• Journal of Environmental Health Sciences
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• v.40 no.5
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• pp.346-354
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• 2014

Objectives: Although a number of epidemiologic studies have examined the association between air pollution and mortality, data limitations have resulted in fewer studies of particulate matter with an aerodynamic diameter of ${\leq}2.5{\mu}m$ ($PM_{2.5}$). We conducted a time-series study of the acute effects of particulate matter with an aerodynamic diameter of ${\leq}10{\mu}m$($PM_{10}$) and $PM_{2.5}$ on the increased risk of death for all causes and cardiovascular mortality in Seoul, Korea from 2006 to 2010. Methods: We applied the generalized additive model (GAM) with penalized splines, adjusting for time, day of week, holiday, temperature, and relative humidity in order to investigate the association between risk of mortality and particulate matter. Results: We found that $PM_{10}$ and $PM_{2.5}$ were associated with an increased risk of mortality for all causes and of cardiovascular mortality in Seoul. A $10{\mu}g/m^3$ increase in the concentration of $PM_{10}$ corresponded to 0.44% (95% Confidence Interval [CI]: 0.25-0.63%), and 0.95% (95% CI: 0.16-1.73%) increase of all causes and of cardiovascular mortality. A $10{\mu}g/m^3$ increase in the concentration of $PM_{2.5}$ corresponded to 0.76% (95% CI: 0.40-1.12%), and 1.63% (95% CI: 0.89-2.37%) increase of all causes and cardiovascular mortality. Conclusion: We conclude that $PM_{10}$ and $PM_{2.5}$ have an adverse effect on population health and that this strengthens the rationale for further limiting levels of $PM_{10}$ and $PM_{2.5}$ in Seoul.

• 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.

• 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.

• Journal of environmental and Sanitary engineering
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• v.24 no.3
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• pp.37-54
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• 2009

The study has analyzed impacts and intensity of weather that affect $PM_{10}$ concentration based on PM10 forecast conducted by the city of Seoul in order to identify ways to improve the accuracy of PM10 forecast. Variables that influence $PM_{10}$ concentration include not only velocity and direction of the wind and rainfalls, but also those including secondary particulate matter, which were identified to greatly influence the concentration in complicated manner as well. In addition, same variables were found to have different impacts depending on seasons and conditions of other variables. The study found out that improving accuracy of $PM_{10}$ concentration forecast face some limits as it is greatly influenced by the weather. As an estimation, this study assumed that basic research units and artificially estimated pollutant emissions, study on mechanisms of secondary particulate matter productions, observatory compliment, and enhanced forecaster's expertise are needed for better forecast.

• Land and Housing Review
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• v.10 no.3
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• pp.1-7
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• 2019

Indoor particulate matter(PM) is a carcinogen and needs to be removed and managed. It is generally reduced and removed through ventilation and filtration. Owing to the recent occurrence of high-concentration fine dust and yellow dust in the atmosphere, however, it is difficult to expect the purification of indoor air through the simple introduction of the outside air. For residential buildings, in particular, they are highly dependent on natural ventilation but the lack of natural ventilation is worsening because concerns over the inflow of external pollutants are increasing. Therefore, this study designed and manufactured a window ventilation system that does not require a duct to improve the maintenance and management problems of general ventilation system, and constructed indoor PM concentration change data through performance evaluation.

• 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.

• Tuberculosis and Respiratory Diseases
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• v.84 no.1
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• pp.55-66
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• 2021

Background: Particulate matter 10 (PM₁₀; airborne particles <10 ㎛) inhalation has been demonstrated to induce airway and lung diseases. In this study, we investigate the effects of PM₁₀ inhalation on RNA expression in lung tissues using a murine model. Methods: Female BALB/c mice were affected with PM₁₀, ovalbumin (OVA), or both OVA and PM₁₀. PM₁₀ was administered intranasally while OVA was both intraperitoneally injected and intranasally administered. Treatments occurred 4 times over a 2-week period. Two days after the final challenges, mice were sacrificed. Full RNA sequencing using lung homogenates was conducted. Results: While PM₁₀ did not induce cell proliferation in bronchoalveolar fluid or lead to airway hyper-responsiveness, it did cause airway inflammation and lung fibrosis. Levels of interleukin 1β, tumor necrosis factor-α, and transforming growth factor-β in lung homogenates were significantly elevated in the PM₁₀-treated group, compared to the control group. The PM₁₀ group also showed increased RNA expression of Rn45a, Snord22, Atp6v0c-ps2, Snora28, Snord15b, Snora70, and Mmp12. Generally, genes associated with RNA splicing, DNA repair, the inflammatory response, the immune response, cell death, and apoptotic processes were highly expressed in the PM₁₀-treated group. The OVA/PM₁₀ treatment did not produce greater effects than OVA alone. However, the OVA/PM₁₀-treated group did show increased RNA expression of Clca1, Snord22, Retnla, Prg2, Tff2, Atp6v0c-ps2, and Fcgbp when compared to the control groups. These genes are associated with RNA splicing, DNA repair, the inflammatory response, and the immune response. Conclusion: Inhalation of PM₁₀ extensively altered RNA expression while also inducing cellular inflammation, fibrosis, and increased inflammatory cytokines in this murine mouse model.

• Tuberculosis and Respiratory Diseases
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• v.65 no.6
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• pp.504-511
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• 2008

Background: Particulate matter may be toxic to human tissue. Ambient air particulate matter ${\leq}10{\mu}m$ in aerodynamic size ($PM_{10}$), which changes under different environmental conditions, is a complex mixture of organic and inorganic compounds. The Asian dust event caused by meteorological phenomena can also spread unique particulate matter in affected areas. We evaluated production of ROS, $TGF-{\beta}$, fibronectin, and $NF{\kappa}B$ by exposing normal epithelial cells to Asian dust particulate matter. Methods: Bronchial epithelial cells were exposed to 0, 50, ${\leq}100{\mu}g/ml$ of a suspension of $PM_{10}$ for 24 h. ROS were detected by measurement of DCF release from DCF-DA by FACScan. $TGF-{\beta}$, fibronectin, and $NF{\kappa}B$ were detected by western blotting. Results: $PM_{10}$ exposure increased the expression of $TGF-{\beta}$, fibronectin, and $NF{\kappa}B$. ROS production and $TGF-{\beta}$ levels were significantly higher with 50 or ${\leq}100{\mu}g/ml$ $PM_{10}$. Fibronectin and $NF{\kappa}B$ production were significantly higher after ${\leq}100{\mu}g/ml$ of $PM_{10}$. Conclusion: $PM_{10}$ from Asian dust particles might have fibrotic potential in bronchial epithelial cells via ROS induction after $PM_{10}$ exposure.

• 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 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.