When cooking, especially during processes such as grilling or frying, harmful gases are generated. Among them, fine dust is a major factor causing lung cancer, and to eliminate it, range hoods are commonly used. Particularly, when using portable cooking device like gas stoves, it is even more challenging to remove fine dust. In this study, a cooking device was developed to combine steam and cooled air in the air-curtain area to recover fine dust generated during the cooking process, allowing it to be collected as food. The device was evaluated according to criteria defined independently and validated through experiments. As a result, all criteria were satisfied within the specified range.

Particulate matter is known to have adverse effects on health, making it crucial to accurately gauge its concentration levels. While the recent advent of low-cost air sensors has enabled real-time measurement of particulate matter, discrepancies in concentrations can arise depending on the sensor used, the measuring environment, and the manufacturer. In light of this, we aimed to propose a method to calibrate measurements between low-cost air sensor devices. In our study, we introduced decision tree techniques, commonly used in machine learning for classification and regression problems, to categorize particulate matter concentration intervals. For each interval, both univariate and multivariate multiple linear regression analyses were conducted to derive calibration equations. The concentrations of PM10 and PM2.5 measured indoors and outdoors with two types of LCS equipment and the GRIMM 11-A device were compared and analyzed, confirming the necessity for distinguishing between indoor and outdoor spaces and categorizing concentration intervals. Furthermore, the decision tree calibration method showed greater accuracy than traditional methods. On the other hand, during univariate regression analysis, the proportion exceeding a PM2.5/PM10 ratio of 1 was significantly high. However, using multivariate regression analysis, the exceedance rate decreased to 79.1% for IAQ-C7 and 89.3% for PMM-130, demonstrating that calibration through multivariate regression analysis considering both PM10 and PM2.5 is more effective. The results of this study are expected to contribute to the accurate calibration of particulate matter measurements and have showcased the potential for scientifically and rationally calibrating data using machine learning.

This study investigates the influence of particulate matter concentrations on the incidence of asthma, focusing on the delayed onset of symptoms and subsequent medical consultations. Analysis incorporates a four-day lag from the initiation of fine dust exposure and compares asthma patterns before and after the World Health Organization's (WHO) classification of fine dust as a Group 1 carcinogen in November 2013. Utilizing daily PM10 data and asthma-related medical visit counts in Seoul from 2008 to 2016, the study additionally incorporates Google search frequencies and newspaper article counts on fine dust to assess public awareness. Results reveal a surge in search frequencies and article publications after WHO announcement, indicating heightened public interest. To standardize the long-term asthma occurrence trend, the daily asthma patient numbers are ratio-adjusted based on annual averages. The analysis uncovers an increase in asthma medical visits 2 to 3 days after fine dust events. Additionally, greater public awareness of fine dust hazards correlates with a significant reduction in asthma occurrence after such events, even within 'normal' fine dust concentrations. Notably, behavioral changes, like limiting outdoor activities, contribute to this decrease. This study highlights the importance of analyzing accumulated medical data over an extended period to identify general public behavioral patterns, deviating from conventional survey methods in social sciences. Future research aims to extend data collection beyond 2016, exploring recent trends and considering the potential impact of decreased fine dust awareness amid the COVID-19 pandemic.

