서울 인구의 미세먼지 노출 예측에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 보건대학원 환경보건학과, 2021.8. 곽수영.Outdoor concentrations of particulate matter with an aerodynamic diameter of < 2.5 µm (PM2.5) often used as a surrogate for population exposure to PM2.5 in epidemiological studies. However, people spend the majority of their time indoors; therefore, the relationship between indoor and outdoor PM2.5 concentrations should be considered in the estimation of population exposure to PM2.5. The overall objective of this study was to estimate population exposure to PM2.5 using time-location patterns and outdoor PM2.5 concentrations which was applied by the relationship between indoor and outdoor PM2.5 concentration. Additionally, direction of refining estimation of population exposure to PM2.5 was explored to provide useful insights for further study for the estimates of population exposure to particulate matter. Study 1 (chapter 2) aimed to develop a population exposure model to PM2.5 and identify the determinants associated with the high-exposure group. The input data for the population exposure model were 3984 time-location patterns from Korea Time Use Survey data, outdoor PM2.5 concentrations from ambient air quality monitoring station (AQMS), and the microenvironment-to-outdoor concentration (M/O ratio) of PM2.5 at seven microenvironments in Seoul, Korea. A probabilistic approach was used to develop the Korea simulation exposure model (KoSEM). The determinants for the population exposure group were identified using a multinomial logistic regression analysis. Population exposure to PM2.5 varied significantly among the three seasons. The mean ± standard deviation of population exposures to PM2.5 was 21.3 ± 4.0 μg/m3 in summer, 9.8 ± 2.7 μg/m3 in autumn, and 29.9 ± 10.6 μg/m3 in winter. Exposure to PM2.5 higher than 35 μg/m3 mainly occurred in winter. Gender, age, working hours, and health condition were identified as significant determinants in the exposure groups. The high PM2.5 exposure group was characterized as a higher proportion of males of a lower age with fewer working hours. Study 2 (chapter 3) aimed to predict real-time indoor PM10 concentration in the daycare centers, kindergartens, and elementary schools using outdoor environmental data. Indoor PM10 concentrations were measured in 54 daycare centers, 12 kindergartens, and 21 elementary schools in Seoul, Korea, using a real-time monitor (AirGuard K) over a period of one year. Multiple linear regression models were used to predict real-time indoor PM10 concentration in these educational facilities using outdoor PM10 and meteorological data as inputs. Four formations (original, ratio of indoor-to-outdoor, root-transformation, and log-transformation) for dependent variable were compared to determine the best performance of the model. A 10-fold cross-validation