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Abstract:
Eastern China is heavily polluted by nitrogen dioxide, particulate matter with
aerodynamic diameter below 2.5 μm (PM2:5), and other air
pollutants. These pollutants vary on a variety of temporal and
spatial scales, with many temporal scales that are nonperiodic
and nonstationary, challenging proper quantitative characterization
and visualization. This study uses a newly compiled
EOF–EEMD analysis visualization package to evaluate
the spatiotemporal variability of ground-level NO2, PM2:5,
and their associations with meteorological processes over
Eastern China in fall–winter 2013. Applying the package
to observed hourly pollutant data reveals a primary spatial
pattern representing Eastern China synchronous variation in
time, which is dominated by diurnal variability with a much
weaker day-to-day signal. A secondary spatial mode, representing
north–south opposing changes in time with no constant
period, is characterized by wind-related dilution or a
buildup of pollutants from one day to another.
We further evaluate simulations of nested GEOS-Chem
v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal
variability of pollutants. GEOS-Chem underestimates
NO2 by about 17 μgm -3 and PM2:5 by 35 μgm -3 on average
over fall–winter 2013. It reproduces the diurnal variability
for both pollutants. For the day-to-day variation, GEOSChem
reproduces the observed north–south contrasting mode
for both pollutants but not the Eastern China synchronous
mode (especially for NO2). The model errors are due to a
first model layer too thick (about 130 m) to capture the nearsurface
vertical gradient, deficiencies in the nighttime nitrogen
chemistry in the first layer, and missing secondary organic
aerosols and anthropogenic dust. CMAQ overestimates
the diurnal cycle of pollutants due to too-weak boundary
layer mixing, especially in the nighttime, and overestimates
NO2 b about 30 μgm -3 and PM2:5 by 60 μgm -3. For the
day-to-day variability, CMAQ reproduces the observed Eastern
China synchronous mode but not the north–south opposing
mode of NO2. Both models capture the day-to-day variability
of PM2:5 better than that of NO2. These results shed
light on model improvement. The EOF–EEMD package is freely available for noncommercial uses.
