• Journal of Environmental Health Sciences
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• v.30 no.5 s.81
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• pp.455-468
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• 2004

The purpose of this paper is to understand the time series and origin of a chemical component and to compare the difference during yellow sand episodes for analysis $PM_{10}$ chemical components in the region of west in Korean Peninsula, 1999-2001. An annual mean concentration of $PM_{10}$ is $29.1\;{\mu}g/m^3$. A monthly mean and standard deviation of $PM_{10}$ concentration are very high in spring but there is no remarkably seasonal variation. Also, water soluble ionic component of $PM_{10}$ be influenced by double more total anion than total cation, be included $NO_{3}^-\;and\;SO_{4}^{2-}$ for the source of acidity and $NH_{4}^+$ to neutralize. Tracer metals of $PM_{10}$ slowly increases caused by emitted for soil and ocean (Fe, Al, Ca, Mg, Na) and Zn, Pb, Cu, Mn for anthropogenic source. According to method of enrichment factor (E.F) and statistics, assuming that the origin of metal component in $PM_{10}$ most of element in the Earth's crust e.g. Mg, Ca, Fe originates soil and Cu, Zn, Cd, Pb derives from anthropogenic sources. The ionic component for $Na^{+}\;Cl^-,\;Mg^{2+}\;and\;Ca^{2+}$ and Mg, Al, Ca, Fe originated by soil component largely increase during yellow sand period and then tracer metal component as Pb, Cd, Zn decrease. According to factor analysis, the first group is ionic component ($Na^+,\;Mg^{2+},\;Ca^{2+}$) and metal component (Na, Fe, Mn and Ni) be influenced by soil. The second group, Mg, Cr also be influenced by soil particle.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.370-373
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• 2005

We have developed Hybrid algorithm for yellow sand detection. Hybrid algorithm is composed of three methods using infrared bands. The first method used the differential absorption in brightness temperature difference between $11\mu m\;and\;12\mu m$ (BID _1), through which help distinguish the yellow sand from various meteorological clouds. The second method uses the brightness temperature difference between $3.7\mu m\;and\;11\mu m$ (BID_2). The technique would be most sensitive to dust loading during the day when the BID _2 is enhanced by reflection of $3.7\mu m$ solar radiation. The third one is a newly developed algorithm from our research, the so-called surface temperature variation method (STY). We have applied the three methods to MODIS for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. PCI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between PCI and MODIS aerosols optical depth (AOD) shows remarkable good correlations during daytime and relatively good correlations over the land.

• 한국해양학회지
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• v.30 no.1
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• pp.1-12
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• 1995

The modal analysis was carried out for the total of 216 subface sediments of southern continental shelf of Korean peninsula. Sandy mud or muddy sand distributed in the range of 70∼100 m water depth revealed the bimodal type (sand and mud components). The relations of textural parameters obtained from every modal were consistent with those of shallow marine sediments. The characteristics of sand component between bimodal were as follows: (1) the distributions of mean grain size, sorting, shell content were repeatedly distributed like the directions of depth contour lines. (2) Sand component was composed of medium to fine sand (Mz, 1-3$\psi$) containing many shell fragments, a few pebbles, and iron-stained quartz. (3) The surface of quartz revealed the concordial breakage and V-shaped features formed at high energy environment. (4) In CM-pattern, sand component was plotted in rolling and bottom suspension area. These characteristics imply that sand component probably derives from shoreface sediments deposited at the beach environment.

• Proceedings of the KSRS Conference
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• v.1
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• pp.236-238
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) data. The algorithm is the hybrid algorithm that has used two methods combined together. The first method used the differential absorption in brightness temperature difference between $11{\mu}m$ and $12{\mu}m$ (BTD1). The radiation at 11 ${\mu}m$ is absorbed more than at 12 ${\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m$ and $11{\mu}m$ (BTD2). The technique would be most sensitive to dust loading during the day when the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. We have applied the three methods to MTSAT-1R for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. As produced Principle Component Image (PCI) through the PCA is the correlation between BTD1 and BTD2, errors of about 10% that have a low correlation are eliminated for aerosol detection. For the region of aerosol detection, aerosol index (AI) is produced to the scale of BTD1 and BTD2 values over land and ocean respectively. AI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between AI and OMI aerosol index (AI) shows remarkable good correlations during daytime and relatively good correlations over the land.

• Journal of Environmental Health Sciences
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• v.26 no.2
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• pp.41-48
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• 2000

This study has been carried out to determine the seasonal characteristics of concentration of various ionic (CI-, NO3-, SO42-, Na+, NH+, K+, Ca2+) and heavy metallic (Pb, Mn, Cu, Ni) species in Pusan from August 1997 to April 1998. The concentrations of CI-, Na+, K+ were higher during summer with 2.98 ${\mu}{\textrm}{m}$/㎥. Seasonal variation of total concentration of but the concentration of NH4+ was higher during winter with 2.46${\mu}{\textrm}{m}$/㎥. Seasonal variation of total concentration of heavy metals(Pb, Cu, Mn, Ni) were 186.0 ng/㎥ in summer, 222.6 ng/㎥ in autumn, and 135.83 ng/㎥ in winter. Over the seasons inspected, the concentration of Mn was higher in coarse particles than fine particles and concentration of Ni was higher in fine particles than coarse particles. during yellow sand period, the concentration of TSP was increased about two times than that of other period. SO42-, Ca2+ concentrations were higher than other ionic components because of soil particles. The concentration of Ni showed 94.62ng/㎥ was increased about 4~5 times than other period. Principal component of the yellow sand, SO42-, Ca2+ could be discreased by rainfall and washout effect of atmospheric aerosol was higher in coarse particles than fine particles. Results from PCA(principal component analysis) showed that major pollutant was NaCl by seasalt particulate and (NH4)2SO4.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.403-406
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.

