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Influence of China and Dangjin Industrial Estate on PM10 고하고 있다

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Influence of China and Dangjin Industrial Estate on PM10 �고�하고 있다 제8회 EDISON SW 활용 경진대회 제8회 EDISON SW 활용 경진대회 서 정부에서도 ‘긴급재난문자의 발송’ 시스템을 Sep 2015, Groningen, Netherlands. Influence of China and Dangjin Industrial Estate on PM10 고하고 있다. 하지만 현재지는 ‘도시 재 <Http://www.essa2015.org/>. <Hal-01216165> 난 발생’에 대한 정보만을 전달하고 있다. 본 Concentration in Pyeongtaek City 연구에서는 확인해보지 지만, 대피 대상자 Lee Ju Eun*, Tran Thanh Son, Lee Mungyu, Joon Ha Kim* 들로 하여 적한 ‘대피시유도’ 정보를 전 School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and 달한다면, 실재 대피에 있어 정보전달이 유의 Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, South Korea 미한 과를 수 있을 것이다. E-mail: [email protected], [email protected], [email protected], [email protected]* 본 논문은 2018년도 정부(과학기정보통 Abstract: Illegal violations were discovered at Hyundai Steel and other fine dust 부)의 재으로 한국연구재 사이스· emission sites in Pyeongtaek and Dangjin areas in 2017. The residents of 교 발(EDISON) 사의 지을 아 Pyeongtaek City assume that the illegal discharge of the factory area deteriorated 수행 연구(NRF-2011-0020576) the air quality of Pyeongtaek City. Cross – correlation and Z-score analysis was made between the air quality components in the vicinity of Pyeongtaek city and the factory area in order to evaluate whether the factory area affected. As a result of the analysis, all area affected by the air from the west part of Korea. However, [1] 영진∙구인(2013), 도시재난 위험성 평가를 the factory area in Dangjin city increase PM10 concentration in Pyeongtaek city. 위한 시뮬레이션 및 GIS 활용안, 한국재학회지, 한국재학회. [2] 재홍∙이준∙홍∙최진(2012), 한국 피난 1. Introduction surrounding area. Hyundai Steel Plant, Dangjin 시뮬레이션 발을 위한 기연구, 한국재소학 Industrial estate and other area were analysed 회 학대회 논문, 한국재소학회. Air pollution is emerging as a social issue in to ascertain where the air quality of Pyeongtaek recent years. As the air quality in the living City originated. [3] 유영(2012), GIS 행위자 기의 비상 대피 quarters of people deteriorates and many 모 발, 한국재학회지, 한국재학회 adverse effects on health are made, many 2. Materials & Methods [4] 이동관∙은∙동근(2018), 재해 경험과 대피 efforts are made in Korea to measure air quality 훈련이 대피 행동에 미치는 영향 분석, 한국재학 and to grasp the current situation [1,2,3]. Illegal 2.1 Site Description 회 학대회 논문, 한국재학회. violations were discovered at Hyundai Steel and The study site is costal area located on the other fine dust emission sites in Pyeongtaek west part of Chungcheongnam-do, Korea (Fig. [5] 최승현∙정용∙도명(2018), Social Force Model and Dangjin areas in 2017 [4]. Pyeongtaek city, 을 활용한 보행자 대피행태 및 정보제 시나리오 1). Five data point were used include Hyundai 분석: 세시를 중심으로, 한국ITS학회논문지, 한국 the area where Dangjin's Hyundai Steel Plant (A), Asan industrial estate (B), Pyeongtaek city ITS학회. and Asan National Industrial estate are (C), Anseong city (D) and Taean-gun (E). concentrated, records more than the national The (A) and (B) are pollution source that can [6] Philippe Caillou, Benoit Gaudou, ArnauD Grignard, average fine dust (PM10) concentration every affect area (C). (D) and (E) are selected to check CHi Quang Truong, Patrick Taillandier. A Simple-to- year, and many citizens are mchoosing the the external factor. use BDI arcHitecture for Agent-based Modeling anD industrial estate as the cause. This study compares PM 10, ozone, nitrogen Simulation. The EleventH Confer- ence of the In this study, the main factor was selected that dioxide, carbon monoxide and sulfur dioxide European Social Simulation Association (ESSA 2015), highly correlated with Pyeongtaek city and 144 145 제8회 EDISON SW 활용 경진대회 with time series data in 2017 and 2018 from performed to confirm the relevance. The cross January to March. correlation analysis shows that all regions has correlation each other (Fig 3). All cross correlation analyses showed a correlation near Lag = 0. The Lag different between each area shows in Table 1. It shows A-E (+2 hr), B-E (+2hr), D-E (+3 hr) and A-D (-1 hr). Figure 1. Site description of measurement point 2.2. Statistical analysis The correlation coefficients were used to select the one parameters with the highest correlation among the data parameters. In order to identify the differences between before and after illegal emissions in B area, B and C were compared between 2017 and 2018 from January to March by Wilcoxon signed-Rank Test. Cross Figure 2. Pearson’s correlation between A, B correlation test was performed between A,B,C,D and C region. and E at 2018 data to analysis between dependency and correspondence. The final Due to cross correlation The final scenario was created through lag different in cross correlation and the scenario was verified using Z-score. All statistical analysis were carried out usig R studio. 