DOI QR코드

DOI QR Code

스마트 그린인프라 기술을 활용한 도로변 미세먼지 저감장치의 성능 및 유지·관리 비용 평가

Evaluation of Performance and Maintenance Cost for Roadside's Particulate Matter Reduction Devices Using Smart Green Infrastructure Technology

  • 송규성 (고려대학교 대학원 환경생태공학과) ;
  • 석영선 (고려대학교 대학원 환경생태공학과) ;
  • 임효숙 (서울과학종합대학원대학교) ;
  • 전진형 (고려대학교 환경생태공학부)
  • Song, Kyu-Sung (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Seok, Young-Sun (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Yim, Hyo-Sook (Seoul School of Integrated Sciences and Technologies) ;
  • Chon, Jin-Hyung (Division of Environmental Science and Ecological Engineering, Korea University)
  • 투고 : 2022.05.12
  • 심사 : 2022.08.02
  • 발행 : 2022.08.30

초록

The Green Purification Unit System (GPUS) is a green infrastructure facility applicable to the roadside to reduce particulate matter from road traffic. This study introduces two types of GPUS (type1 and type2) and assesses the performance and maintenance costs of each of them. The GPUS's performance analysis used the data collected in November 2021 after the installation of the GPUS type1 and type2 at the study site in Suwon. The changes in the particulate matter concentration near the GPUS were measured. The maintenance cost of GPUS type1 and type2 was assessed by calculating the initial installation cost and the management and repair cost after installation. The results of the performance analysis showed that the GPUS type1, which was manufactured by combining plants and electric dust collectors, had a superior particulate matter reduction performance. In particular, type1 produced a greater effect of particulate matter reduction in the time with a high concentration (50㎍/m3 or higher) of particulate matter due to the operation of electric dust collectors. GPUS type2, which was designed in the form of a plant wall without applying an electric dust collector, showed lower reduction performance than type1 but showed sufficiently improved performance compared to the existing band green area. Meanwhile, the GPUS type1 had three times higher costs for the initial installation than GPUS type2. In terms of costs for managing and repairing, it was evaluated that type1 would be slightly more costly than type2. Finally, this study discussed the applicability of two types of GPUS based on the result of the analysis of their particulate matter performance and maintenance cost at the same time. Since GPUS type2 has a cheaper cost than type1, it could be more economical. However, in the area suffering a high concentration of particulate matter, GPUS type1 would be more effective than type2. Therefore, the choice of GPUS types should rely on the status of particulate matter concentration in the area where GPUS is being installed.

키워드

과제정보

본 결과물은 환경부의 재원으로 한국환경산업기술원의 도시 생태계 건강성 증진 사업의 지원을 받아 연구되었습니다(2020002770002).

