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A data-integrated simulation model to forecast ground-level ozone concentration

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Abstract

Elevated ground-level ozone is hazardous to people’s health and destructive to the environment. This research develops a novel data-integrated simulation to forecast ground-level ozone (SIMGO) concentration based on a real data set collected from seven monitoring sites in the Dallas-Fort Worth area between January 1, 2005 and December 31, 2007. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for building the simulation. Classification and Regression Trees (CART), data mining tools for prediction and classification, were used to develop two tree structures in order to forecast ground-level ozone based on factors such as solar radiation and outdoor temperature. Kernel density estimation is used to estimate continuous distributions for the ground-level ozone concentration for seven days in advance. One week forecasts obtained from SIMGO for different months of a year is presented.

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Authors and Affiliations

  1. Olin Business School, Washington University in St. Louis, Saint Louis, MO, 63130, USA

    Durai Sundaramoorthi

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  1. Durai Sundaramoorthi
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Correspondence to Durai Sundaramoorthi.

Appendix

Appendix

  1. 1.

    Ground-level ozone (GO)

  2. 2.

    Previous day’s ground-level ozone (PD_GO)

  3. 3.

    Previous year’s ground-level ozone on the same day (PY_GO)

  4. 4.

    Previous day’s nitric oxide level (PD_NOX)

  5. 5.

    Previous year’s nitric oxide level on the same day (PY_NOX)

  6. 6.

    Previous day’s solar radiation (PD_SR)

  7. 7.

    Previous year’s solar radiation on the same day (PY_SR)

  8. 8.

    Previous day’s outdoor temperature (PD_OT)

  9. 9.

    Previous year’s outdoor temperature on the same day (PY_OT)

  10. 10.

    Previous day’s wind speed (PD_WS)

  11. 11.

    Previous year’s wind speed on the same day (PY_WS)

  12. 12.

    Previous day’s maximum wind gust (PD_MWG)

  13. 13.

    Previous year’s maximum wind gust on the same day (PY_MWG)

  14. 14.

    Previous day’s resultant wind speed (PD_RWS)

  15. 15.

    Previous year’s resultant wind speed on the same day (PY_RWS)

  16. 16.

    Previous day’s resultant wind direction (PD_RWD)

  17. 17.

    Previous year’s resultant wind direction on the same day (PY_RWD)

  18. 18.

    Previous day’s standard deviation of horizontal wind direction (PD_HWD)

  19. 19.

    Previous year’s standard deviation of horizontal wind direction on the same day (PY_HWD)

  20. 20.

    Day of the week (D)

  21. 21.

    Month of the year (M)

  22. 22.

    Data collection location (L)

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Sundaramoorthi, D. A data-integrated simulation model to forecast ground-level ozone concentration. Ann Oper Res 216, 53–69 (2014). https://doi.org/10.1007/s10479-012-1163-9

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  • DOI: https://doi.org/10.1007/s10479-012-1163-9

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