• Journal of Korean Society for Atmospheric Environment
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• v.29 no.2
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• pp.217-227
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• 2013

Passive air samplers (PAS) have been developed since the early 2000s and widely used for the atmospheric monitoring of persistent organic pollutants (POPs). PAS are useful especially for the investigation of source-receptor relationship of POPs because they provide higher spatial resolution data. In Korea, however, only a few research groups have conducted POPs monitoring using PAS. One of the reasons for the limited application of PAS might be due to a complicated calculation method for air concentration. In this study, therefore, we introduced the principle of polyurethane foam (PUF)-PAS, which has been most widely used in the world, and provided an example of the calculation of air concentration of polycyclic aromatic hydrocarbons (PAHs). As all data tables and equations for this calculation were provided, this method can be used for the conversion of the amount of POPs (ng) in a PUF disk to air concentration ($ng/m^3$).

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.1
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• pp.107-115
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• 1993

This study was carried out to find the characteristics of surface ozone concentration data obtained during 1988-1991 by the Korea Ministry of Environment. Seasonal data (spring, summer, autumn and winter) wre obtained in May, August, November and February respectively at Kwanghwamun in Seoul. The pollutants analyzed in this study are $SO_2, TSP, CO, NO, NO_2 and NO_2/NO$. Atmospheric factors such as solar radiation, wind speed, relative humidity, cloud amount and atmospheric temperature are also analyzed. The influence of pollutants and atmospheric factors that affect ozone concentration were analyzed by statistical method. The results are summarized as follows : 1. The ozone concentration varied seasonally. The maximum values were 23 ppb in spring, 33 ppb in summer, 16 ppb in autumn and 13 ppb in winter. So the seasonal ozone value was highest in Summer. 2. Te diurnal concentration of ozone was highest during 2-4 P. M. and was very low in the morning and evening. 3. The maximal correlation coefficients of each season between ozone concentration and the influencing pollutants or atmospheric factors asr as follows ; a. spring, r = 0.44(solar radiation) b. summer, r = -0.59(relative humidity) c. autumn, r = -0.55(relative humidity) d. winter, r = -0.58($NO_2$) 4. The major factor affecting the ozone concentration in spring was solar radiation, Relative humidity was the first affecting factor in summer, autumn and $NO_2$ concentration was dominant in winter.

• Journal of Environmental Science International
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• v.31 no.1
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• pp.87-97
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• 2022

In this study, type analysis was conducted along with the advancement of basic data to calculate the maximum damage caused by strong winds during the typhoon period. The result of the damage by region showed that in 2012, the difference in damage was clearly distinguished as the region was classified in detail. In addition, the result of the annual damage in 2011 was strong on the west coast, and in 2016, the damage to the southeast coast was significant. In 2012, the 3-second gust was relatively stronger on the west and southeast coasts than in 2011, and the winds blew stronger along the southeast coast in 2016. Monthly damage data showed that the damage to the west coast was high in August, and the damage to the southeast coast was high in October from 2002 to 2019. The 3-second gust showed the result of wide expansion throughout the southern coast of the Korean Peninsula in October. As a result, the damage differs for type bacause the intensities and paths of typhoons vary depending on their characteristics, the 3-second gust blows differently by region based on regional characteristics, and the sale price is considered in metropolitan cities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1431-1445
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• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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• pp.554-569
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• 2017

Sensitivity analysis on $PM_{10}$ forecasting simulations was carried out by using two different initial and boundary conditions of meteorological fields: NCEP/FNL (National Centers for Environmental Prediction/Final Analysis) reanlaysis data and NCEP/GFS (National Centers for Environmental Prediction/Global Forecast System) forecasting data, and the comparisons were made between two different simulations. The two results both yielded lower $PM_{10}$ concentrations than observations, with relatively lower biased results by NCEP/FNL than NCEP/GFS. We explored the detailed individual meteorological variables to associate with $PM_{10}$ prediction performance. With the results of NCEP/FNL outperforming GFS, our conclusion is that no particular significant bias was found in temperature fields between NCEP/FNL and NCEP/GFS data, while the overestimated wind speed by NCEP/GFS data influenced on the lower $PM_{10}$ concentrations simulation than NCEP/FNL, by decreasing the duration time of high-$PM_{10}$ loaded air mass over both coastal and metropolitan areas. These comparative characteristics of FNL against GFS data such as maximum 3~4 m/s weaker wind speed, $PM_{10}$ concentration control with the highest possible factor of 1.3~1.6, and one or two hour difference of peak time for each case in this study, were also reflected into the results of statistical analysis. It is implying that improving the surface wind speed fluctuation is an important controlling factor for the better prediction of $PM_{10}$ over Korean Peninsula.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.5
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

