Industrial complex areas are an important factor that contributes to economic development. However, these areas also produce a significant amount of noxious air pollutants. In particular, PM10 emitted from industrial complex areas can lead to detrimental effects on human health. This study was conducted to survey environmental conditions and measure the concentration of PM10 in indoor and outdoor living spaces in Yeosu and Gwangyang. In Yeosu, the level of PM10 concentration in indoor living spaces was measured at 49.38 μg/m3, while the outdoor level was measured at 43.60 μg/m3. In Gwangyang, the PM10 concentration in indoor living spaces was measured at 20.08 μg/m3 while the outdoor measurement came to 18.14 μg/m3. When analyzing the percentage of those who were highly concerned with environmental pollution among a survey group of 200 people, Yeosu City had a rate of 76.5%, while Gwangyang City had a comparable rate of 73%. When looking at the time-activity patterns of residents in both locations, Yeosu residents spend more than 80.0% of their time inside their living spaces, while Gwangyang residents spend more than 70.0% of time inside their living spaces. These high rates are largely due to the fact that most of the residents of these areas are elderly. In both Yeosu and Gwangyang, it was found that 40% of residents use TV and radio rather than newspapers or handouts to obtain information about environmental pollution problems in their area.

PURPOSES : This study predicts the concentration of suspended road dust (PM10) by analyzing meteorological, traffic, and atmospheric environmental data acquired at various angles, and attains a comprehensive understanding of the influencing factors of suspended road dust. METHODS : Experimental field methods were applied and statistical analyses were conducted. Field experiments were conducted using a vehicle-based measurement of suspended dust (PM10) to measure its concentration at the measurement site while maintaining a constant driving vehicular speed. Statistical analysis demonstrated the effects of the concentration of suspended dust on changes in meteorological and environmental factors and lanes per traffic volume at the time of measurement. Finally, a multiple linear regression model was applied to identify the factors which affected the generation of suspended dust. RESULTS : The analysis of suspended road dust concentrations according to the lanes per traffic volume and environmental factors showed that suspended dust concentrations increased at increasing driving speeds. In addition, the background concentration at the monitoring station was higher at high-wind speeds (>3.0 m/s) than at low-wind speeds (<1.6 m/s), but the suspended dust concentrations were higher at low-wind speeds. During the temperature inversion period from evening to morning, the suspended effects of traffic and meteorological factors were greater than the background concentration at the station. Multiple linear regression analysis showed that excluding yellow-dust days, which are known to affect atmospheric pollution levels, the accuracy of the model improved and resulted in increases in background PM10, vapor pressure, sea-level pressure, visibility, after-rainfall time, and in decreases in insolation and precipitation during low-wind speed conditions. CONCLUSIONS : At low-wind speeds, 5 days after rain, and when the relative humidity was higher than 72%, suspended dust was found to be higher than atmospheric PM10 concentration and may increase at increasing driving speeds and section lane traffic volumes. However, the volume of measured data in this study is limited to determining the patterns of suspended dust, as the silt loading of the operational road or the effects of prominent variables were not considered in this study. However, we identified prominent factors related to road-suspended dust for real-time road-dust predictions.

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.

최근 동아시아 지역에서 인위적 배출량의 감소에도 불구하고, 봄철에 한국에서는 잦은 연무 사례가 발생하고 있 다. 북동 태평양에서 자주 발생하는 대기 블로킹은 지구 규모 대기 변동과 동아시아 지역의 서풍 기류를 정체시키기도 한다. 2019년 3월 동아시아 지역의 온난하고 정체적인 종관 기상 특성이 알래스카 대기 블로킹이 발생한 6-7일 후에 일어나고 있었다. 특히, 2019년 3월 18-24일에 발생한 알래스카 대기 블로킹은 3월 25-28일 동안 한국에서 일평균 미 세먼지(particulate matter; PM10) 질량농도가 50 μg m−3을 넘는 고농도 PM10 연무 사례가 발생하는 데 영향을 미치고 있 었다. 한편, WRF-Chem 모델을 활용하여 한국의 고농도 PM10 연무 사례에 대한 인위적 배출의 장거리 수송 기여도는 30-40%를 나타내고 있었다. PM10 에어로졸 구성 성분인 황산염, 질산염, 암모늄, 블랙 카본, 유기 탄소, 기타 무기물의 장거리 수송 기여도는 각각 10-15, 20-25, 5-10, 5-10, 5-10, 15-20%를 나타내었다. 질소 산화물이 온난하고 정체적인 대기에서 암모늄과의 광화학 반응으로 형성된 질산암모늄은 한국의 고농도 PM10 연무 사례에 대한 장거리 수송 기여도 가 PM10 에어로졸 중 가장 큰 비중을 나타내고 있었다.

