1. Synthesized CMAQ A BRAVO community Product
Address Questions to Bret Schichtel

2. Biases in Modeled Source Attribution Estimates
REMSAD Monthly Source Attribution Results
July
October
July - Underestimated at all sites – Mexican influence in west TX
October - Overestimated at all sites – Eastern US influence
Jul – Aug: underestimated in west TX close agreement in East (compensating errors)
NOTE: CMAQ has similar but less severe biases

3. Synthesis Inversion – Removing Source Attribution Biases
Each source region is multiplied by a source attribution scaling coefficient aj to account for biases in emission, kinetics and/or transport for each source region.
SO4 (Predicted)i,l = a1*(Mexico) i,l + a2*(Texas)i,l + a3*(E US)i,l
+ a4*(W US)i,l + a5*(Boundary Conditions)i,l
aj = 1 if CMAQ “perfectly” fits the observed values in a least squares sense
The relationship is inverted to find aj which are then used to adjust the source attribution estimates

4. Bayesian Least Squares
Incorporate prior estimates of the regression coefficients and their variance.
J(a) = Sjk (yi – S Giu au) Xjk (yk – S Gkv av) + S(au – zu) Wuv (av-zv)
Minimize square residuals
Minimize squared difference between prior and post estimates
Minimize J(a) to find the unknown scaling coefficient a
z: the prior estimates of the source attribution scaling coefficients
W: the variance of the prior estimates

5. Application of Bayesian Least Squares to Synthesized CMAQ
Prior estimates of scaling coefficients z = 1 (perfect model)
Variance of priors (diagonals of W) = 1 (model is good within a factor of 2)
All 5 coefficients were free to vary

6. Which BRAVO Sites to Include
Which BRAVO Sites to Include? Exploring the spatial variability in source attribution scaling coeff.
The synthesis inversion was applied to each monitoring site using the daily data in three time periods to derive spatially varying but temporally constant coefficients for each time period.
Time periods with upon unique transport flow directions:
7/9 – 8/5: Transport primarily from Mexico, and southeast TX
8/6 – 9/19: Transport primarily from Mexico, east Texas and Eastern US
9/20 – 10/28: Transport could be from any direction
The Bayesian least square regression was used for each monitoring site and time range.

8. Which BRAVO Sites to Include?
t-test was used to determine how different the coefficients were from the coefficients at Big Bend
All sites with t > 3 for a given source region which contributed 10% or more was deemed to be significantly different from Big Bend

9. Synthesized CMAQ – Temporally Varying Coefficients – Optimum Results
Daily scaling coefficients were derived by incorporating data from all sites that were not significantly different from Big Bend for a given time period
Three consecutive days were incorporated into each inversion with the coefficients applied to the middle day.
Performance Stats
Synthesized CMAQ
Original CMAQ r
0.89
0.72 r2
0.8
0.52
Avg Obs (mg/m3)
2.58
Avg Pred (mg/m3)
2.17
Bias (%)
-16
RMS Error (%) 36 61

10. Synthesized CMAQ – Average % Contribution During BRAVO
Percentages in () are the original CMAQ results

11. Synthesized CMAQ – % Contribution for Each BRAVO Sulfate Episode

12. Synthesized CMAQ – Daily % Contribution

13. Synthesized REMSAD Results
Identical process was used on the REMSAD source attribution estimates.
Notables:
the 4 eastern US source regions were combined into one source region
Prior variances for source regions in:
Mexico and Eastern US = 4
Texas = 2
Western US and BC = 0.5

14. Average Source Contribution to Big Bend’s Sulfate from 7/9 – 10/28/1999

15. Synthesized CMAQ – The Ultimate BRAVO Data Integration Research Collaboration Effort
Inputs from all three air quality models
Measured data from BRAVO, IMPROVE and CASTNet monitoring networks
Incorporation of source oriented and receptor oriented modeling concepts - Hybrid modeling
Incorporates Inputs from all research groups involved in BRAVO