(BRAVO) Study: Final Report. - Desert Research Institute

Final Report — September 2004
tracers to their sources, but the same HYSPLIT model did very badly when it was applied
with MM5 winds. The role of the winds was reversed when TrMB was exercised with the
CAPITA Monte Carlo model. Then the performance with MM5 was pretty good but that
with EDAS was poor. This means that some evaluation of performance is required in order
to use the TrMB method.
A similar sensitivity to choice of wind field was reflected when the CAPITA Monte
Carlo (CMC) model was used alone. It was able to approximate the apportionment of tracer
from the nearby sources using the MM5 wind fields, but did better at apportioning the more
distant tracer sources using EDAS winds. The CMC method was not able to reproduce the
observed day-today tracer concentration variation at the receptor sites, however.
The Forward Mass Balance Regression (FMBR) method was able to identify the
Eagle Pass tracer release location from the K-Bar tracer measurements. It could also
estimate the release rate if data from all tracer sampling sites were used.
Turning to performance at estimating the concentrations of sulfur compounds in an
artificial “reality” defined by application of the REMSAD model, TrMB with CMC
trajectories and the MM5 wind fields was able to attribute study-average sulfate to various
source regions with an accuracy of about 10 percentage points. The FMBR method
performed relatively well at this task, except for a 10-percentage-point overestimation of the
simulated Texas contribution and a comparable underestimation of the contribution from
areas west of the BRAVO Study area.
Actual sulfate concentrations were predicted with relatively small average bias by
both CMAQ-MADRID and REMSAD. Both models tracked many of the sulfate peaks, but
neither model was able to predict all of the major sulfate peaks. Tracking of SO 2 peaks was
poorer for both models, presumably because the SO 2 plumes were less dispersed. The
coefficient of determination, r 2 , at Big Bend National Park was less than 0.27 for REMSAD
predictions of sulfate, but it was 0.52 for CMAQ. When all of the monitoring sites in the
study area were considered, r 2 for sulfate was 0.38 for REMSAD and 0.47 for CMAQ. Mean
absolute gross errors for both models were in the vicinity of 65%. Both models greatly
overestimated sulfur (especially SO 2 ) in the eastern part of the study area.
The synthesis inversion process, which scaled the CMAQ and REMSAD predictions
using measurements, improved the performance of both models, to the extent that r 2 for
sulfate at Big Bend was greater than 0.7 (which means that these hybrid models explained
more than 70% of the variation in the concentrations) and the bias was about –18%.
Because the different methods have differing uncertainties, the final attribution
assessment of the BRAVO Study involved taking into account the performance demonstrated
here as the attribution findings of the various methods were merged into a reconciled
assessment that reflected the weight of evidence produced by the varying methods. This
final attribution assessment reconciliation is presented in Chapter 12, and is based on the
individual assessments that are described in Chapters 10 and 11.
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