DATA FUSION & the CAAQS

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

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

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

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

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).

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

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

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

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

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

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

PM2.5 Model Estimate

With DATA FUSION

(Data Fusion) – (Model Estimates)