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 projection at 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)