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

The Korean Association of Geographic Information Studies (한국지리정보학회)
권호 표지
DOI QR코드

DOI QR Code

Spatial Distribution of Urban Heat and Pollution Islands using Remote Sensing and Private Automated Meteorological Observation System Data -Focused on Busan Metropolitan City, Korea-

위성영상과 민간자동관측시스템 자료를 활용한 도시열섬과 도시오염섬의 공간 분포 특성 - 부산광역시를 대상으로 -

  • HWANG, Hee-Soo (Dept. of Urban Planning and Engineering, Pusan National University) ;
  • KANG, Jung Eun (Dept. of Urban Planning and Engineering, Pusan National University)
  • 황희수 (부산대학교 도시공학과) ;
  • 강정은 (부산대학교 도시공학과)
  • Received : 2020.07.30
  • Accepted : 2020.08.24
  • Published : 2020.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

During recent years, the heat environment and particulate matter (PM10) have become serious environmental problems, as increases in heat waves due to rising global temperature interact with weakening atmospheric wind speeds. There exist urban heat islands and urban pollution islands with higher temperatures and air pollution concentrations than other areas. However, few studies have examined these issues together because of a lack of micro-scale data, which can be constructed from spatial data. Today, with the help of satellite images and big data collected by private telecommunication companies, detailed spatial distribution analyses are possible. Therefore, this study aimed to examine the spatial distribution patterns of urban heat islands and urban pollution islands within Busan Metropolitan City and to compare the distributions of the two phenomena. In this study, the land surface temperature of Landsat 8 satellite images, air temperature and particulate matter concentration data derived from a private automated meteorological observation system were gridded in 30m × 30m units, and spatial analysis was performed. Analysis showed that simultaneous zones of urban heat islands and urban pollution islands included some vulnerable residential areas and industrial areas. The political migration areas such as Seo-dong and Bansong-dong, representative vulnerable residential areas in Busan, were included in the co-occurring areas. The areas have a high density of buildings and poor ventilation, most of whose residents are vulnerable to heat waves and air pollution; thus, these areas must be considered first when establishing related policies. In the industrial areas included in the co-occurring areas, concrete or asphalt concrete-based impervious surfaces accounted for an absolute majority, and not only was the proportion of vegetation insufficient, there was also considerable vehicular traffic. A hot-spot analysis examining the reliability of the analysis confirmed that more than 99.96% of the regions corresponded to hot-spot areas at a 99% confidence level.

최근 온도상승으로 인한 폭염 증가와 대기 풍속의 약화가 상호작용하면서 열환경과 미세먼지(PM10)가 문제가 되고 있다. 도시지역 내에서 다른 지역들보다 온도와 대기오염 농도가 높은 도시열섬과 도시오염섬 현상이 나타나고 있음이 알려져 있으나, 공간데이터로 구축 가능한 미세 자료의 부족 등으로 이를 함께 살펴본 연구는 많지 않았다. 최근 위성영상과 민간통신업체의 인프라에서 측정한 빅데이터들이 생산되면서 온도와 대기오염에 대한 세밀한 공간분포 분석이 가능하게 되었다. 이에 본 연구는 부산광역시를 대상으로 도시열섬과 도시오염섬의 공간적 분포패턴을 살펴보고 두 현상의 분포 특성을 비교 분석하고자 하였다. 연구에는 Landsat 8 위성영상의 지표면온도와 민간자동관측시스템에서 도출된 대기온도, 미세먼지농도 데이터를 30m*30m 단위로 격자화하여 공간분석을 수행하였다. 분석 결과, 도시열섬과 도시오염섬이 동시에 발생하는 대표적인 지역들로 취약 주거지역과 공업지역들이 다수 포함되어 있었다. 부산시의 대표적 주거 취약지역으로 알려진 서동, 반송동 등의 주요 정책이주지가 포함되었는데 해당 지역은 소규모 필지에 건축물의 밀도가 상당히 높은 지역으로 통풍, 환기 등에 문제가 많은 주거지역이다. 이러한 지역의 주민 중 상당수는 폭염과 대기오염에 대한 대응 능력이 낮아 관련 정책 수립 시 우선적으로 이 지역들이 고려될 필요가 있다. 도시열섬과 도시오염섬의 동시발생지역에 포함된 공업지역들은 콘크리트나아스콘 기반의 불투수면의 비중이 높고, 식생이 부족할 뿐 아니라 교통량도 많은 것으로 나타났다. 도시열섬과 도시오염섬 분석에 대한 신뢰성을 살펴보기 위해 핫스팟분석을 진행한 결과, 99.96% 이상의 지역이 99% 신뢰수준의 핫스팟지역에 해당함을 확인할 수 있었다.

