도로에서 발생하는 대기오염의 주요 원인은 자동차 등의 연료연소로 인해 발생하는 미세먼지(PM), 질소산화물(NOX), 황산화물(SOX), 암모니아(NH3), 오존(O3) 등이며, 특히 미세먼지와 질소산화물은 도로를 이용하는 운전자와 보행자의 건강에 부정적인 영향을 미치는 것으로 알려져 있다. 본 연구에서는 버스정류장에 설치되는 미세먼지 저감시설의 미세먼지 저감효과를 분석하기 위하여 미세먼지 저 감능력을 실증할 수 있는 실대형 미세먼지 실증인프라와 실규모의 버스정류장을 이용하였다. 미세먼지 실증인프라에서 미세먼지 저감 시설이 설치되는 실험군(2곳)과 미설치되는 대조군(1곳)을 대상으로 미세먼지(PM10) 발생농도를 측정하였으며, 미세먼지 저감시설의 미 세먼지 저감효과를 분석하기 위하여 미세먼지(PM10)의 발생확률과 확률밀도함수를 산정할 수 있는 통계학적 방법인 Anderson-Darling 테스트(AD 테스트)를 이용하여 분석하였다. 미세먼지 저감시설의 미세먼지 저감효과는 대기질지수(AQI)의 기준을 준용하여 실험군ㆍ 대조군의 미세먼지 농도발생확률을 비교하여 정량적ㆍ정성적으로 분석하였다. 미세먼지(PM10) 농도발생확률 산정결과, AQI ‘보통’의 경우, 실험군 측정지점 1, 2와 대조군의 농도발생확률은 각각 77.24%, 63.26%, 0.00%로 대조군에 비해 실험군의 측정지점 1, 2에서 높 게 나타났으며, AQI ‘나쁨’의 경우, 실험군 측정지점 1, 2와 대조군의 농도발생확률은 각각 21.70%, 35.09%, 100.00%로 나타나 실험군 내의 미세먼지(PM10) 발생농도가 대조군과 비교해 개선되는 것으로 분석되었으며, 대조군 내부의 미세먼지 농도의 변화는 거의 없는 것으로 나타났다. 일반적으로 미세먼지를 측정하는 방식인 중량법과 베타선법을 통한 미세먼지 저감효과 분석방법은 시간당 평균으로 측정한 미세먼지 농도만 비교 가능하므로 정성적인 효과분석이 미비해 본 연구를 통해 소개한 통계학적 방법이 정량적 분석 뿐만 아 니라 정성적 분석에도 효과적일 것으로 기대하고 있다.

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.

In this study, we investigated the Indoor and Outdoor concentrations of PM10 in Y area, Jeollanam-do. We conducted personal exposure concentration estimates, and Exposure and Risk Assessments using the Time-weighted Average Model. The concentration of Indoor PM10 was 49.38 μg/m3 and that of Outdoor PM10 was 48.02 μg/m3, with the Indoor/ Outdoor Ratio value being 1 or more, and it was found that there was an indoor source of pollution. The Indoor/Outdoor Cr ratio value was 1 or more, and the source of Cr was confirmed to be indoor. Based on our analysis, there was a positive correlation between heavy metals Ni, Cr, and Mn (p<0.05). Using the Time-Weighted Average model, we determined the PM10 personal exposure concentration to be 49.36 μg/m3 and confirmed the feasibility of this model in utilizing the PM10 personal exposure concentrations. In this study, the findings are likely to provide useful data that can be used to determine the concentration of indoor pollutants that are not easy to survey. However, to accurately evaluate indoor air quality, more factors need to be considered and evaluated.

