Journal of Urban Science (도시과학)

Urban Science Institute (인천대학교 도시과학연구원)
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Analyzing Impact of the Effect of Large-scale Green Space on Air Pollution in the Seoul Metropolitan Area

수도권의 대규모 녹지공간이 대기오염에 미치는 영향 분석

  • Received : 2020.11.18
  • Accepted : 2020.12.30
  • Published : 2020.12.30
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Abstract

This study aims to analyze the relations among greenbelt, air pollution empirically in order to assess the environmental effects of the greenbelt in the Seoul metropolitan area, objectively. For this purpose, this study conducts an empirical analysis of impacts of greenbelt on urban air pollution using a multiple-regression model. The major findings are summarized as follows. As a result of an empirical analysis of the impacts of greenbelt on air pollution, it is found that the characteristics of the city have impacts on air pollution concentration. It is found that the population and employment are the causes of increases in CO and NO2 concentrations, and the number of employees in the manufacturers has impacts on increases of O3 and SO2, while power plants have impacts on PM10, CO and NO2. Intersections have impacts on O3 and SO2, while the areas of the roads have impacts on CO and NO2. In addition, as for the spatial distribution of air pollutants, it is found that CO and NO2 concentrations are relatively higher in the center of the Seoul metropolitan area, while PM10, O3 and SO2 concentrations are relatively higher in the suburbs. It is found that air pollution concentration is low in greenbelt zone. In the greenbelt zone, PM10, CO and SO2 concentrations are low.

