• Journal of Environmental Policy
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• v.7 no.1
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• pp.1-29
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• 2008

The Ministry of Environment(MoE) of Korea has recently established the Environmental Health Act. This Act contains a clause related to implementation of Health Impact Assessment(HIA). So, selecting a landfill which was expected to have an influence on human health among major development projects, this study carried out the human risk assessment due to inhalation exposure to landfill gas emission and attempted to measure the possibility of domestic application of HIA in the future. The process for HIA on landfill site extension focusing on human risk assessment is as follows: The first step is to presume and calculate the amount of landfill gas emissions using LandGEM, The second step is to carry out exposure assessment using K-SCREEN Model which is used for predicting the concentration in a conservative method. The last step is to carry out human risk assessment of carcinogenic and non-carcinogenic substances. It is considered that it is likely to apply a technique for human risk assessment due to inhalation exposure to landfill gas emission performed here more specifically in the case of implementing HIA. In addition, it is also believed that more systematic studies are needed to overcome some weak points and limits found in this study and if these weak points and limits are improved more reliable outcomes will be produced.

• Atmosphere
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• v.26 no.2
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• pp.257-275
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• 2016

Asian dust is a seasonal meteorological phenomenon influencing most East Asia, irregularly occurring during spring. Unusual heavy Asian dust event in winter was observed in Seoul, Korea, with up to $1,044{\mu}g\;m^{-3}$ of hourly mean $PM_{10}$, in 22~23 February 2015. Causes of such infrequent event has been studied using both ground based and spaceborne observations, as well as numerical simulations including ECMWF ERA Interim reanalysis, NOAA HYSPLIT backward trajectory analysis, and ADAM2-Haze simulation. Analysis showed that southern Mongolia and northern China, one of the areas for dust origins, had been warm and dry condition, i.e. no snow depth, soil temperature of ${\sim}0^{\circ}C$, and cumulative rainfall of 1 mm in February, along with strong surface winds higher than critical wind speed of $6{\sim}7.5m\;s^{-1}$ during 20~21 February. While Jurihe, China, ($42^{\circ}23^{\prime}56^{{\prime}{\prime}}N$, $112^{\circ}53^{\prime}58^{{\prime}{\prime}}E$) experienced $9,308{\mu}g\;m^{-3}$ of hourly mean surface $PM_{10}$ during the period, the Asian dust had affected the Korean Peninsula within 24 hours traveling through strong north-westerly wind at ~2 km altitude. KMA issued Asian dust alert from 1100 KST on 22nd to 2200 KST on 23rd since above $400{\mu}g\;m^{-3}$ of hourly mean surface $PM_{10}$. It is also important to note that, previously to arrival of the Asian dust, the Korean Peninsula was affected by anthropogenic air pollutants ($NO_3^-$, $SO_4^{2-}$, and $NH_4^+$) originated from the megacities and large industrial areas in northeast China. In addition, this study suggests using various data sets from modeling and observations as well as improving predictability of the ADAM2-Haze model itself, in order to more accurately predict the occurrence and impacts of the Asian dust over the Korean peninsula.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• The Korean Journal of Pesticide Science
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• v.11 no.4
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• pp.281-288
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• 2007

This research was carried out to propose the Korean method for estimating the agricultural occupational pesticide exposure level in orchard. The UK-POEM (UK-Predictive Operator Exposure Model) was proposed as a bench-marking model and analysed its performance properties. To extrapolate the Korean agricultural conditions, application equipment, application method, work rate per day, application volume and spraying time of pesticide was surveyed for Korean 204 orchard farmhouse. This survey indicate that the major application equipments are speed sprayer(64.9%) and motor sprayer(33.9%). When they spayed the pesticide with a speed sprayer, they worked for more than 4 hours on area of 4 ha per day. In case of using motor sprayer, they worked for more than 4 hours on area of 1 ha. Based on the above survey result, Korean method for estimating the pesticide exposure level of agricultural worker was proposed finally.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.167-175
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• 2020

The purpose of this paper is to provide directional and policy references to develop a higher level of service quality and consumer-oriented e-commerce platform. This paper has established a model of consumer behavior of Chinese fresh agricultural e-commerce using customer satisfaction theory and cognitive value theory, and used survey and SPS23.0 to verify hypothesis. Studies have shown that when consumers consume fresh agricultural products, product quality, logistics and distribution service quality, interactive quality of e-commerce platform, and product price and cognitive value have a positive effect on consumer behavior. This study is meaningful in the study of consumer behavior of fresh agricultural e-commerce, and in the case of fresh agricultural e-commerce companies, consumer behavior can be understood. In the model constructed in this paper, the relationship between each influencing factor and consumer behavior is considered comprehensively, but the possible relationship between fine molecular factors has not been studied and analyzed. In the future learning process, it is necessary to make clear the characteristics and particularity of the industry, think about its influencing factors comprehensively and make in-depth analysis.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.4
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• pp.353-359
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• 1988

