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Mapping PM 2.5 – Methods and Uncertainties

Presented by: Tami H. Funk Timothy S. Dye Alan C. Chan Craig B. Andersen Bryan M. Penfold Sonoma Technology, Inc. Petaluma, CA U.S. EPA National Air Quality Conference: It’s Not Just About Ozone Anymore February 2-5, 2003 San Antonio, TX Mapping PM 2.5 – Methods and Uncertainties

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Mapping PM 2.5 – Methods and Uncertainties

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  1. Presented by: Tami H. Funk Timothy S. Dye Alan C. Chan Craig B. Andersen Bryan M. Penfold Sonoma Technology, Inc. Petaluma, CA U.S. EPA National Air Quality Conference: It’s Not Just About Ozone Anymore February 2-5, 2003 San Antonio, TX Mapping PM2.5 – Methods and Uncertainties 902090-2324

  2. Overview • Mapping issues • Uncertainty analysis of common mapping methods • A method to improve PM2.5 mapping • Understanding PM2.5 characteristics can improve mapping • Alternative displays of PM2.5 data

  3. Continuous PM2.5 Monitors Continuous Ozone Monitors Mapping Issues (1 of 2) • Limited number of continuous PM2.5 monitors in the U.S.

  4. Mapping Issues (2 of 2) • PM2.5 is a regional and local pollutant • PM2.5 characteristics vary from region to region (e.g. urban vs. rural) • Varying terrain and seasonal characteristics also affect consistency of mapping

  5. 0.06 4.5 5.2 8.6 5.5 3.7 Base Map 1.0 8.0 PM2.5 Data Points Interpolation Uncertainty (µg/m3) 6.0 PM2.5 Contours PM2.5 – July 18, 2002 24-hr Averages Uncertainty Analysis Overall Uncertainty of Interpolation Uncertainty Analysis - Mapping Ozone, PM2.5,Temperature Inverse Distance Weighted (IDW) & Kriging July 15 – 19, 2002 (regional PM episode)

  6. Uncertainty Analysis - Results Summary of IDW results: Summary of kriging results: *RMSE = Root Mean Square Error

  7. A Method to Improve PM2.5 Mapping Cokriging: surrogate data used to augment spatial coverage of PM2.5 monitors: • visibility data as a mapping surrogate • population density or urban boundaries as surrogates • topography to define regions of influence

  8. Base Map Data Points Surrogate Data Contours Ozone – July 18, 2002 Average 8-hr Maximum Reduced Uncertainty? Temperature °F Example - Cokriging

  9. Understanding the Characteristics of PM2.5 • A better understanding of PM2.5 by region is needed to improve mapping results • Explore relationships with possible surrogate data sets • ASOS visibility data, satellite data, digital elevation data, etc.

  10. PM2.5 AQI as Point Values PM2.5 AQI Values by County Alternative Displays of PM2.5 Data

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