method was used to evaluate the accuracy of the prediction models. Daycare centers showed the highest indoor PM10 concentration. Root-transformed models with high accuracy were developed to predict the real-time indoor PM10 concentration in educational facilities every 10 min. The R2 of the prediction models were 0.64 in the daycare centers, 0.45 in the kindergartens, and 0.43 in the elementary schools. The 24 h profile of the predicted indoor PM10 was similar to the measured PM10 concentration. Study 3 (chapter 4) aimed to determine the spatial variations of five air pollutants (PM2.5, PM10, NO2, CO, and O3) at city-scale and small-scale areas in Seoul, Korea in four seasons. Hourly concentrations of the five air pollutants at 25 AQMSs in Seoul from December 2017‒December 2018 were obtained from a Korean government website (AirKorea). In-situ measurements in small-scale (1 km2) areas were conducted to obtain the hourly concentrations of five air pollutants at eight monitoring sites surrounding the AQMS in Guro-gu, Seoul. Spatial autocorrelations were determined using Morans index analyses. High PM2.5 and PM10 concentrations were observed in winter, whereas high O3 concentrations occurred in spring and summer. The hourly mean concentrations of air pollutants at in-situ monitoring sites (IMSs) were generally higher than those at the nearby AQMS, whereas the O3 concentrations at IMSs were lower in spring and summer. Seasonal spatial autocorrelation patterns of air pollutants in the city-scale area did not reflect the variation in the small-scale area. Through these studies, population exposure to PM2.5 in Seoul was predicted by season using a population exposure model. The methodology could provide useful insights to conduct more accurate PM exposure assessment with less resources rather than without direct measurements. The population exposure model for PM2.5 could be used to implement effective interventions and evaluate the effectiveness of control policies to reduce exposure.미세먼지 (Particulate matter, PM)의 노출은 여러 역학 연구에서 호흡기계 및 심혈관계 질병의 유병률과 조기사망률을 증가시킨다고 밝혀진 바 있다. 특히 초미세먼지 (PM2.5) 는 공기역학적 직경이 2.5 μm 인 입자상 물질로 입자 크기가 미세하여 흡입시 코 점막에서 걸러지지 않고 폐포까지 도달하여 많은 건강영향을 유발한다. 초미세먼지 노출 관리에 대한 중요성과 필요성은 커지고 있으며 국민의 건강을 보호하기 위해서는 국민이 미세먼지에 얼마나, 어디서, 어떻게 노출되고 있는지를 파악할 수 있는 개인노출 연구가 필수적이다. 미세먼지에 대한 개인이 노출되는 정도를 정확히 파악하기 위해서는 개인의 시간활동패턴에 따라 언제 (time), 어디서 (microenvironment), 어떤 활동 (activity)을 했는지에 따라 실제 활동공간에서의 노출 농도와 머무른 시간활동 양상을 파악하여야 한다. 하지만 이는 여러 시간적, 경제적 제약이 따른다는 한계점이 있다. 많은 역학연구에서는 국가 대기측정망에서 측정된 실외 대기오염물질 농도를 개인노출 농도와 같다고 간주하여 개인노출을 간접적으로 추정하려는 시도들이 있었다. 하지만 많은 선행연구에서 실외 미세먼지 농도만으로 개인노출 농도를 추정하는 것은 그 오차가 크다고 규명 하였으며, 정확한 미세먼지 개인노출 평가를 하기 위해서는 실내와 실외 모두에서의 노출 수준을 고려하여야 한다. 본 연구의 목표는 초미세먼지 실외농도와 실내외 농도비를 활용한 인구집단 초미세먼지 노출모델을 개발하여 서울 시민의 계절별 초미세먼지 노출 분포를 예측하고 고노출 인구집단의 특성을 파악하는 것이며, 개발한 모델에서 나아가 추후 연구에서 인구집단 노출 예측을 고도화 시킬 수 있는 방법에 대해 고찰하였다. 