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

Estimation of Representative Area-Level Concentrations of Particulate Matter(PM10) in Seoul, Korea

미세먼지(PM10)의 지역적 대푯값 산정 방법에 관한 연구 - 서울특별시를 대상으로

  • SONG, In-Sang (Department of Geography, Seoul National University) ;
  • KIM, Sun-Young (Institute of Health and Environment, Seoul National University)
  • Received : 2016.09.27
  • Accepted : 2016.11.22
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Many epidemiological studies, relying on administrative air pollution monitoring data, have reported the association between particulate matter ($PM_{10}$) air pollution and human health. These monitoring data were collected at a limited number of fixed sites, whereas government-generated health data are aggregated at the area level. To link these two data types for assessing health effects, it is necessary to estimate area-level concentrations of $PM_{10}$. In this study, we estimated district (Gu)-level $PM_{10}$ concentrations using a previously developed pointwise exposure prediction model for $PM_{10}$ and three types of point locations in Seoul, Korea. These points included 16,230 centroids of the largest census output residential areas, 422 community service centers, and 610 centroids on the 1km grid. After creating three types of points, we predicted $PM_{10}$ annual average concentrations at all locations and calculated Gu averages of predicted $PM_{10}$ concentrations as representative Gu-estimates. Then, we compared estimates to each other and to measurements. Prediction-based Gu-level estimates showed higher correlations with measurement-based estimates as prediction locations became more population representative ($R^2=0.06-0.59$). Among the three estimates, grid-based estimates gave lowest correlations compared to the other two(0.35-0.47). This study provides an approach for estimating area-level air pollution concentrations and assesses air pollution health effects using national-scale administrative health data.

미세먼지($PM_{10}$)의 건강영향에 대한 많은 연구들은 정부의 대기오염 측정자료를 이용해서 악영향을 보고했다. 정부 대기오염 측정자료가 제한된 수의 측정소에서 생산되는 반면, 사망률이나 유병률과 같은 정부생산 건강결과 자료는 지역별로 집계되어 공개된다. 따라서 정부에서 생산하는 건강통계자료를 이용해서 건강영향을 분석하기 위해서는, $PM_{10}$ 농도의 지역적인 대푯값을 산출할 필요가 있다. 본 연구에서는 서울특별시를 대상으로 이전 연구에서 개발된 점 사상에 대한 $PM_{10}$ 농도 예측 모형을 이용하여 구별 대푯값을 산정하였다. 이를 위해, 세 가지 종류의 위치들을 대상으로 지점들을 생성한 후, 그 지점들에 예측한 $PM_{10}$ 농도의 구별 평균으로 구별 대푯값을 구했다. 세 가지 위치는 16,230개 집계구 내 가장 넓은 주거지역의 중심점, 424개 동 주민센터, 610개 1km 격자의 중심점이었다. 위치별 구별 대푯값들을 비교하기 위하여 측정치와의 관련성 및 추정치 간 관련성을 탐색하였다. 측정치와의 비교 결과, 측정치와 세 가지 구별 대푯값 추정치들 간의 관련성은 위치의 인구 대표성이 높아짐에 따라 향상되었고($R^2=0.06-0.59$), 상호비교에서는 격자 중심점을 이용한 추정치가 다른 추정치들과의 관련성이 상대적으로 낮았다(0.35-0.47). 본 연구는 $PM_{10}$의 지역별 평균 농도를 추정함으로써 향후 정부 통계에 기반한 전국 규모의 지역 단위 건강영향분석 연구에 기여할 수 있을 것으로 기대된다.

