Model EvaluationComparing Model Output to Ambient Data

Christian Seigneur

AER

San Ramon, California

Major Issues when Comparing Models and Measurements

• 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

• It will be less for a secondary pollutant

Point measurement

Model grid average

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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 m

PM Size FractionModal Representation

To have a more accurate comparison with data:

• Convert ds to da

• Calculate accumulation and coarse mode fractions below 2.5 m

• Correct for the measurement error

PM Size FractionRepresentation with 2 Size Sections

To have a more accurate comparison with data:

• Select ds corresponding to da = 2.5 m using an average particle density

• It is not appropriate to correct for the measurement error

PM Size FractionRepresentation 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)

Nitrate

Sulfate

Ammonium: underestimated?

Organics: over- or underestimated?

BC: factor of 2?

Other: some volatilization? some water?

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