DOI QR코드

DOI QR Code

Assessing Factors Linked with Ozone Exceedances in Seoul, Korea through a Decision Tree Algorithm

  • Park, Sun-Kyoung (Hanyang Cyber University)
  • Received : 2016.01.18
  • Accepted : 2016.02.15
  • Published : 2016.02.29

Abstract

Since prolonged exposure to elevated ozone ($O_3$) concentrations is known to be harmful to human health, appropriate control strategies for ozone are needed for the non-attainment area such as Seoul, Korea. The goal of this research is to assess factors linked with the 1-hour ozone exceedance through a decision tree model. Since ozone is a secondary pollutant, lag times between ozone and explanatory variables for ozone formation are taken into account in the model to improve the accuracy of the simulation. Results show that while ozone concentrations of the previous day and $NO_2$ concentrations in the morning are major drivers for ozone exceedances in the early afternoon, meteorology plays more important role for ozone exceedances in the late afternoon. Results also show that a selection of lag times between ozone and explanatory variables affect the accuracy of predicting 1-hour ozone exceedances. The result analyzed in this study can be used for developing control strategies of ozone in Seoul, Korea.

Keywords

Ozone;Exceedance;Decision tree algorithm;$NO_2$;Control strategy

Acknowledgement

Supported by : Hanyang Cyber University

References

  1. Sousa, S. I. V., Martins, F., Alvim-Ferraz, M., Pereira, M., 2007, Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Env. Model. Softw., 22(1), 97-103. https://doi.org/10.1016/j.envsoft.2005.12.002
  2. Wang, X. P., Mauzerall, D., 2004, Characterizing distributions of surface ozone and its impact on grain production in China, Japan and South Korea: 1990 and 2020, Atmos. Env., 38(26), 4383-4402. https://doi.org/10.1016/j.atmosenv.2004.03.067
  3. Lee, D., Lee, Y., Jang, K., Yoo, C., Kang, K., Lee, J., Jung, S., Park, J., Lee, S., Han, J., Hong, J., Lee, S., 2011, Korean National Emissions Inventory System and 2007 Air Pollutant Emissions, Asian J. Atmos. Env., 5(4), 278-291. https://doi.org/10.5572/ajae.2011.5.4.278
  4. Marmur, A., Park, S., Mulholland, J., Tolbert, P., Russell, A. G., 2006, Source apportionment of $PM_{2.5}$ in the southeastern United States using receptor and emissions-based models: Conceptual differences and implications for time-series health studies, Atmos. Env., 40(14), 2533-2551. https://doi.org/10.1016/j.atmosenv.2005.12.019
  5. Moon, S. S., Kang, S., Jitpitaklert, W., Kim, S., 2012, Decision tree models for characterizing smoking patterns of older adults, Exp. Syst. App., 39(1), 445-451. https://doi.org/10.1016/j.eswa.2011.07.035
  6. Pandey, S. K., Kim, K., Chung, S., Cho, S., Kim, M., Shon, Z., 2008, Long-term study of NOx behavior at urban roadside and background locations in Seoul, Korea, Atmos. Env., 42(4), 607-622. https://doi.org/10.1016/j.atmosenv.2007.10.015
  7. Park, S. K., Cobb, C., Wade, K., Mulholland, J., Hu, Y., Russell, A. G., 2006a, Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations, Atmos. Env., 40, S563-S573. https://doi.org/10.1016/j.atmosenv.2005.11.078
  8. Park, S. K., Marmur, A., Kim, S., Tian, D., Hu, Y., McMurry, P., Russell, A. G., 2006b, Evaluation of fine particle number concentrations in CMAQ, Aeros. Sci. Tech., 40(11), 985-996. https://doi.org/10.1080/02786820600907353
  9. Park, S. K., Marmur, A., Russell, A. G., 2013, Environ-mental Risk Assessment: Comparison of Receptor and Air Quality Models for Source Apportionment, Human& Eco. Risk Assess., 19(5), 1385-1403. https://doi.org/10.1080/10807039.2012.730475
  10. Park, S. K., Russell, A. G., 2013, Regional adjustment of emission strengths via four dimensional data assimilation, Asia-Pacific J. Atmos. Sci., 49(3), 361-374. https://doi.org/10.1007/s13143-013-0034-x
  11. Qian, W., Kang, H., Lee, D., 2002, Distribution of seasonal rainfall in the East Asian monsoon region, Theor. App. Climat., 73(3-4), 151-168. https://doi.org/10.1007/s00704-002-0679-3
  12. Richter, A., Burrows, J., Nuss, H., Granier, C., Niemeier, U., 2005, Increase in tropospheric nitrogen dioxide over China observed from space, Nature, 437(7055), 129-132. https://doi.org/10.1038/nature04092
