DOI QR코드

DOI QR Code

Analysis of PM2.5 Pattern Considering Land Use Types and Meteorological Factors - Focused on Changwon National Industrial Complex -

토지이용 유형과 기상 요인을 고려한 PM2.5 발생 패턴 분석 - 창원국가산업단지를 중심으로 -

  • SONG, Bong-Geun (Institute of Industrial Technology, Changwon National University) ;
  • PARK, Kyung-Hun (School of Civil, Environmental and Chemical Engineering, Changwon National University)
  • 송봉근 (창원대학교 산업기술연구원) ;
  • 박경훈 (창원대학교 토목환경화공융합공학부)
  • Received : 2022.02.17
  • Accepted : 2022.03.16
  • Published : 2022.06.30

Abstract

This study analyzed the PM2.5 pattern by using data measured for one year from June 2020 to May 2021 by 21 low-cost sensors installed near the Changwon National Industrial Complex in Changwon, Gyeongsangnam-do. For the PM2.5 pattern, the land use types around the measuring points and meteorological factors such as air temperature and wind speed were considered. The PM2.5 concentration was high from November to March in winter, and from 1 to 9 in the morning and early in the morning by time zone. The concentration of PM2.5 was higher as it got closer to the industrial area, but the concentration was lower in the residential area and public facility area. In terms of meteorological factors, the higher the air temperature and wind speed, the lower the concentration of PM2.5. As a result of this study, it was possible to identify the PM2.5 patter near Changwon National Industrial Complex. This result will be useful data that can be used in urban and environmental planning to improve air quality including PM2.5 in urban area in the future.

본 연구는 경상남도 창원시 국가산업단지 인근에 설치된 21개의 PM2.5 간이 측정기에서2020년 6월부터 2021년 5월까지 1년 동안 측정된 자료를 활용하여 PM2.5의 발생 패턴을 분석하였다. PM2.5의 발생 패턴은 측정지점 주변의 토지이용현황과 기온 및 풍속의 기상적인 요인을 고려하였다. PM2.5 농도는 계절별로는 겨울철인 11월부터 3월까지, 시간대별로는 새벽과 이른 아침인 1시부터 9시까지가 높았다. PM2.5는 공업지역에 인접할수록 농도가 높았으나, 주거지역과 공공시설지역은 농도가 낮았다. 기상적인 요인에서는 높은 기온과 풍속일수록 PM2.5의 농도는 낮았기 때문에 기상 상태는 PM2.5의 확산에 영향을 미치는 것으로 판단된다. 본 연구의 결과는 창원국가산업단지 인근의 PM2.5 발생 패턴을 파악할 수 있었다. 이 결과는 향후 도시지역의 PM2.5를 포함한 대기질을 개선하기 위해 도시 및 환경계획에서 활용할 수 있는 유용한 자료가 될 것이다.

Keywords

Acknowledgement

이 논문은 2021년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학 협력기반 지역혁신사업(2021RIS-003)과 창원시 도시생태현황지도 제작 및 바람길 조성방안 용역의 연구비 지원으로 수행된 연구결과임

