• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.131-137
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• 2022

Public interest has been increasing the focus on the management of exposure to pollutants and the related health effects. This study reviewed exposure assessment methodologies and addressed future directions. Exposure can be assessed by direct (exposure monitoring) or indirect approaches (exposure modelling). Exposure modelling is a cost-effective tool to assess exposure among individuals, but direct personal monitoring provides more accurate exposure data. There are several population exposure models: stochastic human exposure and dose simulation (SHEDS), air pollutants exposure (APEX), and air pollution exposure distributions within adult urban population in Europe (EXPOLIS). A South Korean population exposure model is needed since the resolution of ambient concentrations and time-activity patterns are country specific. Population exposure models could be useful to find the association between exposure to pollutants and adverse health effects in epidemiologic studies. With the advancement of sensor technology and the internet of things (IoT), exposure assessment could be applied in a real-time surveillance system. In the future, environmental health services will be useful to protect and promote human health from exposure to pollutants.

• Asian Journal of Atmospheric Environment
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• v.8 no.4
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• pp.202-211
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• 2014

In this study, long-term rainfall data with irregular spatial distribution in Seoul, Korea, were separated into individual precipitation events by the inter-event time definition of 6 hours. Precipitation washout of $PM_{10}$ and $NO_2$ concentrations in the air considering various complex factors were analyzed quantitatively. Concentrations of $PM_{10}$ and $NO_2$ in the atmosphere were lower under condition of rainfall compared to that of non-precipitation, and a noticeable difference in average $PM_{10}$ concentrations was observed. The reduction of concentrations of $PM_{10}$ and $NO_2$ by rainfall monitored at road-side air monitoring sites was also lower than that of urban air monitoring sites due to continuous pollutant emissions by transportation sources. Meanwhile, a relatively smaller reduction of average $PM_{10}$ concentration in the atmosphere was observed under conditions of light rainfall below 1 mm, presumably because the impact of pollutant emission was higher than that of precipitation scavenging effect, whereas an obvious reduction of pollutants was shown under conditions of rainfall greater than 1 mm. A log-shaped regression equation was most suitable for the expression of pollutant reduction by precipitation amount. In urban areas, a lower correlation between precipitation and reduction of $NO_2$ concentration was also observed due to the mobile emission effect.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

• 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 the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1153-1164
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• 2008

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Uijeongbu monitoring site in Korea. The result showed that both overall and monthly ARE models are suited for describing the ozone concentration. In the ARE model, seven meteorological variables and four pollution variables are used as the as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide(SO2), Nitrogen dioxide(NO2), Cobalt(CO), and Promethium 10(PM10). Also, the high level ozone data (over 80ppb) have been analyzed four ARE models, General ARE, HL ARE, PM10 add ARE, Temperature add ARE model. The result shows that the General ARE, HL ARE, and PM10 add ARE models are suited for describing the high level of ozone data.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1117-1124
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• 2010

The PM10 (Promethium 10) data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly PM10 data at the southern part of the Gyeonggi-Do, Suwon monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables for the PM10 data set. The six meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, radiation, and amount of cloud. The four air pollution explanatory variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result showed that the monthly ARE models explained about 13-49% for describing the PM10 concentration.

• Environmental Engineering Research
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• v.24 no.4
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• pp.690-698
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• 2019

Air pollution has attracted ever-increasing attention because of its substantial influence on air quality and human health. To better understand the characteristics of long-range transported pollution, the single particle chemical composition and size were investigated by the single particle aerosol mass spectrometry in Fuzhou, China from 17th to 22nd January, 2016. The results showed that the haze was mainly caused by the transport of cold air mass under higher wind speed (10 m·s^-1) from the Yangtze River Delta region to Fuzhou. The number concentration elevated from 1,000 to 4,500 #·h^-1, and the composition of mobile source and secondary aerosol increased from 24.3% to 30.9% and from 16.0% to 22.5%, respectively. Then, the haze was eliminated by the clean air mass from the sea as indicated by a sharp decrease of particle number concentration from 4,500 to 1,000 #·h^-1. The composition of secondary aerosol and mobile sources decreased from 29.3% to 23.5% and from 30.9% to 23.1%, respectively. The particles with the size ranging from 0.5 to 1.5 ㎛ were mainly in the accumulation mode. The stationary source, mobile source, and secondary aerosol contributed to over 70% of the potential sources. These results will help to understand the physical and chemical characteristics of long- range transported pollutants.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1677-1689
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• 2022

Air pollution is social issue that has long-term and short-term harmful effect on the health of animals, plants, and environments. Chimneys are the primary source of air pollutants that pollute the atmosphere, so their location and type must be detected and monitored. Power plants and industrial complexes where chimneys emit air pollutants, are much less accessible and have a large site, making direct monitoring cost-inefficient and time-inefficient. As a result, research on detecting chimneys using remote sensing data has recently been conducted. In this study, YOLOv5-based chimney detection model was generated using BUAA-FFPP60 open dataset create for power plants in Hebei Province, Tianjin, and Beijing, China. To improve the detection model's performance, data split and data augmentation techniques were used, and a training strategy was developed for optimal model generation. The model's performance was confirmed using various indicators such as precision and recall, and the model's performance was finally evaluated by comparing it to existing studies using the same dataset.

• Journal of Sensor Science and Technology
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• v.29 no.2
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• pp.75-81
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• 2020

We introduce a new visible light communication (VLC) method in which three-level return-to-zero (RZ) modulation is used for flicker-free transmission. In the VLC transmitter, the three-level RZ modulation ensures that the average optical power is constant; thus, a flicker-free light-emitting diode (LED) light is achieved. In the VLC receiver, a resistor-capacitor high-pass filter is used for generating spike signals, which are used for data recovery while eliminating the 120 Hz optical noise from adjacent lighting lamps. In transmission experiments, we applied this method for wireless transmission of an air quality sensor message using the visible light of an LED array. This configuration is useful for the construction of indoor wireless sensor networks for air pollution monitoring using LED lights.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.4
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• pp.317-325
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• 2002

An air pollution monitoring station has been operated at Tokchok Island since April 1999 to characterize the background atmosphere in the vicinity of the Yellow Sea. In this study, eight chemical species in PM$_{2.5}$ and three gaseous species were analyzed. A total of 53 samples were collected for the analysis of PM$_{2.5}$ and gaseous species from April, 1999 to April, 2001. The overall mean mass concentration of PM$_{2.5}$ was 20.8 $\mu\textrm{g}$/㎥ and the eight soluble ionic species accounted for about 46.8% of PM$_{2.5}$ mass. Approximately 80% of samples appeared to experience the chloride loss effect. Air pollutant sources of PM$_{2.5}$ measured at Tokchok Island were qualitatively identified by the principal component analysis. It was found that five principal components are secondary aerosol, soil, incineration, phase change of nitrate, and ocean.and ocean.