본 연구는 대도시에서 미세먼지 없는 학교 부지를 찾는 Model Eliciting Activity (이하 MEA) 활동을 통해 고 등학교 학생들의 문제 해결 특성을 조사하기 위한 것이다. 5차시로 개발된 MEA 활동에 79명의 고등학교 2학년 학생 들이 참여 하였으며, MEA 활동지를 주요 데이터로 수집하였다. 학생들이 작성한 활동지의 개방형 질문에 대한 답을 기반으로 학생들의 문제 해결 모델을 귀납적 및 질적 방법으로 분석하였다. 먼저 학생들이 다른 데이터보다 어떤 데이 터를 우선적으로 사용했는지 순서를 분석한 후 주어진 데이터 세트를 어떻게 상호 연결하여 순서를 결정하는지 분석하 였다. 분석결과 학생들은 미세먼지 배출량이 많은 곳을 기피하기 위해 미세먼지 배출농도, 산업단지 분포 등 미세먼지 와 직접적으로 관련된 데이터를 먼저 활용하는 경향이 있음을 알 수 있었다. 흥미롭게도 MEA 활동에서 고등학생의 문 제 해결 특성은 매우 다양하여 76명의 학생이 총 61가지 유형의 문제 해결 모델을 제작한 것으로 나타났다. 문제를 해 결하기 위해 동일한 순서의 데이터를 사용하는 학생의 최대 수는 6명으로 학생들의 문제 해결 방법은 매우 다양함을 보여준다. 그러나 공통적으로 미세먼지 농도가 높은 곳을 제외하는 방법으로 미세먼지 배출과 직접적으로 관련된 데이 터를 먼저 선택하는 특성을 보였다.

본 연구는 복잡한 도심주변의 지형과 다양한 배출원이 존재하고 있는 세종시 부강면과 조치원읍 일원을 대상으 로 대기오염 확산의 물리적인 특성을 분석하여 오염 물질의 확산을 가정했을 때의 특징을 분석하였다. 부강면의 경우 토지의 기복이 심한 전형적인 분지형지역으로서, 대기오염물질의 확산이 불량하여 대기 오염이 심화되는 지형상의 특성을 내포하고 있다. 지형조건의 특성상 강한 풍속으로 이류된 대기오염물질의 확산이 용이하지 않아 국지적으로 정체되 어 대기오염물질의 공간적 확산 보다는 정체에 의한 대기 환경 악화의 간접 요인이 될 수 있음을 나타낸다. 또한 이러 한 경우 지형적 가열율의 차이 등에 의한 급속한 온도 상승 경향이 나타날 가능성이 있어 향후 먼지 돔현상(dust dome) 등과 관련하여 오염물질의 확산과 관련한 정책제안에 있어서 세심한 논의가 필요하다. 고농도와 비고농도 사례 일을 분석했을 때 조치원읍의 경우 부강면에 비하여 주변 배출원의 영향이 다소 높은 상태이다. 또한 고농도과 비고농 도의 사례에 대한 분석에서 특정한 풍계에 따라 고농도일의 사례가 됨과 동시에 지형적으로 경사지에 속해 서쪽에서 이류된 오염물질이 쉽게 유입되는 상황으로 분석된다. 이러한 국지 지형에 따른 난류특성 변수들인 마찰속도(friction velocity), 확산속도(convective velocity), 현열 속(sensible heat flux) 등에 의한 연직 혼합고는 대기오염물질의 확산강도 에 중요한 요인임을 알 수 있었다. 이와 관련하여 배출원관리에 따른 논의가 부강면 사례에서 보다는 조치원읍에서 상 대적으로 필요함을 분석하였다.

The importance of urban green space creation is increasingly recognized as the most realistic and efficient approach for fine dust mitigation in urban areas. Particularly considering the characteristics of domestic cities, the application of buffer green spaces along roads can maximize the efficiency of fine dust reduction without the need for separate green space creation. Accordingly, this study analyzed the fine dust mitigation effects based on the types of plantings in the central dividers and roadside trees in Jeonju City, Jeollabuk-do. To do this, we controlled various external variables of urban space and considered the planting arrangement types in the central dividers, carrying out the analysis using a CFD simulation. The simulation results confirmed that the central dividers with plantings demonstrated more effective ultrafine dust reduction than those without. Moreover, the arrangement of roadside trees showed a greater ultrafine dust reduction effect when adopting a multilayered structure compared to a single layer. Based on these findings, we concluded that installing both trees and shrubs simultaneously in the central dividers and along roads was effective for ultrafine dust mitigation. On this basis, we quantified the dust reduction effects of plants in urban street environments and proposed planting guidelines for roadside green spaces to improve air quality.

