The Phe ' bus test FPT-1 was carried out to investigate the fission product release, transportation, and distribution in the reactor coolant system and the simulated containment during a severe accident. The test ...
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The Phe ' bus test FPT-1 was carried out to investigate the fission product release, transportation, and distribution in the reactor coolant system and the simulated containment during a severe accident. The test was divided into three phases: the core degradation, aerosol transport, and containment phenomena. In this work, a parametric sensitivity and uncertainty analysis of the two first phases, i.e., core degradation and aerosol transport, is con-ducted with the focus on aerosol *** study was done in a frame of WP4 of the European MUSA project whose main objective is to develop a methodology for severe accident uncertainty *** to the wide scope of the MUSA project, this study focuses not only on the UQ analysis but also on the parametric dependency and data interpretation. The main subject was investigating the uncertainty of the MELCOR 2.2 aerosol models and the relation between the chosen parameters as well as evaluating the usability of essential analytical tools available for such *** calculations were carried out using the integral severe accident code MELCOR2.2 and the generic input deck of the FPT-1 experiment. The uncertainty quantification tool DAKOTA was used as a statistical tool. It was coupled with MELCOR within the SNAP *** uncertain parameters related with the aerosol release and transport were investigated. Two figures of merit were chosen to be the amount of deposition of Cs in the reactor coolant circuit, and the mass of air-borne aerosol in the containment. Preliminary analysis showed a significant correlation between the aerosol dynamic and agglomeration shape factors, density and minimum aerosol diameter with both FOMs. In addition to the standard uncertainty analysis, five MELCOR/DAKOTA UQ sensitivity calculations with modified parameters were performed and analyzed. Additionally, the impact of the parameters was investigated by excluding the most correlated ones from the analysis. The results con
Radiative balance, local climate, and human health are all significantly influenced by aerosol. Recent severe air pollution over Lahore, a city in Pakistan calls for more thorough research to determine the negative im...
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Radiative balance, local climate, and human health are all significantly influenced by aerosol. Recent severe air pollution over Lahore, a city in Pakistan calls for more thorough research to determine the negative impacts brought on by too many aerosols. To study regional aerosol characteristics and their differences from various aspects, in-depth and long-term (2007-2020) investigations of the columnar aerosol properties over the urban environment of Lahore were carried out by using AERONET data. The Aerosol Optical Depth (AOD400) and Angstrom Exponent (AE400-870) vary from low values of 0.10 to a maximum value of 4.51 and from 0.03 to 1.81, respectively. The huge differences in the amount of AOD440 as well as AE440-870 show the large fluctuation of aerosol classes because of various sources of their emission. During the autumn and winter seasons, the decreasing trend of the optical parameters of aerosols like Single Scattering Albedo (SSA) and Asymmetry Parameter (ASY) with increasing wavelength from 675 to 1020 nm indicates the dominance of light-absorbing aerosols (biomass burning (BB) and industrial/urban (UI). Due to the long-distance dust movement during spring, summer, and autumn, coarse mode particles predominated in Lahore during the study period. Dust type (DD) aerosols are found to be the dominant one during spring (46.92%), summer (54.31%), and autumn (57.46%) while urban industry (BB/UI) was dominant during the winter season (53.21%). During each season, the clean continental (CC) aerosols are found to be in negligible amounts, indicating terrible air quality in Lahore City. The present research work fills up the study gap in the optical properties of aerosols in Lahore and will help us understand more fully how local aerosol fluctuation affects regional climate change over the urban environment of Lahore.
In this study, we investigate the impact of sea fog over the Yellow Sea on air quality with the direct effect of aerosols for the entire year of 2016. Using the WRF-CMAQ two-way coupled model, we perform four model si...
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In this study, we investigate the impact of sea fog over the Yellow Sea on air quality with the direct effect of aerosols for the entire year of 2016. Using the WRF-CMAQ two-way coupled model, we perform four model simulations with the up-to-date emission inventory over East Asia and dynamic chemical boundary conditions provided by hemispheric model simulations. During the spring of 2016, prevailing west-southwesterly winds and anticyclones caused the formation of a temperature inversion over the Yellow Sea, providing favorable conditions for the formation of fog. The inclusion of the direct effect of aerosols enhanced its strength. On foggy days, we find dominant changes of aerosols at an altitude of 150-200 m over the Yellow Sea resulted by the production through aqueous chemistry (similar to 12.36% and similar to 3.08% increases in sulfate and ammonium) and loss via the wet deposition process (similar to-3.79% decrease in nitrate);we also find stronger wet deposition of all species occurring in PBL. Stagnant conditions associated with reduced air temperature caused by the direct effect of aerosols enhanced aerosol chemistry, especially in coastal regions, and it exceeded the loss of nitrate. The transport of air pollutants affected by sea fog extended to a much broader region. Our findings show that the Yellow Sea acts as not only a path of long-range transport but also as a sink and source of air pollutants. Further study should investigate changes in the impact of sea fog on air quality in conjunction with changes in the concentrations of aerosols and the climate.
aerosols over the oceanic region significantly influence Earth's energy budget and climate change. Therefore, this study examines the spatiotemporal patterns of MODIS retrieved aerosol optical depth, sea surface t...
