Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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Analysis of PM2.5 Distribution Contribution using GIS Spatial Interpolation - Focused on Changwon-si Urban Area -

GIS 공간내삽법을 활용한 PM2.5 분포 특성 분석 - 창원시 도시지역을 대상으로 -

  • MUN, Han-Sol (Dept. of Environmental Engineering, Changwon National University) ;
  • SONG, Bong-Geun (Institute of Industrial Technology, Changwon National University) ;
  • SEO, Kyeong-Ho (Gyeongsangnamdo Office of Education) ;
  • KIM, Tae-Hyeung (School of Civil, Environmental and Chemical Engineering, Changwon National University) ;
  • PARK, Kyung-Hun (School of Civil, Environmental and Chemical Engineering, Changwon National University)
  • 문한솔 (창원대학교 환경공학과) ;
  • 송봉근 (창원대학교 산업기술연구원) ;
  • 서경호 (경상남도교육청) ;
  • 김태형 (창원대학교 토목환경화공융합공학부) ;
  • 박경훈 (창원대학교 토목환경화공융합공학부)
  • Received : 2020.03.20
  • Accepted : 2020.04.21
  • Published : 2020.06.30
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Abstract

The purpose of this study was to analyze the distribution characteristics of spatial and temporal PM2.5 in urban areas of Changwon-si, and to identify the causes of PM2.5 by comparing the characteristics of land-use, and to suggest the direction of reduction measures. As the basic data, the every hour average from September 2017 to August 2018 of Airpro data, which has measurement points in kindergartens, elementary schools, and some middle and high schools in Changwon-si was used. Also, by using IDW method among spatial interpolation methods of GIS, monthly and time-slot distribution maps were constructed, and based on this, spatial and temporal PM2.5 distribution characteristics were confirmed. First, to verify the accuracy of the Airpro data, the correlation with AirKorea data managed by the Ministry of Environment was confirmed. As a result of the analysis, R2 was 0.75~0.86, showing a very high correlation and the data was judged that it was suitable for the study. In the monthly analysis, January was the highest year, and August was the lowest. As a result of analysis by time-slot, The clock-in time at 06-09 was the highest, and the activity time at 09-18 was the lowest. By administrative district, Sangnam-dong, Happo-dong, and Myeonggok-dong were the most severe regions of PM2.5 and Hoeseong-dong was the lowest. As a result of analyzing the land-use characteristics by administrative area, it was confirmed that the ratio of traffic area and commercial area is high in the serious area of PM2.5. In conclusion, the results of this study will be used as basic data to grasp the characteristics of PM2.5 distribution in Changwon-si. Also, it is thought that the severe regions and the direction of establishing reduction measures derived from this study can be used to prepare more effective policies than before.

본 연구는 창원시 도시지역을 대상으로 PM2.5의 시·공간적인 분포 특성을 분석하고, 토지이용특성과의 비교를 통해 PM2.5 발생 요인을 파악하여 저감 방안 방향을 제시하고자 하였다. 기초 자료로 창원시 내 유치원, 초등학교와 일부 중·고등학교에 측정지점을 두고 있는 Airpro 자료의 2017년 9월부터 2018년 8월까지의 매 1시간 평균값을 활용하였다. 그리고 GIS의 공간내삽법 중 IDW 기법을 활용하여 월별, 시간대별 분포 지도를 구축하였고 이를 바탕으로 시·공간적인 PM2.5 분포 특성을 확인하였다. 먼저 Airpro 자료의 정확성을 검증하고자 환경부에서 관리하는 AirKorea 자료와의 상관성을 확인하였고, 분석 결과 R2이 0.75~0.86으로 매우 높은 상관성이 나타나 연구에 적합하다고 판단되어 분석을 진행하였다. 월별 분석에서는 1월이 연중 가장 높았고, 8월이 가장 낮았다. 시간대별 분석 결과 출근시간인 06-09시가 가장 높았고 활동시간인 09-18시가 가장 낮게 나타났다. 행정구역별로는 상남동, 합포동, 명곡동이 PM2.5 심각 지역으로, 회성동이 가장 낮은 지역으로 나타났다. 행정구역별 토지이용 특성을 분석한 결과 PM2.5 심각 지역 내에 교통지역과 상업지역의 비율이 높은 것을 확인하였다. 결론적으로 본 연구 결과는 창원시의 PM2.5 분포 특성을 파악할 기초자료로 활용될 것이다. 또한 본 연구에서 도출된 심각 지역 및 저감 방안수립 방향은 기존보다 더욱 효과적인 정책 마련에 활용할 수 있을 것으로 판단된다.

