• Journal of Preventive Medicine and Public Health
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• v.49 no.4
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• pp.205-219
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• 2016

Objectives: No children-specified review and meta-analysis paper about the short-term effect of fine particulate matter ($PM_{2.5}$) on hospital admissions and emergency department visits for asthma has been published. We calculated more precise pooled effect estimates on this topic and evaluated the variation in effect size according to the differences in study characteristics not considered in previous studies. Methods: Two authors each independently searched PubMed and EMBASE for relevant studies in March, 2016. We conducted random effect meta-analyses and mixed-effect meta-regression analyses using retrieved summary effect estimates and 95% confidence intervals (CIs) and some characteristics of selected studies. The Egger's test and funnel plot were used to check publication bias. All analyses were done using R version 3.1.3. Results: We ultimately retrieved 26 time-series and case-crossover design studies about the short-term effect of $PM_{2.5}$ on children's hospital admissions and emergency department visits for asthma. In the primary meta-analysis, children's hospital admissions and emergency department visits for asthma were positively associated with a short-term $10{\mu}g/m^3$ increase in $PM_{2.5}$ (relative risk, 1.048; 95% CI, 1.028 to 1.067; $I^2=95.7%$). We also found different effect coefficients by region; the value in Asia was estimated to be lower than in North America or Europe. Conclusions: We strengthened the evidence on the short-term effect of $PM_{2.5}$ on children's hospital admissions and emergency department visits for asthma. Further studies from other regions outside North America and Europe regions are needed for more generalizable evidence.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.898-904
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• 2021

This experimental study aims to investigate the use of a wet electrostatic precipitator as a post-treatment device to satisfy the strict emission regulations for sulfur oxides and particulate matter (PM). The inlet/outlet of a wet electrostatic precipitator was installed in a funnel using a marine four-stroke diesel engine (STX-MAN B&W) consuming marine heavy fuel oil (HFO) with a sulfur content of about 2.1%. Measurements were then obtained at the outlet of the wet electrostatic precipitator; an optical measuring instrument (OPA-102), and the weight concentration measurement method (Method 5 Isokinetic Train) were used for the PM measurements and the Fourier transform infrared (FT-IR; DX-4000) approach was used for the sulfur oxide measurements. The experimenst were conducted by varying the engine load from 50%, to 75% and 100%; it was noted that the PM reduction efficiency was a high at about 94 to 98% under all load conditions. Additionally, during the process of lowering the exhaust gas temperature in the quenching zone of the wet electrostatic precipitator, the sulfur dioxide (SO₂) values reduced because of the cleaning water, and the reduction rate was confirmed to be 55% to 81% depending on the engine load.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.47 no.1
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• pp.75-84
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• 2021

In this study, we investigated inhibitory effect of Tripeptide against particulate matter (PM)-induced damage in human keratinocytes. PM-induced cell death was inhibited by Tripeptide and the activity of aryl hydrocarbon receptor (AhR) also inhibited by Tripeptide resulting in reduced expression of its downstream targets, cytochrome P450 family 1 subfamily A member 1 (CYP1A1) and cyclooxygenase-2 (COX-2), which are responsible for toxic metabolites production and inflammation. Furthermore, PM-induced expressions of pro-inflammatory cytokines, matrix metalloproteinase-1 (MMP-1) and apoptosis-related factors were decreased by anti-oxidant activity of Tripeptide. From these results, it has been shown that the Tripeptide has protective effect against PM-induced skin damage not only through the inhibiting of keratinocyte death but also through the inhibiting the secretion of several damage-inducing factors to adjacent skin tissue. And the results suggested that Tripeptide with anti-pollution effect could be applied as a new functional cosmetic material.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.6
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• pp.789-803
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• 2003