PURPOSES : In this study, a model was developed to estimate the concentrations of particulate matter (PM2.5 and PM10) in expressway tunnel sections. METHODS : A statistical model was constructed by collecting data on particulate matter (PM2.5 and PM10), weather, environment, and traffic volume in the tunnel section. The model was developed after accurately analyzing the factors influencing the PM concentration. RESULTS : A machine learning-based PM concentration estimation model was developed. Three models, namely linear regression, convolutional neural network, and random forest models, were compared, and the random forest model was proposed as the best model. CONCLUSIONS : The evaluation revealed that the random forest model displayed the least error in the concentration estimation model for (PM2.5 and PM10) in all tunnel section cases. In addition, a practical application plan for the model developed in this study is proposed.

시설 재배 시, 미세먼지의 잦은 발생은 피복재의 광투과율을 감소시키고 이는 작물의 생육에 간접적인 영 향을 미칠 수 있다. 본 연구에서는 미세먼지 발생에 따른 폴리에틸렌(PE)과 폴리올레핀(PO) 필름의 광투과율 변화 를 조사하고, 피복재의 광투과율 감소에 따른 봄철 재배 오이의 생육 변화를 확인하였다. 미세먼지 발생 챔버를 이 용하여 PE와 PO 필름을 지속적으로 미세먼지에 노출시켰을 때, PE 필름에서 미세먼지 발생에 의한 광투과율 감소 가 PO 필름보다 크게 나타났다. PE 필름에 인위적으로 먼지를 부착시켜서 대조구 대비 10, 20 및 30% 광투과율 감 소 처리구를 설정한 후 오이를 재배하였을 때, 3월 말 이후 재배 후반기에서의 오이 생육은 광투과율 감소 처리구에 서 증가하였으나, 누적 수확량은 대조구에서 가장 높았다. 봄철 오이 재배에서 미세먼지 발생에 의한 광투과율 감 소는 3월 말 이후 시설 내 고온 노출에 의한 생육 지연을 줄일 수 있었으나, 전 생육 기간 동안의 광투과율 감소는 입 사광량의 감소 및 광합성의 저하로 오이의 총 수확량을 감소시켰다.

환경오염에 의한 미세먼지의 증가로 피부는 산화적 손상과 노화가 가속화된다. 본 연구에서는 선발된 한약재 추출물의 항산화, hyaluronic acid, filaggrin, MMP-1, ROS 항목을 평가함으로써 PM10으 로 부터의 각질형성세포 보호 효능을 확인하였다. 그 결과 1,1-diphenyl-2-picrylhydrazyl(DPPH), 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid(ABTS), FRAP assay에서 농도의존적으로 항산화능 이 증가하는 것을 확인하였다. 각질형성세포에 PM10 300 ㎍/㎖을 단독으로 처리한 군에서는 hyaluronic acid 및 filaggrin이 50% 이상 감소하였으며, 고량강, 유백피, 토복령 추출물을 처리한 군에서는 증가하였 다. MMP-1의 경우 PM10 단독처리군에는 55% 이상 증가하였으나, 추출물을 처리한 경우 감소하여 콜라 겐, 엘라스틴의 분해를 저해하는 것으로 평가된다. 또한 제브라피쉬 배아를 이용한 ROS 측정의 경우 추출 물을 처리하였을 때 감소되는 것을 확인하였다. 특히 토복령 추출물의 25 μg/ml에서 음성대조군과 유사 한 형광의 세기를 나타내어 ROS의 생성이 유의적으로 감소한 것을 확인하였다. 본 연구를 통하여 선별된 한약재 소재인 고량강, 유백피, 토복령은 미세먼지로부터 피부를 보호하거나 개선할 수 있는 소재로서 피 부 개선을 위한 안티에이징 제품으로 활용될 수 있을 것으로 사료된다.

