• Environmental and Resource Economics Review
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• v.12 no.3
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• pp.469-485
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• 2003

This paper reports on the results of epidemiological investigation of daily health effects in the elderly associated with daily exposure to particulate matters in Korea. Our main focus is on the potential difference in health effects between PM10 and PM2.5. While the Korean environmental authority has set an ambient standard for PM10, the government currently does not monitor PM2.5, which has no national standard. A daily data on respiratory symptoms as well as PM concentrations are collected for a total of 120 days. Using a probit model, we find statistically significant negative health effects of PM2.5 on respiratory symptoms among the nonsmoking elderly, while PM10 does not show such effects from the estimation. This result suggests that, for air quality regulatory purposes, PM2.5 can be a more appropriate air pollutant than PM10.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.1-13
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• 2022

This study aims to examine the trend and characteristics of newspaper articles concerned with fine particle matter. Newspaper articles since 1995 collected from Bigkinds were analyzed using text mining techniques, sentiment analysis and regression analysis. Air pollution measurement and domestic pollutants appeared frequently previously, but "China" became the keyword in the 2010s along with political action, the effects on the health, AD/PR, and domestic pollutants. Korea JoongAng Daily, Hankyoreh and Kyunghyang Shinmun have had more focused on political regulations whereas most regional daily newspapers on emission sources and reduction measures at the regional level. The results of this study are expected to be used as a reference for understanding the trend of newspaper articles. Future work includes further analysis and discussion of fine particle pollution condition and news reports in the post-COVID era.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.919-932
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• 2019

Fine particulate matter (PM2.5) is not only affected by anthropogenic emissions, but also intensifies, migrates, decreases by hydrometeorological factors. Therefore, it is essential to understand relationships between the hydrometeorological factors and PM2.5 concentration. In Korea, PM2.5 concentration is measured at the ground observatories and estimated data are given to locations where observatories are not present. In this way, the data is not suitable to represent an area, hence it is impossible to know accurate concentration at such locations. In addition, it is hard to trace migration, intensification, reduction of PM2.5. In this study, we analyzed the relationships between hydrometeorological factors, acquired from Global Land Data Assimilation System (GLDAS), and PM2.5 by means of Bayesian Model Averaging (BMA). By BMA, we also selected factors that have meaningful relationship with the variation of PM2.5 concentration. 4 PM2.5 concentration models for different seasons were developed using those selected factors, with Aerosol Optical Depth (AOD) from MODerate resolution Imaging Spectroradiometer (MODIS). Finally, we mapped the result of the model, to show spatial distribution of PM2.5. The model correlated well with the observed PM2.5 concentration (R ~0.7; IOA ~0.78; RMSE ~7.66 ㎍/㎥). When the models were compared with the observed PM2.5 concentrations at different locations, the correlation coefficients differed (R: 0.32-0.82), although there were similarities in data distribution. The developed concentration map using the models showed its capability in representing temporal, spatial variation of PM2.5 concentration. The result of this study is expected to be able to facilitate researches that aim to analyze sources and movements of PM2.5, if the study area is extended to East Asia.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1891-1900
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• 2021

Fine particulate matter (FPM; diameter ≤ 2.5 ㎛) is frequently found in metropolitan areas due to activities associated with rapid urbanization and population growth. Many adolescents spend a substantial amount of time at school where, for various reasons, FPM generated outdoors may flow into indoor areas. The aims of this study were to estimate FPM concentrations and categorize types of FPM in schools. Meteorological and chemical variables as well as satellite-based aerosol optical depth were analyzed as input data in a random forest model, which applied 10-fold cross validation and a grid-search method, to estimate school FPM concentrations, with four statistical indicators used to evaluate accuracy. Loose and strict standards were established to categorize types of FPM in schools. Under the former classification scheme, FPM in most schools was classified as type 2 or 3, whereas under strict standards, school FPM was mostly classified as type 3 or 4.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2001.11a
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• pp.233-234
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• 2001

1997년부터 2000년 사이의 하절기(5~9월)의 평일(월~토요일)과 일요일(공휴일 포함)의 매 시간평균 미세먼지 및 오존의 농도특성을 분석하기 위하여 질소산화물(NOx), 오존(O$_3$), 미세먼지(PM10)등의 대기 오염물질을 한 시간단위로 자동측정하는 서울의 27개 측정소의 측정자료를 분석하였다. (중략)

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.807-817
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• 2014

This paper considers multivariate time series modelling of PM10 data in Korea collected from 2008 to 2011. We consider both temporal and spatial dependencies of PM10 by applying the sparse vector autoregressive (sVAR) modelling proposed by Davis et al. (2013). It utilizes the partial spectral coherence to measure cross correlation between different regions, in turn provides the sparsity in the model while balancing the parsimony of model and the goodness of fit. It is also shown that sVAR performs better than usual vector autoregressive model (VAR) in forecasting.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.473-481
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• 2017

It is well known that the air quality of 92% of the world is known to exceed the standard of WTO and the death caused by air pollution is almost 6 million per year. The $PM_{2.5}$ concentration in Korea is the second most serious among the OECD countries following Turkey. Since the $PM_{2.5}$ has a direct effect on the respiratory system, it has been actively studied in domestic and foreign countries. But current research on the $PM_{2.5}$ is limited in weather factor or air pollutants. In this paper, we consider the influence of spatial neighbor with weather factor or air pollutants using spatial panel model. We applied the proposed method to 25 borough of Seoul in Korea. The result shows a significant effect of spatial neighbor on the $PM_{2.5}$ concentration fields.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.987-998
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• 2013

This paper considers the statistical characteristics on the air quality (PM10) of Korea collected hourly in 2011. PM10 in Korea exhibits very strong correlations even for higher lags, namely, long range dependence. It is power-law tailed in marginal distribution, and generalized Pareto distribution successfully captures the thicker tail than log-normal distribution. However, slowly decaying autocorrelations may confuse practitioners since a non-stationary model (such as changes in mean) can produce spurious long term correlations for finite samples. We conduct a statistical testing procedure to distinguish two models and argue that the high persistency can be explained by non-stationary changes in mean model rather than long range dependent time series models.

• The Journal of Bigdata
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• v.6 no.2
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• pp.39-49
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• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.1
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• pp.59-77
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• 2008

The purpose of this study is to investigate the impact of traffic-related air pollution on the urban population in the Metropolitan Seoul area. In particular, this study analyzes urban population exposure to traffic-related particulate materials(PM). For the purpose, this study examines the relationships between traffic flows and PM concentration levels during the last fifteen years. Traffic volumes have been decreased significantly in recent year in Seoul, however, PM levels have been declined less compare to traffic volumes. It may be related with the rapid growth in the population and vehicle numbers in Gyenggi, the outskirt of Seoul, where several New Towns have been developed in the middle of 1990's. The spatial pattern of commuting has changed, and thus and travel distances and traffic volumes have increased along the main roads connecting CBDs in Seoul and New Towns consisting of large residential apartment complexes. These changes in traffic flows and travel behaviors cause increasing exposure to traffic-related air pollution for urban population over the Metropolitan Seoul area. GIS techniques are applied to analyze the spatial patterns of traffic flows, population distributions, PM distributions, and passenger flows comprehensively. This study also analyzes real time base traffic flow data and passenger flow data obtained from T-card transaction database applying data mining techniques. This study also attempts to develop a space-time model for assessing journey-time exposure to traffic related air pollutants based on travel passenger frequency distribution function. The results of this study can be used for the implications for sustainable transport systems, public health and transportation policy by reducing urban air pollution and road traffics in the Metropolitan Seoul area.