• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.30 no.2
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• pp.236-248
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• 2020

Objectives: The purpose of this study was to evaluate the concentrations of particulate matter generated during coffee roasting and to study various factors affecting the concentrations. Methods: Differences in concentration levels were investigated based on various factors to understand the emission rates of particulate matter over time and to compare the mass and number concentrations according to their size. Sampling was performed in closed laboratories without the operation of air conditioning or ventilation. Optical Particle Sizer(OPS) was used as a measuring device. An OPS measures using a light-scattering method. Sampling was performed for sixty minutes at one-minute intervals. The background concentration was measured for about 30 minutes before starting of coffee roasting. The concentrations of particulate matter generated during coffee roasting were monitored until roasted coffee beans were removed from the roaster and cooled down. Several factors affecting the concentrations of particulate matter were investigated, which includes the origins of green beans, the roasting level, and the input amount of green beans. Results: The results of this study may be summarized as follows: 1) There was no difference in particulate matter concentration levels by the origin of the green beans, but a statistically significant difference in concentration levels by roasting level and the input amount of green beans; The higher the roasting level, the higher was the particulate matter concentration. The more green beans we put in the roaster, the higher were the concentrations; 2) The PM10 mass concentrations increased over time. The average concentration after roasting was higher than the average concentration during roasting; 3) In the distribution of mass and number concentration by particle diameter, the majority of particles was below 2.5 ㎛. Conclusions: Persons who work in roastery cafes can be exposed to high concentrations of particulate matter. Therefore, personal exposure and risk assessment should be conducted for roastery cafe workers.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1138-1148
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• 2021

In this study, we developed a 'shared-use mobility'-mounted local particulate matter monitoring terminal technology to measure the actual particulate matter concentration around me. As a mobile terminal device in the form of an IoT sensor platform, it is designed to be separated into a control module and a sensor module to minimize interference between sensors and to consider the optimal observation position of each sensor. As a result of the field test, it was confirmed that particulate matter was locally different depending on time and space even within the same area. In addition, it was confirmed that the concentration of particulate matter in the relevant section differed by up to 100 times compared to the surrounding area due to specific sources of particulate matter such as unpaved roads. In addition, we positively reviewed the applicability of the service in the real-time metaverse environment using this result. Through technological advancement and application of multiple shared-use mobilities, we expect to be able to provide new services for practical smart city air environment monitoring, such as localized particulate matter information, air pollution event information, and identification of causes of particulate matter.

• Journal of Environmental Health Sciences
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• v.32 no.6
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• pp.533-538
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• 2006

The nano-particles are known to influence the environmental protection and human health. The relationships between transient vehicle operation and nano-particle emissions are not well-known, especially for diesel passenger vehicles with DPF(Diesel Particulate Filter). In this study, two diesel passenger vehicles were measured on a chassis dynamometer test bench. The particulate matter (PM) emission of these vehicles was investigated by number and mass measurement. The mass of the total PM was evaluated using the standard gravimetric measurement method, and the total number concentrations were measured on a ECE15+EUDC driving cycle using Condensation Particle Counter (CPC). According to the investigation results, total number concentration was $1.14{\times}10^{11}$M and mass concentration was 0.71mg/km. About 99% of total number concentration was emitted during the $0{\sim}400s$ because of engine cold condition. In high temperature and high speed duration, the particulate matter was increased but particle concentration was emitted not yet except initial engine cold condition According to DPF performance deterioration, the particulate matter was emitted 2 times and particle concentration was emitted 32 times. Thus DPF performance deterioration affects particle concentration more than PM.

• Journal of Environmental Science International
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• v.29 no.6
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• pp.623-631
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• 2020

This study analyzes the effect of negative air ions on the concentration of airborne particulate matter and evaluates the expected purification efficiency of open spaces for particulate matter by investigating the amount of negative air ions generated by plants. This study establishes a negative air ion generation treatment environment, plant environment, and control environment to measure the purification efficiency of particulate matter under the conditions of each, analyzing the expected purification efficiency by designing a particulate matter purification model. Results show that the amount of generated negative air ion according to environment was negative air ion generation treatment environment > plant environment > control environment; this order also applies to the particulate matter purification efficiency. Moreover, it took 65 min for the negative ion generation treatment environment, 90 min for the plant environment, and 240 min for the control environment to reach the standard expected purification efficiency of particulate matter concentration of 960 mg/㎥ for PM₁₀. For PM_2.5, with the designated maximum concentration of 700 mg/㎥, it took 60 min for the negative ion generation treatment environment, 80 min for the plant environment, and more than 240 min for the control environment. Based on these results, the expected purification efficiency compared to the control environment was quadrupled in the negative ion generation treatment environment and tripled in the plant environment on average.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1208-1215
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• 2017

Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.