Keywords

References

  1. An, S.M., Kim, S.J. and Lee, H.C. 2016. A study on the urban area microclimate management direction. Korea Research Institute for Human Settlements. pp.1-105.
  2. Bae, H.J. 2014. Effects of short-term exposure to $PM_{10}$ and $PM_{2.5}$ on mortality in Seoul. Journal or Environmental Health Sciences 40(5):346-354.
  3. Cho, S.H., Kim, H.W., Han Y.J. and Kim W.J. 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
  4. Crutzen, P.J. 2004. New directions: the growing urban heat and pollution"island" effect-impact on chemistry and climate. Atmospheric Environment 38(21):3539-3540. https://doi.org/10.1016/j.atmosenv.2004.03.032
  5. Fahmy, M. and Sharples, S. 2009. On the development of an urban passive thermal comfort system in Cairo, Egypt. Building and Environment 44(9):1907-1916. https://doi.org/10.1016/j.buildenv.2009.01.010
  6. Feizizadeh, B. and Blaschke, T. 2013. Examining urban heat island relations to land use and air pollution: multiple endmember spectral mixture analysis for thermal remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(3):1749-1756. https://doi.org/10.1109/JSTARS.2013.2263425
  7. Han, J.S., Lee, S.M., Chung, Y.M., Park, M.C. and Kim, S.H. 2019. Research on the reduction of PM and GHG from ground freight transportation. Korea Environment Institute. pp.1-216.
  8. Hardin, A.W., Liu, Y., Cao, G. and Vanos, J.K. 2018. Urban heat island intensity and spatial variability by synoptic weather type in the northeast U.S.. Urban Climate 24:747-762. https://doi.org/10.1016/j.uclim.2017.09.001
  9. He, G.-X., Yu, C.W.F., Lu, C. and Deng, Q.-H. 2013. The influence of synoptic pattern and atmospheric boundary layer on PM10 and urban heat island. Indoor and Built Environment 22(5):796-807. https://doi.org/10.1177/1420326X13503576
  10. Hoffmann, P., Krueger, O. and Schlunzen, K.H. 2012. A statistical model for the urban heat island and its application to a climate change scenario. International Journal of Climatology 32:1238-1248. https://doi.org/10.1002/joc.2348
  11. Honjo, T. 2019. Analysis of urban heat island movement and intensity in Tokyo metropolitan area by AMeDAS data. Journal of Agricultural Meteorology 75(2):84-91. https://doi.org/10.2480/agrmet.D-18-00026
  12. Huang, Q., Huang, J., Yang, X., Fang, C. and Liang, Y. 2019. Quantifying the seasonal contribution of coupling urban land use types on urban heat island using land contribution index: a case study in Wuhan, China. Sustainable Cities and Society 44:666-675. https://doi.org/10.1016/j.scs.2018.10.016
  13. Hwang, K.S., Lee, S.A., Kim, H.J. and Kim, J.L. 2015. Analysis of sex, age and seasonal mortality according to diurnal temperature range(DTR). Korean Journal of Scientific Criminal Investigation 9(1):30-37.
  14. Hwang, S.Y., Moon, J.Y. and Kim, J.J. 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.
  15. Jang, A.S. 2014. Impact of particulate matter on health. Journal of the Korean Medical Association 57(9):763-768. https://doi.org/10.5124/jkma.2014.57.9.763
  16. Jang, Y.K. and Kim, J.W. 1991. A study on the relation of urban heat island and air pollution in Seoul area. Journal of Korean Society for Atmospheric Environment 7(1):49-53.
  17. Je, M.H. and Jung, S.H. 2018. Urban heat island intensity analysis by landuse types. Journal of the Korea Contents Association 18(11):1-12. https://doi.org/10.5392/JKCA.2018.18.11.001
  18. Jo, S.K. and Park, H.Y. 2013. A study on the spatial structure in the housing complex of the political migration through the'Power Theory'based on the Banyeo 1 district in the city of Busan. Journal of the Regional Association of Architectural Institute of Korea 15(2):73-82.
  19. Khamis, N., Sin, T.C. and Hock, G.C. 2018. Segmentation of residential customer load profile in peninsular Malaysia using jenks natural breaks. 2018 IEEE 7th International Conference on Power and Energy(PECon). Kuala Lumpur, Malaysia, Malaysia, Dec. 3-4, 2018. pp.128-131.
  20. Kim, H.S., Jang, E.S., Kim, S.H., Yoo, J.H. and Lee, S.W. 2011. A study on sasang constitutional classification methods based on ROC-curve using the personality score. Korea Journal of Oriental Medicine 17(2):107-113.