This study obtained the following conclusions using the measurement results of indoor and outdoor PM10 with regard to cardiovascular disease patients in Cheongju-area in November 2020. Most of the PM10 has an I/O ratio of less than 1, which is an outdoor source. Since we measured once and twice time, Without the air purifier device’s working status, there were no concentration changes of PM10 in the first and second indoor areas. As for the concentration of PM10 according to the living environment, the distribution of PM10 is higher indoors than outdoors when the residential area is 30 m2 or more, and the outdoor PM10 concentration tends to be high when the distance to the road is within 50 m. The more time spent indoors, the higher the indoor PM10 concentration. The smaller the ventilation time and frequency, the longer the cooking time was, and the higher the number of cooking times, the higher the concentration of PM10 could be. The indoor PM10 contribution ratio through multiple regression analysis showed the possibility of increasing indoor PM10 as β = 28.590 when the time spent indoors was longer than 16 hours (p<0.05). The result regarding PM10 exposure reveals that PM10 can be inhaled not only indoors but also outdoors, and the subjects of this study appear to have lived indoors for about 16 hours or longer on a daily basis, which may affect their health regardless of gender.

This study was conducted as a part of the research for the “Development of Big Data Analysis Techniques and AI-based Integrated Support System for Energy-Environment Management.” We collected research results on characterization of distribution of fine dust and re-analyzed using meta-analysis techniques to build “big data” with high potential for school environments. The results of prior studies on the characteristics of fine dust concentration distribution in a school environment conducted in Korea were collected and re-analyzed the results using the metaanalysis technique. In this manner, the variables that could be used to derive the independent variables needed to produce the e-coding book prior to the big data collection, were first derived. The possibility of using the data as independent variables was then evaluated. In this study, three variables: “elementary school vs. middle school vs. high school,” “general classroom vs. special classroom,” and “new classroom vs. old classroom” were evaluated for their application as major classification variables with priority. The necessity of being derived as a major classification variable was examined by testing the difference in fine dust concentration distribution in the school environment by each variable case. Results showed that “elementary school vs. middle school vs high school” and “general classroom vs. special classroom” could be used as independent variables, while “new classroom vs. old classroom” was less likely to be used as an independent variable.

The goal of this study was to measure the indoor and outdoor fine and ultrafine particulate matter concentrations (PM10, PM1.0) of some houses in Yeosu and in S university in Asan from March to September 2018. PM10 concentration in indoor air in Yeosu area was 18.25 μg/m3, while for outdoor air it was 14.53 μg/m3. PM1.0 concentration in indoor air in the Asan area was 1.70 μg/m3, while for outdoor air it was 1.76 μg/m3, showing a similar trend. Heavy metal concentrations in the Yeosu region were the highest, at Mn 2.81 μg/m3, Cr 1.30 μg/ m3, and Ni 1.11 μg/m3 indoors. Outside, similar concentrations were found, at Cr 3.44 μg/m3, Mn, 2.60 μg/m3, and Ni 1.71 μg/m3. Our analysis of indoor and outdoor PM concentrations in the Asan region, which was carried out using the MOUDI (Micro-orifice Uniform Deposit Impactor) technique, found that PM concentration is related to each particle size concentration, as the concentration of 18 μm and 18-10 μm inside tends to increase by 3.2- 1.8 μm and 0.56-0.32 μm.

최근 미세먼지 농도가 높은 날이 늘면서 국민들의 관심도 증가하고 있다. 미세먼지의 분포는 공간적으로 상이하며 그 발생도 지역별로 다르게 기인하는데도 불구하고 미세먼지 저감을 위한 정책은 차별성 없이 이루어지고 있기 때문에 미세먼지의 공간적 이질성을 반영한 연구가 필요하다. 본 연구에서는 미세먼지 농도에 영향을 미치는 자연요소와 인문요소를 함께 고려하여 요인을 선정 후 OLS, GWR, GWRR기법을 이용하여 미세먼지 분포의 공간 패턴을 분석하였다. 연구결과는 다음과 같다. 첫째, OLS 분석 결과 자연요인의 경우 강수량과 대기정체일이 적을수록 그리고 주변고도가 낮을수록 미세먼지의 높은 농도의 기여율이 높았으며, 축사나 공업시설과 같은 인문요인의 경우 대부분 미세먼지와 양의 관계가 있음이 파악되었다. 둘째, GWRR 분석 결과 각 하위 지역별로 미세먼지의 분포에 영향을 주는 변수나 그 정도는 다르게 나타났다. 셋째, GWRR의 효용성 평가 결과 GWRR이 다른 두 모델에 비해 향상된 결과를 보였으며, 이는 미세먼지 뿐만 아니라 다양한 대기오염물질의 분석에도 적용이 가능함을 확인하였다.

