Ozone Exposure Assessment by Population Characteristics: A Case Study for High Ozone Days in Busan

인구특성을 고려한 노출평가: 부산지역 고농도 오존일 사례연구

  • Received : 2014.01.14
  • Accepted : 2015.04.21
  • Published : 2015.04.28


Objectives: Photochemical ozone pollution is associated with increased mortality risk. This study aims to assess the population exposure to ozone according to population characteristics for high ozone days in the Busan metropolitan region (BMR). Methods: The ozone exposure assessment in this study was performed using the WRF-CMAQ simulated ozone concentrations and the population data in the BMR. The settled and daytime population and their activity were considered to conduct the static and dynamic ozone exposure assessment. Results: Applying a static exposure assessment, in case that high ozone occurred throughout Busan area, the highest exposure levels were evaluated in urban neighborhoods. In case of ozone pollution in outer Busan, because sensitive groups have been relatively higher exposure, this case was also evaluated as part of that should not be overlooked. The dynamic exposure was higher than static exposure because the number of population exposed to ozone of high concentration is increased. This approach is important in a regard consider that daytime population distribution when high ozone occur. Conclusion: This study shows the different population exposure according to various ozone distributions for each episode day. Considering demographic characteristic such as population density and activity should be important to understanding the population exposure assessment when ozone pollution occurs.


