• Journal of Environmental Health Sciences
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• v.47 no.3
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• pp.259-266
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• 2021

Objectives: This study was conducted to identify the emissions characteristics of total particulate matter (TPM), fine dust (PM₁₀, PM_2.5), and gaseous pollutants (SOx, NOx) in iron and steel manufacturing facilities in order to investigate emissions factors suitable for domestic conditions. Methods: Total particulate matter (TPM), fine dust (PM₁₀, PM_2.5), and gas phase materials were investigated at the outlet of electric arc furnace facilities using a cyclone sampling machine and a gas analyzer. Results: The concentrations of TPM ranged from 1.64 to 3.14 mg/Sm³ and the average was 2.47 mg/Sm³. Particulate matter 10 (PM₁₀) averaged 1.49 mg/Sm³ with a range of 0.92 to 1.99 mg/Sm³, and the resulting ratio of PM₁₀ to TPM was around 60 percent. PM_2.5/PM₁₀ ranged from 33.7 to 47.9% and averaged 41.6%. Sulfur oxides (SOx) were not detected, and nitrogen oxides (NOx) averaged 6.8 ppm in the range of 5.50 to 8.67 ppm. TPM emission coefficients per product output were in the range of 0.60 to 1.26 g/kg, 0.13 to 0.79 g/kg for PM₁₀ and 0.12 to 0.36 g/kg for PM_2.5, and showed many differences from the emissions coefficients previously announced. An emissions coefficient for NOx is not currently included in the domestic notices, but the results were calculated to be 0.42 g/kg per product output. Conclusions: Investigation and research on emissions coefficients that can reflect the characteristics of various facilities in Korea should be conducted continuously, and the determination and application of unique emissions coefficients that are more suitable for domestic conditions are needed.

• Safety and Health at Work
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• v.12 no.1
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• pp.114-118
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• 2021

Background: Exposure to particulate matter (PM) emitted from vehicle exhaust might disrupt systemic function and elevate the risk of cardiovascular disease. In this study, we examined the changes of cardiometabolic biomarkers among vehicle inspectors exposed daily to PM0.25 and components. Methods: This cross-sectional study was conducted at two vehicle inspection centers, Pulogadung and Ujung Menteng, located in East Jakarta, Indonesia. The exposed respondents were 43 workers from vehicle inspection centers, and the unexposed group consisted of 22 staff officers working in the same locations. Vehicle exhaust particulate matter was measured for eight hours using a Leland Legacy personal pump attached to a Sioutas Cascade Impactor. The used filters were 25 and 37-mm quartz filters. The particulate matter concentration was analyzed using a gravimetric method, whereas trace elements were analyzed using energy dispersive X-ray fluorescence. An EEL Smoke Stain Reflectometer analyzed black carbon. Results: The personal exposure concentrations of PM0.25 were 10.4-fold higher than those in unexposed groups. Calcium and sulfur were the major components in the obtained dust, and their levels were 3.3- and 7.2-fold higher, respectively, in the exposed group. Based on an independent-samples t-test, high-density lipoprotein, triglyceride, HbA1c, total immunoglobulin E, high-sensitivity C-reactive protein, tumor necrosis factor-alpha, and nitric oxide levels were significantly different between the groups. Conclusions: In summary, it was suggested that PM0.25 exposure from vehicle exhaust might affect cardiometabolic biomarkers change.

• Journal of the Korean Ceramic Society
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• v.49 no.3
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• pp.226-230
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• 2012

This paper presents a novel structure for a diesel particulate filter (DPF) with inclined gas paths, which was designed so that the gas paths offered a fluent flow of exhaust gases, and particulate matter (PM) was collected at pores formed in the body. The alumina porous filter was prepared by a conventional sintering process at $1200^{\circ}C$ for 2 h. Straight gas paths with $30^{\circ}$ of inclination from the gas flow direction were formed in the filter body. It is shown that this filter structure worked as a PM filter, in which 90.2% of soot filtration efficiency and 59.6 mbar of pressure drop were achieved.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.109-121
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• 2020

The purpose of this study is to find out primary policies for reducing PM(particulate matter) as well as for improving the quality of life. Serious particulate matters cause to diverse healthcare and economy problems including business transactions. Unfortunately, until recently there are very few researches regarding the decision-making process for particulate matter policies. This study has applied the AHP(Analytic Hierarchy Process) method to develop cooperative policy making processes. The upper layer of this hierarchy analysis consists of four parts, i.e., transportation, production facility, living environment, and urban planning management. And each upper layer parts has their own three policies. 25 experts including policy-makers, academic researchers and industrial specialists have decided the primary policies and directions. The most significant PM policy is the mandatory reduction of air pollution and suspension of factory operation in the production industry. The results of this study can lead to guidelines for making environmental policies.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.92-99
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• 2021

Light-scattering fine particulate matter monitors can measure particulate matter (PM) concentrations in every second and can be designed in a portable size. They can measure the concentrations of various PM sizes (PM_1.0, PM_2.5, PM_4.0 and PM₁₀) with a single sensor. They measure the number and size of particulate matters and convert them to weight per volume (concentration). These devices show a large error for asian dust. This paper proposes a scheme that compensates the PM_2.5 concenstration error for asian dust by multiple linear regression machine learning in light-scattering PM monitors. This scheme can be effective with only two or three types of PM sizes. The experimental results compare a beta-ray PM monitor of national institute of environmental research and a light-scattering PM monitor during a month. The correlation coefficient (R²) of theses two devices was 0.927 without asian dust, but it was 0.763 due to asian dust during the entire experimental period and improved to 0.944 by the proposed machine learning.

