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Model Evaluation
Comparing Model Output to Ambient Data
Christian Seigneur
AER
San Ramon, California
Major Issues when Comparing
Models and Measurements
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Spatial averaging
Temporal averaging
PM size fractions
Semi-volatile species
Carbonaceous species
“Other” PM
Spatial Averaging
• Spatial variability for a
primary pollutant can be
up to a factor of 2.5
(maximum/minimum) for
a grid resolution of 4 km
Point
measurement
+
• It will be less for a
secondary pollutant
Model
grid average
Temporal Averaging
• Models and measurements are consistent for short
periods (1 to 24-hour averaging)
• Lack of daily measurements (1 in 3 days for STN
and IMPROVE) leads to approximations of
seasonal and annual measured values
• It is preferable to conduct model performance
evaluations using time periods consistent with the
measurements
PM Size Fraction
Do the current model representations of PM size
fractions (i.e., three modes, two size sections and
multiple size sections) correctly represent
measured PM2.5?
Sampling PM2.5
Measurements do not
have a sharp particle
diameter cut-off:
PM2.5 includes some
coarse particles and
some fine particles are
not sampled.
PM Size Fraction
• Inertial impaction measurements (e.g., FRM) use the
aerodynamic diameter of the particles to define the size
fraction
– the aerodynamic diameter, da, is the diameter of a
spherical particle of unit density that behaves like the
actual particle
• Models simulate particle dynamics using the Stokes
diameter
– the Stokes diameter, dS, is the diameter of a spherical
particle that behaves like the actual particle
PM Diameters
dS = da / (particle density)1/2
Particle density is a function of location and time
If one uses an average PM2.5 density of 1.35 g/cm3,
dS for PM2.5 should be 2.15 mm
PM Size Fraction
Modal Representation
To have a more accurate
comparison with data:
• Convert ds to da
• Calculate accumulation
and coarse mode
fractions below 2.5 mm
• Correct for the
measurement error
PM Size Fraction
Representation with 2 Size Sections
To have a more accurate
comparison with data:
• Select ds corresponding
to da = 2.5 mm using an
average particle density
• It is not appropriate to
correct for the
measurement error
PM Size Fraction
Representation with Multiple Size Sections
To have a more accurate
comparison with data:
• Convert ds to da using the
simulated particle density
• Correct for the
measurement error
Semi-Volatile Species
HNO3 & nitrate
NH3 & ammonium
Organic compounds
Water
Their particulate mass can be under- or
overestimated
Semi-Volatile Species
Losses associated with filter-based sampling:
• Sampling losses (volatilization) may occur because of
– decrease in concentrations of gas-phase precursor
concentrations due to losses before the filter
– increase in temperature during sampling
– decrease in pressure after the filter
• Storage and transport losses can be minimized
• Losses during the laboratory analysis appear to be
negligible
Ammonium Nitrate
• Sampling losses for ammonium nitrate have been
estimated to be significant for Teflon filters (PM2.5 mass):
– 28% on average in Los Angeles (Hering & Cass, 1999)
– 9 to 92% in California (Ashbaugh & Eldred, 2004)
– Losses are typically higher in summer
• Nitrate is thought to be well collected on Nylon filters but
some ammonium could be volatilized (speciated PM2.5)
Organic Compounds
• Sampling losses of organic PM can be significant
– about 50% in Riverside, CA (Pang et al., 2002)
• Adsorption of gaseous organic compounds can
take place on quartz filters
Water
PM measurements may include some water
PM model results typically exclude the particulate
water, which could lead to a small underestimation
of PM2.5
Carbonaceous Species
The difference between black carbon (BC) and
organic carbon (OC) is operational:
IMPROVE and STN use different techniques
~factor of 2 difference for BC (Chow et al., 2001)
~10% difference for OC
For modeling, the emissions and ambient
determinations of BC should be based on the same
operational technique
Estimating Organic PM
Organic mass is not measured but estimated from
measured organic carbon using a scaling factor
– the default value is 1.4
– it can range from 1.2 to 2.6
Turpin and Lim (2001) recommend
– 1.6 for urban PM
– 2.1 for non-urban PM
“Other” PM
IMPROVE defines “other” PM as soil (oxides of
Si, Ca, Al, Fe and Ti), non-soil K and NaCl
“Other” PM can also be defined as the difference
between PM2.5 and the measured components
(with some water)
In the models, “other” PM is typically defined as
the difference between PM2.5 and the measured
components (without water)
PM2.5 Chemical Composition
(IMPROVE, STN)
Other:
some volatilization?
some water?
BC: factor of 2?
Organics:
over- or underestimated?
Nitrate
Sulfate
Ammonium: underestimated?
Recommendations
• Evaluate models with the finest spatial and
temporal resolutions feasible
• Take sampling artifacts for semi-volatile
compounds into account when interpreting the
results
• Use realistic scaling factors to convert OC to
organic PM
• Conduct separate performance evaluations for PM
monitoring networks that use different sampling
techniques