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aerosols over the oceanic region significantly influence Earth's energy budget and climate change. Therefore, this study examines the spatiotemporal patterns of MODIS retrieved aerosol optical depth, sea surface temperature, Chlorophyll-a, Sea-WiFS retrieved aerosol optical depth, and MERRA-2 model dust mass concentrations over the Arabian Sea (4o N-26o N to 50o E- 78o E) during the period 2002-2020. The effects of dust aerosols on phytoplankton blooms using the Mann-Kendall and Theil Sen-slope are also analyzed in this study. An increase in aerosol optical depth (AOD) was observed within the latitudes of (4o N-26o N). The AOD shows an increasing trend at 13.4% per year over the Arabian Sea (AS). The highest value of Chl-a (-7 mgm 3) was observed along the coast of Pakistan, Iran, and Oman. Moreover, a negative correlation is found between AOD and sea surface temperature (SST) along the coast of Oman and Somalia, while a strong positive correlation (-0.99) is observed between AOD and Chlorophyll-a (Chl-a) along the coasts of Oman and Somalia.
Surface-enhanced Raman spectroscopy (SERS) is conducted from single aerosol particles held in a linear electro-dynamic quadrupole trap. SERS measurements from two representative types of ambient aerosol particles, sem...
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Surface-enhanced Raman spectroscopy (SERS) is conducted from single aerosol particles held in a linear electro-dynamic quadrupole trap. SERS measurements from two representative types of ambient aerosol particles, semi-liquid and solid aerosols, are demonstrated;aerosol composed of adenine where the metallic nanoparticles (MNPs) are volume distributed throughout the particle and aerosol composed of polystyrene latex (PSL) beads where the MNPs are surface coated. An enhancement factor > 10(6) is demonstrated from 5 mu m aerosols containing trace amounts of adenine (0.1% by mass), with a detection limit of 10(-8) M corresponding to 5 x 10(5) molecules (equivalent to 100 ag in mass or a 50 nm diameter sphere), and a ratio of 100 adenine molecules per Ag NP. SERS signal intensities are linear with particle adenine concentration up to a saturation point. Both the linearity and enhancement factor were confirmed by SERS measurements of adenine as bulk suspensions. The SERS spectra of adenine as bulk suspensions were explored as a function of excitation wavelength ranging from 400 to 800 nm. The two main Raman peaks of adenine at 738 and 1336 cm(-1) exhibit SERS maxima for excitation in the 450-500 nm range for commercially available 40 nm spherical Ag nanoparticles (NPs) used in this study, which shifts to longer wavelengths with the addition of NaCl. Shifts in SERS and spontaneous Raman shifts were observed between aqueous and dry adenine, in agreement with the literature, demonstrating the utility of SERS to possibly study water uptake of aerosols. SERS is measured from MNP surface-coated PSL beads with an enhancement factor of 30 for 5 mu m PSLs. Theoretical extrapolation demonstrates that the enhancement factor will increase for decreasing particle size with an estimated enhancement factor of 140 for 1 mu m PSLs.
This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with ...
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This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with its ability to probe the atmosphere at very high spatial and temporal resolution and measure backscattered signals, has become an invaluable tool for studying these atmospheric components. However, the complexity and diversity of lidar technology requires advanced data processing and analysis methods, where machine learning has emerged as a powerful approach. This survey focuses on the application of various machine learning techniques, including supervised and unsupervised learning algorithms and deep learning models, to extract meaningful information from lidar observations. These techniques enable the detection, classification, and characterization of aerosols and clouds by leveraging the rich features contained in lidar signals. In this article, an overview of the different machine learning architectures and algorithms employed in the field is provided, highlighting their strengths, limitations, and potential applications. Additionally, this survey examines the impact of machine learning techniques on improving the accuracy, efficiency, and robustness of aerosol and cloud real-time detection from lidar observations. By synthesizing the existing literature and providing critical insights, this survey serves as a valuable resource for researchers, practitioners, and students interested in the application of machine learning techniques to lidar technology. It not only summarizes current state-of-the-art methods but also identifies emerging trends, open challenges, and future research directions, with the aim of fostering advancements in this rapidly evolving field.
