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Using Auxiliary Data for Air Pollutant Spatial Interpolation

Using Auxiliary Data for Air Pollutant Spatial Interpolation. Liyun Xie May 6, 2004. Background and Rationale. Air pollutants (fine particles, heavy metal, ozone, etc.) are harmful to human health Low resolution of monitoring data

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Using Auxiliary Data for Air Pollutant Spatial Interpolation

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  1. Using Auxiliary Data for Air Pollutant Spatial Interpolation Liyun Xie May 6, 2004

  2. Background and Rationale • Air pollutants (fine particles, heavy metal, ozone, etc.) are harmful to human health • Low resolution of monitoring data • Possible relationship between air pollutant data and other data • PM2.5 mass concentration and visibility • Lead concentration and emission • Using spatial data analysis tool to interpolate/ extrapolate the air pollutant map

  3. Data • Fine particles • Daily mean from June 24, 2003 to Jun 28, 2003 • Eastern states in US • PM2.5 Concentration (µg/m3): EPA AIR database • Visibility (light extinction coeff, Mm-1): National Weather Service • Lead • Annual mean in 2000 • 4 states: MO, IL, ID, OH • Concentration (µg/m3): EPA TRI database • Emission (lbs/y): EPA AIR database

  4. Methods and Tools • Interpolation methods • IDW • Kirging • CoKirging • Tools • Correlation: Excel 2003 • Variogram: VarioWin 2.21 • Developed by Yvan Pannatier, 1996 • Interpolation: ESRI ArcGis 8.3

  5. Data Flow Download Pollutant Data Download Auxiliary Data Analyze spatial distribution Analyze correlation Decide model and parameters IDW Interpolation Kirging Interpolation CoKirging Interpolation Compare methods

  6. Results - Lead • Correlation and Variogram

  7. Results – PM 2.5 • Monitoring Location

  8. Results – PM 2.5 (Cont.) • Correlation

  9. Results – PM 2.5 (Cont.) • Variogram (6/26/03) • PM 2.5 • Visibility

  10. Results – PM 2.5 (Cont.) • Interpolation Methods Comparison • Surface estimation maps

  11. Results – PM 2.5 (Cont.) • Interpolation Methods Comparison • Estimation difference maps

  12. Results – PM 2.5 (Cont.) • Interpolation Methods Comparison • RMSE

  13. Results – PM 2.5 (Cont.) • Temporal trends

  14. Summary • Lead • Not suitable for spatial interpolation • PM 2.5 • Kirging and CoKirging are better than IDW • Comparing to Kirging, CoKiring doesn’t improve interpolation

  15. Recommendations • More monitoring locations for air pollutants • Weather conditions • Transportation modeling • Improve software for spatial data analysis

  16. Acknowledgements • Dr. Falke • Dr. Turner

  17. Thank You! Any Questions

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