• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.125-132
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• 2022

The Ministry of Environment recently analyzed the output data of 10 fine dust measuring stations and, as a result, announced that about 60% had an error that the existing atmospheric measurement concentration was higher. In order to accurately predict fine dust, the wind direction and measurement position must be corrected. In this paper, in order to solve these problems, fuzzy rules are used to solve these problems. In addition, in order to calculate the fine particulate sensation index actually felt by pedestrians on the street, a computer simulation experiment was conducted to calculate the fine particulate sensation index in consideration of weather conditions, temperature conditions, humidity conditions, and wind conditions.

• Journal of Environmental Health Sciences
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• v.45 no.1
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• pp.30-41
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• 2019

Objectives: The effects of particulate matter and ozone on health are being reported in a number of studies. These effects are likely to be stronger on the elderly population, but studies in this regard are scarce. The purpose of this study was to examine the effects of particulate matter ${\leq}2.5{\mu}m$ and ozone on the acute health status of the elderly population. Methods: In order to analyze the health status of the elderly population, the NHIS-Senior Cohort data was used. In this study of people 60 years or older in Seoul, the number of outpatient visits and ER visits between 2002 and 2013 were calculated. Each disorder and the lag effect were analyzed separately. Particulate matter and ozone were analyzed using both the single exposure model and the adjusted multi-exposure model. Results: In the single exposure analysis with PM2.5 as the exposure variable, with each increase of $10{\mu}g/m^3$, the number of outpatient visits increased by 1.0081 times, vascular disease 1.0065 times, chronic pulmonary disease 1.0086 times, and diabetes 1.0055 times. In the multi-exposure model adjusting for ozone, the number of outpatient visits increased by 1.0066 times. There was a one-day lag effect and 1.0066 times increase between PM2.5 and ER visits in the multi-exposure model and 1.0057 times when adjusted for ozone (p value <0.10). There was a one-day lag effect in all multi-exposure models with ozone as the main variable, and when the particulate matter was adjusted, there was a one-day delay and 1.0143 times increase in ER visits. Conclusions: In our study, an increase in the number of outpatient and ER visits in the elderly population in accordance with the increase in PM2.5 and ozone was found. The association found in our study could also produce a socioeconomic burden. Future studies need to be performed in regards to younger populations and other air pollutants.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.04a
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• pp.512-514
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• 2008

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.04a
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• pp.188-190
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• 2008

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2019.10a
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• pp.212-212
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• 2019

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.620-622
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• 2018

The need for particulate matter prediction algorithms has increased as social interest in the effects of human on particulate matter increased. Many studies have proposed statistical modelling and machine learning techniques based prediction models using weather data, but it is difficult to accurately set the environment and detailed conditions of the models. In addition, there is a need to design a new prediction model for missing data in domestic weather monitoring station. In this paper, fine dust prediction is performed using multi-layer perceptron network as a previous study for particulate matter prediction. For this purpose, a prediction model is designed based on weather data of three monitoring station and the suitability of the algorithm for particulate matter prediction is evaluated through comparison with actual data.

• Land and Housing Review
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• v.11 no.4
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• pp.93-98
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• 2020

This study examined the particulate removal performance of three different types of air filters inside a heat exchanger. Of interest was the ability of each filter type in reducing the transmission of outdoor particulate matter of PM10 from entering an apartment while the heat exchanger was in operation. The study tested one commonly used medium filter (E11 grade) and two HEPA filters (H13 grade) of different thicknesses. Two different concentrations of particulate matter were used in the experiment to address different ambient air quality conditions in Korea, 32.75 ㎍/㎥ and 67.26 ㎍/㎥. Study results indicated that under the particulate matter concentration of 32.75 ㎍/㎥, all three filters were capable of removing more than 95% of the fine dust. However at a particulate matter concentration of 67.26 ㎍/㎥, the medium E11 grade filter was only able to remove about 90% of the particulates whereas the HEPA H13 grade filters were able to remove 95% or more of the particulates. The thicker HEPA filter (40T) was also more effective in removing particulates than the thinner HEPA filter (20T) by about 1.6 to 3 percentage points. Based on the findings of this study, it is recommended that HEPA filters of 20T thickness or greater be used during the high air pollution seasons of winter and spring in Korea while medium filters can be used during the other seasons to reduce outdoor air pollution transmission indoors.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.141-150
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• 2021

Fine dust is a substance that greatly affects human health, and various studies have been conducted in this regard. Due to the human influence of particulate matter, various studies are being conducted to predict particulate matter grade using past data measured in the monitoring network of Seoul city. In this paper, predictive model have focused on particulate matter concentration in May, 2019, Seoul. The air pollutant variables were used to training such as SO₂, CO, NO₂, O₃. The predictive model based on Adaboost, and training model was dividing PM₁₀ and PM_2.5. As a result of the prediction performance comparison through confusion matrix, the Adaboost model was more conformable for predicting the particulate matter concentration grade. Although air pollutant variables have a higher correlation with PM2.5, training model need to train a lot of data and to use additional variables such as traffic volume to predict more effective PM₁₀ and PM_2.5 distribution grade.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.51-63
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• 1997

Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.

• Journal of Preventive Medicine and Public Health
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• v.19 no.1 s.19
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• pp.130-136
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• 1986

The particle size of suspended particulates was measured by a Anderson air sampler from Mar. 1982 to Feb. 1984 in a part of Seoul. It was concluded as follows : 1) The arithmetic concentration of suspended particulates was $147.8{\mu}g/m^3$ in Spring, 136.9 in Summer, 131.9 in Autumn and 158.1 in Winter respectively. 2) The cumulative distribution of suspended particulates size in logarithmic diagram showed similar to normal log distribution. 3) The atmospheric particulate matters showed a bimodal size distribution on the base of unit particle concentrations, which divided at approximately $2{\mu}m$ in the diameter. 4) While the fine particulates less than $2.1{\mu}m\;was\;35.4{\sim}45.0%$, the coarse particulates was $55.0{\sim}64.5%$. 5) The higher the concentration of suspended particulates, the more increased the ratio of fine particulates. The higher the concentration of suspended particulates, the lower median size of suspended particulate as well. 6) The respirable dust particulates less than $4.7{\mu}m\;was\;52.2{\sim}62.9%$ in seasonal average through the 2 year samples. With the above result, air pollution concerned with public health could be evaluated and the control measures also are suggested.