• Korean Journal of Agricultural Science
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• v.47 no.1
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• pp.59-66
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• 2020

Particulate matter (PM) is regarded as a major threat to public health and safety in urban areas. Despite a variety of efforts to systemically monitor the distribution of PM, the limited amount of sampling sites may not provide sufficient coverage over the areas where the monitoring stations are not located in close proximity. This study examined the capacity of using remotely sensed data to estimate the PM10 and PM2.5 concentrations in Seoul, South Korea. Multiple linear regression models were developed using the multispectral band data from the Moderate-resolution imaging spectro-radiometer equipped on Terra (MODIS) and Operational Land Imager equipped on Landsat 8 (Landsat 8) and meteorological parameters. Compared to MODIS-derived models (r² = 0.25 for PM10, r² = 0.30 for PM2.5), the Landsat 8-derived models showed improved model reliabilities (r² = 0.17 to 0.57 for PM10, r² = 0.47 to 0.71 for PM2.5). Landsat 8 model-derived PM concentration and ground-truth PM measurements were cross-validated to each other to examine the capability of the models for estimating the PM concentration. The modeled PM concentrations showed a stronger correlation to PM10 (r = 0.41 to 0.75) than to PM2.5 (r = 0.14 to 0.82). Overall, the results indicate that Landsat 8-derived models were more suitable in estimating the PM concentrations. Despite the day-to-day fluctuation in the model reliability, several models showed strong correspondences of the modeled PM concentrations to the PM measurements.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.83-92
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• 2015

Concerning the national health, it is important to develop an accurate prediction method of atmospheric particulate matter (PM) because being exposed to such fine dust can trigger not only respiratory diseases as well as dermatoses, ophthalmopathies and cardiovascular diseases. The National Institute of Environmental Research (NIER) employs a decision tree to predict bad weather days with a high PM concentration. However, the decision tree method (even with the inherent unstableness) cannot be a suitable model to predict bad weather days which represent only 4% of the entire data. In this paper, while presenting the inaccuracy and inappropriateness of the method used by the NIER, we present the utility of a new prediction model which adopts boosting with quantile loss functions. We evaluate the performance of the new method over various ${\tau}$-value's and justify the proposed method through comparison.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.118-129
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• 2016

Many epidemiological studies, relying on administrative air pollution monitoring data, have reported the association between particulate matter ($PM_{10}$) air pollution and human health. These monitoring data were collected at a limited number of fixed sites, whereas government-generated health data are aggregated at the area level. To link these two data types for assessing health effects, it is necessary to estimate area-level concentrations of $PM_{10}$. In this study, we estimated district (Gu)-level $PM_{10}$ concentrations using a previously developed pointwise exposure prediction model for $PM_{10}$ and three types of point locations in Seoul, Korea. These points included 16,230 centroids of the largest census output residential areas, 422 community service centers, and 610 centroids on the 1km grid. After creating three types of points, we predicted $PM_{10}$ annual average concentrations at all locations and calculated Gu averages of predicted $PM_{10}$ concentrations as representative Gu-estimates. Then, we compared estimates to each other and to measurements. Prediction-based Gu-level estimates showed higher correlations with measurement-based estimates as prediction locations became more population representative ($R^2=0.06-0.59$). Among the three estimates, grid-based estimates gave lowest correlations compared to the other two(0.35-0.47). This study provides an approach for estimating area-level air pollution concentrations and assesses air pollution health effects using national-scale administrative health data.

• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.486-495
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• 2021