• Journal of Environmental Science International
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• v.14 no.3
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• pp.311-325
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• 2005

Elements of atmospheric environment, temperature, humidity and wind, at the compus of KNU(Kyungpook National University) were investigated by the observations. The observed data were compared with those of DWS (Daegu Weather Station). The simulations of wind field and dispersions of polluted gases were conducted by MUKLIMO under the various conditions. The results show that the atmospheric environment of KNU are suitable but the campus does not play role as a heat sink in the city. The simulations of wind field show the air flows and wind channels in the campus clearly. The exhausted gases by motor vehicles on the northside street of campus affect very much to the campus with $NW(300^{\circ})$ wind. The running cars in the campus are also pollute much on the campus with the various wind directions. The characteristics of environmental conditions, various meteorological fields, wind channels, and dispersion of exhausted gases at the campus of KNU were understood quantitatively in the study.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.6
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• pp.649-661
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• 2008

The impact of the detailed surface data on regional meteorological fields in complex coastal area is studied using RAMS. Resolutions of topography and land use data are very important to numerical modeling, because high resolution data can reflect correct terrain height and detail characteristics of the surface. Especially, in complex coastal region such as Gwangyang area, southern area in Korean Peninsula, high resolution topography and land use data are indispensable for accurate modeling results. This study investigated the effect of resolutions of terrain data using SRTM with 3 second resolution topography and KLU with 1 second resolution land use data. Case HR was the experiment using high resolution data, whereas Case LR used low resolution data. In Case HR, computed surface temperature was higher than Case LR along the coastline and wind speed was $1{\sim}2m/s$ weaker than Case LR. Time series of temperature and wind speed indicated great agreement with the observation data. Moreover, Case HR indicated outstanding results on statistical analysis such as regression, root mean square error, index of agreement.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.4
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• pp.487-497
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• 2007

Increase of the greenhouse gases emissions during last century has led remarkable changes in our environment and climate system. Continuous monitoring of atmospheric constituents over the world is positively necessary to understand these changes around us. The concentrations of greenhouse gases ($CO_2,\;CH_4,\;N_2O,\;CFCs$) have been continuously measured at Global Climate Change Monitoring station in Gosan, Jeju since January, 2002. In this study, the variation characteristics of greenhouse gases as well as their annual, seasonal and diurnal trend using the data from January, 2002 to December, 2005 were analyzed. The raw data which was used in the analysis were validated with the methods recommended by WDCGG (World Data Center for Greenhouse Gases). The concentration of $CO_2$ was increasing continuously by 2.1 ppm/year, while $CH_4$ did not show any increasing or decreasing trend clearly for 4 years. The concentration of $N_2O$ was slightly increasing and CFCs were decreasing except CFC-12 which has longer lifetime compared with other CFCs. The variations of the greenhouse gases at Gosan were shown to be consistent with the global trend. But the concentration level of $CO_2$ in Korea was more or less higher than abroad.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.2
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• pp.119-126
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• 2002

Recently, the flux community in Korea established a new regional flux network, so-called KoFlux, which will provide an infrastructure for collecting, synthesizing, and analysing long-term measurements of energy and mass exchange between the atmosphere and the various vegetated surfaces. KoFlux requires the collection of long time series of raw data, and a large amount of data are expected to accumulate due to continuous flux observations at each KoFlux sites. Therefore, we need a systematic and efficient tool to manage these raw data. As a part of this effort, a computer program far processing raw data measured from micrometeorological field experiments was developed for the flux community in Korea. In this paper, we introduce this program for processing raw data to estimate fluxes and other turbulent statistics and explain the micrometeolological processes coded in this data-processing program. Also, we show some examples on how to run the program and handle the outputs for the unique purpose of research interest.