Journal of Environmental Health Sciences (한국환경보건학회지)

Korean Society of Environmental Health (한국환경보건학회)
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Existing Population Exposure Assessment Using PM2.5 Concentration and the Geographic Information System

지리정보시스템(GIS) 및 존재인구를 이용한 초미세먼지(PM2.5) 노출평가

  • Jaemin, Woo (Department of Health and Safety, Daegu Catholic University) ;
  • Gihong, Min (Department of Health and Safety, Daegu Catholic University) ;
  • Dongjun, Kim (Department of Health and Safety, Daegu Catholic University) ;
  • Mansu, Cho (Department of Health and Safety, Daegu Catholic University) ;
  • Kyeonghwa, Sung (Center of Environmental Health Monitoring, Daegu Catholic University) ;
  • Jungil, Won (Department of Environmental and Health, Chungbuk Provincial University) ;
  • Chaekwan, Lee (Institute of Environmental and Occupational Medicine, Medical School, Inje University) ;
  • Jihun, Shin (Department of Health and Safety, Daegu Catholic University) ;
  • Wonho, Yang (Department of Health and Safety, Daegu Catholic University)
  • 우재민 (대구가톨릭대학교 보건안전학과) ;
  • 민기홍 (대구가톨릭대학교 보건안전학과) ;
  • 김동준 (대구가톨릭대학교 보건안전학과) ;
  • 조만수 (대구가톨릭대학교 보건안전학과) ;
  • 성경화 (대구가톨릭대학교 환경보건모니터링센터) ;
  • 원정일 (충북도립대학교 환경보건학과) ;
  • 이채관 (인제대학교 의과대학 환경.산업의학연구소) ;
  • 신지훈 (대구가톨릭대학교 보건안전학과) ;
  • 양원호 (대구가톨릭대학교 보건안전학과)
  • Received : 2022.11.23
  • Accepted : 2022.12.16
  • Published : 2022.12.31
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Abstract

Background: The concentration of air pollutants as measured by the Air Quality Monitoring System (AQMS) is not an accurate population exposure level since actual human activities and temporal and spatial variability need to be considered. Therefore, to increase the accuracy of exposure assessment, the population should be considered. However, it is difficult to obtain population data due to limitations such as personal information. Objectives: The existing population defined in this study is the number of people in each region's grid. The purpose is to provide a methodology for evaluating exposure to PM2.5 through existing population data provided by the National Geographic Information Institute. Methods: The selected study period was from October 26 to October 28, 2021. Using PM2.5 concentration data measured at the Sensor-based Air Monitoring Station (SAMS) installed in Guro-gu and Wonju-si, the concentration for each grid was estimated by applying inverse distance weights through QGIS version 3.22. Considering the existing population, population-weighted average concentration (PWAC) was calculated and the exposure level of the population was compared by region. Results: The outdoor PM2.5 concentration as measured through the SAMS was high in Wonju-si on all three days. Wonju-si showed an average 22% higher PWAC than Guro-gu. As a result of comparing the PWAC and outdoor PM2.5 concentration by region, the PWAC in Guro-gu was 1~2% higher than the observed value, but it was almost the same. Conversely, observations of Wonju-si were 10.1%, 11.3%, and 8.2% higher than PWAC. Conclusions: It is expected that the Geographic Information System (GIS) method and the existing population will be used to evaluate the exposure level of a population with a narrow activity radius in further research. In addition, based on this study, it is judged that research on exposure to environmental pollutants and risk assessment methods should be expanded.

Keywords

Acknowledgement

본 연구는 환경부의 재원으로 한국환경산업기술원의 환경성질환 예방관리 핵심 기술개발사업의 지원을 받아 수행되었습니다(과제번호: 2021003320008).

