DATA FUSION & the CAAQS

1. DATA FUSION& the CAAQS

2. What’s special about the CAAQS scenarios • Most of the scenario studies performed to date by REQA had for objective to evaluate the impact of a measure • The model estimated concentrations were therefore used only to calculate differences • The purpose of the CAAQS scenarios is to estimate the projected metrics

3. EPA Guidance • Use model estimates in a “relative” rather than “absolute” sense. • To do so, calculate at monitoring sites, a relative response factor (RRF), ie • Projected values are obtained by multiplying the model projectionat site k by the corresponding RRF

4. Example for 98th percentile of 24-hour average for PM2.5

5. Generating a 2D field • Kriging of the projected observations • Very sparse network in Canada • Meteorology, chemistry & emissions in unmonitored sites not represented in final product • Model output • Accounts for meteorology, chemistry and emissions everywhere • Uncertainties in estimates

6. Generating 2D fields • Data fusion • Attempts to take advantage of the strengths of both datasets: observations and model estimates • Recommended by the EPA • Expertise within ASTD/MSC in using data fusion to generate the Canadian Precipitation Analysis (CaPA).

7. Going the CaPA way... • The software used is known as MIST (Moteur d’Interpolation STatistique) • Mist offers: • Choice of interpolation method • Choice of variogram model • Possibility to group observations • Prescribe correlation length • Prescribe correlation range • Mist

8. Going the CaPA way... • Mist uses kriging to interpolate analysis increments, i.e. • In an operation setting, the variogram obtained from the previous analysis is used to perform the next one • After each analysis, the variogram parameters are updated

9. Using MIST for CAAQS scenarios • For each metric, the increments were calculated using the 2006 observations and model estimated values • An interpolation of the increments is performed with a prescribed correlation length of a=100km and a correlation range of 20*a

10. Using MIST for CAAQS scenarios (cont’d) • An empirical variogram is calculated where h is the separation distance, and n(h) is the number of pairs of increments which are separated by the distance h

11. Variogram for 98th percentile of 24-hour average PM2.5

12. Using MIST for CAAQS scenarios (cont’d) • The empirical variogram is then fitted using a variogram model. • It was found that the exponential model provides a good fit • The model provides an estimated of the correlation length a • The analysis is redone using as a correlation range 6*a where h is the separation distance, and n(h) is the number of pairs of increments which are separated by the distance h

13. PM2.5 Model Estimate

14. With DATA FUSION

15. (Data Fusion) – (Model Estimates)