activity;exposure;numerical air quality modeling;ozone;population


  1. Moon N, Kim S, Seo J. Sensibility Study for PBL Scheme of WRF-CMAQ. Journal of Korean Society for Atmospheric Environment. 2011; 27(6): 791-804.
  2. Kim J, Kim J, Hong J, Jung D, Ban S, Lee Y. Assessment of changed input module with SMOKE Model. Journal of Korean Society for Atmospheric Environment. 2008; 24(3): 284-299.
  3. Oh IB, Kim YK, Kim CH. An observational and numerical study of the effects of the late sea breeze on ozone distributions in the Busan metropolitan area, Korea. Atmospheric Environment. 2006; 40(7): 1284-1298.
  4. Otte, TL, Pouliot G, Pleim JE, Young JO, Schere KL, Wong DC, et al. Linking the Eta model with the Community Multiscale Air Quality (CMAQ) modeling system to build a national air quality forecasting system. Weather and Forecasting. 2005; 20(3): 367-384.
  5. Taha H. Urban surface modification as a potential ozone air-quality improvement strategy in California: a mesoscale modelling study. Boundary-layer meteorology. 2008; 127(2): 219-239.
  6. Bae H. Effects of short-term exposure to PM10 and PM2.5 on Mortality in Seoul. J. Envion Health Sci. 2014; 40(5): 346-354.
  7. Oh IB, Lee JH, Sim CS, Kim Y, Yoo CI. An Association between air pollution and the prevalence of allergic rhinitis in the Ulsan Metropolitan Region. J. Envion Health Sci. 2010; 36(6): 465-471.
  8. Lee W, Hwang M-K, Kim Y-K. Health Vulnerability Assessment for PM10 in Busan. J Environ Health Sci. 2014; 40(5): 355-366.
  9. Son JY, Kim Y-S, Cho Y-S, Lee J-T. Prediction Approaches of Personal Exposure from Ambient Air Pollution Using Spatial Analysis: A Pilot Study Using Ulsan Cohort Data. Journal of Korean Society for Atmospheric Environment. 2009; 25(4): 339-346.
  10. Bell ML, McDermott A, Zeger SL, Samet JM Dominici F. Ozone and short-term mortality in 95 US urban communities, 1987-2000. Jama. 2004; 292(19): 2372-2378.
  11. Ito, K., De Leon SF, Lippmann M. Associations between ozone and daily mortality: analysis and meta-analysis. Epidemiology. 2005; 16(4): 446-457.
  12. Kang JE, Bang JH, Oh IB, Kim YK. Estimation and Variation of an Exposed Population of a Vulnerable Group to High Ozone Episodes. Journal of Environmental Science International. 2014; 23(4): 697-705.
  13. Furtaw, EJ. An overview of human exposure modeling activities at the USEPA's National Exposure Research Laboratory. Toxicology and industrial health. 2001; 17(5-10): 302-314.
  14. Georgopoulos PG, Wang SW, Vyas VM, Sun Q, Burke J, Vedantham R, et al. A source-to-dose assessment of population exposures to fine PM and ozone in Philadelphia, PA, during a summer 1999 episode. Journal of Exposure Science and Environmental Epidemiology. 2005; 15(5): 439-457.
  15. Baklanov A, Hanninen O, SlOrdal LH, Kukkonen J, Bjergene N, Fay B, et al. Integrated systems for forecasting urban meteorology, air pollution and population exposure. Atmospheric Chemistry and Physics. 2007; 7(3): 855-874.
  16. Beckx C, Int Panis L, Arentze T, Janssens D, Torfs R, Broekx S, Wets G. A dynamic activity-based population modelling approach to evaluate exposure to air pollution: methods and application to a Dutch urban area. Environmental Impact Assessment Review. 2009; 29(3): 179-185.
  17. Beckx C, Int Panis L, Uljee I, Arentze T, Janssens D, Wets G. Disaggregation of nation-wide dynamic population exposure estimates in The Netherlands: Applications of activity-based transport models. Atmospheric Environment. 2009; 43(34): 5454-5462.
  18. Kousa A, Kukkonen J, Karppinen A, Aarnio P, Koskentalo T. A model for evaluating the population exposure to ambient air pollution in an urban area. Atmospheric Environment. 2002; 36(13): 2109-2119.
  19. Skamarock WC, Klemp J, Dudhia J, Gill DO, Barker DM, Wang W, et al. A description of the Advanced Research WRF version 3. NCAR Technical Note, NCAR/TN-468+STR. Mesoscale and Micro scale Meteorology Division, National Center for Atmospheric Research, Boulder, Colorado, USA; 2008.
  20. Byun DW, Ching JKS. Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, EPA Report N.EPA-600/R-99/030, Office of Research and Development, US Environmental Protection Agency, Washington, DC; 1999.
  21. McCoy, J., Johnston, K., Environmental systems research institute. Using ArcGIS spatial analyst: GIS by ESRI. Environmental Systems Research Institute; 2001.
  22. Zhang Q, Streets DG, Carmichael GR, He KB, Huo H, Kannari A, et al. Asian emissions in 2006 for the NASA INTEX-B mission. Atmospheric Chemistry and Physics. 2009; 9(14): 5131-5153.
  23. Hogrefe C, Rao ST, Kasibhatla P, Hao W, Sistla G, Mathur R, et al. Evaluating the performance of regional-scale photochemical modeling systems: Part II-Ozone predictions. Atmospheric Environment. 2001; 35(24): 4175-4188.
  24. National Institute of environmental research. Studies on the optimization method for improving the accuracy of air quality modeling, NIER-SP2013-210; 2014.
  25. Oh IB, Kim YK, Hwang M-K. Effects of late seabreeze on ozone distributions in the coastal urban area. Journal of Korean Society for Atmospheric Environment. 2004; 20(3): 345-360.
  26. Ding A, Wang T, Zhao M, Wang T, Li Z. Simulation of sea-land breezes and a discussion of their implications on the transport of air pollution during a multi-day ozone episode in the Pearl River Delta of China. Atmospheric Environment. 2004; 38(39): 6737-6750.
  27. Hwang, MK, Kim YK, Oh IB, Lee HW, and Kim CH. Identification and interpretation of representative ozone distributions in association with the sea breeze from different synoptic winds over the coastal urban area in Korea. Journal of the Air & Waste Management Association. 2007; 57(12): 1480-1488.
  28. Byun MR, Seo US. How to Measure Daytime Population in Urban Streets: Case of Seoul Pedestrian Flow Survey. The Korean Association for Survey Research. 2011; 12(2): 27-50.
  29. Han JS, Kim HG. A Study on GIS Methods for Estimating "Index for Populat ion Generator" Based on socio-economic factor. Korea Spatial Information Society, Conference on Geospatial Information. 2009; 262-264.

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