첫번째 연구(chapter 2)는 인구집단 초미세먼지 노출모델을 개발하여 서울 시민의 계절별 초미세먼지 노출분포를 도출하여 고노출 인구집단의 특성을 파악하였다. 모델의 입력값은 통계청 2014년 생활시간조사의 계절별 평일의 서울 시민 시간활동패턴 3984개 (여름: 960, 가을: 1898, 겨울: 1126)와 7개의 미세환경 (집, 직장/학교, 기타장소, 식당, 도보, 자동차, 대중교통)에서의 계절별 초미세먼지 농도이다. 미세환경에서의 초미세먼지 농도는 서울시 25개구 도시대기측정망에서의 5개년 (2015-2019년) 실외 초미세먼지 농도 dataset과 미세환경 실측을 통해 산출한 미세환경 및 실외 농도비 (M/O ratio) 각 분포를 곱하여 산출하였다. 산출한 미세환경별 초미세먼지 농도는 10분 단위의 계절별 시간활동 패턴에 각각 적용하여 확률론적 예측 방법 (몬테카를로 시뮬레이션)을 통해 서울시민의 계절별 초미세먼지 개인노출 분포를 도출하였다. PM2.5 개인노출 평균은 여름철에 21.3 ± 4.0 μg/m3 이었고, 가을철에 9.8 ± 2.7 μg/m3 이었고, 겨울철에 29.9 ± 10.6 μg/m3 이었다. 노출군은 도출한 노출 분포에서 상위 20%에 해당하는 사람들을 고노출군, 하위 20%를 저노출군, 나머지 60%를 기준 노출군으로 정의하였다. 고노출군의 PM2.5 개인노출 평균은 45.4 ± 13.1 μg/m3 이었고, 저노출군 평균은 20.4 ± 2.1 μg/m3, 기준 노출군 평균은 27.9 ± 3.4 μg/m3 이었다. 고노출에 영향을 준 인자는 성별, 나이, 일한시간, 건강상태였으며, 고노출군은 남자일수록, 나이가 어릴수록, 월수입이 높고 일한 시간이 긴 사람으로 특성되었다. 두번째 연구(chapter 3)는 여러 실외변수들을 이용하여 교육 기관에서의 실시간 실내 미세먼지 농도를 예측하는 모델을 개발하였다. 예측 모델 개발에는 데이터 특성을 반영한 4가지 모델 형태 (Original, I/O ratio, Root, Log)를 고려하였고, 9가지 실외변수를 활용하여 다중회귀모델을 개발하였다. 실외변수는 실외공기질 자료, 실외 기후관측 자료 (온도, 습도, 강수량, 풍향, 풍속, 기압, 일사량, 일조량) 이었고, 반응변수는 서울이 87곳에서의 교육기관 (어린이집 54곳, 유치원 12곳, 초등학교 21곳) 교실에서의 1년 동안 10분 단위의 실시간 실내 미세먼지 농도를 활용하였다. 최종 개발된 예측 모델은 변수를 루트변형하고 요일-시간 상호작용 변수를 포함하였고 가장 주요한 설명변수는 실외 미세먼지 농도였다. 예측 모델의 R2는 어린이집은 0.64, 유치원은 0.45, 초등학교는 0.43이었으며 실측된 실내 미세먼지 농도의 시간대별 추이를 잘 예측하여 좋은 예측력을 나타냈다. 본 실내 예측 모델은 직접 측정을 하지 않더라도 공개된 국가자료를 활용하여 실내 미세먼지 농도 예측 가능성을 보여줬으며 본 모델을 활용하여 노출에 대한 중재 (Intervention)를 할 수 있다. 세번째 연구(chapter 4)는 서울시 내 공간 크기에 따라 city-scale과 보다 더 작은 1 km2의 작은 공간 단위 (small-scale)로 나누어 5가지 대기오염물질 (PM10, PM2.5, NO2, CO, O3) 농도의 계절별 공간적 패턴에 대해 파악하였다. 대기오염물질의 시공간적 농도 변이의 이해와 고찰을 바탕으로 실외농도를 개인노출 평가에 적용한다면 보다 더 정확한 개인노출 평가가 가능하다. 2017년 12월부터 2018년 12월까지의 서울시 25개구 도시대기측정망에서의 대기오염물질 시간 농도는 에어코리아 홈페이지 (http://www.airkorea.or.kr)에서 다운로드 받았다. 구로구 내 작은 공간 단위 (1 km2)에서 대기오염물질 시간 농도는 도시대기측정망의 측정법과 동일한 공정시험기준에 따라 차량을 이용하여 측정하였다. 측정 지점은 서울시 구로구 도시대기측정망 1곳을 기준으로 5 km x 5 km test-bed를 설정하여 1 km2 크기의 공간 단위로 8곳을 선정하고, 한 계절당 각 지점에서 약 10일 동안 농도를 4계절 반복 측정하였다. 분석 방법은 Morans 지수를 이용하여 대기오염물질 농도의 전역적, 국지적 공간 자기상관성을 파악하였다. 25개구 도시 대기측정망에서의 대기 오염물질 농도와 보다 더 8곳의 작은 공간단위에서의 오염물질 농도는 계절별로 유의한 차이를 나타냈다. 일반적으로 도시대기측정망에서의 오염물질 농도가 높았지만 오존의 경우 봄과 여름에 더 작은 공간 단위에서의 농도가 더 높았다. 25개구 도시 대기측정망의 농도를 활용한 공간 자기상관성은 구로구 내 8곳의 작은 공간 단위의 공간 자기상관성과 다른 패턴을 나타내어 작은 공간까지 대변할 수 없었다. 보다 더 정확한 대기오염물질에 대한 개인노출을 예측하기 위해서는 city-scale보다 더 작은 공간 단위의 대기오염물질 농도 변이가 고려 되어야 하겠다. 본 연구는 인구집단 노출 모델을 개발하여 서울 시민의 초미세먼지 노출 분포를 도출하였고 고노출의 특성을 파악하여 노출 중재에 대한 과학적인 근거를 마련하였다. 본 연구에서 개발한 초미세먼지 인구집단 노출 모델을 통해 개인노출을 직접 측정을 하지 않아도 개인의 노출 수준을 예측할 수 있어 경제적인 노출평가를 할 수 있으며 궁극적으로 초미세먼지 고노출 인구집단에 대한 국가 정책 수립의 기초자료로 활용될 수 있다. 또한 보다 더 실제 노출을 반영할 수 있는 노출평가 모델을 보완, 발전시킬 수 있는 방향에 대한 연구를 통해 추후 인구집단 노출평가에 대한 정확도와 활용도를 높일 수 있는 통찰력을 제공할 수 있다. 실내 미세환경의 미세먼지 농도를 실외 변수만으로도 예측할 수 있는 가능성을 확인하였고 실외 미세먼지 농도를 인구집단 노출모델에 적용시 예측 공간의 단위에 대한 신중한 접근 관점을 제공하였다. 