• Journal of Environmental Health Sciences
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• v.29 no.3
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• pp.91-96
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• 2003

This study was intended as an investigation of characteristics of background site atmospheric respirable particulate matters(RPM), and fine particles(<2.5 ${\mu}{\textrm}{m}$). The particle size distributions during the phenomenon of Yellow Sand(YS) occurs from April, 2001. Atmospheric aerosol particulate matter was directly collected on the Jeju island between 1 to 30, April, 2001 using an eight-stage cascade impacter(particle size range: 0.43-11 ${\mu}{\textrm}{m}$), and cyclone separator(cut size: 2.5, 10 ${\mu}{\textrm}{m}$). The episode of YS observed in background monitoring site, Kosan and appeared 2 times at sampling period. The mass concentrations of fine and coarse particles for YS episode were 34.2 and 59.6 $\mu\textrm{g}$/㎥, respectively, which were significantly increased amounts compared to 13.3 and 13.0 $\mu\textrm{g}$/㎥ for NonYS(NYS). Most size distributions had two peaks, one at 0.43∼.65 ${\mu}{\textrm}{m}$ and the other at 3.3${\mu}{\textrm}{m}$4.7 ${\mu}{\textrm}{m}$. The result of analysis of water-soluble ion component indicated that sulfate was mainly ion component, but nitrate and calcium ion was significantly increased at the YS episode.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.383-390
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• 2024

Sand is a vital component within a concrete admixture for variety of structures and is classified as one of the crucial bulk material used. Assessing the Fineness Modulus (FM) of sand is an essential part of concrete production process because FM significantly affects the workability, cost-effectiveness, porosity, and concrete strength. Traditional sand quality inspection methods, like Sieve Analysis Test, are known to be laborious, time-consuming, and cost ineffective. Previous studies had mainly focused on measuring the physical characteristics of individual sand particles rather than real-time quality assessment of sand, particularly its FM during concrete production. This study introduces an image-based method for detecting flawed sand through deep learning techniques to evaluate the quality of sand used in concrete. The method involves categorizing sand images into three groups (Unavailable, Stable, Dangerous) and seven types based on FM. To achieve a high level of generalization ability and computational efficiency, various deep learning architectures (VGG16, ResNet-101 and MobileNetV3 small), were evaluated and chosen; with the inclusion of transfer learning to ensure model accuracy. A dataset of labeled sand images was compiled. Furthermore, image augmentation techniques were employed to effectively enlarge this dataset. The models were trained using the prepared dataset that were categorized into three discrete groups. A comparative analysis of results was performed based on classification performance metrics which identified the VGG16 model as the most effective achieving an impressive 99.87% accuracy in identifying flawed sand. This finding underscores the potential of deep learning techniques for assessing sand quality in terms of FM; positioning this research as a preliminary investigation into this topic of study.

• Journal of Conservation Science
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• v.32 no.1
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• pp.75-88
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• 2016

In this study, 5 mural paintings in the Buddhist temples of Joseon era were researched for component analysis on the soil contained in the walls. The results of particle size analysis showed that the ratio of particle contents were different in each layer. In the finishing layer, the distribution of the middle sand fraction is higher than that of the middle layer. The results of XRD analysis showed that quartz, feldspar, and clay mineral are the main components of sand, suggesting similar mineral composition to that of ordinary soil component. It seems weathered rocks were used for construction of the walls. The main chemical components detected from EDX analysis were Si, Al, Fe, and K. Also the SEM images showed sand or clay sized minerals. In conclusion, the walls of the buddhist mural paintings in Joseon Dynasty had been constructed by using the loess, and had been produced by using mixture of clay and sand particles of different sizes for each layer. This study identified the characteristics of the materials and the manufacturing technologies used on the walls of mural paintings of Buddhist temples in Joseon era.

• The Korean Journal of Ecology
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• v.21 no.4
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• pp.321-328
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• 1998

To describe the major environmental factors operating in coastal wetland and to characterize the distribution of the plant species over the wetland in relation to the major environmental gradients, 12 soil physical and chemical properties were determined. The gradient of water and osmotic potential of soil, electrical conductivity, sodium and chloride content and soil texture alsong the three habitat types of salt marshes, salt swamp and sand dune were occurred. The 24 coastal plant communities from principal component analysis (PCA) on the 12 variables were at designated as a gradient for soil texture and water potential related with salinity by Axis I and as a gradient for soil moisture and total nitrogen gradient by Axis II On Axis I were divided into 3 groups (1) 9 salt marsh communities including Salicornia herbacea communities (2) 5 salt swamp communities including Scirpus fluviatilis communities and (3) 10 sand dune communities including Jmperata cylindrica communities on Axis II were divided into 2 groups (1) salt marsh and sand dune communities, and (2) 3 salt swamp communities. The results could account for the zonation of plant communities on coastal wetland observed alsong envionmental gradients.