The purpose of this study is to compare the usefulness between Gaussian dispersion model and receptor model with the experimental result of the dispersion tracing of the particulate pollutants from Taean coal-fired power plants. For this purpose, the component analysis of the collected PM 10 samples was performed. In order to trace the pollution sources, factor analysis was done with the result of the component analysis. As a result of the correlativity analysis of the fifteen power plants' profiles offered by US EPA, the correlativity of No.11202 source profile showed highest rate up to 84.5%. Thus it was adopted as proper one and the contribution rate by each pollution source was calculated by Chemical Mass Balance (CMB)-8 model. The contribution rate, which was the effect rate of the power plants on each measuring point, were calculated with a range of 24∼52％ and the standard error was below 0.9 $\mu\textrm{g}$/㎥. This indicates the selection of the source profile was appropriate. Also, the concentrations of each point were calculated by the ISCST3 which is suggested by US EPA as one of the regulatory Gaussian dispersion model. The calculation result showed that the predicted concentration was 50∼58 $\mu\textrm{g}$/㎥, comparing with the measured result of 9∼65 $\mu\textrm{g}$/㎥. It was found that the concentration calculated by ISCST3 was underpredicted. It was thought that the receptor model was more favorable than the Gaussian dispersion model in estimating the effect of the particulate matter on a certain receptive point.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.3
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• pp.233-240
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• 2017

To distinguish between high particulate matter (HPM) and light Asian dust (LAD) events, aerosol optical properties from GOCI were investigated in Seoul from 2014 to 2016. The poor air quality case caused by fine atmospheric particulate matter (i.e., 80<$PM_{10}$<$400{\mu}g/m^3$) is clearly separated from the case of heavy Asian dust that generally shows the $PM_{10}$ concentration more than $400{\mu}g/m^3$. In this study, we have found eight cases for the poor air quality and divided them into the two events(i.e., HPM and LAD). In case of aerosol optical depth (AOD), there was no big difference between two events. However, Angstrom exponent (AE) for HPM events was greater than 1, while that for LAD events less than 1. As a result of comparing aerosol type, non-absorbing fine mode aerosols were dominant for HPM events, but coarse and absorbing coarse mode aerosols for LAD events. Therefore, AE and aerosol type from GOCI can be used to distinguish between two events effectively.

• Environmental Engineering Research
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• v.21 no.2
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• pp.121-131
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• 2016

In most African urban areas, Particulate Matters (PM) concentration exceeds by far the WHO limits. In these areas, plants can play a key role in removing particles. In this study, we evaluated three ornamental species (Jatropha interrigima, Ficus benjamina, Barleria prionitis) used in Abidjan (Ivory Coast). Leaf-encapsulated saturation isothermal remnant magnetisation (SIRM) were measured and the relationship between PM captured and leaf wettability were done. The sampling were performed at roadsides and Parks. Firstly, Leaf-encapsulated and total leaf SIRM were quantified and the wettability was determined by drop contact angles (DCA). Secondly, the relationship between leaf SIRM and wettability was found. Results showed that leaf SIRM was two to ten times higher at roadsides than in Parks. Total leaf SIRM was also higher on mature leaves in Main roads suggesting a particle accumulation in leaves over time especially in waxy species (Ficus benjamina). This species encapsulated other than 20% of total leaf SIRM. All tested species were highly-wettable ($40^{\circ}$ < DCA < $90^{\circ}$). Thus, Jatropha interrigima with its leaf trichomes and F. benjamina with its leaf waxes were more wettable. A significantly positive correlation was found between wettability intensity and leaf SIRM.

• Journal of Preventive Medicine and Public Health
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• v.37 no.4
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• pp.300-305
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• 2004

