• 한국입자에어로졸학회지
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• 제5권4호
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• pp.171-178
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• 2009

$PM_{2.5}$ is one of critical air pollutants due to its high absorbability of heavy metallic fumes, PAH and bacillary micro organisms. Such a fine particulate matter is often formed through various nucleation processes including condensation. This study attempts to find the nucleation behaviors of $PM_{2.5}$ arisen from coal power stations using a classical heterogeneous Fletcher's theory. The numerical simulation by C-language could approximate the nucleation process of $PM_{2.5}$ from water vapor, of which approach revealed the required energy for embryo formation and embryo size and nucleation rate. As a result of the calculation, it was found that wetting agents could affect the particle nucleation in vapor condensation. In particular, critical contact angle relates closely with the vapor saturation. Particle condensation could be reduced by lowering the angles. The wetting agents aid to decrease the contact angle and surface tensions, thereby may contribute to save the formation energy.

• 대한원격탐사학회지
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• 제38권6_1호
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• pp.1191-1205
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• 2022

복잡하고 광범위한 원인을 가진 대기오염물질 중 particulate matter (PM)은 입자의 크기에 따라 분류된다. 그 중 PM_2.5는 그 크기가 매우 작아 사람이 흡입하면 인간의 호흡기나 심혈관에 질병을 유발할 수 있다. 이러한 위험에 대비하기 위해서는 국가 중심의 관리와 사전에 예방할 수 있는 모니터링 및 예측이 중요하다. 본 연구는 고농도 미세먼지의 발생이 잦은 서울시의 PM_2.5를 local data assimilation and prediction system (LDAPS) 기상 관련 인자 15가지와 aerosol optical depth (AOD), 화학인자 4가지를 독립변수로 하여 앙상블 모델 두 가지 random forest (RF)와 extreme gradient boosting (XGB)로 예측하고자 하였다. 예측에 사용된 두 모델의 성능 평가와 인자 중요도 평가를 수행하였으며, 계절별 모델 분석도 수행하였다. 예측 정확도 결과, RF가 R² = 0.85, XGB가 R² = 0.91의 높은 예측 정확도를 보이며 XGB가 RF보다 PM_2.5 예측에 적합한 모델임을 확인하였다. 계절별 모델 분석 결과, 봄에 농도가 높은 관측 값과 비교하여 예측 수행이 잘 되었다고 할 수 있다. 본 연구는 다양한 인자를 이용하여 서울시의 PM_2.5를 예측하였고, 좋은 성능을 보이는 앙상블 기반의 PM_2.5 예측 모델을 구축하였다.

• 한국대기환경학회지
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• 제31권2호
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• pp.97-104
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• 2015

The pollution of particulate matter (PM) is considered one of the hot socioenvironmental issues at present time. In this study, we investigated the distribution of fine particulate matter ($PM_{2.5}$) in Wangsimni commercial areas in Seoul, Korea to learn more about its environmental behavior in an urban area. Our analysis of $PM_{2.5}$ was made to distinguish the $PM_{2.5}$ pollution levels between three different types of site characteristics: (1) densely populated area, (2) thinly populated area, and (3) traffic roadside. Moreover, to assess the temporal trends in our study, the concentration levels of $PM_{2.5}$ were also compared between weekdays and weekends and between early in the afternoon and evening. The average concentration of $PM_{2.5}$ from densely and thinly populated areas were measured as $36.0{\pm}13.1$ and $32.3{\pm}11.2{\mu}g/m^3$, respectively. If the results are compared between different time bands, there were apparent differences between weekdays ($29.6{\pm}10.8{\mu}g/m^3$) and weekends ($36.9{\pm}12.1{\mu}g/m^3$). Such difference was also evident between noon ($27.8{\pm}5.8{\mu}g/m^3$) and evening ($38.3{\pm}13.7{\mu}g/m^3$). According to our research, concentration of $PM_{2.5}$ in the study area was affected more sensitively by time zone rather than the population density. The measurement data was also analyzed by drawing concentration map of $PM_{2.5}$ in the Wangsimni commercial areas based on data contouring method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• 한국환경보건학회지
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• 제39권2호
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• pp.144-150
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• 2013