참고문헌

  1. Abhijith KV. Kumar P. Gallagher J. McNabola A. Baldauf R. Pilla F. Broderick B. Di Sabatino S. and Pulvirenti B. 2017. Air pollution abatement performances of green infrastructure in open road and built up street canyon environments (A review). Atmospheric Environment. 162: 71-86. https://doi.org/10.1016/j.atmosenv.2017.05.014
  2. Ahn R. and Hong S. 2021. Characteristics of Particulate Matter 2.5 by Type of Space of Urban Park - Focusing on the Song sang hyeon Plaza in Busan- . Journal of the Korean Institute of Landscape Architecture. 49(6): 37-48. (in Korean) https://doi.org/10.9715/KILA.2021.49.6.037
  3. Baldauf, R. 2017. Roadside vegetation design characteristics that can improve local, near road air quality. Transportation Research Part D: Transport and Environment. 52: 354-361. https://doi.org/10.1016/j.trd.2017.03.013
  4. Choi SI. An JG. and JO YM. 2018a, Review of Analysis Principle of Fine Dust. KIC News. 21(2): 16-23. (in Korean)
  5. Choi TY. Moon HG. Kang DI. and Cha JG. 2018b. Analysis of the Seasonal Concentration Differences of Particulate Matter According to Land Cover of Seoul - Focusing on Forest and Urbanized Area -. J. Environ. Impact Assess. 27(6): 635-346. (in Korean)
  6. Di Q. Dai L. Wang Y. Zanobetti A. Choirat C. Schwartz JD. and Dominici F. 2017. Association of short term exposure to air pollution with mortality in older adults. JAMA. 318(24): 2446-2456. https://doi.org/10.1001/jama.2017.17923
  7. Eisenman TS. Churkina G. Jariwala SP. Kumar P. Lovasi GS. Pataki DE. and Whitlow TH. 2019. Urban trees, air quality, and asthma: An interdisciplinary review. Landscape and urban planning. 187: 47-59. https://doi.org/10.1016/j.landurbplan.2019.02.010
  8. Han Y. Lee J. Haiping G. Kim KH. Wanxi P. Bhardwaj N. Oh JM. and Brown R JC. 2022. Plant-based remediation of air pollution(A review). Journal of Environmental Management. 301(2): 113860.
  9. Huang HL. Chuang YH. Lin TH. Lin C. Chen YH. Hung JY. and Chan TC. 2021. Ambient cumulative PM2.5 exposure and the risk of lung cancer incidence and mortality: A retrospective cohort study. International Journal of Environmental Research and Public Health. 18(23): 12400.
  10. Hwang KI. Han BH. Kwark JI. and Park SC. 2018. A Study on Decreasing Effects of Ultra-fine Particles (PM2.5) by Structures in a Roadside Buffer Green - A Buffer Green in Songpa-gu, Seoul -. Journal of the Korean Institute of Landscape Architecture. 46(4): 61-75. (In Korean) https://doi.org/10.9715/KILA.2018.46.4.061
  11. Janhall S. 2015. Review on urban vegetation and particle air pollution-Deposition and dispersion. Atmospheric En vironment. 105: 130-137. https://doi.org/10.1016/j.atmosenv.2015.01.052
  12. Jeong MS, and Lim HJ. 2019. A Study of Evaluating Streetscape Green Environments to Improve Urban Street Green Spaces - A Case Study of Jeonju City -. Journal of the Korean Society of Environmental Restoration Technology. 22(3): 55-71. (In Korean)
  13. Kim B. Yoon EJ. Kim S. and Lee DK. 2020. The Effects of Risk Perceptions Related to Particulate Matter on Outdoor Activity Satisfaction in South Korea. International Journal of Environmental Research and Public Health. 17(5): 1613.
  14. Kim JS. and Lee DK. 2014. Cost-Benefit Analysis for Planting Type of Street Trees. Journal of the Korean Society of Environmental Restoration Technology. 17(6): 9-37. (In Korean)
  15. Kim MK. 2021. Analysis of the Priority of Evaluation Criteria and Detailed Index for Selecting Street Trees. Journal of the Korean Institute of Landscape Architecture. 49(1): 42-53. https://doi.org/10.9715/KILA.2021.49.1.042
  16. Kim SW. Lee DK. and Bae CY, 2021. Analysis of the effect of street green structure on PM2.5 in the walk space. Journal of the Korean Society of Environmental Restoration Technology. 24(4): 61-75. (In Korean) https://doi.org/10.13087/KOSERT.2021.24.4.61
  17. Kim WJ. Woo SY. Yoon CR. and Kwak MJ. 2018. Evaluation on the Reduction Effect of Particulate matter through Green Infrastructure and Its Expansion Plans; Seoul Institute: Seoul, Korea. (In Korean)
  18. Korea Environment Institute. 2019, A Study on the Development of Integrated Fine Dust Management Strategy. 9-12. (In Korean)
  19. Korea Institute of Civil Engineering and Building Technology. 2018. Development of Proof Technique for Particulate Matters Reduction in Urban Roadside. Research report to Korea Institute of Construction Technology. (In Korean)
  20. Korea Institute of Civil Engineering and Building Technology. 2021. Construction Work Standard Item. Research report to Korea Institute of Construction Technology. (In Korean)
  21. Lee HY. and Kim NJ. 2017. The Impact of Fine Particular Matter Risk Perception on the Outdoor Behavior of Recreationists: An Application of the Extended Theory of Planned Behavior. The Tourism Sciences Society of Korea. 41(7): 27-44. (In Korean)
  22. Ministry Of Environment, NIER. 2019. Air Pollution Measurement Network Installation/Operation Guidelines. 31: 128. (In Korean)
  23. Morakinyo TE. and Lam YF. 2016. Simulation study of dispersion and removal of particulate matter from traffic by road-side vegetation barrier. Environmental Science and Pollution Research. 23: 6709-6722. https://doi.org/10.1007/s11356-015-5839-y
  24. Nowak DJ. Crane DE. and Stevens JC. 2006. Air pollution removal by urban trees and shrubs in the United States. Urban Forestry & Urban Greening. 4(3-4): 115-123. https://doi.org/10.1016/j.ufug.2006.01.007
  25. Pugh MTA. Mackenzie AR. Whyatt JD. and Hewitt CN. 2012. Effectiveness of green infrastructure for improvement of air quality in urban street canyons. Environmental Science & Technology. 46: 7692-7699. https://doi.org/10.1021/es300826w
  26. Related Ministries Joint. 2019. Comprehensive plan on fine dust management. Report to Related Ministries Joint. (In Korean)
  27. Salmond JA. Tadaki M. Vardoulakis S. Arbuthnott K. Coutts A. Demuzere M. Dirks KN. and Wheeler BW. 2016. Health and climate related ecosystem services provided by street trees in the urban environment. Environmental Health. 15(1): 95-111. https://doi.org/10.1186/s12940-016-0178-0
  28. Seok Y. Song K. Han H. and Lee J. 2021. Derivation of Green Infrastructure Planning Factors for Reducing Particulate Matter - Using Text Mining -. Journal of the Korean Institute of Landscape Architecture. 49(5): 79-96. (In Korean) https://doi.org/10.9715/KILA.2021.49.5.079
  29. Seok Y. Yim H. Moon T. and Chon J. 2022. Street Tree Planning to Improve Public Health and Ecosystem Resilience in Urban Areas: A Scenario Analysis Using a System Dynamics Model. International Journal of Environmental Research and Public Health. 19(3): 1625.
  30. Song KS. Han HS. Park JH. and Lee HK. 2015. Comparative Study on Leakage Area and Fine Dust(PM10, PM2.5) Removal Efficiency of The Bio-Purification System. Korean Society for Environmental Technology. 16(3): 233-238. (In Korean)
  31. Tong Z. Baldauf RW. Isakov V. Deshmukh P. and Max Zhang K. 2016. Roadside vegetation barrier designs to mitigate near-road air pollution impacts. Science of The Total Environment. 541: 920-927. https://doi.org/10.1016/j.scitotenv.2015.09.067
  32. https://air.gg.go.kr/default/esData.do?mCode=A010010000
  33. https://www.data.go.kr/data/15080202/fileData.do
  34. https://www.ilyoweekly.co.kr/news/newsview.php?ncode=1065574484689465