Objectives : The exposure to particulate air pollution during the pregnancy has reported to result in adverse pregnancy outcome such as low birth weight, preterm birth, still birth, and intrauterine growth retardation (IUGR). We aim to assess whether prenatal exposure of particulate matter less than 10 (m in diameter ($PM_{10}$) is associated with preterm birth in Seoul, South Korea. Methods : We included 382,100 women who delivered a singleton at 25-42 weeks of gestation between 1998 and 2000. We calculated the average PM10 exposures for each trimester period and month of pregnancy, from the first to the ninth months, based on the birth date and gestational age. We used three different models to evaluate the effect of air pollution on preterm birth; the logistic regression model, the generalized additive logistic regression model, and the proportional hazard model. Results : The monthly analysis using logistic regression model suggested that the risks of preterm birth increase with PM10 exposure between the sixth and ninth months of pregnancy and the highest risk was observed in the seventh month (adjusted odds ratio=1.07, 95% CI=1.01-1.14). We also found the similar results using generalized additive model. In the proportional hazard model, the adjusted odds ratio for preterm births due to PM10 exposure of third trimester was 1.04 (95% CI=0.96-1.13) and PM10 exposure between the seventh month and ninth months of pregnancy was associated with the preterm births. Conclusions : We found that there were consistent results when we applied the three different models. These findings suggest that air pollution exposure during the third trimester pregnancy has an adverse effect on preterm birth in South Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.259-267
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• 2020

This study measured the Particulate Matter (PM) concentration according to altitude (30 m, 60 m, 90 m, 120 m, and 150 m) in three different environments: a construction site, natural environment (arboretum), and residential area. PM_2.5 and PM₁₀ values at 30 m above the construction site were 18.63 ㎍/㎥ and 24.23 ㎍/㎥ while values at 150 m were 10.89 ㎍/㎥ and 10.61 ㎍/㎥, respectively, indicating the average concentration decreased as altitude increased. PM_2.5 and PM₁₀ values at 30 m above the natural environment were 9.03 ㎍/㎥ and 11.21 ㎍/㎥ while those at 150 m were 3.42 ㎍/㎥ and 3.57 ㎍/㎥, respectively, showing lower average concentrations as altitude increased. PM_2.5 and PM₁₀ values at 30 m above the residential area were 10.65 ㎍/㎥ and 12.06 ㎍/㎥ while those at 150 m were 4.24 ㎍/㎥ and 5.17 ㎍/㎥, also demonstrating lower PM concentrations as altitude increased. The PM concentrations decreased as altitude increased at all tested sites and also decreased between environments in the following order: construction site, residential area, and natural environment. The results of this study are significant because PM concentrations were measured at various altitudes at different land-use sites. The results are expected to serve as basic data for decision-making in both regional and urban planning.

• Journal of Forest and Environmental Science
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• v.33 no.2
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• pp.113-121
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• 2017

In flight particulate matter particularly emissions generated by incomplete combustion processes has become a subject of global concern due to the health problems and environmental impacts associated with them. This has compelled most countries to set standards for coarse and fine particles due to their conspicuous impacts on environment and public health. This contribution therefore explores forest fire emissions and how its particulates affects air quality, damage to vegetation, water bodies and biological functions as architects for lung diseases and other degenerative illnesses such as oxidative stress and aging. Soot was collected from simulated forest fire using a clean glass surface and carefully transferred into amber vials for analysis. Volatile components of soot were collected over 10 mL dichloromethane and analyzed using a QTOF Premier-Water Corp Liquid Chromatography hyphenated to a mass selective detector (MSD), and Gas Chromatograph coupled to a mass spectrometer (GC-MS). To characterize the size and surface morphology of soot, a scanning electron microscope (SEM) was used. The characterization of molecular volatiles from simulated forest fire emissions revealed long chain compounds including octadec-9-enoic acid, octadec-6-enoic acid, cyclotetracosane, cyclotetradecane, and a few aromatic hydrocarbons (benzene and naphthalene). Special classes of organics (dibenzo-p-dioxin and 2H-benzopyran) were also detected as minor products. Dibenzo-p-dioxin for instance in chlorinated form is one of the deadliest environmental organic toxins. The average particulate size of emissions using SEM was found to be $11.51{\pm}4.91{\mu}m$. This study has shown that most of the emissions from simulated forest fire fall within $PM_{10}$ particulate size. The molecular by-products of forest fire and particulate emissions may be toxic to both human and natural ecosystems, and are possible precursors for various respiratory ailments and cancers. The burning of a forest by natural disasters or man-made fires results in the destruction of natural habitats and serious air pollution.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.