Background: Particulate matter (PM) is one of the leading causes of premature death worldwide. Previous studies in South Korea have applied a relative risk calculated from Western populations when estimating the disease burden attributable to PM. However, the relative risk of PM on health outcomes may not be the same across different countries or regions. Objectives: This study aimed to estimate the premature deaths and socioeconomic costs attributable to long-term exposure to PM in South Korea. We considered not only the difference in PM concentration between regions, but also the difference in relative risk. Methods: National monitoring data of PM concentrations was obtained, and missing values were imputed using the AERMOD model and linear regression model. As a surrogate for relative risk, hazard ratios (HRs) of PM for cardiovascular and respiratory mortality were estimated using the National Health Insurance Service-National Sample Cohort. The nation was divided into five areas (metropolitan, central, southern, south-eastern, and Gangwon-do Province regions). The number of PM attributable deaths in 2018 was calculated at the district level. The socioeconomic cost was derived by multiplying the number of deaths and the statistical value of life. Results: The average PM₁₀ concentration for 2014~2018 was 45.2 ㎍/m³. The association between long-term exposure to PM₁₀ and mortality was heterogeneous between areas. When applying area-specific HRs, 23,811 premature deaths from cardiovascular and respiratory disease in 2018 were attributable to PM₁₀ (reference level 20 ㎍/m³). The corresponding socioeconomic cost was about 31 trillion won. These estimated values were higher than that when applying nationwide HRs. Conclusions: This study is the first research to estimate the premature mortality caused by long-term exposure to PM using relative risks derived from the national population. This study will help precisely identify the national and regional health burden attributed to PM and establish the priorities of air quality policy.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.128-135
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• 2022

Because construction work is conducted outdoors, construction workers are affected by harmful environmental factor. Especially, Particulate Matter (PM₁₀) is one of the harmful environmental factors with a diameter of 10㎍/m³ or less. When PM₁₀ is inhaled by human, it can cause fatal impact on the human. Contrary to the various analyses of health impact on PM₁₀, the research on the relationship between construction accidents and PM₁₀ are few. Therefore, this study aims to conduct the relative frequency analysis which find out the correlation between construction accidents and PM₁₀, and the modified PM₁₀ grade is suggested to expect accidents probability caused by PM₁₀ in the construction industry. This study is conducted by four steps. i) Establishment of the database; ii) Classification of data; iii) Analysis of the Relative Frequency of accidents in the construction industry by PM₁₀ concentration; iv) Modified PM₁₀ groups to classify the impact of PM₁₀ on accident. In terms of frequency analysis, the most accidents were occurred in the average concentration of PM₁₀ (32㎍/m³). However, we found that the relative frequency of accident was increased as the concentration of PM₁₀ increased. This means the higher PM₁₀ concentration can cause more accidents during construction. In addition, PM₁₀ concentration was divided as 6 groups by the WHO, but the modified PM₁₀ grade by the relative frequency on accident was suggested as 3 groups.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.6
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• pp.713-721
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• 2013

This study is about emission characteristics of HAPs and particulate matters emitted by spray of paint and organic solvent usually used in vehicle paint facilities. To analyze emission characteristics of HAPs and particulate matters emitted from vehicle paint facilities are calculated based on the measuring emission quantity of pollutants based on the amount of paint used (kg) and unit area ($m^2$) by paint manufacturers (J company, K company, and R company). In cases of paint manufacturers (J, K, and R), average emission factors of VOCs, carbonyl compound, particulate matter, and PAHs per 1 kg of paint were 327.81 g/kg, 5.98 g/kg, 336.70 g/kg, and 0.0078 g/kg respectively. The average emission factors of VOCs, carbonyl compounds, particulate matters, and PAHs by unit area were $171.55g/m^2$, $3.10g/m^2$, $176.27g/m^2$, and $0.0036g/m^2$ respectively.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.99-105
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• 2022

Public attention to the indoor environment of underground subway stations, which is a representative multi-use facility, has been increasing along with the increase in indoor activities. In underground stations, fine iron oxide, which affects the health of users, is generated because of the friction between wheels and rails. Among particulate pollutant reduction technologies, plants have been considered as a non-chemical air purification method, and their effects in reducing certain chemical species have been identified in previous studies. The present study aimed to derive the total quantitative and qualitative reduction effects of a bio-filter system comprising air purifying plants, installed in an underground subway station. The experiment proceeded in two ways. First, PM(particulate matter) reduction effect by vegetation biofilter was monitored with the IAQ(indoor air quality) station. In addition, chemical speciation analysis conducted on the samples collected from the experimental and control areas where plants and irrigation using SEM-EDS(scanning electron microscopy-energy dispersive X-ray spectroscopy). This study confirmed the effect of the vegetation bio-filter system in reducing the accumulation of particulate pollutants and transition and other metals that are harmful to the human body.