  13. Rohli, R. V., Hsu, S., Blanchard, B., Fontenot, R., 2003, Short-range prediction of tropospheric ozone concentrations and exceedances for Baton Rouge, Louisiana, Weather & Forecast., 18(2), 371-383. https://doi.org/10.1175/1520-0434(2003)018<0371:SPOTOC>2.0.CO;2
  14. Schlink, U., Dorling, S., Pelikan, E., Nunnari, G., Cawley, G., Junninen, H., Greig, A., Foxall, R., Eben, K., Chatterton, T., Vondracek, J., Richter, M., Dostal, M., Bertucco, L., Kolehmainen, M., Doyle, M., 2003, A rigorous inter-comparison of ground-level ozone predictions, Atmos. Env., 37(23), 3237-3253. https://doi.org/10.1016/S1352-2310(03)00330-3
  15. Kim, N. K., Kim, Y., Morino, Y., Kurokawa, J., Ohara, T., 2013, Verification of NOx emission inventory over South Korea using sectoral activity data and satellite observation of $NO_2$ vertical column densities, Atmos. Env., 77, 496-508. https://doi.org/10.1016/j.atmosenv.2013.05.042
  16. Kim, S. W., Yoon, S., Won, J., Choi, S., 2007, Ground-based remote sensing measurements of aerosol and ozone in an urban area: A case study of mixing height evolution and its effect on ground-level ozone concentrations, Atmos. Env., 41(33), 7069-7081. https://doi.org/10.1016/j.atmosenv.2007.04.063
  17. Abdul-Wahab, S. A., Bakheit, C. S., Al-Alawi, S. M., 2005, Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations, Env. Model. Softw., 20(10), 1263-1271. https://doi.org/10.1016/j.envsoft.2004.09.001
  18. Bauer, G., Deistler, M., Scherrer, W., 2001, Time series models for short term forecasting of ozone in the eastern part of Austria, Environmetrics, 12(2), 117-130. https://doi.org/10.1002/1099-095X(200103)12:2<117::AID-ENV448>3.0.CO;2-N
  19. Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J., 1984, Classification and Regression Trees, Wadsworth International Group, Belmont CA, U. S. A.
  20. Burrows, W. R., Benjamin, M., Beauchamp, S., Lord, E. R., McCollor, D., Thomson, B., 1995, CART Decision-tree statistical-analysis and prediction of summer season maximum surface ozone for the Vancouver, Montreal, and Atlantic regions of Canada, J. App. Met., 34(8), 1848-1862. https://doi.org/10.1175/1520-0450(1995)034<1848:CDTSAA>2.0.CO;2
  21. Chu, H. J., Lin, C. Y., Liau, C. J., Kuo, Y. M., 2012, Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree, Atmos. Env., 60, 142-152. https://doi.org/10.1016/j.atmosenv.2012.06.032
  22. Gardner, M. W., Dorling, S. R., 2000, Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmos. Env., 34(1), 21-34. https://doi.org/10.1016/S1352-2310(99)00359-3
  23. Han, S. Q., Bian, H., Feng, Y. C., Liu, A., Li, X., Zeng, F., Zhang, X., 2011, Analysis of the Relationship between $O_3,\;NO\;and\;NO_2$ in Tianjin, China, Aeros. Air Quality Res., 11(2), 128-139. https://doi.org/10.4209/aaqr.2010.07.0055
  24. He, Y., Uno, I., Wang, X., Ohara, T., Sugirnoto, N., Shimizu, A., Richter, A., Burrows, J., 2007, Variations of the increasing trend of tropospheric $NO_2$ over central east China during the past decade, Atmos. Env., 41(23), 4865-4876. https://doi.org/10.1016/j.atmosenv.2007.02.009
  25. Heo, J. S., Kim, D., 2004, A new method of ozone forecasting using fuzzy expert and neural network systems, Sci. Tot. Env., 325(1-3), 221-237. https://doi.org/10.1016/j.scitotenv.2003.11.009
  26. Jorquera, H., Perez, R., Cipriano, A., Espejo, A., Letelier, M., Acuna, G., 1998, Forecasting ozone daily maximum levels at Santiago, Chile, Atmos. Env., 32(20), 3415-3424. https://doi.org/10.1016/S1352-2310(98)00035-1
  27. Kim, K. H., Choi, Y., Kim, M., 2005, The exceedance patterns of air quality criteria: a case study of ozone and nitrogen dioxide in Seoul, Korea between 1990 and 2000, Chemosphere, 60(4), 441-452. https://doi.org/10.1016/j.chemosphere.2004.12.067

Cited by

  1. Assessing the impact of air pollution on mortality rate from cardiovascular disease in Seoul, Korea vol.23, pp.4, 2018, https://doi.org/10.4491/eer.2018.063
  2. Improving Environmental Sustainability by Characterizing Spatial and Temporal Concentrations of Ozone vol.10, pp.12, 2018, https://doi.org/10.3390/su10124551