature, were efficiently scavenged, resulting in their lower concentration within the interstitial aerosol during fog episodes. Oligomerization with hydroxy and carbonyl functional groups during AFP (activating fog period) and DFP (dissipating fog period), respectively, accompanied by acidic aerosol (having catalytic ability) and high aerosol liquid water content conditions was found to be significant. Whereas the fragmentation process was dominant along with functionalization of −RCOOH or carbonyl (aldehyde/ketone) and −RCOOH moieties during FP (fog period) and PoFP (post-fog period), respectively. Transition metals play an important role in aqueous production of secondary organic aerosol (SOA) especially during the night-time. Crustal sources had the highest scavenging efficiency along with WSOC playing an important role in nucleation scavenging. Fine droplets had a higher concentration of species with a larger fraction of highly oxidized organic matter (OM) as compared to coarse or medium size droplets. Also, a new approach to calculate absorption by black carbon (BC) and brown carbon (BrC) was proposed, which found the water-soluble brown carbon (WSBrC) absorption value in aerosol to be up to 1.8 times higher than that measured in their corresponding aqueous extracts. Organic aerosol plays a vital role in facilitating fog formation and is responsible for the longer residence time in the ambient atmosphere. Ammonia plays an important role in stabilizing organic aerosol and aids to this recurring haze–fog–haze cycle that is dominant during wintertime in the IGP. Therefore, controlling the major anthropogenic sources of organic aerosol and ammonia should be our top priority in this part of the world.

Emerging investigator series: aqueous-phase processing of atmospheric aerosol influences dissolution kinetics of metal ions in an urban background site in the Po Valley.

Metals are an important atmospheric aerosol component; their impacts on health and the environment depend also on their solubility, dissolution kinetics and chemical form in which they are present in the aerosol (e.g., oxidation state, inorganic salt or oxide/hydroxide, organic complex). In this study, we investigated the impact of fog processing on the solubility and dissolution of metals in PM2.5 samples collected in an urban background site in Padova (Italy). For each sample, we determined the solubility and dissolution kinetics of 17 elements in a solution simulating fog water in the winter season in the Po Valley (pH 4.7, T 5 °C, and water content ∼0.5 g m-3). We also determined water-soluble inorganic and organic compounds having ligand properties. We used the model E-AIM IV to calculate the aerosol liquid water (ALW) content and pH, and we used the model Visual MinteQ to determine the speciation picture of the most important elements under conditions of both deliquescent aerosol (ALW and pH calculated using E-AIM IV, ambient temperature) and simulated fog. We found that the dissolution of Al, Cu, and Fe metal ions, predicted to be largely coordinated with organic compounds under fog conditions, was either immediate or considerably faster in samples collected on days with observed fog events compared with those collected on days having drier conditions. For readily soluble elements, such as As, Cd, Cr, Sr, and Zn, such an effect was not observed. Our study highlights the importance of coordination chemistry in atmospheric aerosol and fog in determining the bioavailability of particle-bound metals.

Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager

Sea fog significantly threatens the safety of maritime activities. This paper develops a sea fog dataset (SFDD) and a dual branch sea fog detection network (DB-SFNet). We investigate all the observed sea fog events in the Yellow Sea and the Bohai Sea (118.1{\deg}E-128.1{\deg}E, 29.5{\deg}N-43.8{\deg}N) from 2010 to 2020, and collect the sea fog images for each event from the Geostationary Ocean Color Imager (GOCI) to comprise the dataset SFDD. The location of the sea fog in each image in SFDD is accurately marked. The proposed dataset is characterized by a long-time span, large number of samples, and accurate labeling, that can substantially improve the robustness of various sea fog detection models. Furthermore, this paper proposes a dual branch sea fog detection network to achieve accurate and holistic sea fog detection. The poporsed DB-SFNet is composed of a knowledge extraction module and a dual branch optional encoding decoding module. The two modules jointly extracts discriminative features from both visual and statistical domain. Experiments show promising sea fog detection results with an F1-score of 0.77 and a critical success index of 0.63. Compared with existing advanced deep learning networks, DB-SFNet is superior in detection performance and stability, particularly in the mixed cloud and fog areas.

The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city.

Severe fog events during winter months in India are a serious concern due to the higher incidence of road accidents, flight delays and increased occurrence of respiratory diseases. The present paper is an attempt to study the twenty fog samples collected from the rooftop of an academic building of Guru Nanak Dev University, Amritsar, India from November 2017 to January 2018. Fog samples were analysed for various parameters viz. pH, electrical conductivity (EC), chloride (Cl−), nitrate (NO3−) and sulphate (SO42−) levels. The pH, EC, and Cl−, NO3− and SO42− levels in the fog samples were estimated as 6.3–7.9, 240–790 μS cm−1, 108–2025 μeq L−1, 105–836 μeq L−1 and 822–5642 μeq L−1, respectively. It was noticed that sulphate was the dominant anion in fog samples. The SO42− to NO3− molar ratio in the fog was estimated as 7.6 which suggests the burning of fossil fuel as the major pollutant from vehicular exhausts. Multiple regression analysis was performed to evaluate the effect of PM2.5/PM10 ratio and relative humidity (RH) on visibility. A box-cox plot of power transformation produced better model fitting, employing a square root transformation of the visibility which indicated that the PM2.5/PM10 and RH have an exponential effect on visibility.

Portability analysis of data mining models for fog events forecasting

AbstractThis article describes an analytical method for comparing geographical sites and transferring fog forecasting models, trained by Data Mining techniques on a fixed site, across Italian airports. This portability method uses a specific intersite similarity measure based on the Euclidean distance between the performance vectors associated with each airport site. Performance vectors are useful for characterizing geographical sites. The components of a performance vector are the performance metrics of an Ensemble descriptive model. In the tests carried out, the comparison method provided very promising results, and the forecast model, when applied and evaluated on a new compatible site, shows only a small decrease in performance. The portability schema provides a meta‐learning methodology for applying predictive models to new sites where a new model cannot be trained from scratch owing to the class imbalance problem or the lack of data for a specific learning. The methodology offers a measure for clustering geographical sites and extending weather knowledge from one site to another.