• Journal of the Korean Chemical Society
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• v.58 no.3
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• pp.267-276
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• 2014

The collection of $PM_{10}$ and $PM_{2.5}$ fine particulate matter samples was made at the 1100 m site of Mt. Halla of Jeju Island, located at the atmospheric boundary layer (ABL) of background area, during the fall of 2012. Their ionic and elemental species were analyzed, in order to investigate the chemical compositions and size distribution characteristics. In $PM_{2.5}$ fine particles ($d_p$ < $2.5{\mu}m$), the concentrations of the secondary formed nss-$SO{_4}^{2-}$, $NH_4{^+}$ and $NO_3{^-}$ species were 4.84, 1.98, and $1.27{\mu}g/m^3$, respectively, showing 58.2% of the total $PM_{2.5}$ mass. On the other hand, their concentrations in $PM_{10-2.5}$ coarse particles (2.5 < $d_p$ < $10{\mu}m$) were 0.63, 0.21 and $1.10{\mu}g/m^3$, respectively, occupying 22.8% of the total $PM_{10-2.5}$ mass. The comparative study of size distribution has resulted that $NH_4{^+}$, nss-$SO{_4}^{2-}$, $K^+$ and $CH_3COO^-$ are mostly existed in fine particles, and $NO_3{^-}$ is distributed in both fine and coarse particles, but $Na^+$, $Cl^-$, $Mg^{2+}$ and nss-$Ca^{2+}$ are rich in coarse particle mode.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.5
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• pp.547-553
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• 2016

Black carbon (BC) contained in particulate matter (PM) originating from the exhaust gases of ships' diesel engines has been receiving great attention as a cause of glacial melting and warming in the polar regions. In this study, we took samples from various locations of PM emitted from the training ship (T/S) HANBADA's main engine, in cooperation with the Korea Maritime and Ocean University. We analyzed the structure and characteristics of these samples using high-resolution transmission electron microscopy (HR-TEM) and applied our findings as fundamental research for developing PM reduction technology. We also employed our results to determine appropriate preemptive action to meet upcoming PM/BC regulations. In addition, we confirmed the emission trend of pollutants from exhaust gases under various engine operating conditions using an exhaust gas analyzer. Results obtained from the analysis of HR-TEM images showed that the structure of the PM is chain-like wispy agglomerates consisting of a number of individual spherical particles. As the sampling location was moved away from the turbo charger (T/C) towards the funnel, more condensates were observed at a low temperature and the molecular structure of the PM lost its characteristic BC structure as an amorphous structure gradually appeared. Furthermore, through the analysis of exhaust gases, we predicted a decrease in PM concentration in the exhaust stream as engine rpm increase.

• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.185-196
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• 2024

Objectives: Excess mortality associated with long-term exposure to fine particulate matter (PM_2.5) has been documented. However, research on the disease burden following short-term exposure is scarce. We investigated the cause-specific mortality burden of short-term exposure to PM_2.5 by considering the potential non-linear concentration-response relationship in Korea. Methods: Daily cause-specific mortality rates and PM_2.5 exposure levels from 2010 to 2019 were collected for 8 Korean cities and 9 provinces. A generalized additive mixed model was employed to estimate the non-linear relationship between PM_2.5 exposure and cause-specific mortality levels. We assumed no detrimental health effects of PM_2.5 concentrations below 15 ㎍/m³. Overall deaths attributable to short-term PM_2.5 exposure were estimated by summing the daily numbers of excess deaths associated with ambient PM_2.5 exposure. Results: Of the 2 749 704 recorded deaths, 2 453 686 (89.2%) were non-accidental, 591 267 (21.5%) were cardiovascular, and 141 066 (5.1%) were respiratory in nature. A non-linear relationship was observed between all-cause mortality and exposure to PM_2.5 at lag0, whereas linear associations were evident for cause-specific mortalities. Overall, 10 814 all-cause, 7855 non-accidental, 1642 cardiovascular, and 708 respiratory deaths were attributed to short-term exposure to PM_2.5. The estimated number of all-cause excess deaths due to short-term PM_2.5 exposure in 2019 was 1039 (95% confidence interval, 604 to 1472). Conclusions: Our findings indicate an association between short-term PM_2.5 exposure and various mortality rates (all-cause, non-accidental, cardiovascular, and respiratory) in Korea over the period from 2010 to 2019. Consequently, action plans should be developed to reduce deaths attributable to short-term exposure to PM_2.5.