During a volcanic eruption, copious amounts of volcanic gas, aerosol droplets, and ash are released into the stratosphere, potentially impacting radiative feedback. One of the most significant volcanic gases emitted i...
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During a volcanic eruption, copious amounts of volcanic gas, aerosol droplets, and ash are released into the stratosphere, potentially impacting radiative feedback. One of the most significant volcanic gases emitted is sulphur dioxide, which can travel long distances and impact regions far from the source. This study aimed to investigate the transport of sulphur dioxide and sulphate aerosols from the Tonga volcanic eruption event, which occurred from the 13th to the 15th of January 2022. Various datasets, including Sentinel-5 Precursor (TROPOMI), the Ozone Monitoring Instrument (OMI), and the Ozone Mapping and Profiler Suite (OMPS), were utilized to observe the transport of these constituents. The TROPOMI data revealed westward-traveling SO2 plumes over Australia and the Indian Ocean towards Africa, eventually reaching the Republic of South Africa (RSA), as confirmed by ground-based monitoring stations of the South African Air Quality Information System (SAAQIS). Moreover, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) demonstrated sulphate aerosols at heights ranging from 18 to 28 km with a plume thickness of 1 to 4 km. The results of this study demonstrate that multiple remote sensing datasets can effectively investigate the dispersion and long-range transport of volcanic constituents.
The presence of airborne viruses in bioaerosols is a significant and ongoing public health concern. In this context, this study aimed to develop a new filter that integrates nanosilver into a TiO2-chitosan matrix (nan...
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Air pollution (urban, industrial or rural) has been linked to a myriad of human ailments despite clear mechanistic associations that are often not thoroughly established. Daily variability of fine aerosols in a survei...
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Air pollution (urban, industrial or rural) has been linked to a myriad of human ailments despite clear mechanistic associations that are often not thoroughly established. Daily variability of fine aerosols in a surveillance campaign in south Japan shows a striking coevolution between their trace elements (metal and metalloid, MM) content and Kawasaki disease (KD) admissions, suggesting a strong dynamical link. These aerosol MM could instigate an immune response that, along with genetic susceptibility, would lead to KD development. This association may account for over 40% of the total variability in the disease, being dominated by a clear sub-weekly cycle (SWC1). Thanks to both an unprecedented daily KD epidemiological record going back to 1970, light detection and ranging (LIDAR) atmospheric backscattering profiles for the interval 2010-2016 and HYSPLIT simulations with numerous sensitivity analyses, we can trace this SWC1 variability to occur concomitantly from sub-seasonal to interannual timescales in both KD and aerosols. This SWC1 appears to connect or disconnect Japan to air intrusions from above the planetary boundary layer (PBL), having their source in industrial and agricultural areas in NE Asia and points to a stronger case for an agricultural source for the exposure as opposed to urban pollution. The KD maxima always occur in full synchrony with the arrival of very small (mu m;PM1) particles showing that ultrafine aerosols appear as a necessary cofactor in the occurrence of KD and sets the field to associate other similar human diseases. Our study shows how signal-detection approaches can be useful to uncover hidden associations between the environment and human health, otherwise unnoticed and help set new early-warning systems for disease prevention.
Many respiratory diseases, including COVID-19, can be spread by aerosols expelled by infected people when they cough, talk, sing, or exhale. Exposure to these aerosols indoors can be reduced by portable air filtration...
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Many respiratory diseases, including COVID-19, can be spread by aerosols expelled by infected people when they cough, talk, sing, or exhale. Exposure to these aerosols indoors can be reduced by portable air filtration units (air cleaners). Homemade or Do-It-Yourself (DIY) air filtration units are a popular alternative to commercially produced devices, but performance data is limited. Our study used a speaker-audience model to examine the efficacy of two popular types of DIY air filtration units, the Corsi-Rosenthal cube and a modified Ford air filtration unit, in reducing exposure to simulated respiratory aerosols within a mock classroom. Experiments were conducted using four breathing simulators at different locations in the room, one acting as the respiratory aerosol source and three as recipients. Optical particle spectrometers monitored simulated respiratory aerosol particles (0.3-3 mu m) as they dispersed throughout the room. Using two DIY cubes (in the front and back of the room) increased the air change rate as much as 12.4 over room ventilation, depending on filter thickness and fan airflow. Using multiple linear regression, each unit increase of air change reduced exposure by 10%. Increasing the number of filters, filter thickness, and fan airflow significantly enhanced the air change rate, which resulted in exposure reductions of up to 73%. Our results show DIY air filtration units can be an effective means of reducing aerosol exposure. However, they also show performance of DIY units can vary considerably depending upon their design, construction, and positioning, and users should be mindful of these limitations.
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