Opuntia ficus-indica (OFI), or Pricky Pear Cactus, is effective in cough, fever, pain and anti-inflammatory action, and asthma. This study aims to investigate the effect of OFI stem extract on the respiratory system of animal models induced by ovalbumin (OVA) and fine dust (PM10) and to analyze the indicator substances of OFI stem extract. In the OFI stem 50% ethanol extract (OFI-50E) administration group, the number of immune cells and inflammatory cytokines in the lungs and BAL decreased to a similar level to the positive control group administered with dexamethasone. In addition, OVA-specific IgE and airway hyper-reactivity (AHR) were significantly reduced. Also, the deposition of PM10 observed through staining of lung tissue was clearly reduced in the OFI-50E 200 mg/kg administration group. The anti-inflammatory mechanism in the lung was found to obstruct the production of inflammatory cytokines by impeding the NF-kB and MAPK pathways through the inhibition of IRAK-1 active cells. The main component of OFI stem 50% ethanol extract was identified to be narcissin. According to the study results, OFI is expected to be a respiratory health functional food.

선박용 엔진에서 배출되는 배기가스에는 다량의 수분과 미세먼지를 포함하고 있다. 미세먼지에는 여과성 미세먼지와 배기 배 출 후 액상으로 변화하는 응축성 미세먼지가 포함되어 있으며 배출 전에 걸러지는 고체상 미세먼지보다 응축성 미세먼지가 더 많은 것으 로 보고되고 있다. 본 연구에서는 배기가스의 배기열과 수분을 회수하고 응축성 미세먼지를 제거하기 위한 실험장치를 실험실 내의 가스 보일러 배기가스를 이용하여 테스트 하였다. 배기가스는 1차적으로 냉각방식으로 수분과 응축성 미세먼지가 제거되고 2차적으로 흡수제 방식에 의해 추가적으로 수분이 제거되었다. 상대습도 측정에 의한 배기가스 수분 제거율을 계산하면 1단계 배기냉각 방식으로 73%, 2단 계 흡수제 방식으로 90% 제거되는 것으로 측정되었다. 이 과정에서 응축성 미세먼지는 80~90% 제거되는 것으로 측정되었다. 개발 시스템 에 의해 회수된 열은 공정열로 활용할 수 있으며, 회수된 물은 수처리 과정을 통해 공정수로 활용할 수 있다. 또한 현재 관리 규제가 되고 있지 않지만 미세먼지의 주요 원인인 응축성 미세먼지를 효과적으로 제거할 수 있을 것으로 기대된다.

Low-cost particulate matter (PM) sensors based on the light scattering principle measure the concentration of particles by the change of scattering intensity after light is irradiated onto the particles. It has been reported that when the relative humidity is high, water vapor may cause the expansion of airborne particles and affect the accuracy of the light scattering method for PM measurement, but it has also been shown that the effect of humidity is not significant or even negligible. Therefore, to determine the effect of humidity on the Plantower PMS7003 light scattering sensor, in this study, a BAM1020 (Beta Attenuation Monitoring) was installed alongside to continuously monitor the ambient atmospheric PM concentration for approximately four weeks. The sensors collected data at 10-minute intervals, resulting in a 1-hour average for comparative analysis. To accurately measure humidity, the performance of the Arduino + DHT22 humidity sensor was also pre-evaluated using a series of saturated salt solutions. The humidity was grouped into five intervals and analyzed by visual analysis. The results confirmed that there was no significant correlation between PM2.5 differences and humidity, which were randomly and uniformly distributed around the mean. However, since in the very low and high concentration ranges based on the beta-ray monitor measurements, the difference between the light scattering sensor PM10 measurement and the reference value is much larger than the difference between the PM2.5 and the reference value., there is an additional need to investigate the appropriate correction method for dust season or PM10. The results show that the outcomes of the light scattering sensor are more influenced by particle size and concentration than by humidity.