Subway trains with air cleaners have been newly deployed in the Seoul Metro system. The purpose of this study was to determine differences regarding in-cabin particulate matter with respect to concentrations less than 10 um (PM10) and 2.5 um (PM2.5) through the operation of air cleaners in subway trains. One subway train newly installed with in-cabin air cleaners on Seoul Metro Line number 2 was chosen monitoring in 2020. In-cabin air cleaners were turned-on at both front and back areas while those in the middle area were turned-off while the train was running. In-cabin PM10 and PM2.5 concentrations were measured in each area using a real-time aerosol monitor. Average in-cabin PM10 concentrations were statistically significantly lower (by 15%) in areas with air cleaners turned-on (43.8±12.1 μg/m3) compared to those areas where the air cleaners were turned-off (51.4±15.0 μg/m3). Average incabin PM2.5 concentrations were significantly lower (by 14%) in areas with air cleaners turned on (33.7±12.2 μg/m3) compared to those areas where air cleaners were turned-off (39.2± 14.4 μg/m3). In-cabin PM10 and PM2.5 concentrations ratios were similar regardless of area with air cleaners turned-on or turned-off. The in-cabin PM10 and PM2.5 concentrations were not associated with commute time. Use of air cleaners in subway trains effected reductions in in-cabin PM10 and PM2.5 concentrations.

2020년 중국의 COVID-19 폐쇄는 한국의 풍상측에 위치한 중국의 대기오염 배출량을 감소시켰다. 몽골 북부로 부터 중국 동부를 거쳐 한반도에 이르는 지역에서는 2020년 1~2월에 기온 아노말리가 양(+)으로 온난하였고, 2020년 1월에는 동서류 아노말리가 음(−)으로 정체적인 특징을 보였다. 2019년 12월~2020년 3월에 한국 중부 서쪽의 석모리와 파도리에서 중국 배출량 감소의 영향에 따라 PM10, NO2, O3 농도 변동이 나타났다. 파도리에서 PM10, O3 월평균 농도와 최근 4년의 월평균 농도의 비는 2019년 12월과 비교하여 중국의 COVID-19 폐쇄 이후인 2020년 1~3월에 각각 0.7~4.7%, 9.2~22.8%로 감소하였다. 2020년 1월 중국의 춘절 기간에는 석모리와 파도리에서 PM10, NO2, O3 농도가 최근 4년의 춘절 기간과 마찬가지로 감소하였다. 그러나 2020년 1월 평균 농도가 최근 4년 1월과 비교하여 감소한 것은 중국 춘절 전후의 기간에도 배출량이 감소하였던 것과 관련 있다. 2020년 1~3월 석모리의 PM10, NO2, O3 농도의 비 ( /M)는 각각 70.8~89.7%, 70.5~87.1%, 72.5~97.1%이었고, 파도리에서도 각각 79.6~93.5%, 67.7~84.9%, 83.7~94.6% 로 추정 월평균(M)보다 월평균(Os)이 감소하였다. 2020년 1월에 몽골 북부로부터 중국 동부와 한반도에 이르는 지역의 온난화로 인한 광화학 반응으로 최근 4년과 비교하여 AOD가 높게 나타났으나 2020년 3월에는 풍상측인 중국에서 2차 에어로졸을 생성하는 전구물질 배출 감소로 최근 4년과 비교하여 낮은 AOD 분포를 보였던 것으로 분석되었다.

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 investigated 180 students’ indoor environmental awareness of rest spaces and measured the indoor and outdoor concentrations of PM10, TVOCs, and HCHO in 8 rest spaces from October 2019. 89.4% of the students responded that they use rest spaces at least once a day and most of the respondents are using rest spaces in the university. The largest number of students responded to the tight space as the main cause of air pollution in rest spaces. 62.1% of the students answered they experienced health symptoms from using rest spaces. Among them, 32.5% said they experienced irritation symptoms of eyes, neck, nose, and 12.1% answered that they experienced headaches. Indoor PM10, TVOCs, and HCHO levels did not exceed indoor air quality recommendations nor the maintenance standard for multi-use facilities. Indoor PM10, TVOCs, and HCHO levels did not exceed indoor air quality recommendations nor the maintenance standard for multi-use facilities. According to the type of rest space, concentrations of PM10, TVOCs, and HCHO were higher among the closed-type than open-type rest space. Even if the concentration of pollutants is less than the environmental standard, continuous exposure may cause negative health effects. In addition, considering that 62.1% of the respondents experienced health symptoms, it is deemed necessary to take measures to manage indoor environments in rest spaces and to develop measures to reduce pollutants.

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.