Variations in photosynthetic capacities of leaves differing in thickness were explained on the basis of relationships between gas exchange and internal leaf structure. The relative importance of gas diffusion and of biochemical processes as limiting for leaf photosynthesis was also determined. Mesophyll cell surface was considered to be the limiting internal site for gas diffusion. and cell volume to be indicative of the sink capacity for CO$_2$ fixation. Increases in cell surface area were assumed to reduce proportionately mesophyll resistance to the liquid phase diffusion of CO$_2$. Increased cell volume was thought to account for a proportional increase in reaction rates for carboxylation, oxygenation. and dark respiration. This assumption was tested using chamber-grown Glycine max (L.) Merr. cv. Amsoy plants. Plants were grown under 200, 400, and 600 ${\mu}$mol photons m$\^$-2/ s$\^$-1/ of PAR to induce development of various leaf thickness. Photosynthetic CO$_2$ uptake rates were measured on the 3rd and 4th trifoliolate leaves under 1000 ${\mu}$mol photons m$\^$-2/ s$\^$-1/ of PAR and at the air temperature of 28 C. A pseudo -mechanistic photosynthesis model was modified to accommodate the concept of cell surface area as well as both cell volume and surface area. Both versions were used to simulate leaf photosynthesis. Computations based on volume and surface area showed slightly better agreement with experimental data than did those based on the surface area only. This implies that any single factor, whether it is photosynthetic model utilized in this study was suitable for relating leaf thickness to leaf productivity.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.105-118
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• 2022

The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.387-398
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• 2023

The COVID-19 outbreak has significantly impacted human lifestyles and patterns. It was recommended to avoid face-to-face contact and over-crowded indoor places as much as possible as COVID-19 spreads through air, as well as through droplets or aerosols. Therefore, if a person who has contacted a COVID-19 patient or was at the place where the COVID-19 patient occurred is concerned that he/she may have been infected with COVID-19, it can be fully expected that he/she will search for COVID-19 symptoms on Google. In this study, an exploratory data analysis using deep learning models(DNN & LSTM) was conducted to see if we could predict the number of confirmed COVID-19 cases by summoning Google Trends, which played a major role in surveillance and management of influenza, again and combining it with data on the number of confirmed COVID-19 cases. In particular, search term frequency data used in this study are available publicly and do not invade privacy. When the deep neural network model was applied, Seoul (9.6 million) with the largest population in South Korea and Busan (3.4 million) with the second largest population recorded lower error rates when forecasting including search term frequency data. These analysis results demonstrate that search term frequency data plays an important role in cities with a population above a certain size. We also hope that these predictions can be used as evidentiary materials to decide policies, such as the deregulation or implementation of stronger preventive measures.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.1
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• pp.93-106
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• 2016

This study aimed to estimate and evaluate the thermal mitigation of the urban tree canopy on the summer outdoor environment by quantitative use of mean radiant temperature. This study applied the SOLWEIG model based $T_{mrt}$ comparison method by using both (1) urban tree canopy presence examples and (2) urban tree canopy absence examples as constructed from airborne LiDAR system based three-dimensional point cloud data. As a result, it was found that an urban tree canopy can provide a decrease in the entire domain averaged daily mean $T_{mrt}$ about $5^{\circ}C$ and that the difference can increase up to $33^{\circ}C$ depending both on sun position and site conditions. These results will enhance urban microclimate studies such as indices (e.g., wind speed, humidity, air temperature) and biometeorology (e.g., perceived temperature) and will be used to support forest based public green policy development.

• Korean Journal of Food Science and Technology
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• v.29 no.2
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• pp.292-301
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• 1997

In drying process, to minimize the quality degradation by improved drying process and pretreatment methods, carrots, cabbages and radishes were dried and rehydrated. Physico-chemical properties of product were analyzed to determine the optimum pretreatment method and drying models were applied to explain drying mechanisms. Microwave, steam and water were used prior to drying as blanching method. In consideration of physical properties, optimum treatment time was decided that microwave was 1 min, steam and water were each 10 min. Control, steam, water, microwave and osmotic dehydration were treated prior to drying as pretreatment individually, osmotic dehydration was lower than the other treatmemt in drying efficiency, but carotene content was higher than the others. The effect continued after rehydration. Ten panelists tested dried and rehydrated carrots. After rehydration, the quality of air dried product with osmotic dehydration was superior to freeze dried without treatment. The fittness of drying models were conducted in order to explain the mechanism of drying each process. Quadratic model was most fittable to explain during drying. However, in rehydration process, no fittable model was found.