3. Results & discussion Pearson’s correlation analysis was performed Figure 3. Cross-correlation A to B, C, D and E to select the main correlated parameters in January to March 2018 data (Fig. 2). Table1. Lag different due to the cross- It showed the highest significant linear 3correlation analysis relationship in PM10 than other parameters. Correlation Lag (hr) Correlation Lag (hr) A-B 0~0.5 B-E 2~2.5 Therefore, PM10 was selected as a key indicator and used for data analysis. A-C 1 C-D 0.5 Cross correlation of PM10 in each area was A-E 2 C-E 3 B-C 0~0.5 D-E 3.5 146 제8회 EDISON SW 활용 경진대회 제8회 EDISON SW 활용 경진대회 with time series data in 2017 and 2018 from performed to confirm the relevance. The cross It means that the highest correlation peak m C-E and D-E. E area is red line and A, C and D January to March. correlation analysis shows that all regions has oving one direction from E to D. When the lati area is blue line. correlation each other (Fig 3). All cross tude and longitude of each area were compar Z-score analysis was performed more clear correlation analyses showed a correlation near ed, the difference of Lag was observed in the ly confirm that the direction of the air came Lag = 0. The Lag different between each area order of the longitude different from E to the shows in Table 1. It shows A-E (+2 hr), B-E host. The latitude and longitude in each area a from the west (Fig 4). A, C, and D were com (+2hr), D-E (+3 hr) and A-D (-1 hr). re E (36.8, 126.28), A (36.97, 126.70), B (36.95, pared based on E area. In the E region, the 126.86), C (37.0, 126.91), D (37.0, 127.16) differ position of the graph inflection point was cl ence in longitude between PM10 concentratio ose to the upper graph, and the inflection p Figure 1. Site description of measurement point ns indicates that the atmosphere affecting the oint was larger in the far region. Confirm the air quality of Pyeongtaek comes from China, w wind direction, analysis of proportional check 2.2. Statistical analysis hich is west of E region. by curve fitting between Lag and longitude (Fig The correlation coefficients were used to select 5.). R2 is 0.99, that Lag difference is the one parameters with the highest correlation proportional to the Longitude difference. Ther among the data parameters. In order to identify efore, we can confirm that the PM10 air qua the differences between before and after illegal lity in A, B, C, D, and E is affected from the emissions in B area, B and C were compared same source located in the west. between 2017 and 2018 from January to March by Wilcoxon signed-Rank Test. Cross Figure 2. Pearson’s correlation between A, B correlation test was performed between A,B,C,D and C region. and E at 2018 data to analysis between dependency and correspondence. The final Due to cross correlation The final scenario was created through lag different in cross correlation and the scenario was verified using Z-score. All statistical analysis were carried out Figure 5. Proportional check by curve fitting in usig R studio. Lag and Longitude 3. Results & discussion Pearson’s correlation analysis was performed Figure 3. Cross-correlation A to B, C, D and E to select the main correlated parameters in January to March 2018 data (Fig. 2). Table1. Lag different due to the cross- It showed the highest significant linear 3correlation analysis relationship in PM10 than other parameters. Correlation Lag (hr) Correlation Lag (hr) A-B 0~0.5 B-E 2~2.5 Therefore, PM10 was selected as a key indicator and used for data analysis. A-C 1 C-D 0.5 Figure 5. Mahn-Whitney Test in A, B, C, D and A-E 2 C-E 3 Cross correlation of PM10 in each area was Figure 4. Z-score plot comparison between A-E, B-C 0~0.5 D-E 3.5 E site 146 147 제8회 EDISON SW 활용 경진대회 Mahn-Whitney Test showed with box plot in 4. Conclusion Fig 5. The A, B, C and D site showed similar Correlation analysis shows that PM10 air mean value. However, C site has low mean than quality is correlated with the whole area, and it other region. E site place west part than other is confirmed that the air influencing the air regions (near China) and due to z-score the quality comes from the west due to the wind direction is west to east.
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