References

  1. Briggs, D.J., de Hoogh, C., Gulliver, J., Wills, .J., Elloitt, P., Kingham, S., and Smallbone, K., 2000. A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments. Science of The Total Environment 253:151-167. https://doi.org/10.1016/S0048-9697(00)00429-0
  2. Choi, T.Y., Moon, H.G., Kang, D.I., and Cha, J.G. 2018. Analysis of the seasonal concentration differences of particulate matter according to land cover of seoul. Journal of Environmental Impact Assessment 27(6):635-646. https://doi.org/10.14249/EIA.2018.27.6.635
  3. Changwon. 2021. Changwon biotope map production and wind corridor planning. Report of Changwon. pp.283-298.
  4. Dones, E., Panis, L.I., Van Poppel, M., Theunis, J., and Wets, G. 2012. Personal exposur e to black carbon in transport microenviron ments. Atmospheric Environment 55:392-398. https://doi.org/10.1016/j.atmosenv.2012.03.020
  5. Gao, M., Cao, J., and Seto, E. 2015. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi'an, China. Environmental Pollution 199:56-65. https://doi.org/10.1016/j.envpol.2015.01.013
  6. Guo, L., Luo, J., Yuan, M., Huang, Y., Shen, H., Li, T. 2019. The influence of urban planning factors on PM2.5 pollution exposure and implications: A case study in China based on remote sensing, LES, and GIS data. Science of the Total Environment 659:1585-1596. https://doi.org/10.1016/j.scitotenv.2018.12.448
  7. Harper, A., Baker, P.N., Xia, Y., Kuang, T., Zhang, H., Chen, Y., Han, T-L., and Gulliver, J. 2021. Development of spatiotemporal land use regression models for PM2.5 and NO2 in Chongqing, China, and exposure assessment for the CLIMB study. Atmospheric Pollution Research 12:101096. https://doi.org/10.1016/j.apr.2021.101096
  8. Ikram, J., Tahir, A., Kazmi, H., Khan, Z., Javed R., and Masood, U., 2012. View: implementing low cost air quality monitoring solution for urban areas. Environmental Systems Research 1:10. https://doi.org/10.1186/2193-2697-1-10
  9. Jeon, B.I. 2010. Characteristics of Spatiotemporal 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
  10. 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
  11. Jerrett, M., Arain, M., Kanaroglou, P. Beckerman, B., Crouse, D., Gilbeert, N., Brook, J.R., Finkelstein, N., and Finkelstein, M.M. 2007. Modeling the intraurban variability of ambient traffic pollution in Toronto, Canada. Journal of Toxicology and Environmental Health A 70:200-212. https://doi.org/10.1080/15287390600883018
  12. Kim, H., Park, C., and Jung, S. 2016. Analysis on effective range of temperature observation network for evaluating urban thermal environment. Korea Institute of Ecological Architecture and Environment 16(6):69-75.
  13. Kim, H., Park, J.J., and Kwark, J. 2019. Development of construction materials and application technologies for particulate matter reduction using photocatalytic materials. Journal of the Korean Society of Civil Engineers 67(8):87-89.
  14. Lee, T.S., Song, B.G., Han, C.B., and Park, K.H. 2011. Analysis of the GIS-based water cycle system for effective rainwater manage ment 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
  15. Lin, Y., Yuan, X., Zhai, T., and Wang, J. 2020. Effects of land-use patterns on PM2.5 in China's developed coastal region: exploration and solutions. Science of the Total Environment 703:135602. https://doi.org/10.1016/j.scitotenv.2019.135602
  16. Liu, C., Henderson, B.H., Wang, D., Yang, X., and Peng, Z. 2016. A land use regression application into assessing spatial variation of intra-urban fine particulate matter(PM2.5) and nitrogen dioxide(NO2) concentrations in City of Shanghai, China. Science of The Total Environment 565:607-615. https://doi.org/10.1016/j.scitotenv.2016.03.189
  17. Liu, Z., Xie, M., Tian, K., and Gao, P. 2017. GIS-based analysis for population exposure to PM2.5 air pollution-A case study of Beijing. Journal of Environmental Sciences 59:48-53. https://doi.org/10.1016/j.jes.2017.02.013
  18. Lu, D., Mao, W., Yang, D., Zhao, J., and Xu, J. 2018. Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China. Atmospheric Pollution Research 9: 705-713. https://doi.org/10.1016/j.apr.2018.01.012