향후 추가 연구를 통해 초미세먼지 인구집단 노출모델을 국내 다른지역, 다른 대기오염물질에 대해 확대, 적용하여 개발한 노출평가 모델의 범용성을 확인할 수 있다.Chapter I. Introduction 1 1.1. Background 2 1.2. Objectives 24 References 27 Chapter II. A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul 34 2.1. Introduction 35 2.2. Methods 39 2.3. Results 47 2.4. Discussion 60 2.5. Conclusions 65 References 66 Chapter III. Prediction models using outdoor environmental data for real-time PM10 concentrations in daycare centers, kindergartens, and elementary schools 72 3.1. Introduction 73 3.2. Methods 76 3.3. Results 80 3.4. Discussion 92 3.5. Conclusions 97 References 98 Chapter IV. Seasonal spatial variation of five air pollutants (PM2.5, PM10, NO2, CO, and O3) at city-scale and small-scale areas in Seoul 104 4.1. Introduction 105 4.2. Methods 108 4.3. Results 116 4.4. Discussion 128 4.5. Conclusions 133 References 134 Chapter V. Summary and Conclusions 142 Supplements 146 국문초록 178 List of Tables . Table 1-1. KAAQSs by criteria air pollutants 4 Table 1-2. IAQ standards and guideline (PM2.5, PM10, CO2, CO, and NO2) in indoor multi-use facilities managed by the Korea Ministry of Environment 6 Table 1-3. Seasonal times spent for Seoul population in 2014 11 Table 1-4. Summary of population exposure models to air pollutants in previous studies 20 Table 1-5. A summary of refinement of KoSEM in this study 23 Table 2-1. Times spent in different microenvironments by season. 48 Table 2-2. Descriptive statistics of the PM2.5 M/O ratio in various microen vironments by season 52 Table 2-3. Demographic and socio-economic characteristics of the exposure groups for PM2.5 56 Table 2-4. Determinants associated with the PM2.5 exposure group by a multinomial logistic regression model 59 Table 3-1. Descriptive statistics of the average indoor and outdoor variables during one year 81 Table 3-2. Spearman correlation coefficients among indoor PM10 concen tration, outdoor PM10 concentration, and meteorological variables 83 Table 3-3. The comparison of the RMSE (μg/m3) among the four types of transformed models with day-time predictor 85 Table 3-4. The rate of exceeding the KAAQS of PM10 (75 μg/m3) 90 Table 3-5. Agreement of measured PM10 (Standard of > 75 μg/m3) and diff erent levels of predicted 10 min PM10 91 Table 4-1. The noncompliance rates (%) of the KAAQSs of air pollutants by season 119 Table 4-2. Pearson correlation coefficients among five air pollutants in 25 AQMSs in Seoul (gray) and IMSs in Guro-gu, Seoul (white) by season 121 Table 4-3. Pearson correlation coefficients of five air pollutants between IMSs and AQMS by season 122 Table 4-4. Global spatial autocorrelation analysis of air pollutants at 25 AQ MSs in Seoul over four seasons 123 Table 4-5. Global spatial autocorrelation analysis of air pollutants at 8 IMSs in Guro-gu over four seasons 124 List of