Keywords

References

  1. Atkinson, R. W., S. Kang, H. R. Anderson, I. C. Mills, and H. A. Walton. 2014. Epidemiological time series studies of $PM_{2.5}$ and daily mortality and hospital admissions: a systematic review and meta-analysis. Thorax 69(7):660-665. https://doi.org/10.1136/thoraxjnl-2013-204492
  2. Brindley, P., S. M. Wise, R. Maheswaran, and R. P. Haining. 2005. The effect of alternative representations of population location on the areal interpolation of air pollution exposure. Computers, Environment and Urban Systems 29(4):455-469. https://doi.org/10.1016/j.compenvurbsys.2004.01.001
  3. Brook, R. D., S. Rajagopalan, C. A. Pope 3rd, J. R. Brook, A. Bhatnagar, A. V. Diez-Roux, F. Holguin, Y. Hong, R.V. Luepker, M.A. Mittleman, A. Peters, D. Siscovick, S.C. Smith, L. Whitsel, and J.D. Kaufman. 2010. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation 121(21):2331-2378. https://doi.org/10.1161/CIR.0b013e3181dbece1
  4. Cressie, N. A. C. 1993. Statistics for spatial data(revised edition). John Wiley & Sons, New York, U.S. p.900.
  5. Eum, Y.S., I.S. Song, H.C. Kim, J.H. Leem, and S.Y. Kim. 2015. Computation of geographic variables for air pollution prediction models in South Korea. Environmental Health Toxicology 30(e2015010). https://doi.org/10.5620/eht.e2015010
  6. Hao, Y., H. Flowers, M. M Monti, and J. R Qualters. 2012. U.S. census unit population exposures to ambient air pollutants. International Journal of Health Geographics 11(3):1-9. https://doi.org/10.1186/1476-072X-11-1
  7. Hengl, T. 2007. A practical guide to geostatistical mapping of environmental variables. JRC Scientific and Technical Reports. European Commission, Luxembourg. p.157.
  8. Jeong, J.C. 2014. A spatial distribution and time series change of $PM_{10}$ in Seoul City. Journal of the Korean Association of Geographic Information Studies 17(1):61-69 (정종철. 2014. 서울시 PM10 공간분포 분석과 시계열 변화. 한국지리정보학회지 17(1):61-69). https://doi.org/10.11108/kagis.2014.17.1.061
  9. Kang, Y.O., E.J. Yoon, and J.H. Jung. 2007. A study on the delineation of census output areas in Korea. Journal of the Korean Urban Geographical Society 10(1):15-36 (강영옥, 윤은주, 정재희. 2007. 소지역 통계구역 획정방안 연구. 한국도시지리학회지 10(1):15-36).
  10. Keller, J.P, C. Olives, S.Y. Kim, L. Sheppard, P. D. Sampson, A. A. Szpiro, A.P. Oron, J. LindstrOm, S. Vedal, and J. D. Kaufman. 2015. A unified spatiotemporal modeling approach for prediction of multiple air pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Environmental Health Perspectives 123(4):301-309. https://doi.org/10.1289/ehp.1408145
  11. Kim, S.Y. 2016. National exposure prediction approach for $PM_{10}\;and\;NO_2$ in South Korea. Proceedings of the Conference of International Society for Environmental Epidemiology and International Society of Exposure Science - Asia Chapter 2016. Sapporo, Japan, June 26-29, 2016. p.93.
  12. Kim, S.Y., L. Sheppard, S. Bergen, A.A. Szpiro, P.D. Sampson, J.D. Kaufman, and S. Vedal. 2016. Prediction of fine particulate matter chemical components for the Multi-Ethnic Study of Atherosclerosis Cohort: a comparison of two modeling approaches. Journal of Exposure Science and Environmental Epidemiology 26(5):520-528. https://doi.org/10.1038/jes.2016.29
  13. Mercer, L.D., A.A. Szpiro, L. Sheppard, J. Lindstrom, S.D. Adar, R.W. Allen, E.L. Avol, A.P. Oron, T.Larson, L.-J.S. Liu, and J.D. Kaufman. 2011. Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen($NO_x$) for the Multi -Ethnic Study of Atherosclerosis and Air Pollution(MESA Air). Atmospheric Environment 45(26):4412-4420. https://doi.org/10.1016/j.atmosenv.2011.05.043
  14. Min, K.D., Y.S. Eum, H.J. Kwon, and S.Y. Kim. 2015. Community air pollution monitoring design for epidemiological application in Seoul. Proceedings of the International Conference of the Korean Society of Environmental Health and Toxicology. Jeju, Korea, June 17, 2015. p.105.
  15. Ministry of Environment. 2011. Guidelines for installation and management of national air quality monitoring networks. p.409 (환경부. 2011. 대기오염측정망 설치 운영지침. 409쪽).
  16. Ministry of Environment. 2016. http://egis.me.go.kr. Accessed October 31, 2016. (환경부. 2016. 환경지리정보시스템 웹페이지).
  17. Montgomery, D.C., E.A. Peck, and G. G. Vining. 2001. Introduction to linear regression analysis (third edition). John Wiley & Sons, New York, U.S. p.310, 534, 535, 642.
  18. Parenteau, M.P. and M. C. Sawada. 2012. The role of spatial representation in the development of a LUR model for Ottawa, Canada. Air Quality, Atmosphere and Health 5(3):311-323. https://doi.org/10.1007/s11869-010-0094-3
  19. Park, H.J., H.S. Shin, Y.H. Roh, K.M. Kim, and K.H. Park. 2012. Estimating forest carbon stocks in Danyang using kriging methods for aboveground biomass. Journal of the Korean Association of Geographic Information Studies 15(1):16-33 (박현주, 신휴석, 노영희, 김경민, 박기호. 2012. 크리깅 기법을 이용한 단양군의 산림 탄소저장량 추정-지상부 바이오매스를 중심으로-. 한국지리정보학회지 15(1):16-33). https://doi.org/10.11108/kagis.2012.15.1.016
  20. Pope III, C. A. and D.W. Dockery. 2006. Health effects of fine particulate air pollution : lines that connect. Journal of the Air & Waste Management 56(6):709-742. https://doi.org/10.1080/10473289.2006.10464485
  21. Sampson, P.D., M. Richards, A.A. Szpiro, S. Bergen, L. Sheppard, T.V. Larson, and J.D. Kaufman. 2013. A regionalized national universal kriging model using Partial Least Squares regression for estimating annual $PM_{2.5}$ concentrations in epidemiology. Atmospheric Environment 75:383-392. https://doi.org/10.1016/j.atmosenv.2013.04.015
  22. Stehman, S.V. and W. S. Overton. 1996. Spatial sampling. Practical Handbook of Spatial Statistics. CRC Press, Boca Raton, USA. pp.31-64.
  23. Wackernagel, H. 2003. Multivariate geostatistics : an introduction with applications. Springer-Verlag, Berlin, Germany. p.387.
  24. Yi, S.J., H. Kim, and S.Y. Kim. 2016. Exploration and application of regulatory $PM_{10}$ measurement data for developing long-term prediction models in South Korea. Journal of Korean Society for Atmospheric Environment 32(1):114-126 (이선주, 김호, 김선영. 2016. $PM_{10}$ 장기노출 예측모형 개발을 위한 국가 대기오염측정 자료의 탐색과 활용. 한국대기환경학회지 32(1): 114-126). https://doi.org/10.5572/KOSAE.2016.32.1.114
  25. Yoo, C.J. and K.W. Nam. 2014. Spatio-temporal distribution analysis of oneperson household -the case of Busan City-. Journal of the Korean Association of Geographic Information Studies 17(2):59-71 (유창주, 남광우. 2014. 1인가구의 시공간적 분포 분석 -부산시를 사례로-. 한국지리정보학회지 17(2):59-71). https://doi.org/10.11108/kagis.2014.17.2.059

