Model Evaluation Comparing Model Output to Ambient Data

1. Model EvaluationComparing Model Output to Ambient Data Christian Seigneur AER San Ramon, California

2. Major Issues when Comparing Models and Measurements • Spatial averaging • Temporal averaging • PM size fractions • Semi-volatile species • Carbonaceous species • “Other” PM

3. Spatial Averaging • Point • measurement • 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 • + • Model • grid average

4. 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

5. 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?

6. 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.

7. 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

8. 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

9. 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 mm • Correct for the measurement error

10. PM Size FractionRepresentation 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

11. 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

12. Semi-Volatile Species • HNO3 & nitrate • NH3 & ammonium • Organic compounds • Water • Their particulate mass can be under- or overestimated

13. 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

14. 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)

15. 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

16. 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

17. 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

18. 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

19. “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)

20. PM2.5 Chemical Composition(IMPROVE, STN) • Other: • some volatilization? • some water? • Nitrate • Sulfate • BC: factor of 2? • Organics: • over- or underestimated? • Ammonium: underestimated?

21. 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