Figures . Figure 1-1. The overall outline of the study 26 Figure 2-1. Structure of the KoSEM II-PM2.5 44 Figure 2-2. Daily average of outdoor PM2.5 concentrations by season from 2015‒2019 50 Figure 2-3. Cumulative frequency of population exposures to PM2.5 by season using a probabilistic simulation 54 Figure 3-1. Comparison between the measured and predicted indoor PM10 concentration every 10 min during 24 h 88 Figure 4-1. Locations of monitoring sites in Seoul. Yellow star represents AQMSs and blue squares represents IMSs 109 Figure 4-2. Hourly concentrations of air pollutants at 25 AQMSs (blue), 1 AQMS in Guro-gu (pink), and 8 IMSs in Guro-gu (white) 118 Figure 4-3. LMIs of air pollutants in eight IMSs by season 127 List of Supplements . Table S-1. IAQ standards and guidelines in indoor multi-use facilities 147 Table S-2. IAQ guidelines in public transportations managed by the Korea Ministry of Environment 148 Table S-3. IAQ standards and guidelines in kindergartens, schools, and university managed by the Korea Ministry of Education 149 Table S-4. IAQ standards in workplace managed by Korea Ministry of Employment and Labor 150 Table S-5. Descriptive statistics of socio-demographic characteristics of population by seasons 151 Table S-6. Specifications of the MicroPEM 153 Table S-7. Sample size of microenvironment measurement 154 Table S-8. Means of PM2.5 concentrations in the seven microenvironments by season 155 Table S-9. Specifications of the AirGuard K 156 Table S-10. Outdoor environmental parameters as input variable for multiple linear regression models 157 Table S-11. Time Tables at educational facilities 159 Table S-12. KAAQSs of air pollutants 160 Table S-13. Latitude and longitude of monitoring sites in Seoul 161 Table S-14. Measurement schedules in eight IMSs over one year 162 Table S-15. Means of PM2.5 concentrations (μg/m3) both IMSs and AQMS in Guro-gu 163 Table S-16. Means of PM10 concentrations (μg/m3) both IMSs and AQMS in Guro-gu 164 Table S-17. Means of NO2 concentrations (ppm) both IMSs and AQMS in Guro-gu 165 Table S-18. Means of CO concentrations (ppm) both IMSs and AQMS in Guro-gu 166 Table S-19. Means of O3 concentrations (ppm) both IMSs and AQMS in Guro-gu 167 Table S-20. LMIs of PM2.5 at 25 AQMSs in Seoul by season 168 Table S-21. LMIs of PM10 at 25 AQMSs in Seoul by season 169 Table S-22. LMIs of NO2 at 25 AQMSs in Seoul by season 170 Table S-23. LMIs of CO at 25 AQMSs in Seoul by season 171 Table S-24. LMIs of O3 at 25 AQMSs in Seoul by season 172  Figure S-1. 24-h profiles of hourly average of outdoor PM2.5 concentrations over 5 year 173 Figure S-2. Scatter plots in educational facilities 174 Figure S-3. Relationship between gravimetric measurement and AirGuard K concentration using 2-day average of PM10 176 Figure S-4. Location of AQMSs and educational facilities in Seoul 177박