  21. Kim, J.H. and Yoon, Y.H. 2011. Effect of thermal environment by green roof and land cover change in detached housing area. Journal of Environmental Policy 10(1):27-47. https://doi.org/10.17330/joep.10.1.201103.27
  22. Kim, J.S. and Kang, J.E. 2018. Effects of compact spatial characteristics on the urban thermal environment. Journal of the Urban Design Institute of Korea Urban Design 19(1):21-36.
  23. Kim, J.S. and Kim, H.Y. 2020. Analysis on the characteristics of heat wave vulnerable areas using landsat 8 data and vulnerability assessment analysis. Journal of the Korean Association of Geographic Information Studies, 23(1):1-14.
  24. Kim, O.J., Kim, S.Y., Kwon, H.Y. and Kim, H. 2017. Data issues suggestions in the national health insurance servicenational sample cohort for assessing the long-term health effects of air pollution focusing on mortality. Journal of Health Informatics and Statistics 42(1):89-99. https://doi.org/10.21032/jhis.2017.42.1.89
  25. Kim, W.S. 2016. Policy options to manage high-pollution on-road diesel vehicles based on excessive emission grades in Seoul. The Seoul Institute. pp.1-27.
  26. Kim, Y.H. and Baik, J.J. 2005.Spatial and temporal structure of the urban heat island in Seoul. Journal of Applied Meteorology 44(5):591-605. https://doi.org/10.1175/JAM2226.1
  27. Kim, Y.J., Kang, D.H. and Ahn, K.H. 2011. Characteristics of urban heat-island phenomena caused by climate changes in Seoul, and alternative urban design approaches for their improvements. Journal of the Urban Design Institute of Korea Urban Design 12(3):5-14.
  28. Kim, Y.K. 2017. An analysis of traffic flows and land-use on urban air pollution concentrations using geographic information system. Journal of Transport Research 24(2):67-81. https://doi.org/10.34143/jtr.2017.24.2.67
  29. Kwon, Y.M., Byun, J.H. and Kang, N.W. 2019. A study on the alternative selection of eco-friendly modification techniques for small diesel trucks. Journal of Korean Society of Transportation 37(2):135-147. https://doi.org/10.7470/jkst.2019.37.2.135
  30. Kyunghyang Shinmun. 2018. Port city Busan air pollution is more serious than Seoul an d Daegu. http://news.khan.co.kr/kh_news/khan_art_view.html?art_id=201808272055015(Accessed July 2020).
  31. Lai, L.-W. and Cheng, W.-L. 2009. Air quality influenced by urban heat island coupled with synoptic weather patterns. Science of the Total Environment 407:2724-2733. https://doi.org/10.1016/j.scitotenv.2008.12.002
  32. Lee, C.Y., Kim, S.M. and Choi, Y.S. 2019. Application of hot spot analysis for interpreting soil heavy-metal concentration data in abandoned mines. Journal of the Korean Association of Geographic Information Studies 22(2):24-35. https://doi.org/10.11108/KAGIS.2019.22.2.024
  33. Lee, J.H., Ryu, J.E., Choi, Y.Y., Chung, H.I., Jeon, S.W., Lim, J.H. and Choi, H.S. 2019. Spatial estimation of forest species diversity index by applying spatial interpolation method - based on 1st forest health management data-. Journal of the Korean Society of Environmental Restoration Technology 22(4):1-14.
  34. Lee, K.I., Ryu, J.E., Jeon, S.W., Jung, H.C. and Kang, J.Y. 2017. Analysis of the effect of heat island on the administrative district unit in Seoul using LANDSAT image. Korean Journal of Remote Sensing 33(5_3):821-834. https://doi.org/10.7780/kjrs.2017.33.5.3.6
  35. Lee, S.H. and Kang, J.E. 2019. Risk assessment of heavy snowfall considering climate change: focusing on damage to roads and buildings. Journal or the Korean Society of Hazard Mitigation, 19(2):57-68. https://doi.org/10.9798/KOSHAM.2019.19.2.57
  36. Lee, S.H., Kang, J.E., Park, C.S., Yoon, D.K. and Yoon, S.Y. 2020. Multi-risk assessment of heat waves under intensifying climate change using Bayesian Networks. International Journal of Disaster Risk Reduction 50:101704. https://doi.org/10.1016/j.ijdrr.2020.101704
  37. Li, H., Meier, F., Lee, X., Chakraborty, T., Liu, J., Schaap, M. and Sodoudi, S. 2018. Interaction between urban heat island and urban pollution island during summer in Berlin. Science of the Total Environment 636:818-828. https://doi.org/10.1016/j.scitotenv.2018.04.254