Objectives: Cooking activity in indoor environments can generate particulate matter. The objective of this study was to determine the concentrations of ultrafine particles (UFP), $PM_{2.5}$, and $PM_{10}$ in cooking and non-cooking areas of major department stores in Seoul. Methods: Eighteen department stores in Seoul, Korea were measured for concentrations of particulate matter. Using real-time monitors, concentrations of UFP, $PM_{2.5}$ and $PM_{10}$ were simultaneously measured in cooking and non-cooking areas on the floor with a food court and a non-cooking floor. Results: The concentrations of UFP, $PM_{2.5}$ and $PM_{10}$ were significantly higher in cooking areas than in noncooking areas and non-cooking floors (p<0.05). UFP and $PM_{2.5}$ were significantly correlated in cooking areas and non-cooking areas but not in non-cooking floors. $PM_{2.5}$ were consisted of approximately 81% in $PM_{10}$ and highly correlated with $PM_{10}$ in all places. Conclusion: A higher correlation between UFP and $PM_{2.5}$ was shown on cooking floor than on non-cooking floor in department stores. High levels of fine particles were caused by cooking activities at food courts. The further management of PM is needed to improve the indoor PM levels at food courts in department stores.

• 한국BIM학회 논문집
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• 제12권4호
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• pp.104-115
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• 2022

Fine Dust in Korea has been classified as a social disaster since 2019 due to continuous increase in concentration of Particulate Matter 10(PM 10) and PM 2.5. The fine dust issue has negative physical and mental impacts, especially on vulnerable population including children and the elderly. Seoul metropolitan government have installed fine dust shelters since 2019. However, there is a lack of research that evaluates spatiotemporal distribution of these facilities. Therefore, the first aim of this study is to find the relationship between PM levels and dust scattering construction sites, or air pollutant emission sites through in depth spatial analyses. The second purpose is to analyze the spatial distribution of PM shelters in Seoul, and to evaluate the location efficiency of them. Kernel density, krigging, and network analyses were conducted, and floating population was considered instead of census data for this research. The reults of network analysis based on the road system showed that Yangcheon-gu, Songpa-gu, Seongbuk-gu, and Dobong-gu were found to need additional fine dust shelters. Also, the results from analyzing the floating population that includes children and the elderly showed that Songpa-gu, Seodaemun-gu, Gangdong-gu, Seocho-gu, and Dongdaemun-gu need more placements of find dust shelters. The results of this study are expected to provide implications for urban planners to enhance find dust shelter placement in urban areas, and vulnerable population issues would be considered in many ways.

• 한국산림과학회지
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• 제110권2호
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• pp.129-140
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• 2021

본 연구는 한국 남부지역의 주요 조경수 가시나무(Quercus myrsinifolia), 종가시나무(Quercus glauca), 참가시나무(Quercus salicina), 동백나무(Camellia japonica), 왕벚나무(Prunus × yedoensis) 등 5수종을 대상으로 계절별 미세먼지 흡착량 및 수종별 잎 표면 미세구조와의 관계를 연구하였다. 계절별 미세먼지 흡착량 범위는 1월(31.51~110.44 ㎍/cm²), 11월 (23.20~79.30 ㎍/cm²), 5월(22.68~76.90 ㎍/cm²), 8월(9.88~49.91 ㎍/cm²) 순으로, 8월보다 1월에 54.4% 더 높은 미세먼지 흡착량을 보였다. 잎 표면에 홈이 있고 털을 갖고 있으며, 왁스층 함량이 높은 Q. salicina는 미세먼지 입자 크기별 흡착량이 높게 유지되었으며, 광택이 있고 잎 표면이 매끄러우며, 왁스층 함량이 낮은 C. japonica와 Prunus × yedoensis는 계절별 미세먼지 흡착량이 낮았다. 엽면적 크기, 기공밀도 및 기공 길이의 증가는 PM 흡착량의 감소와 관련이 있고 반면, 잎 표피 거칠기, 왁스층 함량, 기공 폭의 증가는 PM 흡착량의 증가와 관련이 있었다. 또한, 잎 표면 왁스층 함량이 증가할수록 잎 표면 PM 흡착량도 증가하였으며, PM₁₀, PM_2.5 보다는 PM_0.2와 관련이 높은 것으로 확인되었다. 또한, 앞으로 개별 수종에 대한 미세먼지 저감 효율을 정량적으로 판단할 수 있는 기준을 통한 저감 수종 선발과 더불어 미세먼지 저감을 위한 숲 조성 가이드라인 또한 제시되어야 할 것으로 판단된다.