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Long-term exposure to particulate matter (PM) with aerodynamic diameters &lt; 10 (PM10) and 2.5 mu m (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i. e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i. e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015-2016. The results show that the RF-based models produced good performance resulting in R-2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 mu gm(-3) for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for esti-mating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i. e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i. e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i. e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ)

Ambient particulate matter (PM10) concentrations in major urban areas of Korea during 1996–2010

AbstractIn this study, ambient particulate matter pollution was investigated using monthly PM10 concentration data collected from seven major cities in Korea from 1996 to 2010. The highest mean value for the whole study period is seen from the capital city, Seoul (63.2±17.9μg m–3), while the lowest is from Ulsan (46.7±14.8μg m–3). The concentrations of PM10 in all cities exhibited seasonal variations with the peak values occurring consistently in spring (March or April). The PM10 data in each city consistently exhibited strong correlations (p<0.01) with gaseous pollutants (SO2, NO2, and CO), except for O3 (p>0.05). The analysis of long term trends of PM10 levels indicates a weak but consistent decline in concentrations in most cities with the relative average annual reductions of between 0.4 and 2.8% y–1

Assessing Health Vulnerability to Air Pollution in Seoul Using an Agent-Based Simulation

This study aims to investigate the exposure to air pollution in Seoul and the consequent health effects in Seoul South Korea, and suggest possible solutions using agent-based modelling (ABM). ABM is a useful technique that can simulate pollution generation and exposure, mobility patterns of unique individuals, and explore future scenarios. The first study compared Universal Kriging and Generalised Additive Models to spatially interpolate pollution station data over Seoul. A new method was discovered to enhance the accuracy of NO2 on roads. Next, ABM was used to evaluate potential health loss for a set of demographic groups after being cumulatively exposed to particulates (PM10), with a nominal heath impact threshold of 100µg/m3. Finally, a traffic simulation examined the coupled problem of non-exhaust emissions and behaviour and estimate exposure to PM10 for groups of drivers and pedestrians in central Seoul. Having tested the sensitivity to calibrated parameters, scenarios of traffic restriction and modification of pedestrian behaviour to avoid polluted areas was investigated. With less difference between interpolation methods, the result showed a remarkable contrast between roadside and background NO2 as well as a daily cycle, while PM10 had a small variance between hours but had greater seasonal oscillation. The first ABM study showed that disparities in health may arise as a result of differences in socioeconomic status, especially when the group was exposed over a long period, and road proximity caused additional health loss. In the traffic simulation study, extreme PM10 was found along roadways, but although drivers were exposed to extreme values, longer exposure for pedestrians led to higher health risks. Despite the absence of reliable data linking exposure to actual health effects, it is possible to make progress with ABM. In addition, pollution exposure can vary by commuting patterns and the urban development of one’s location. Scenarios can be advantageous for healthcare policy – to aid the most vulnerable groups and districts

Spatiotemporal variations of air pollutants (O-3, NO2, SO2, CO, PM10, and VOCs) with land-use types

The spatiotemporal variations of surface air pollutants (O-3, NO2, SO2, CO, and PM10) with four land-use types, residence (R), commerce (C), industry (I) and greenbelt (G), have been investigated at 283 stations in South Korea during 2002-2013, using routinely observed data. The volatile organic compound (VOC) data at nine photochemical pollutant monitoring stations available since 2007 were utilized in order to examine their effect on the ozone chemistry. The land-use types, set by the Korean government, were generally consistent with the satellite-derived land covers and with the previous result showing anti-correlation between O-3 and NO2 in diverse urban areas. The relationship between the two pollutants in the Seoul Metropolitan Area (SMA) residence land-use areas was substantially different from that outside of the SMA, probably due to the local differences in vehicle emissions. The highest concentrations of air pollutants in the diurnal, weekly, and annual cycles were found in industry for SO2 and PMPM10, in commerce for NO2 and CO, and in greenbelt for O-3. The concentrations of air pollutants, except for O-3, were generally higher in big cities during weekdays, while O-3 showed its peak in suburban areas or small cities during weekends. The weekly cycle and trends of O-3 were significantly out of phase with those of NO2, particularly in the residential and commercial areas, suggesting that vehicle emission was a major source in those areas. The ratios of VOCs to NO2 for each of the land-use types were in the order of I (10.2) &gt; C (8.7) &gt; G (3.9) &gt; R (3.6), suggesting that most areas in South Korea were likely to be VOC-limited for ozone chemistry. The pollutants (NO2, SO2, CO, and PMPM10 except for O-3 have decreased, most likely due to the effective government control. The total oxidant values (OX = O-3 + NO2) with the land-use types were analyzed for the local and regional (or background) contributions of O-3, respectively, and the order of OX (ppb) was C (57.4) &gt; R (53.6) &gt; I (50.7) &gt; G (45.4), indicating the greenbelt observation was close to the backgroundopen

