Since 1997, hundreds of
summertime
flights designed to measure O3, CO, SO2, and
aerosol
optical and microphysical properties over the Mid-Atlantic have been
conducted
as part of the Regional Atmospheric Measurement, Modeling, and
Prediction
Program (RAMMPP). To investigate
diurnal patterns, median morning and afternoon profile values were
calculated. Little diurnal variation
was identified in the CO, SO2, and Ångström
exponent profiles. Ozone values were
larger in the afternoon
due to photochemical production. Lower
free tropospheric O3, subject to long range transport, was
invariant
at ~55 ppb. The single scattering
albedo increased from morning to afternoon (0.94 vs. 0.93 in the
boundary
layer) due to SO2 oxidation. In
the morning and afternoon single scattering albedo
profiles, the
values decreased with altitude, likely due to the preferential rain out
of SO42-
dominated particles over black carbon particles. An
agglomerative,
hierarchical cluster analysis of backtrajectory
data in conjunction with the vertical profile data was used to identify
the
source regions and characteristic transport patterns during summertime
pollution episodes. Eight clusters were
identified. The clusters were divided
into morning and afternoon profiles. The
northern Ohio River Valley was identified as the
predominant source
of power plant pollution, with large O3 values, highly
scattering
particles, and large aerosol optical depth. Flow
from
the southern Ohio River Valley, in contrast,
brought little
pollution. The greatest afternoon O3
values occurred during periods of stagnation when transport was minimal
and
photochemical production was encouraged.
North-northwesterly and northerly flow brought the
least
pollution
overall. Ozone
transport was quantified by calculating
the ratio of residual layer O3 in upwind morning profiles to
downwind, afternoon O3 profiles. The
greatest
transported O3 came from the Ohio
River Valley. The least O3 was
transported
during periods of clean, northerly flow and when stagnation dominated. The use of the
clustering
techniques with aircraft vertical profile data provided greater insight
into
the transport processes, dynamics, and underlying mechanisms that drive
pollution events than when used with surface-based data.
The results will be useful in designing
regional pollution control strategies, validating air quality models,
and
predicting future pollution episodes.
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