PURPOSES : Fine dust significantly affects the atmospheric environment, and various measures have been implement to reduce it. The aim of this study is to reduce fine dust on roads by implementing porous pavements and a clean road system using the low-impact development technique. METHODS : We conducted quality tests (draindown, cantabro loss rate, tensile strength ratio, dynamic stability, and indoor permeability coefficient tests) and performance evaluation (dynamic modulus and Hamburg wheel-tracking tests) on the porous asphalt mixture. Subsequently, we constructed a porous pavement road in a test bed and conducted a permeability test. In the test bed, we installed a nozzle, a water tank, and a fluid pump to water the roadside. After the clean road system was completely installed, we measured the concentration of fine dust before and after water was sprayed. Additionally, we conducted a total suspended solids (TSS) test to confirm the reduction in re-suspended dust. RESULTS : All results from the quality test of the porous asphalt mixture satisfy the standards stipulated by the Ministry of Land, Infrastructure and Transport. Results from the dynamic modulus test show a low plastic deformation resistance but a high fatigue crack resistance. The results from the Hamburg wheel-tracking test satisfy the U.S. Department of Transportation standards. After the porous pavement was constructed, a permeability test was conducted, and the result satisfies the standard value. Using a particle counter, we measured the concentration of fine dust before and after water spraying, and results show 12.08% and 10.23% for PM10 and PM2.5 particles, respectively. The results from the TSS test show that after the initial water spray, almost all re-suspended dust are removed from a road. In unfavorable road conditions, almost all re-suspended dust are removed after a second water spray. CONCLUSIONS : The results of all of quality tests performed on a porous asphalt mixture satisfy the standards. By applying the results to a test bed, the problem of securing water is solved. Using the clean road system, 12.08% and 10.23% of PM10 and PM2.5 particles are removed, respectively. The system removes PM10 particles (larger particles) more effectively compared with PM2.5 particles. IN the future, we plan to revise the maintenance plan such that the porous pavement can exhibit long-term performance. Because pipe freezing may occur in the winter, we plan to analyze the periodic maintenance plan of the porous pavement and develop a solution to mitigate the issue of freezing pipes in the winter.

In this study, the distribution characteristics of particulate matter (PM) in subway platforms were investigated, and the performance of hybrid filter systems was determined through the removal efficiency of PM according to various flow rates and filter structures. The hybrid filter systems were constructed in magnetic systems as (Magnet-Magnet (MM) filters and Magnet-Cascade (MC) filters). PM removal efficiencies of these filters were investigated at a subway platform for three days including weekdays and weekends. The compositions of collected PM were also analyzed. Based on the PM measurement in the subway platforms, it was confirmed that the operation of trains had a significant effect on the increase of PM concentration, and a large number of PMs were less than 1 μm in size. For the MC filter, the removal efficiency of PM1 based on the number of particles was up to 30.5%, demonstrating a relatively high removal efficiency in comparison with the MM filter. In terms of PM10, PM removal efficiencies of the MC filter with respect to the mass concentration and the number of particles were 48.3% and 14.5%, respectively. For the MC filter, it was found that the PM removal efficiency was enhanced with the increase in the flow rate. Moreover, the relatively large particle size PM (i.e., 7.5 μm - 10 μm) denoted a maximum removal efficiency of 97% in terms of the number of particles. All PMs collected by the filter were Fecontaining PMs. As a field experiment using the hybrid filter, the applicability of magnetic particle control technology was approved. Based on this result, it is expected that this study will be used as background research for the development of fine dust control technologies in a subway environment.

Black carbon (BC), which is mainly contained in fine particulate matters, is one of the typical anthropogenic air pollutants that are generated from the incomplete combustion process and discharged into the atmosphere, and its various health effects particularly on children have been a growing concern. In this study, BC and particulate matters were closely analyzed in an elementary school adjacent to a high- traffic road in a large metropolitan city. The investigation showed that black carbon behaved similarly to ultrafine dust of 0.3 μm or less in the air, accounting for 20%-40% of it. The occurrence of high concentration outdoor pollution influences the BC content in indoor particulate matters. The average I/O value was 0.7 during the class-hours, and 0.8 without students. However, when students played in the classroom, the range of BC concentrations varied from 0.25 to 1.15, wider than 0.41-1.13 without students. Although this study was conducted with regard to just one elementary school, it can be considered to represent the typical air quality status of domestic schools, and it is believed to present valuable data which can be utilized to assist with preparing measures to enhance the air quality management of schools.