SOURCE IDENTIFICATION OF PM10 AND SO2 IN A MULTI-INDUSTRIAL CITY OF KOREA

Department of Urban and Environmental Engineering (Environmental Science and Engineering)Ulsan is the largest industrial city of South Korea. A large area of the city is covered by automobile, shipbuilding, petrochemical, and non-ferrous industrial complexes. Among criteria air pollutants (CAPs), particulate matters (PM) and sulfur dioxide (SO2) directly related to the main industries are major environmental concerns in Ulsan. Basically, the effect of local sources is crucial for these pollutants. Also, long-range atmospheric transport (LRAT) from China is an important source of CAPs, especially for PM10. However, there has been no studies dealing with LRAT and local pollution of CAPs together in Ulsan. In this study, we collected and interpreted hourly data on CAPs measured at 14 automatic monitoring stations. The conditional bivariate probability function (CBPF), a receptor model, was used in order to identify local pollution sources of PM10 and SO2. An air dispersion model, California puff (CALPUFF), was also used to evaluate the influence of the industrial emissions by using 2012 Clean Air Policy Support System (CAPSS) data. The correlation analysis between the concentrations derived by CALPUFF and the monitoring data was conducted to identify the influence of local industrial sources. For LRAT of PM10, the potential source contribution function (PSCF) and cluster analysis of back-trajectories were performed. Totally, the monitoring data, modelling results, and back-trajectory data were derived at the hourly data set. These parameters were processed using statistical analysis, such as c-tree and random forest to assess the major sources between local and LRAT effects for each month. The hourly data of PM10 showed the highest level in April and May and the lowest in August and December. Besides, the highest and the lowest concentrations of SO2 were observed in July and December, respectively. CBPF results indicated that the petrochemical industry and road traffic were the main local sources of PM10, whereas SO2 concentration was greatly influenced by the petrochemical industry. From CALPUFF results, both PM10 and SO2 were dispersed from the industrial areas to the residential areas in summer. The PSCF and cluster analysis results showed the potential LRAT sources of PM10 was china in spring. Lastly, the importance between the local and LRAT impacts in each month was identified by the statistical analysis. The local impacts of PM10 and SO2 were the largest in summer and decreased in winter. The LRAT of PM10 was observed when high levels of PM coming from China occurred in spring. This study can be a basis to identify the local and long-distance sources of CAPs in other cities.clos

Possible link between Arctic Sea ice and January PM10 concentrations in South Korea

In this study, we investigated the possible teleconnection between PM10 concentrations in South Korea and Arctic Sea ice concentrations at inter-annual time scales using observed PM10 data from South Korea, NCEP R2 data, and NOAA Sea Ice Concentration (SIC) data from 2001 to 2018. From the empirical orthogonal function (EOF) analysis, we found that the first mode (TC1) was a large-scale mode for PM10 in South Korea and explained about 27.4% of the total variability. Interestingly, the TC1 is more dominantly influenced by the horizontal ventilation effect than the vertical atmospheric stability effect. The pollution potential index (PPI), which is defined by the weighted average of the two ventilation effects, is highly correlated with the TC1 of PM10 at a correlation coefficient of 0.75, indicating that the PPI is a good measure for PM10 in South Korea at inter-annual time scales. Regression maps show that the decrease of SIC over the Barents Sea is significantly correlated with weakening of high pressure over the Ural mountain range region, the anomalous high pressure at 500 hPa over the Korean peninsula, and the weakening of the Siberian High and Aleutian low. Moreover, these patterns are similar to the correlation pattern with the PPI, suggesting that the variability of SIC over the Barents Sea may play an important role in modulating the variability of PM10 in South Korea through teleconnection from the Barents Sea to the Korean peninsula via Eurasia