  38. Li, H., Zhou, Y., Wang, X., Zhou, X., Zhang, H. and Sodoudi, S. 2019. Quantifying urban heat island intensity and its physical mechanism using WRF/UCM. Science of the Total Environment 650:3110-3119. https://doi.org/10.1016/j.scitotenv.2018.10.025
  39. Miles, V. and Esau, I. 2017. Seasonal and spatial characteristics of urban heat islands(UHIs) in northern west siberian cities. Remote Sensing 9(10):989. https://doi.org/10.3390/rs9100989
  40. Myeong, S.J. 2009. A study on strategies to mitigate urban heat island effects as part of climate change adaptation in urban areas. Korea Environment Institute. pp.1-32.
  41. Nam Goung, S.J., Choi, K.Y., Hong, H.J., Yoon, D.K., Kim, Y.S., Park, S.H., Kim, Y.K. and Lee, C.M. 2019. Study on the selection and application of a spatial analysis model appropriate for selecting the radon priority management target area. J Environ Health Sci 45(1):82-96.
  42. Park, C.Y., Lee, D.K., Sung, S.Y., Park, J.H. and Jeong, S.G. 2016. Analyzing the diurnal and spatial variation of surface urban heat island intensity distribution - focused on 30 cities in Korea-. Journal of Korea Planning Association 51(1):125-136. https://doi.org/10.17208/jkpa.2016.02.51.1.125
  43. Plocoste, T., Jacoby-Koaly, S., Molinie, J. and Petit, R.H. 2014. Evidence of the effect of an urban heat island on air quality near a landfill. Urban Climate 10:745-757. https://doi.org/10.1016/j.uclim.2014.03.007
  44. Ryu, Y.H., Baik, J.J. and Lee, S.H. 2013. Effects of anthropogenic heat on ozone air quality in a megacity. Atmospheric Environment 80:20-30. https://doi.org/10.1016/j.atmosenv.2013.07.053
  45. Shirani-bidabadi, N., Nasrabadi, T., Faryadi, S., Larijani A. and Shadman Roodposhti, M. 2019. Evaluating the spatial distribution and the intensity of urban heat island using remote sensing, Case study of Isfahan city in Iran. Sustainable Cities and Society 45:686-692. https://doi.org/10.1016/j.scs.2018.12.005
  46. Steeneveld, G.-J., Klompmaker, J.O., Groen, R.J.A. and Holtslag, A.A.M. 2018. An urban climate assessment and management tool for combined heat and air quality judgements at neighbourhood scales. Resources, Conservation and Recycling 132:204-217. https://doi.org/10.1016/j.resconrec.2016.12.002
  47. Suh, M.S., Hong, S.K. and Kang, J.H. 2009. Characteristics of seasonal mean diurnal temperature range and their causes over South Korea. Atmosphere 19(2):155-168.
  48. Swamy, G., Shiva Nagendra, S.M. and Schlink, U. 2017. Urban heat island(UHI) influence on secondary pollutant formation in a tropical humid environment. Journal of the Air & Waste Management Association 67(10):1080-1091. https://doi.org/10.1080/10962247.2017.1325417
  49. Taha, H. 2017. Characterization of urban heat and exacerbation: development of a heat island index for California. Climate 5(3):59. https://doi.org/10.3390/cli5030059
  50. Woo, K.S., Kim, D.E. and Chae, S.M. 2019. High temperature-related mortality in Korea: a meta-analysis of the empirical evidence. Health and Social Welfare Review 39(2):10-36. https://doi.org/10.15709/hswr.2019.39.2.10
  51. Yi, C.Y., Kim, K.R., An, S.M. and Choi, Y.J. 2014. Impact of the local surface characteristics and the distance from the center of heat island to suburban areas on the night temperature distribution over the Seoul metropolitan area. Journal of the Korean Association of Geographic Information Studies 17(1): 35-49. https://doi.org/10.11108/kagis.2014.17.1.035
  52. Yim, J.S. and Lee, G.H. 2017. Estimating urban temperature by combining remote sensing data and terrain based spatial interpolation method. Journal of the Korean Cartographic Association 17(2):75-88. https://doi.org/10.16879/jkca.2017.17.2.075
  53. Yoshikado, H. and Tsuchida, M. 1996. High levels of winter air pollution under the influence of the urban heat island along the shore of Tokyo bay. Journal of Applied Meteorology 35(10):1804-1813. https://doi.org/10.1175/1520-0450(1996)035<1804:HLOWAP>2.0.CO;2