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

Final Report — September 2004
For sulfate, the model overestimated the daily sulfate load at Big Thicket on 64 of the
88 days with valid measurement data. The statistics in Table 9-21 indicate that the model
was able to capture daily fluctuation in fine particulate sulfate concentrations there, but it
regularly overestimated their values by approximately 50%.
Daily and weekly total sulfur fluctuations at Big Thicket were fairly well captured by
the model, albeit with regular overestimations of peaks at twice the observed values, but the
model failed to represent the lower and more consistent longer-term average concentrations.
As was indicated in Table 9-20, the correlation between total sulfur predictions and
observations deteriorates when weekly averages are considered, although the bias decreases.
The weekly total sulfur concentrations observed at Big Thicket ranged from 1.2 to 2.4 µg/m 3
S, with a mean of approximately 2 µg/m 3 S and coefficient of variation (standard
deviation/mean) of 0.20; in contrast, predicted total sulfur concentrations for Big Thicket
ranged from 2.1 to 6.0 µg/m 3 S, with a mean concentration of approximately 3.5 µg/m 3 S and
coefficient of variation (standard deviation/mean) of 0.35. Overall, at Big Thicket the ratio
of weekly mean predicted total sulfur concentrations to the mean observed total sulfur
concentrations was about 1.8.
Other Particulate Matter Components and PM 2.5 Mass Concentration. The
ability of the CMAQ-MADRID model to reproduce daily variations of the components of
PM 2.5 other than sulfate can be evaluated at K-Bar, which is the only location where all five
major directly-measurable components of fine particulate matter – ammonium, elemental
(black) carbon, nitrate, organic mass and sulfate – were determined. In addition to the five
major components and total mass, an “other” category composed of soil elements, sodium
and chloride, and all other unidentified species will be used in the following discussion.
Recall that, as shown in Table 9-18, the CMAQ-MADRID model reproduced total
PM 2.5 mass throughout the network with a negative mean bias (-0.39 µg/m 3 ) and a positive
mean normalized bias (16%).
As for the organic mass, the discussion there showed predictions versus
measurements at six stations exhibited a coefficient of determination (r 2 = 0.61; see Table
9-19) that indicates that the model could reproduce fairly accurately the fluctuations in
organic mass. The model predictions show a mean normalized bias (-50%) nearly equal in
magnitude but opposite in sign to the normalized error, indicating that the model regularly
predicted approximately half of the organic mass observed at the monitoring stations. 5
Table 9-22, a modified version of Table 9-18, lists model performance statistics
determined from comparing predictions to observations on the 103 days (out of 119
simulation days) when concentrations of all 5 major fine particulate components and total
5 Consistent with the conventional IMPROVE methodology, a factor of 1.4 was used to convert
organic carbon measurements to organic mass in order to account for other atoms such as hydrogen,
oxygen and nitrogen. Recent studies suggest that this factor may need to be increased depending on
location (Turpin and Lim, 2001), but doing so would further increase the degree of underestimation of
organic mass by the model.
9-60