Cited by

  1. Data Issues and Suggestions in the National Health Insurance Service-National Sample Cohort for Assessing the Long-term Health Effects of Air Pollution Focusing on Mortality vol.42, pp.1, 2017, https://doi.org/10.21032/jhis.2017.42.1.89
  2. Association between Long-Term Exposure to Particulate Matter Air Pollution and Mortality in a South Korean National Cohort: Comparison across Different Exposure Assessment Approaches vol.14, pp.10, 2017, https://doi.org/10.3390/ijerph14101103
  3. An Approach to Estimating National-scale Annual-average Concentrations of PM2.5 before 2015 when National Air Quality Monitoring Data are Available in South Korea vol.34, pp.6, 2018, https://doi.org/10.5572/KOSAE.2018.34.6.806
  4. Web-Based Visualization of Scientific Research Findings: National-Scale Distribution of Air Pollution in South Korea vol.17, pp.7, 2020, https://doi.org/10.3390/ijerph17072230
  5. Incident cardiovascular disease and particulate matter air pollution in South Korea using a population-based and nationwide cohort of 0.2 million adults vol.19, pp.1, 2020, https://doi.org/10.1186/s12940-020-00671-1
  6. Association between ambient air pollution and high-risk pregnancy: A 2015–2018 national population-based cohort study in Korea vol.197, pp.None, 2016, https://doi.org/10.1016/j.envres.2021.110965
  7. 경기도 지역 미세먼지 관리를 위한 권역 범주화 연구 vol.30, pp.4, 2021, https://doi.org/10.14249/eia.2021.30.4.237