To reduce subway passengers’ exposure to PM 10 (particulate matter less than 10 micrometers), management of PM 10 concentration in underground stations is critical. In this study, we attempted to investigate the distribution of airflow PM 10 concentration in an underground station. The numerical simulations were performed using computational fluid dynamics. In order to apply to CFD, measurement of air volume (supplied and exhausted air) and PM 10 concentration were conducted at the concourse and platform areas of the underground station. The results of the simulation agreed with the actual PM 10 concentration, and we confirmed the distribution of PM 10 concentration depending on air volume conditions. This result will be helpful to reduce the PM 10 in an underground station when using ventilation system.

In this study, we measured the concentration of Particulate Matter (PM10), Formaldehyde (HCHO), and Total Bacteria Count (TBC) at three facilities: elderly care centers, day care centers, and postnatal care centers located in the cities of Seoul, Sejong, Daegu and Ulsan from January 1, 2012 to December 31, 2015. PM10 concentration was the highest in the day care centers and HCHO concentration was the highest in the postnatal care centers. TBC concentration was the highest in the day care centers. Comparing the different cities, PM10 concentration was the highest in Sejong, HCHO concentration was the highest in Seoul, and TBC concentration was the highest in Daegu. This study is expected to be helpful in preventing damage to health from the contaminated indoor air at sensitive facilities, and can be used as basic data for indoor air quality management.

The objectives of this study were to investigate the effects of PM10 and O3 concentration on the symptoms of allergic diseases. The questionnaire was used to determine whether or not symptoms of allergic diseases were present from September to October 2012. The air pollution concentration data used was the corresponding point CEM (continuous emission monitoring) data. The average concentration of PM10 was 56.09 μg/m3 in the control area, and the concentration in the exposed area was 40.44 μg/m3. In the two areas, concentration of O3 was 28.73 ppb and 28.74 ppb, respectively. The total average concentrations of PM10 and O3 were 45.66 μg/m3 and 28.73 ppb in the Gwangyang area. The rate of asthma diagnosis was higher in the control area (9.6%) than in the exposed area (4.1%), but the rate of allergy eye disease was higher in the exposed area (23.9%) than in the control area (16.5%). There was a significant difference in the symptoms of some allergic diseases when the relative concentration of PM10 and O3 were high and low.

In this study, we investigated the concentrations of PM10 and CO2 in public transportation vehicles (express bus, train, KTX, and subway) reported by previous indoor air quality (IAQ) surveys carried out from 2005 to 2013 in Korea. The number of valid data for PM10 was 566 and for CO2 was 579, and all data were classified according to whether it was collected during rush-hour or non rush-hour. PM10 and CO2 concentrations in subway cabin during the rush-hour were 1.3 and 1.45 times higher, respectively, than those of non rush-hour (p<0.05) in terms of geometric mean value. PM10 and CO2 concentration of express bus and train during the rush-hour also were 1.23 times higher than those of non rush-hour with relatively weak correlations (p=0.246). Among all PM10 concentrations, 16.9% and 3.8% of PM10 concentrations exceeded the IAQ guidelines (200 μg/m3 for non-rush hour and 250 μg/m3 for rush-hour), respectively. In terms of CO2 concentrations, 10.5% and 3.0% of them exceeded the IAQ guidelines (2,500 ppm for non rush-hour and 3,000 ppm for rush-hour), respectively. As a result, concentrations of PM10 and CO2 were estimated to be dominantly influenced by the operation characteristics of public transportation, such as degree of congestion and type of vehicle. In order to improve the IAQ of public transportation vehicles, specific air purification and ventilation systems are needed, depending on the characteristics of public transportation vehicles.

The concern of fine particle (PM2.5) management of outdoor environments has been increasing due to its exposure and related health effects in Korea. As a result, PM2.5 standard in atmosphere environment was regulated in 2015. On the other hand, indoor PM2.5 standard has been required because most people spent their times in indoor environments. In this study, we measured the PM2.5 and PM10 concentrations both indoor and outdoor environments of public-use facilities such as underground stations, underground shopping centers, and nurseries for 24 hour with filter-weighing method in Seoul and Daegu. Measurement duration was from March to April in 2014 during the Asian dust period. At all measurements, indoor to outdoor (I/O) concentration ratios exceeded 1 except 1 day nursery in Daegu in spite of Asian dust period. The ratios of PM2.5 to PM10 concentrations ranged from 0.63 to 0.75 in indoor environments, and from 0.63 to 0.82 in outdoor, indicating that PM2.5 should be carefully managed in indoor environments as well as outdoor atmosphere.