Keywords

References

  1. Air Visual. 2019. 2018 World air quality report; region & city PM2.5 Ranking. pp. 1-21.
  2. Brauer, M., C. Lencar, L. Tamburic, M. Koehoorn, P. Demers and C. Karr. 2008. A cohort study of traffic related air pollution impacts on birth outcomes. Environmental Health Perspectives 116(5):680-686. https://doi.org/10.1289/ehp.10952
  3. Chen, W., H. Tang and H. Zhao. 2015. Diurnal, weekly and monthly spatial variations of air pollutants and air quality of Beijing. Atmospheric Environment 119(2015):21-34. https://doi.org/10.1016/j.atmosenv.2015.08.040
  4. Cho, H.L. and J.C. Jeong. 2006. A study on spatial and temporal distribution characteristics of coastal water quality using GIS. The Journal of GIS Association of Korea 14(2):223-234
  5. Cho, H.L. and J.C. Jeong. 2009. The distribution analysis of PM10 in Seoul using spatial interpolation methods. Journal of Environmental Impact Assessment 18(1):31-39
  6. Cho, S.H., H.W. Kim, Y.J. Han and W.J. Kim. 2016. Characteristics of fine particles measured in two different functional areas and identification of factors enhancing their concentrations. Journal of Korean Society for Atmospheric Environment 32(1):100-113 https://doi.org/10.5572/KOSAE.2016.32.1.100
  7. Crilley, L. R., M. Shaw, R. Pound, L.J. Kramer, R. Price, S. Young, A.C. Lewis and F.D. Pope. 2018 Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring. Atmospheric Measurement Techniques 11:709-720. https://doi.org/10.5194/amt-11-709-2018
  8. Do, H.S., S.J. Choi, M.S. Park, J.K. Lim, J.D. Kwon, E.K. Kim and H.B. Song. 2014. Distribution characteristics of the concentration of ambient PM-10 and PM-2.5 in Daegu Area. Journal of Korean Society of Environmental Engineers 36(1):20-28 https://doi.org/10.4491/KSEE.2014.36.1.20
  9. Dongnam Regional Statistics Office. 2018. Analysis of Air Environment in Gyeongsangnam-do, 2017 [Fine Dust]. http://kostat.go.kr/regional/dn/dn_ntc/1/1/index.board?bmode=read&bSeq=&aSeq=371231&pageNo=1&rowNum=10&navCount=10&currPg=&searchInfo=srch&sTarget=title&sTxt=%EB%AF%B8%EC%84%B8%EB%A8%BC%EC%A7%80. (Accessed october 26, 2018).
  10. Han, C.W., S.T. Kim, Y.H. Lim, H.J. Bae and Y.C. Hong. 2018. Spatial and temporal trends of number of deaths attributable to ambient PM2.5 in the Korea. The Korean Academy of Medical Sciences 33(30):e193.
  11. Harrison, R.M., D. Laxen, S. Moorcroft and K. Laxen. 2012. Processes affecting concentrations of fine particulate matter in the UK atmosphere. Atmosphere Environment 46:115-124. https://doi.org/10.1016/j.atmosenv.2011.10.028
  12. Hwang, K.I., B.H. Han, J.I. Kwark and S.C. Park. 2018. A study on decreasing effects of ultra-fine particles(PM2.5) by structures in a roadside buffer green -a buffer freen in Songpa-gu, Seoul-. Journal of the Korean Institue of Landscape Architecture 46(4):61-75
  13. Hwang, S.Y., J.Y. Moon and J.J. Kim. 2019. Relationship analysis between fine dust and traffic in Seoul using R. The Journal of The Institute of Internet, Broadcasting and Communication 19(4):139-149
  14. Jeon, B.I. 2010. Characteristics of spaciotemporal variation for PM10 and PM2.5 concentration in Busan. Journal of the Environmental Sciences 19(8):1013-1023 https://doi.org/10.5322/JES.2010.19.8.1013
  15. Jeong, J.C. 2014. A spatial distribution analysis and time series change of PM10 in Seoul city. Journal of the Korean Association of Geographic Information Studies 17(1):61-69 https://doi.org/10.11108/kagis.2014.17.1.061
  16. Jeong, J.C. and S.H. Lee. 2018. Spatial distribution of particulate matters in comparison with land-use and traffic volume in Seoul, Republic of Korea. Journal of Cadastre & Land Information 48(1):123-138
