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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
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

slide2

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
mapping issues 1 of 2

Continuous PM2.5 Monitors

Continuous Ozone Monitors

Mapping Issues (1 of 2)
  • Limited number of continuous PM2.5 monitors in the U.S.
mapping issues 2 of 2
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
uncertainty analysis mapping

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)

uncertainty analysis results
Uncertainty Analysis - Results

Summary of IDW results:

Summary of kriging results:

*RMSE = Root Mean Square Error

a method to improve pm 2 5 mapping
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
example cokriging

Base Map

Data Points

Surrogate Data

Contours

Ozone – July 18, 2002

Average 8-hr Maximum

Reduced Uncertainty?

Temperature °F

Example - Cokriging
understanding the characteristics of pm 2 5
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.