• 한국환경과학회지
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• 제24권7호
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• pp.875-882
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• 2015

In 2013, International Agency for Research on Cancer (IARC) concluded that outdoor air pollution is carcinogenic to humans, with the particulate matter component of air pollution most closely associated with sufficient evidence of increased cancer incidence by exposure to particulate matter component of air pollution. Motor vehicles are one of a major emission sources of fine particle ($PM_{2.5}$) in urban areas. A large number of epidemiological studies have reported a positive association of morbidity or mortality with distance from the roadside. We conducted this study to assess the association of $PM_{2.5}$ concentrations measured at roadside hotspots with those at adjacent residential sites using real-time $PM_{2.5}$ monitors. We conducted real-time $PM_{2.5}$ measurements for rush hour periods (08:00~10:00 and 18:00~20:00) at 9 roadside air monitoring Hotspot sites in metropolitan Seoul over 3 weeks from October 1 to 21, 2013. Simultaneous measurements were conducted in residential sites within a 100 m radius from each roadside air monitoring site. A SidePak AM510 was used for the real-time $PM_{2.5}$ measurements. Medians of roadside $PM_{2.5}$ concentrations ranged from $9.8{\mu}g/m^3$ to $38.3{\mu}g/m^3$, while corresponding median values at adjacent residential sites ranged from $4.4{\mu}g/m^3$ to $37.3{\mu}g/m^3$. $PM_{2.5}$ concentrations of residential sites were 0.97 times of hotspot roadside sites. Distributions of $PM_{2.5}$ concentrations in roadside and residential areas were also very similar. Real-time $PM_{2.5}$ concentrations at residential sites, (100 m adjacent), showed similar levels to those at roadside sites. Increasing the distance between roadside and residential sites, if needed, should be considered to protect urban resident populations from $PM_{2.5}$ emitted by traffic related sources.

• 대기
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• 제33권3호
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• pp.319-330
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• 2023

As the need for PM type observation increases, Surface Particulate Matter Network (SPARTAN), PM samplers analyzes aerosol samples for PM mass concentration and chemical composition, were recently installed at two sites: Yonsei University at Seoul and Ulsan Institute of Science and Technology (UNIST) at Ulsan. These SPARTAN filter samplers and nephelometers provide the PM_2.5 mass concentration and chemical speciation data with aerosol type information. We introduced the overall information and installation of SPARTAN at the field site in this study. After installation and observation, both Seoul and Ulsan sites showed a similar time series pattern with the daily PM_2.5 mass concentration of SPARTAN and the data of Airkorea. In particular, in the case of high concentrations of fine particles, daily average value of PM_2.5 was relatively well-matched. During the Yonsei University observation period, high concentrations were displayed in the order of sulfate, black carbon (BC), ammonium, and calcium ions on most measurement days. The case in which the concentration of nitrate ions showed significant value was confirmed as the period during which the fine dust alert was issued. From the data analysis, SPARTAN data can be analyzed in conjunction with the existing urban monitoring network, and it is expected to have a synergetic effect in the research field. Additionally, the possibility of being analyzed with optical data such as AERONET is presented. In addition, the method of installing and operating SPARTAN has been described in detail, which is expected to help set the stage for the observation system in the future.

• 한국산업보건학회지
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• 제27권1호
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• pp.13-22
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• 2017

Objectives: The objective of this study was to evaluate the exposure levels of door-to-door deliverers to fine particulate matter (PM2.5). Another objective was to confirm the general working patterns of door-to-door deliverers via survey. Methods: In the city of Daegu, ten door-to-door deliverers who wished to join the study were recruited. The general working characteristics of door-to-door deliverers were surveyed using self-reported questionnaires. In the cabin of each car driven by a deliverer, a real-time PM2.5 sampler (Sidepak, Model AM510, TSI Inc., MN, USA) and a GPS device (GPS 741, Ascen, Korea) were installed. Each deliverer was monitored for four days per week so that each day could be monitored at least four times. Results: A total of 40 measurements of PM2.5 concentrations were taken during delivery of parcels. The average exposure levels of door-to-door deliverers to PM2.5 was $44.62{\mu}g/m^3$ ($7-9443{\mu}g/m^3$. Exposure levels to PM2.5 according to the day of the week and coverage areas were not significantly different (p>0.05). Door-to-door deliverers using trucks with older diesel engines manufactured before 2006 had significantly higher exposure levels to PM2.5 than in the case of trucks with diesel engines manufactured after 2006 (p<0.05). Many of the door-to-door deliverers reported the status of having windows open during the delivery task. During delivery services, the working hours spent in residential areas were higher than on roadsides, but exposure levels to PM2.5 in residential areas and on roadsides were $46.17{\mu}g/m^3$ and $49.90{\mu}g/m^3$, respectively. Real-time PM2.5 exposure levels were significantly different between roadways and residential areas (p<0.001). Conclusions: PM2.5 exposure levels of door-to-door deliverers were found to be affected by higher vehicle emissions from the roadsides near their vehicle during deliveries and while driving to other locations compared to by PM2.5 from the diesel engines of their own trucks. Particle concentrations from roadsides and emissions from nearby vehicles through open windows were the main source of PM2.5.