Model Evaluation Comparing 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 +

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 PM 2.5 ?

Sampling PM 2.5 Measurements do not have a sharp particle diameter cut-off: PM 2.5 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, d a, 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, d S, is the diameter of a spherical particle that behaves like the actual particle

PM Diameters d S = d a / (particle density) 1/2 Particle density is a function of location and time If one uses an average PM 2.5 density of 1.35 g/cm 3, d S for PM 2.5 should be 2.15  m

PM Size Fraction Modal Representation To have a more accurate comparison with data: Convert d s to d a Calculate accumulation and coarse mode fractions below 2.5  m Correct for the measurement error

PM Size Fraction Representation with 2 Size Sections To have a more accurate comparison with data: Select d s corresponding to d a = 2.5  m 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 d s to d a using the simulated particle density Correct for the measurement error

Semi-Volatile Species HNO 3 & nitrate NH 3 & 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 (PM 2.5 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 PM 2.5 )

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 PM 2.5

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 PM 2.5 and the measured components (with some water) In the models, “other” PM is typically defined as the difference between PM 2.5 and the measured components (without water)

PM 2.5 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