• Journal of Environmental Science International
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• v.18 no.11
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• pp.1299-1307
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• 2009

In order to measure in-situ suspended particle size, volume concentration of suspended particulate matter and current speed, mooring observation was performed at the Gwangyang Bay by using of an optical instrument, 'LISST-100' and an acoustic instrument, 'ADV'(St. S1). And the sediment flux was obtained based on the concentration of suspended particulate matter and current speeds measured at three lines of Gwangyang Bay during ebb and flood tide of August 2006. To investigate the spatial variation of suspended particulate matter, profiling observations were measured difference echo intensity and beam attenuation coefficient by using of ADCP and Transmissometer (Line A, B, C). The suspended sediment flux rate at the mouth of Gwangyang Bay was observed to be higher during asymmetrical than symmetrical of current speeds. The flux of suspended particulate matter concentration and current speeds were transported to southeastern direction of surface layer and northwestern direction of bottom layer at the western area at line A of Gwangyang Bay. Small suspended particles have been found to increase attenuation and transmission more efficiently than similar large particles using acoustic intensity (ADV/ADCP) or optical transmit coefficient (LISST-100/Transmissometer). The application and problems as using optical or acoustic instruments will be detected for use in time varying calibrations to account for non-negligible changes in complex environments in situ particle dynamics are poorly understood.

• Journal of the Korean Medical Association
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• v.61 no.12
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• pp.749-755
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• 2018

Particulate matter is an air pollutant emitted from both natural and anthropogenic sources, and its adverse health effects have been well documented in time-series analyses and cohort studies. The effect size of particulate matter exposure-a roughly 0.5% increase in mortality for each $10{\mu}g/m^3$ increment of short-term exposure to particulate matter with aerodynamic diameter ${\leq}10{\mu}m$ and approximately a 10% increase for each $10{\mu}g/m^3$ increment of long-term exposure to particulate matter with aerodynamic diameter ${\leq}2.5{\mu}m$-is small compared to other risk factors, but the exposure is involuntary and affects the entire population, which makes particulate matter pollution an important public health issue. The World Health Organization and Korean government have both established guidelines for particulate matter concentrations, but the Korean guideline is less stringent than that of the World Health Organization. The annual mean concentration of particulate matter in Korea is decreasing, but the trend seems to be slowing. In addition to policy efforts to reduce particulate matter emission, personal approaches such as the use of face masks and air purifiers have been recommended. Personal approaches may not solve the fundamental problem, but can provide temporary mitigation until efforts to reduce emission make progress.

• Journal of Auto-vehicle Safety Association
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• v.10 no.3
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• pp.27-31
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• 2018

The People have a bad perception about diesel vehicle because of serious air pollution, increase fine dust and global vehicle company's diesel gate. Starting the project in 2005, Ministry of Environment has been supporting that is exhaust gas reduction devices (DPF) on diesel vehicles in the metropolitan area. During the period of 2017.01.01 to 2017.12.31, 10,030 diesel vehicles installed exhaust gas reduction devices (DPF). Among them, 9,921 diesel vehicles that they have sufficient data for analysis were analyzed amount of particulate matter reduction before and after exhaust gas reduction devices (DPF) was installed. Opacity smoke meter measures the concentration of particulate matter. So concentration of particulate matter was converted into a mass unit, and then calculated the total amount of reduced particulate matter. It was estimated that social benefits is costs required to remove it from the total amount of particulate matter.

• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.719-725
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• 2020

Objectives: With the growth of national interest in fine particulate matter, many complaints about pollutants emitted from air pollution emitting facilities have arisen in recent years. In particular, it is thought that a large volume of particulate pollutants are discharged from workplaces that use Solid Refuse Fuel (SRF). Therefore, particulate contaminants generated from SRF were measured and analyzed in this study in terms of respective particle sizes. Methods: In this study, particulate matter in exhaust gas was measured by applying US EPA method 201a using a cyclone. This method measures Filterable Particulate Matter (FPM), and does not consider the Condensable Particulate Matter (CPM) that forms particles in the atmosphere after being discharged as a gas in the exhaust gas. Results: The mass concentration of Total Suspended Particles (TSP) in the four SRF-using facilities was 1.16 to 11.21 mg/Sm3, indicating a very large concentration deviation of about 10 times. When the fuel input method was the continuous injection type, particulate matter larger than 10 ㎛ diameter showed the highest particle size fraction, followed by particulate matter smaller than 10 ㎛ and larger than 2.5 ㎛, and particulate matter of 2.5 ㎛ or less. Contrary to the continuous injection type, the batch injection type had the smallest particle size fraction of particulate matter larger than 10 ㎛. The overall particulate matter decreased as the operating load factor decreased from 100% to 60% at the batch input type D plant. In addition, as incomplete combustion significantly decreased, the particle size fraction also changed significantly. Both TSP and heavy metals (six items) satisfied the emissions standards. The measured value of the emission factor was 38-99% smaller than the existing emissions factor. Conclusions: In the batch injection facility, the particulate matter decreased as the operating load factor decreased, as did the particle size fraction of the particulate matter. These results will help the selection of effective methods such as reducing the operating load factor instead of adjusting the operating time during emergency reduction measures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.540-542
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• 2019

With many announcements on air pollution and human effects from particulate matters, particulate matter forecasts are attracting a lot of public attention. As a result, various efforts have been made to increase the accuracy of particulate matter forecasting by using statistical modeling and machine learning technique. In this paper, the particulate matter AQI index prediction is performed using the multilayer perceptron neural network for particulate matter prediction. For this purpose, a prediction model is designed by using the meteorological factors and particulate matter concentration values commonly used in a number of studies, and the accuracy of the particulate matter AQI prediction is compared.