  19. Massey, D.D., Kulshrestha, A., and Taneja, A., 2013. Particulate matter concentrations and their related metal toxicity in rural residential environment of semi-arid region of India. Atmospheric Environment 67: 278-286. https://doi.org/10.1016/j.atmosenv.2012.11.002
  20. Mead, M.I., Popoola, O.A.M., Stewart, G.B., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J.J., McLeod, M.W., Hodgson, T.F., Dicks, J., Lewis, A., Cohen, J., Baron, R., Saffell, J.R., and Jones, R.L. 2013. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmospheric Environment 70: 186203.
  21. Mun, H.S., Song, B.G., Seo, K.H., Kim, T.H., and Park, K.H. 2020. Analysis of PM2.5 distribution contribution using GIS spatial interpolation. Journal of the Korean Association of Geographic Information Studies 23(2):1-20. https://doi.org/10.11108/KAGIS.2020.23.2.001
  22. National Institute of Environmental Research. 2018. Low-cost PM2.5 Sensor Guidebook. Report of National Institute of Environmental Research. 1-39.
  23. Nowak, D.J., Hirabayashi, S., Bodine, A., Greenfield, E., 2014. Tree and forest effects on air quality and human health in the United States. Environmental Pollution 193:119-129. https://doi.org/10.1016/j.envpol.2014.05.028
  24. Shi, W., Wong, M.S., Wang, J., and Zhao, Y. 2012. Analysis of airborne particulate matter(PM2.5) over Hong Kong using remote sensing and GIS. Sensors 12:6825-6836. https://doi.org/10.3390/s120606825
  25. Singh, V., Sokhi, R.S., and Kukkonen, J., 2014. PM2.5 concentrations in London for 2008 -A modeling analysis of contributions from road traffic. Journal of the Air & Waste Management Association 64(5): 509-518. https://doi.org/10.1080/10962247.2013.848244
  26. Singh, P. and Verma, P. 2019. Chapter 5-A Comparative study of spatial interpolation technique(IDW and Kriging) for determining groundwater quality. GIS and Geostatistical Techniques for Groundwater Science. 43-56.
  27. Song, B. and Park, K. 2019. Analysis of spatiotemporal urban temperature characteristics by urban spatial patterns in Changwon. City, South Korea. Sustainability 11:3777. https://doi.org/10.3390/su11143777
  28. Song, B.G. and Park, K.H. 2013. Air ventilation evaluation at nighttime for the construction of wind corridor in urban area. Journal of the Korean of Geographic Information Studies 16(2):16-29.
  29. Souan, S., Koehler, K., Thomas, G., Park, J.H., Hillman, M., Halterman, A., and Peters, T.M. 2016. Inter-comparison of low-cost sensors for measuring the mass concentration of occupational aerosols. Aerosol Science and Technology 50(5):462-473. https://doi.org/10.1080/02786826.2016.1162901
  30. Taheri, A., Aliasghari, P., and Hosseini, V. 2019. Black carbon and PM2.5 monitoring campaign on the roadside and residential urban background sites in the city of Tehran. Atmospheric Environment 218:116928. https://doi.org/10.1016/j.atmosenv.2019.116928
  31. Tian, X., Dai, H., Geng, Y., Wilson, J., Wu, R., Xie, Y., and Hao, H. 2018. Economic impacts from PM2.5 pollution-related health effects in China's road transport sector: A provincial -level analysis. Environment International 115:220-229. https://doi.org/10.1016/j.envint.2018.03.030
  32. Xia, T., Nitschlke, M., Zhang, Y., Shah, P., Grabb, S., and Hansen, A. 2015. Traffic-related air pollution and health co-benefits of alternative transport in Adelaide, South Australia. Environment International 74: 281-290. https://doi.org/10.1016/j.envint.2014.10.004
  33. Xu, J., Bechle, M.J., Wang, M., Szpiro, A.A, Vedal, S., Bai, Y.Q., and Marchall, J.D. 2019. National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging, Science of The Total Environment 655: 423-433. https://doi.org/10.1016/j.scitotenv.2018.11.125
  34. Xu, G., Ren, X., Xiong, K., Li, L., Bi, X., and Wu, Q. 2020. Analysis of the driving factors fo PM2.5 concentration in the air: A case study of the Yangze River Delta, China. Ecological Indicators 110:105889. https://doi.org/10.1016/j.ecolind.2019.105889
  35. Xu, J., Jia, C., Yu, H., Xu, H., Ji, D., Wang, C., Xiao, H., and He, J. 2021. Characteristics, sources, and health risks of PM2.5-bound trace elements in representative areas of Northern Zhejiang Province, China. Chemosphere 272:129632. https://doi.org/10.1016/j.chemosphere.2021.129632