  17. Jha, D.K., M. Sabasan, A. Das, N.V. Vinithkumar and R. Kirubagaran. 2011. Evaluation of interpolation technique for air quality parameters in Port Blair, India. Universal Journal of Environmental Research and Technology 1(3):301-310.
  18. Kang, H.C. 2019. An analysis of the causes of fine dust in Korea considering spatial correlation. Environmental and Resource Economics Review 28(3):327-354
  19. Kim, H.J. and W.K. Jo. 2012. Assessment of PM-10 Monitoring stations in Daegu using gis interpolation. Journal of the Korean Society for Geospatial Information Science 20(2):3-13
  20. Kim, J.H., J.H. Choi, and C.S. Kim. 2010. Comparative evaluation of interpolation accuracy for CO2 emission using GIS. Journal of Environmental Impact Assessment 19(6):647-656
  21. Kim, Y.S., K.M. Sim, M.P. Jung and I.T. Choi. 2014. Accuracy comparison of air temperature estimation using spatial interpolation methods according to application of temperature lapse rate effect. Journal of Climate Change Research 5(4):323-329 https://doi.org/10.15531/ksccr.2014.5.4.323
  22. Lee, J.J. 2019. Research for characteristics and management of the fine particulate matter in the region of Gyeonggi Province. Master Thesis of Ajou University. 1-47pp
  23. Lee, T.S., B.G. Song, C.B. Han and K.H. Park. 2011. Analysis of the GIS-based water cycle system for effective rainwater management of Gyeongsangnam-do. Journal of the Korean Association of Geographic Information Studies 14(2):82-95 https://doi.org/10.11108/kagis.2011.14.2.082
  24. Leem, J.H., B.M. Kaplan, Y.K. Shim, H.R. Pohl, C.A. Gotway, S.M. Bullard, J.F. Rogers, M.M. Smith and C.A. Tylenda. 2006. Exposures to air pollutants during pregnancy and preterm delivery. Environmental Health Perspectives 114(6):905-910. https://doi.org/10.1289/ehp.8733
  25. Ministry of Environment AirKorea. 2020. https://www.airkorea.or.kr/web/pastSearch?pMENU_NO=123. (Accessed February 3, 2020).
  26. Park, A.K., J.B. Heo and H. Kim. 2011. Analyses of factors that affect PM10 level of Seoul focusing on meteorological factors and long range transferred carbon monooxide. Korean Association for Particle and Aerosol Research 7(2):59-68
  27. Park, C.S. 2017. Variations of PM10 concentration in Seoul during 2015 and relationships to weather condition. The Association of Korean Photo-Geographers 27(2):47-64 https://doi.org/10.35149/jakpg.2017.27.2.004
  28. Park, J.C. and M.K. Kim. 2013, Comparison of precipitation distributions in precipitation data sets representing 1km spatial resolution over South Korea produced by PRISM, IDW, and Cokriging. Journal of the Korean Association of Geographic Information Studies 16(3):147-163 https://doi.org/10.11108/kagis.2013.16.3.147
  29. Park, S.A. and Shin, H.J. 2017. Analysis of the factors influencing PM2.5 in Korea : focusing on seasonal factors. Journal of Environmental Policy and Administration 25(1):227-248 https://doi.org/10.15301/jepa.2017.25.1.227
  30. Park, S.H. and Ko, D.W. 2018. Investigating the effects of the built environment on PM2.5 and PM10: a case study of Seoul metropolitan city, South Korea. Sustainability 10(12):4552. https://doi.org/10.3390/su10124552
  31. Rattigan, O.V., H.D. Felton, M.S. Bae, J.J. Schwab and K.L. Demerjian. 2010. Multi-year hourly PM2.5 carbon measurements in New York: Diurnal, day of week and seasonal patterns. Atmosphere Environment 44:2043-2053. https://doi.org/10.1016/j.atmosenv.2010.01.019
  32. Song, B.K. and K.H. Park. 2013. Air ventilation evaluation at nighttime for the construction of wind corridor in urban area. Journal of the Korean Association of Geographic Information Studies 16(2): 16-29 https://doi.org/10.11108/kagis.2013.16.2.016
  33. Yeo, M.J. and Y.P. Kim. 2019. Trend of the PM10 concentrations and high PM10 concentration cases in Korea. Journal of Korean Society for Atmospheric Environment 35(2):249-264 https://doi.org/10.5572/KOSAE.2019.35.2.249