The purpose of this study is to develop correction formulas using the results of measurement by PMS 103, which is a weight method measuring device, and by Dusttrak (TSI, USA), DustMate (Turnkey Instrument Ltd., UK), and LD-5 (SIBATA, Japan), which are light scattering measuring devices. The objective is to evaluate and identify new standards (to develop a proposal) in order to complement the limitations of the existing measurement methods of public transportation vehicle indoor air quality by utilizing the three nephelometer type measuring devices. In the case of non-rush hours, the PMS values were estimated using an estimation regression equation. Statistically, the PMS values that were actually measured were not significantly different (p-value=0.4375, 0.4375, 1.000). With respect to the agreement between the two values, ICC was 0.99 in the case of the estimation regression equation using LD-5 values, 0.97 in the case of the estimation regression equation using Dusttrak values, and 0.84 in the case of the estimation regression equation using DustMate values to allow for the identification of agreement at greater levels. In the case of rush hours, the PMS values were estimated using an estimation regression equation. Statistically, the PMS values that were actually measured were not significantly different (p-value=0.3125, 0.6250, 0.8125). With respect to the agreement between the two values, ICC was 0.92 in the case of the estimation regression equation using LD-5 values, 0.91 in the case of the estimation regression equation using Dusttrak values, and 0.89 in the case of the estimation regression equation using DustMate values to allow for the identification of agreement at greater levels.

이 논문에서는 환경변수의 시계열 분포도 작성과 불확실성 모델링을 위해 시공간영역으로 확장된 다중 가우시안 크리깅을 제안하였다. 다중 가우시안 틀 안에서, 우선 정규점수변환된 환경변수를 결정론적 경향 성분과 확률론적 잔차 성분으로 분해하였다. 그리고 시간 경향 모델 계수의 내삽을 통해 경향 성분의 시계열 공간 분포도를 작성하였다. 정상성 잔차 성분의 시공간 상관 구조는 곱-합 시공간 베리오그램 모델을 이용하여 정량화하였고, 이 베리오그램 모델과 시공간 크리깅을 이용하여 국소적 누적 확률분포함수를 모델링하였다. 이 국소적 누적 확률분포함수로부터 평균값과 조건부 분산을 계산하여 공간분포도 작성과 불확실성 분석에 각각 이용하였다. 제안 기법의 적용성 평가를 위해 인천광역시에서 3년간 13개 관측소에서 측정된 월 평균 미세먼지(PM10) 농도 자료를 이용한 시계열 분포도 작성 사례 연구를 수행하였다. 사례연구 결과, 제안 기법을 통해 기존 공간 정규 크리깅에 비해 작은 편향과 높은 예측 능력을 가진 시계열 미세먼지(PM10) 농도 분포도 작성이 가능함을 확인할 수 있었다. 또한 조건부 분산과 특정 농도값을 초과할 확률값들은 해석을 위한 유용한 보조 정보를 제공하였다.