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References

  1. 권용우, 변병설, 이재준, 박지희. (2013). 그린벨트: 개발제한구역 연구, 박영사
  2. 김소정, 박혜령, 이종선. (2020), 지각된 대기오염이 부정 정서, 신체증상 및 미래 조망에 미치는 영향, 한국융합학회논문지, 11(10), pp. 287-298. https://doi.org/10.15207/JKCS.2020.11.10.287
  3. 최승배, 문승호, 강창완, 조장식, 이정형, (2008). SAS/STAT를 이용한 공간예측, 자유아카데미.
  4. 환경부(2020), 2019 대기환경연보. 국립환경과학원
  5. Aguilera, I., Sunyer, J., Fernandez-Patier, R., Hoek, G., Aguirre-Alfaro, A., Meliefste, K., Bomboi-Mingarro, M.T., Nieuwenhuijsen, M.J., Herce-Garraleta, D. & Brunekreef, B. (2008). Estimation of outdoor NOx, NO2, and BTEX Exposure in a Cohort of Pregnant Women Using Land Use Regression Modeling. Environmental Science & Technology. 42: 815-821. https://doi.org/10.1021/es0715492
  6. Amini, H., Seyed, M., Sarah, T., Henderson, B., Naddafi, K., Nabizadeh, R. & Yunesian, M. (2014). Land Use Regression Models to Estimate the Annual and Seasonal Spatial Variability of Sulfur Dioxide and Particulate Matter in Tehran. Iran Science of the Total Environment (488-489): 343-353.
  7. Bengston, D. & Youn, Y.C. (2006). Urban Containment Policies and the Protection of Natural Areas: The Case of Seoul's Greenbelt. Ecology and Society. 11(1): 3.
  8. Briggs, D., Collins, S., Elliot, P., Fischer, P., Kingham, S., Lebret, E., Pryl, K., Hv, Reeuwijk, Smallborne, K., & Avd, Veen, (1997). Mapping Urban Air Pollution Using GIS: A Regression-based Approach. International Journal of Geographical Information Science. 11: 699-718. https://doi.org/10.1080/136588197242158
  9. Cho, H.S. & Choi, M.J. (2014). Effects of Compact Urban Development on Air Pollution: Empirical Evidence from Korea. Sustainability. 6: 5968-5982. https://doi.org/10.3390/su6095968
  10. Dirgawatia, M., Barnesa, R., Wheeler, A.J., Arnolda, A.L., McCaul, K.A., Stuart, A.L., Blake, D., Hinwood, A., Yeape, B., & Heyworth, J.A. (2015). Development of Land Use Regression Models for Predicting Exposure to NO2 and NOx in Metropolitan Perth, Western Australia. Environmental Modelling & Software. 18: 1-10. https://doi.org/10.1016/S1364-8152(02)00041-5
  11. Gilbert, N. L., Goldberg, M. S., Beckerman, B., Brook, J. R., & Jerrett, M., (2005). Assessing Spatial Variability of Ambient Nitrogen Dioxide in Montreal, Canada, with a Land-Use Regression Model. Journal of the Air & Waste Management Association. 55(8): 1059-1063. https://doi.org/10.1080/10473289.2005.10464708
  12. Henderson, S., Beckerman, B., Jerrett, M., & Brauer, M., (2007). Application of Land Use Regression to Estimate Long-Term Concentrations of Trafficrelated Nitrogen Oxides and Fine Particulate Matter. Environmental Science & Technology. 41: 2422-2428. https://doi.org/10.1021/es0606780
  13. Ho, C.C., Chan, C.C., Cho, C.W,, Lin, H.I., Lee, J.H., & Wu, C.F. (2015). Land Use Regression Modeling with Vertical Distribution Measurements for Fine Particulate Matter and Elements in an Urban Area. Science of The Total Environment. 536: 150-160. https://doi.org/10.1016/j.scitotenv.2015.07.051
  14. Jerrett, M., Arain, M.A., Kanaroglou, P., Beckerman, B., Crouse, D.,Gilbert, D., Brook, J.R., Finkelstein, N., & Finkelstein, M.M., (2007). Modeling the Intraurban Variability of Ambient Traffic Pollution in Toronto, Canada. Journal of Toxicology and Environmental Health, Part A. 70: 200-212. https://doi.org/10.1080/15287390600883018
  15. Jerrett, M., Arain, M.A., Kanaroglou, P., Beckerman, B., Crouse, D.,Gilbert, D., Brook, J.R., Finkelstein, N., & Finkelstein, M.M., (2007). Modeling the Intraurban Variability of Ambient Traffic Pollution in Toronto, Canada. Journal of Toxicology and Environmental Health, Part A. 70: 200-212. https://doi.org/10.1080/15287390600883018
  16. Kerckhoffs, J., Wang, M., Meliefste, K., Malmqvist, E., Fischer, P., Janssen, N.A., Beelen, R., & Hoek, G. (2015). A National Fine Spatial Scale Land-Use Regression Model for Ozone. Environmental Research. 140: 440-448. https://doi.org/10.1016/j.envres.2015.04.014
  17. Khan, F. I., & Abbasi, S. A. (1999a). Major Accidents in Process Industries and an Analysis of their Causes and Consequences. Journal of Loss Prevention in Process Industries. 12: 361-378. https://doi.org/10.1016/S0950-4230(98)00062-X
  18. Khan, F. I., & Abbasi, S. A. (1999b). The Worst Chemical Industry Accident of 1990s - What Happened and What Might have been: A Quantitative Study. Process Safety Progress. 18: 135-145. https://doi.org/10.1002/prs.680180304
  19. Lee, J.H., Wu, C., Hoek, G., Hoogh, K., Beelen, R., Brunekreef, B. & Chan, C. (2015). LUR Models for Particulate Matters in the Taipei Metropolis with High Densities of Roads and Strong Activities of Industry, Commerce and Construction. Science of the Total Environment. (514): 178-184.
  20. Li, X., Liu, W., Chen, Z., Zeng, G., Hu, C., Leon, T., Liang, J., Huang, G., Gao, Z., Li, Z., Yan, W., He, X., Lai, M., & He, Y. (2015). The Application of Semicircular-Buffer-Based Land Use Regression Models Incorporating Wind Direction in Predicting Quarterly NO2 and PM10 Concentrations. Atmospheric Environment. 103: 18-24. https://doi.org/10.1016/j.atmosenv.2014.12.004
  21. Madsen, C., Lodrup-Carlsen, K.C., Hoek, G., Oftedal, B., Nafstad, P.,Meliefste, K., Jacobsen, R., Nystad, W., Carlsen, K.H.,& Brunekreef, B.,(2007). Modeling the Intra-Urban Variability of Outdoor Traffic Pollution in Oslo, Norway - A GA2LEN project. Atmospheric Environment. 41: 7500-7511. https://doi.org/10.1016/j.atmosenv.2007.05.039
  22. Matheron, G., (1962), Trait'e de g'ostatistique appliqu'ee, Tome I: M'moires du Bureau de Recherches G'ologiques et Mini'eres, no.14. Paris: Technip Press.
  23. Meng, X., Chen, L., Cai, J., Zou, B., Wu, C.F. & Fu, Q. (2015). A Land Use Regression Model for Estimating the NO2 Concentration in Shanghai, China. Environmental Research. 137: 308-315. https://doi.org/10.1016/j.envres.2015.01.003
  24. Montagne, D. R., Hoek, G., Klompmaker, J.O., Wang, M, Meliefste, K & Brunekreef, B. (2015). Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation. Environmental Science & Technology. 49: 8712−8720. https://doi.org/10.1021/es505791g
  25. Nowak, D.J. & Dwyer, J.F. (2007). Understanding the Benefits and Costs of Urban Forest Ecosystems. Urban and Community Forestry in the Northeast. Springer Netherlands: 25-46.
  26. OECD. (2012). Green Growth and Developing Countries: Consultation Draft. Paris: OECD.
  27. Wheeler, A., Smith, M., Xu, X., Gilberta, N., & Brook, J. (2008). Intra-Urban Variability of Air Pollution in Windsor, Ontario-Measurement and Modeling for Human Exposure Assessment. Environmental Research. 106: 7-16. https://doi.org/10.1016/j.envres.2007.09.004
  28. Yang, J., McBride, J., Zhou, J. & Sun, Z. (2005). The Urban Forest in Beijing and its Role in Air Pollution Reduction. Urban Forestry & Urban Greening. 3: 65-78. https://doi.org/10.1016/j.ufug.2004.09.001