2012-2018년 한반도 시정 변화와 기여요인 분석

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 지구환경과학부, 2020. 8. 박록진.Visibility is determined by light extinctions due to gases and particles in the atmosphere as well as meteorological conditions. In particular, fine particular matters (PM) are one of important factors for affecting visibility, which is thus known to be a representative indicator of sensible air pollution. Despite of continued decreases of PM concentrations in South Korea, the public, however, perceive that PM air pollution in South Korea has worsened over the past. To understand and explain this disparity, we here use long-term hourly visibility observations at six sites in South Korea for 2012-2018 and analyze contributing factors to their variations including PM concentrations, and its chemical compositions along with meteorological conditions. We find that annual mean visibility in Seoul and Daejeon has improved by 0.7 km and 0.4 km, respectively, for the past 7 years, while Gwangju, Ulsan, Jeju, and Baengnyeong have shown its degradations by 0.7 km, 2.9 km, 0.6 km, 1.4 km for the same period. For high PM seasons, the frequency of hourly poor visibility (< 6.7 km) in Seoul, however, has increased by 11% in winter and Daejeon has also shown an increase of poor visibility frequency by 13% in spring. The frequencies of hourly poor visibility in Gwangju, Ulsan, and Baengnyeong have increased by 9%, 15%, 13%, respectively, regardless of seasons. Our analysis reveals that PM composition changes from sulfate to nitrate aerosols are a major factor for increasing hourly poor visibility frequencies in Seoul, Daejeon, and Baengnyeong, whereas meteorological conditions including relative humidity and windspeed changes are important factors for visibility degradations in Gwangju, Ulsan, and Jeju. We find that nitrate aerosols account for about 53% of visibility degradations in all regions. Increases of nitrate aerosol concentrations are driven by NOx emission changes and the reduction of sulfate aerosol concentrations, which makes additional NH3 available for ammonium nitrate production in the atmosphere.시정은 대기의 혼탁도를 나타내는 척도로써 공기 중의 다양한 가스상 물질과 입자상 물질들의 빛 산란과 기상 조건에 의해 결정된다. 특히 체감 대기오염의 대표적인 지표로 알려진 미세먼지는 시정에 영향을 끼치는 중요한 물질 중 하나이다. 그러나 한국의 미세먼지 농도가 지속해서 감소하는 추세임에도 불구하고, 국민들은 과거보다 한국의 대기오염이 더 악화되었다고 인식하고 있다. 이러한 인식의 차이를 이해하고 설명하기 위해 2012년부터 2018년 동안 한국의 6개 지역에서 관측된 시정 자료를 사용하여, 미세먼지를 구성하는 성분 농도(질산염, 황산염, 유기 탄소, 원소 탄소)와 기상 조건(상대습도, 풍속 등)이 시정과 저시정의 빈도율 변화에 얼마나 기여하는지를 분석하였다. 서울과 대전의 연평균 시정은 지난 7년 동안 각각 0.7 km, 0.4 km씩 개선되었으며, 광주, 울산, 제주, 백령도는 각각 0.7 km, 2.9 km, 0.6 km, 1.4 km씩 감소하였다. 광주와 울산, 백령도는 계절과 관계없이 저시정 빈도율이 각각 9%, 15%, 13%씩 증가한 반면, 서울은 겨울에, 대전은 봄에 한정하여11%, 13%씩 증가하였다. 서울과 대전, 백령도의 저시정 빈도율의 증가는 미세먼지를 구성하는 주요 성분이 황산염에서 질산염으로 변화하는 것으로 설명할 수 있는데, 이에 반해 광주, 울산, 제주도의 시정 저하에는 상대습도와 풍속의 변화가 더 크게 기여하고 있음을 알아내었다. 또한, 모든 지역에서 미세먼지 성분들 중 가장 크게 시정 저하에 기여하는 물질은 질산염으로 53% 기여하고 있다. 질산염의 농도 변화는 질소산화물의 배출량 변화와 황산염 농도의 감소로 인한 대기 중의 암모니아 생성으로 설명할 수 있다.1. Introduction 8 2. Methods 12 2.1 Observations 12 2.2 Data reconstruction 15 2.3 Poor visibility frequency 18 3. Results and Discussion 19 3.1 Annual visibility trend 19 3.2 Poor visibility analysis 23 3.3 Conditions affecting poor visibility 29 3.4 Variations of nitrate and sulfate 33 4. Summary and Conclusions 38 BIBLIOGRAPY 40 국문 초록 44Maste

Meteorological characteristics and assessment of the effect of local emissions during high PM10 concentration in the Seoul Metropolitan Area

In this study, we investigate the meteorological characteristics and the effect of local emissions during high PM10 concentrations in the Seoul Metropolitan Area (SMA) by utilizing data from a high-resolution urban meteorological observation system network (UMS-Seoul) and The Air Pollution Model (TAPM). For a detailed analysis, days with PM10 concentrations higher than 80 ??g m-3 for daily average PM10 concentration (classified as unhealthy by the Korean Ministry of Environment) in the Seoul Metropolitan Area (SMA) were classified into 3 Cases. Case I was defined as when the prevailing effect was from outside the SMA. Case II was defined as when the prevailing effect was a local effect with outside. Case III was defined as when the prevailing effect was local. Overall, high PM10 concentrations in the SMA mostly occurred under weak migratory anticyclone systems over the Korean Peninsula during warm temperatures. Prior to the PM10 concentration reaching the peak concentration, the pattern in each case was distinctive. After peak concentrations, however, the pattern for the 3 cases became less distinct. This study showed that nearly 50% of the high PM10 concentrations in the SMA occurred in spring and were governed by the conditions for Case II more than these for Cases I and III. In spring, the main sources of the high PM10 concentrations in the SMA were local emissions due to the predominance of weak winds and local circulation. The simulation showed that the non-SMA emissions were about 63 to 73% contribution to the spring high PM10 concentrations in the SMA. Specifically, local point sources including industrial combustion, electric utility, incineration and cement production facilities scattered around the SMA and could account for PM10 concentrations more than 10 ??g m-3 in the SMA