부산지역에서 PM10 과 PM2.5중의 금속 성분 농도를 파악하기 위하여 2004년 3월부터 2004년 12월까지 조사하였다. PM10의 평균농도는 58.2μg/m3 농도범위는 8.3~161.1μg/m3이었으며, PM2.5의 평균농도는 29.3μg/m3, 농도범위는 2.8~65.3μg/m3였다. PM10의 평균 질량농도는 황사시 121.5μg/m3, 비황사시 56.0μg/m3로 나타났다. 10 이상의 지각농축계수를 보인 성분은 Cd, Cr, Cu, Ni, Pb 및 Zn로서 인위적기원을 받은 것으로 추정된다. PM10과 PM2.5 중 미량금속 성분의 지각농축계수는 황사시보다 비황사시에 높게 나타났으며, 인근의 공단지역으로부터 인위적 오염물질이 수송된 것으로 추정된다. PM10과 PM2.5의 토양입자의 평균 기여율은 각각 15.2%와17.5%였다. 토양기여율의 황사/비황사비는 PM10과 PM2.5에서 각각 1.9와 2.1로 나타났다.

The objective of this study was to estimate air quality trends in the study area by surveying monthly and seasonal concentration trends. To do this, the mass concentration of PM10 samples and the metals, ions, and total carbon in the PM10 were analyzed. The mean concentration of PM10 was 33.9 ㎍/㎥. The composition of PM10 was 39.2% ionic species, 5.1% metallic species, and 26.6% carbonic species (EC and OC). Ionic species, especially sulfate, ammonium, and nitrate, were the most abundant in the PM10 and had a high correlation coefficient with PM10. Seasonal variation of PM10 showed a similar pattern to those of ionic and metallic species. with high concentration during the winter and spring seasons. PM10 showed high correlation with the ionic species NO3 - and NH4 +. In addition, NH4 + was highly correlated with SO4 2- and NO3 -. We obtained four factors through factor analysis and determined the pollution sources using the United States Environmental Protection Agency(U.S. EPA) pollution profile. The first factor accounted for 51.1% of PM10 from complex sources, that is, soil, motor vehicles, and secondary particles: the second factor indicated marine sources; the third factor, industry-related sources; and the last factor, heating-related sources. However, the pollution profile used in this study may be somewhat different from the actual situation in Korea because it was from US EPA. Therefore, to more accurately estimate the pollutants present, it is necessary to create a pollution profile for Korea.

This study was conducted to investigate how PM10 concentration and Relative Humidity (RH) affected visibility in Jinju, Korea. A 9-yr dataset of 1 h averages for visibility, PM10, and RH data was analyzed to examine the correlation between these variables. On average, visibility decreased by 1.4 km for every 10 μg/㎥ increase in PM10 and by 2.1 km for every 10% increase in RH. In general, a negative correlation was observed between visibility and and PM10 concentration. However, under conditions of low PM10 concentration(< 15 μg/㎥) and visibility(< 2 km), there was a positive correlation between these two variables. In this case, RH levels were high (> 75%). A high correlation analysis between two variables need to be under control conditions with RH < 75%, PM10 15~100 μg/㎥, and visibility > 2 km.

The study investigates the characteristics of PM10 concentration in Guducsan air quality observatory and in particular, analyzes the relationship between sudden increase of PM10 concentration in the morning of spring 2014 and meteorological parameters. PM10 concentration in April was 46.9 ㎍/㎥, the highest, followed by 45.5 ㎍/㎥ and 44.6 ㎍/㎥ in March and May, and 21.9 ㎍/㎥ in August. The low concentration in the early morning appeared on 0800 LST in spring, summer, and fall, whereas it emerged on 0900 LST in winter. High concentration in daytime lasted from 1200 LST to 1500 LST in spring and fall, whereas it continued from 1300 LST to 1600 LST in winter. The findings of PM10 concentration and change of meteorological parameters in Guducsan from April 20th to 27th in 2014 are as follows. The low concentration at dawn and in the morning decreased due to strong land breeze. Also, the sudden increase of PM10 concentration in the morning was attributable to low wind speed. Lastly, the sudden decrease of PM10 concentration in the afternoon was attributed to diffusion by strong sea breeze.