• 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 Mechanical Science and Technology
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• v.20 no.1
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• pp.1-12
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• 2006

This work reviews the effects of catalyst formulation and exhaust gas composition on soot oxidation in CDPF (Catalytic Diesel Particulate Filter). DOC's (Diesel Oxidation Catalysts) have been loaded with Pt catalyst (Pt/$Al_{2}O_3$) for reduction of HC and CO. Recent CDPF's are coated with the Pt catalyst as well as additives like Mo, V, Ce, Co, Fe, La, Au, or Zr for the promotion of soot oxidation. Alkali (K, Na, Cs, Li) doping of metal catalyst tends to increase the activity of the catalysts in soot combustion. Effects of coexistence components are very important in the catalytic reaction of the soot. The soot oxidation rate of a few catalysts are improved by water vapor and NOx in the ambient. There are only a few reports available on the mechanism of the PM (particulate matter) oxidation on the catalysts. The mechanism of PM oxidation in the catalytic systems that meet new emission regulations of diesel engines has yet to be investigated. Future research will focus on catalysts that can not only oxidize PM at low temperature, but also reduce NOx, continuously self-cleaning diesel particulate filters, and selective catalysts for NOx reduction.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.241-247
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• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• Environmental Engineering Research
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• v.23 no.2
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• pp.143-149
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• 2018

Air quality conditions and pollution status have been evaluated in the industrial area between Sharjah and Ajman border in UAE. Daily concentrations of $O_3$, CO, $NO_2$, $SO_2$, $PM_{2.5}$, $PM_{10}$, Total Volatile Organic Compounds (TVOC) and Total Suspended Particulate (TSP) have been monitored from Sept. 2015 to April 2016. The monthly average concentrations of $O_3$, CO, $NO_2$, $SO_2$, TVOC were within the UAE ambient air quality standards during the survey period. However, $PM_{10}$ and TSP levels exceeded the recommended limits in Sept. 2015, Oct. 2015 and March 2016. Temporal variations in air quality parameters showed highest levels in March 2016 for $PM_{2.5}$, $PM_{10}$, $NO_2$, TVOC and TSP, whereas $O_3$, $SO_2$ and CO showed relatively low values in this month. $PM_{2.5}$ levels in ambient air were above the EPA guideline of $35{\mu}g/m^3$ in all months. $PM_{2.5}$ was the critical ambient air pollutant with Index for Pollutant ($I_p$) values varying from 103-209, indicating Air Quality Index categories of unhealthy for sensitive groups (62.5%) to unhealthy (25%) to very unhealthy (12.5%). The $I_p$ average values of $PM_{2.5}$ decreased from Sept. 2015 to reach lowest value in Dec. 2015 before increasing gradually, peaking in March 2016. These results suggest the potential health risks associated with $PM_{2.5}$ is low in winter, where the prevailing meteorological conditions of lower temperatures, higher humidity, higher wind speed reduced particulate matter. The results revealed the industrial area is impacted by anthropogenic and natural sources of particulate matter.

• Elastomers and Composites
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• v.58 no.4
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• pp.161-172
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• 2023

Because particulate matter has emerged as a major contributor to air pollution, the tire industry has conducted studies to reduce particulate matters from tires by improving tire performance. In this study, we compared the conventional blending method, in which rubber, filler, and additives are mixed simultaneously, to the Y-blending method, in which masterbatches are blended. We manufactured carbon black (CB)-filled natural rubber (NR)/butadiene rubber (BR) blend and silica-filled epoxidized NR/BR blend compounds to compare the effects of the two blending methods on the physical properties of the compounds and the amount of particulate matter generated. The Y-blending method provided uniform filler distribution in the heterogeneous rubber matrix, improved processability, and exhibited low rolling resistance. This method also improved physical properties owing to the excellent filler-rubber interaction. The results obtained from measuring the generation of particulate matter indicated that, the Y-blending method reduced PM_2.5 particulate matter generation from the CB-filled and silica-filled compounds by 38% and 60%, and that of PM₁₀ by 29% and 67%, respectively. This confirmed the excellence of the Y-blending method regarding the physical properties of truck bus radial tire tread compounds and reduced particulate matter generated.