treasure valley road dust study: final report - ResearchGate

egressions, R 2 and P-values are also reported. R 2 can be thought of as the fraction of the
variance in Log(b) that is explained by the regression. The P-value for a parameter is an indicator
of the probability that the fit for that parameter is not different from zero. In general, a low P-
value suggests that a discernible relationship exists between the dependent (Log(b)) variable and
the independent (Log(s), Log(v), or both) variable(s). Option 1 resulted in very good curve fits
with R 2 ranging from 0.83 (Winter-Canyon-Rural) to 0.99 (Summer-Ada-Rural). When Log(s)
was the only independent variable (option 2) R 2 ranged from 0.77 (Winter-Canyon-Rural) to
0.98 (Summer-Ada-Rural). Option 3 resulted in R 2 ranging from 0.47 (Summer-Canyon-Urban)
to 0.95 (Summer-Ada-Urban). Option 2 was chosen as the best option for use in the Treasure
Valley Road Dust Study for three reasons. First, though option 1 resulted in better curve fits, the
relationship between the variables was not consistent. The exponent, y, for per lane volume was
negative for Summer-Canyon-Urban and Winter Canyon-Rural, suggesting an increase in
emissions potential, b with increasing volume. However, y was positive for all other cases,
suggesting a decrease in b with increasing volume. These results are not physically consistent.
Second, road speed and per lane traffic volume are positively correlated (see Figure 4-2). Thus,
they are not independent variables. Third, road speed is a known quantity while volume is
obtained from TDM model results. This adds uncertainty to the variable v.
Table 4-4. Results of regression of log(emissions factor, b) vs. log (speed, s) and log (volume per lane, v).
Time and Location
Option 1
Regressions of Log(b) vs. Log(s) and Log(v)
Option 2
Log(b) vs. Log(s) only; y=0
Option 3
Log(b) vs. Log(v) only; x=0
Season County Setting R 2 x P x y P y R 2 x P x R 2 y P y
S Ada Rural 0.99 1.07 0.0002 0.12 0.0175 0.98 1.47 0.0000 0.90 0.37 0.0001
S Ada Urban 0.95 0.20 0.6300 0.48 0.0199 0.88 1.39 0.0002 0.95 0.56 0.0000
S Canyon Rural 0.93 1.18 0.1720 0.22 0.2289 0.88 2.05 0.0055 0.86 0.45 0.0076
S Canyon Urban 0.89 4.01 0.1041 -0.42 0.2671 0.77 2.03 0.0496 0.47 0.31 0.2035
W Ada Rural 0.97 0.13 0.7292 0.34 0.0217 0.88 1.32 0.0017 0.97 0.38 0.0000
W Ada Urban 0.91 0.59 0.2447 0.33 0.1083 0.85 1.38 0.0004 0.88 0.53 0.0002
W Canyon Rural 0.83 1.79 0.0341 -0.17 0.2597 0.77 1.05 0.0039 0.54 0.19 0.0366
W Canyon Urban 0.94 1.36 0.1224 0.27 0.3249 0.89 1.86 0.0165 0.73 0.67 0.0637
Considering speed, setting, and time of year as the only parameters that determine road
dust emissions potentials from paved roads, Equation (4-7) simplifies to
b
? x
? C C , S,
T
? s
Eq(4-8)
The final values for C C,S,T and the exponent x are shown in Table 4-5. Note that the table
also lists residential roads but that the x values for those roads are zero (i.e. no dependence on
road speed). The curve fits corresponding to the non-residential entries in Table 4-5 are shown
in Figure 4-3. In general, the regressed values for the exponent x and C C,S,T fit the original
dataset well, within the limits of uncertainty. Table 4-6 shows a breakdown of emissions
potentials and emissions factors based on the year 2000 Treasure Valley Roadway network. In
obtaining the averages in Table 4-6 each link in the Traffic Demand Model was weighted equally
regardless of link length or traffic volume. This averaging scheme provides an overview of the
range of emissions characteristics. A discussion of road dust emissions resulting from individual
activities and road types is provided in the following Chapter.
4-10