References

  1. EEA. Air Quality in Europe 2019. Available: https://www.eea.europa.eu/publications/air-quality-in-europe-2019 [accessed 18 November 2022].
  2. World Health Organization. 9 out of 10 People Worldwide Breathe Polluted Air, but More Countries are Taking Action. Available: https://www.who.int/news-room/detail/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action [accessed 18 November 2022].
  3. Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet. 2017; 389(10082): 1907-1918. https://doi.org/10.1016/S0140-6736(17)30505-6
  4. World Health Organization. Health Effects of Particulate Matter. Copenhagen: World Health Organization Regional Office for Europe; 2013.
  5. Kang C, Park S, Sunwoo Y, Kang B, Lee H. Respiratory health effects of fine particles (PM2.5) in Seoul. J Korean Soc Atmos Environ. 2006; 22(5): 554-563.
  6. Lee SL, Wong WH, Lau YL. Association between air pollution and asthma admission among children in Hong Kong. Clin Exp Allergy. 2006; 36(9): 1138-1146. https://doi.org/10.1111/j.1365-2222.2006.02555.x
  7. Ministry of Environment. Atmospheric Environment Standards. Available: https://www.me.go.kr/mamo/web/index.do?menuId=586 [accessed 17 November 2022].
  8. Yi S, Kim H, Kim S. Exploration and application of regulatory PM10 measurement data for developing long-term prediction models in South Korea. J Korean Soc Atmos Environ. 2016; 32(1): 114-126. https://doi.org/10.5572/KOSAE.2016.32.1.114
  9. Air Korea. About Air Korea. Available: https://www.airkorea.or.kr/web/contents/contentView/?pMENU_NO=91&cntnts_no=1 [accessed 15 November 2022].
  10. Kim J, Ghim Y, Han J, Park S, Shin H, Lee S, et al. Long-term trend analysis of Korean air quality and its implication to current air quality policy on ozone and PM10. J Korean Soc Atmos Environ. 2018; 34(1): 1-15. https://doi.org/10.5572/KOSAE.2018.34.1.001
  11. World Health Organization. Health risk assessment of air pollution: general principles. Geneva: World Health Organization; 2016.
  12. Son J, Kim Y, Cho Y, Lee J. Prediction approaches of personal exposure from ambient air pollution using spatial analysis: a pilot study using Ulsan cohort data. J Korean Soc Atmos Environ. 2009; 25(4): 339-346. https://doi.org/10.5572/KOSAE.2009.25.4.339
  13. Lin YC, Chi WJ, Lin YQ. The improvement of spatial-temporal resolution of PM2.5 estimation based on micro-air quality sensors by using data fusion technique. Environ Int. 2020; 134: 105305. https://doi.org/10.1016/j.envint.2019.105305
  14. Jeon Y, Yang W, Yu S, Lee J, Son B. Personal exposure level and health risk assessment of nitrogen dioxide in an industrial area. J Environ Health Sci. 2008; 34(3): 199-206. https://doi.org/10.5668/JEHS.2008.34.3.199
  15. Gariazzo C, Carlino G, Silibello C, Renzi M, Finardi S, Pepe N, et al. A multi-city air pollution population exposure study: combined use of chemical-transport and random-forest models with dynamic population data. Sci Total Environ. 2020; 724: 138102. https://doi.org/10.1016/j.scitotenv.2020.138102
  16. Kim K, Lee I, Kwak H, Min J. Application study of telecommunication record data in floating population estimation. Seoul Stud. 2015; 16(3): 177-187.
  17. Yoo S, Kim B. A decision-making model for adopting a cloud computing system. Sustainability. 2018; 10(8): 2952. https://doi.org/10.3390/su10082952
  18. Park J, Ryu H, Kim E, Choe Y, Heo J, Lee J, et al. Assessment of PM2.5 population exposure of a community using sensor-based air monitoring instruments and similar time-activity groups. Atmos Pollut Res. 2020; 11(11): 1971-1981.
  19. National Geographic Information Institute. National Statistical Map. Available: http://map.ngii.go.kr/ms/map/NlipMap.do [accessed 18 November 2022].
  20. Ku C, Shin Y, Lee J, Kim H. The study on the grid size regarding spatial interpolation for local climate maps. Geogr J Korea. 2012; 46(4): 489-500.
  21. Kim D, Min G, Choe Y, Shin J, Woo J, Kim D, et al. Evaluation of population exposures to PM2.5 before and after the outbreak of COVID-19. J Environ Health Sci. 2021; 47(6): 521-529. https://doi.org/10.5668/JEHS.2021.47.6.521
  22. Heo J. Advanced exposure assessment based on monitoring sensors estimation for particulate matter in population [dissertation]. [Gyeongsan]: Daegu Catholic University; 2022.
  23. Gupta P, Doraiswamy P, Levy R, Pikelnaya O, Maibach J, Feenstra B, et al. Impact of California fires on local and regional air quality: the role of a low-cost sensor network and satellite observations. GeoHealth. 2018; 2(6): 172-181. https://doi.org/10.1029/2018GH000136
  24. Liu HY, Schneider P, Haugen R, Vogt M. Performance assessment of a low-cost PM2.5 sensor for a near four-month period in Oslo, Norway. Atmosphere. 2019; 10(2): 41. https://doi.org/10.3390/atmos10020041
  25. Ahmed SO, Mazloum R, Abou-Ali H. Spatiotemporal interpolation of air pollutants in the Greater Cairo and the Delta, Egypt. Environ Res. 2018; 160: 27-34.
  26. Alimissis A, Philippopoulos K, Tzanis CG, Deligiorgi D. Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ. 2018; 191: 205-213. https://doi.org/10.1016/j.atmosenv.2018.07.058
  27. National Institute of Environmental Research. Analysis and Interpretation of the Causes of High Particulate Matter Pollution in Gangwon Province. Incheon: National Institute of Environmental Research; 2018.
  28. Cho S, Kim H, Han Y, Kim W. Characteristics of fine particles measured in two different functional areas and identification of factors enhancing their concentrations. J Korean Soc Atmos Environ. 2016; 32(1): 100-113. https://doi.org/10.5572/KOSAE.2016.32.1.100
  29. Brody M, Golub A, Potashnikov V. The effects of increasing population granularity in PM2.5 population-weighted exposure and mortality risk assessment. Environ Health Perspect. 2021; 129(12): 127703. https://doi.org/10.1289/EHP9925
  30. Singh V, Sokhi RS, Kukkonen J. An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London. Environ Pollut. 2020; 257: 113623. https://doi.org/10.1016/j.envpol.2019.113623
  31. Leung DYC. Outdoor-indoor air pollution in urban environment: challenges and opportunity. Front Environ Sci. 2015; 2(69): 1-7.
  32. Kang D, Choi D. A preliminary study to evaluate the impact of outdoor dust on indoor air by measuring indoor/outdoor particle concentration in a residential housing unit. J KIAEBS. 2015; 9(6): 452-459.