Roland R. Draxler

*Journal of the Air Pollution Control
Association*, Vol. 37, No. 6, pp. 708-714, June 1987

**Abstract** - A one-year-long experiment
in which two different tracers were simultaneously released from
two different locations was used to test various hybrid receptor
modeling techniques to estimate the tracer emissions using the
measured air concentrations and a meteorological model. Air
concentrations were measured over an 8-hour averaging time at three
sites 14 to 40 km downwind. When the model was used to
estimate emissions at only one tracer source, 6 percent of the
short-term (8-h) emissions estimates were within a factor of 2 of
the actual emissions. Temporal averaging of the 8-h data enhanced
the precision of the estimate such that after 10 days 42 percent of
the estimates were within a factor of 2 and after six months all of
them (each source-receptor pair) were within a factor of 2. To test
the ability of the model to separate two sources, both tracer
sources were combined, and a multiple linear regression technique
was used to determine the emissions from each source from a time
series of air concentration measurements representing the sum of
both tracers. In general, 50 percent of the short-term estimates
were within a factor of 10, 25 percent were biased low, and in
another 25 percent the regression technique failed. The bias and
failures are attributed to low or no correlation between measured
air concentrations and model calculated dispersion factors.
In the regression method increased temporal averaging did not
consistently improve the emission estimates since the ability of
the model to distinguish emissions between sources was diminished
with increased averaging time. However, including
progressively longer time periods (more data) into the regression
or spatially averaging the data over all the receptors was fond to
be the most effective method to improve the estimated
emissions. At best about 75 percent of the estimated monthly
emission data were within a factor of 10 of the measured
values. This suggests that the usefulness of meteorological
models and statistical methods to address questions of source
attribution requires many data points to reduce the uncertainty in
the emission estimate.

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