An elementary school is a significant facility for educating and cultivating children and it is where they spend most of their days. The purpose of this study is to conduct an analysis on PM10 and TBC concentrations in 110 elementary schools in Suwon. We investigated 110 elementary schools from March 2011 to December in 2012. The mean concentration of PM10 and TBC were 51.3 ± 18.0 μg/m3(GM: 47.9 μg/m3) and 311.0 ± 125.3 CFU/m3 (GM:293.3 CFU/m3), respectively. All measurement concentrations did not exceed the Ministry of Education IAQ guidelines. Also, the concentrations of PM10 and TBC were not affected by various conditions such as Outdoor PM10, school construction year, class student number, temperature(oC) and humidity(%). The results of the study suggest that management of the IAQ plan in elementary schools should be implemented through Daily-Pattern and Activity-Pattern analysis for assessment of IAQ.

The aim of this study is to analyze the distribution of particulate matters including PM_2.5 which is known for severe adverse health effect than PM₁₀ in public facilities. The total 40 public buildings are investigated in this study and they are classified into 11 sub-groups as follows : child-care centers, medical centers, libraries, museums, bus terminals, ports, airports, railway terminals, subway stations, large-scale stores, and indoor parking lots. The mean concentration of PM₁₀ was 38.6㎍/㎥ and that of PM₁₀ in all studied facilities were lower than the Ministry of Environment's control standards. The average concentration of PM_2.5 was 27.2㎍/㎥ and that of PM_2.5 in 18 facilities were exceed the guideline of WHO (24h average value : 25㎍/㎥). The subway stations had the highest indoor level of particulate matters and the waiting area in bus terminals, railway terminals, indoor parking lots had followed in order. When comparing mean value of I/O ratio of PM₁₀, the only I/O ratio of subway stations were greater than one. In the case of PM_2.5, however, the average concentrations of PM_2.5 in indoors of subway stations, bus terminals, and indoor parking lots were higher than those of PM_2.5 in outdoors. The mean concentration of PM₁₀ and PM_2.5 were gradually increased between 6 A.M and 10 A.M and after 6 P.M in most of target buildings with increasing the number of users in thest facilities.

본 연구에서는 한반도 황사 사례 동안 WRF 기상모델과 SMOKE 배출량모델, CMAQ 및 CMAQ-MADRID 대기질 모델을 이용하여 다양한 황사 발생량 경험식에 대한 PM10의 농도를 추정하였다. 특별히 Wang et al.(2000), US EPA 모델, Park and In(2003), GOCART 모델, DEAD 모델의 5가지 황사 발생 경험식이 중국과 몽골 등의 황사 발생량을 추정하기 위해 WRF-SMOKE-CMAQ(MADRID) 모델에 적용되었다. 일기도, 후방궤적 및 위성이미지 분석에 따르면 한반도로의 황사 수송은 절리저기압(위성에서 콤마형 구름)과 관련된 지상 전선의 뒤쪽에서, 그리고 상층 제트류의 발달에 기인한 파의 정체현상과 함께 상층 골에서의 풍속이 하층으로 전이되는 풍하 바람에 의해 생성되었다. 그리고 WRF-SMOKE-CMAQ 모델링 결과, 황사의 시 공간적 분포에 있어서는 Wang et al.(2000)의 경험식이, 평균 편의 및 평균 제곱근 오차에서의 정확도 부분에서는 GOCART 모델의 경험식이 관측값을 보다 잘 모사하는 것으로 나타났다. 또한 Wang et al.의 경험식을 이용한 황사의 연직분포 분석 결과에서 강한 황사 사례(2007년 3월 31에서 4월 1일 800 μg/m3 이상)의 경우는 황사 수송이 한반도 상공 대기 경계층 내를 통과하였기 때문으로, 약한 황사 사례(2009년 3월 16일과 17에 400 μg/m3 이하)의 경우는 황사 수송이 경계층 위를 통과하였기 때문으로 나타났다. 또한 CMAQ 모델과 CAMQ-MADRID 모델에서의 미세먼지(PM10) 민감도 분석 결과에서는 CMAQ-MADRID 모델이 CMAQ 모델에 비해 한반도를 포함한 동아시아 지역에서 최대 25 μg/m3 정도가 높게 모사되었고, 모델 내 구름 액상과정에 의해서는 최대 15 μg/m3 정도가 제거되는 것으로 나타났다.

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.

본 연구에서는 2006년부터 2008년까지 3년간 봄철에 PM10과 PM2.5를 채취하여 질량농도와 금속원소의 화학적 특성, 기상인자와의 관계 분석, 황사 및 비황사시의 미세먼지 특성 그리고 이동경로에 따른 농도의 특성을 고찰하였다. 연구기간동안의 PM10, PM2.5, PM10-2.5평균농도는 각각 126.2±89.8, 85.5±41.6, 40.7±54.9μg/m3이었으며 PM2.5/PM10 및 PM10-2.5/PM2.5 비는 각각 0.70, 0.48이었다. 우리나라의 북서쪽인 북경을 포함한 지역과 서쪽인 상해를 포함한 지역에서 공기덩어리가 이류 할 때 가장 높은 미세먼지농도를 나타내었다.