Particulate Matter Modeling: Scientific Issues & Future Prospects Christian Seigneur AER San Ramon, California, USA EMEP TFMM, 29 November 2006, Paris, France

Major components of a PM air quality model Initial and Boundary Conditions Meteorological Model Emissions Concentrations of gases and PM Droplet Chemistry Wet Deposition Dry Deposition Gas-phase chemistry PM Chemistry and Physics Transport Air Quality PM Model

Model uncertainties: Inputs and formulation Inputs –Emissions –Meteorology –Boundary and initial conditions Formulation (some current issues) –Dry deposition velocities –Treatment of point sources –Treatment of secondary organic aerosols (SOA)

Emissions of PM and precursors Bottom-up emission inventories are improving with inventories at high spatial and temporal resolution becoming more common: –United Kingdom: National atmospheric emissions inventory ( –France: “Inventaire national spatialisé” (INS) under development by the French Ministry of Ecology (to be available in 2008) PM models require accurate emissions of gaseous precursors (NO x, VOC, SO 2, NH 3 ) and primary PM (with size distribution and chemical composition)

Importance of NH 3 emissions Particulate nitrate concentrations decrease significantly when NH 3 emissions decrease by 33% Simulation of the eastern U.S. on 15 January 2002 with CMAQ

Meteorology Winds affect the accuracy of long-range transport Vertical mixing affects PM surface concentrations Clouds (and fogs) enhance secondary PM formation but precipitation removes PM from the atmosphere

Model performance for transport Regional models cannot reliably predict the impact of individual point sources at long distances Comparison of measured and simulated concentrations of a tracer released at 500 km from the receptor during July-October 1999 in Texas ( Pun et al., JGR,, 111, D06302, doi: /2004JD005608, 2006 )

Comparison of two PM models Importance of vertical mixing PM 2.5 concentrations over the U.S. on 6 July 1999 differ primarily because of different algorithms for vertical mixing (Zhang et al., JAWMA, 54, 1478, 2004) CMAQCAMx

Influence of clouds on sulfate: Formation and removal Without clouds With clouds Simulation of 14 July 1995 with CMAQ over the northeastern U.S.

Importance of boundary conditions Sulfate over the United States Simulation of July- October 1999 (REMSAD, M. Barna, National Park Service) Solution: Use of a global model that has undergone satisfactory performance evaluation

Importance of the dry deposition of HNO 3 Particulate nitrate concentrations decrease significantly when the dry deposition velocity of HNO 3 increases by a factor of 3 Simulation of the eastern U.S. on 15 January 2002 with CMAQ

Evolution of plume chemistry Early Plume Dispersion NO/NO 2 /O 3 chemistry 1 2 Mid-range Plume Dispersion Reduced VOC/NO x /O 3 chemistry — acid formation from OH and NO 3 /N 2 O 5 chemistry Long-range Plume Dispersion 3 Full VOC/NO x /O 3 chemistry — acid and O 3 formation PM formation is negligible near the stack (Karamchandani et al., ES&T, 32, 1709,1998)

Effect of an advanced plume-in-grid treatment (APT) on sulfate concentrations Without APTWith APT Simulation of July 2002 over the eastern United States with CMAQ-MADRID: Sulfate due to 14 power plants (Karamchandani et al., AE, 40, 7280, 2006)

Chemical composition of PM: Organics constitute a significant fraction PM 10, Saclay, Paris region, France, July 2000 (Hodzic et al., ACP, 6, 3257, 2006) Other Black Carbon Organic Mass Ammonium Nitrate Sulfate PM 2.5, Boundary Waters, Minnesota, USA, July 2002 (IMPROVE network)

Treatment of SOA formation C gas C particle K C gas C particle H Condensable VOC oxidation products Hydrophilic Hydrophobic Henry’s law for the aqueous phase Raoult’s law for the organic phase

Uncertainties in SOA formation Missing precursors –Isoprene, benzene, sesquiterpenes are now being added to models Large number of condensable products –Use of surrogate SOA compounds Approximations for the partitioning constants Oligomerization –Not currently treated in models Limited interactions between organic and inorganic compounds in particles

Effect of oligomerization on organic PM Effect of pH on SOA oligomerization in an  -pinene simulation (Pun and Seigneur, )

Model performance evaluation Model application Model evaluation Model/Data improvement Regulatory application or forecasting The modeling cycle iterates until performance is good enough for emission strategy design or forecasting

Model performance evaluation: Comparing the model results to data Ambient DataModel Results Model Evaluation Software + Graphics Package Performance Statistics Paired peak error Unpaired peak error Gross error Gross bias Normalized bias Normalized error Root mean square error Coefficient of determination... Graphics Time series Scatter plots Pie charts...

Major issues when comparing models and measurements Spatial averaging Temporal averaging PM size fractions PM chemical composition –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 may lead to approximations of seasonal and annual measured values (e.g., 1 in 3 days for U.S. networks) It is preferable to conduct model performance evaluations using continuous measurements with fine temporal resolution (~ 1 hour)

Diagnostic analysis using fine temporal resolution Observed and simulated (CMAQ-MADRID 2) organic mass in Atlanta, Georgia, USA, July 1999 ( Bailey et al., JGR, in press )

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 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 For example, if one uses an average PM 2.5 density of 1.35 g/cm 3, d S of PM 2.5 in the model is 2.15  m

PM 2.5 chemical composition Nitrate: sampling artifacts Sulfate Ammonium: sampling artifacts Organics: Conversion factor OC => OM? Sampling artifacts BC: factor of 2? Other: some volatilization? some water?

Semi-volatile species HNO 3 & nitrate NH 3 & ammonium Organic compounds Water Their particulate mass can be over- or underestimated due to positive or negative artifacts

Ammonium nitrate Positive artifacts may occur in the absence of upstream denuders to collect gaseous HNO 3 and NH 3 Negative artifacts may occur because of evaporation of ammonium and nitrate from the filter –Significant for Teflon filters (losses are typically higher in summer) –Nitrate is thought to be well collected on Nylon filters impregnated with alkaline substance but some ammonium could be volatilized

Organic compounds Positive artifacts may occur because denuders are rarely used (adsorption of gaseous organic compounds on quartz filters) Negative artifacts may occur due to volatilization of organic PM (it can be significant: about 50% in Riverside, California, according to Pang et al., AST, 36, 277, 2002)

Carbonaceous species The difference between black carbon (BC) and organic carbon (OC) is operational: - temperature evolution protocol - light absorption method Different monitoring networks use different techniques ~factor of 2 difference for BC (Chow et al., AST, 35, 23, 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 (AST, 35, 602, 2001) recommend –1.6 for urban PM –2.1 for non-urban PM

Advanced techniques for evaluating model performance Evaluating spatial and temporal patterns Evaluating the third dimension Comparing different models and/or modeling techniques Evaluating model response

Spatial display of model error can provide insights into possible causes Sulfate error for CMAQ-MADRID in Texas (July-October 1999): - Emissions ? - Coastal meteorology ? ( Pun et al., JGR,, 111, D06302, doi: /2004JD005608, 2006 )

Evaluating the third dimension Use of satellite data: Aerosol Optical Depth August 2001 (Bias = -51%) November 2001 (Bias = -35%) Model simulation: CMAQ Satellite data: MODIS (Zhang et al., AMS annual meeting, 2006)

Comparison of three SOA models The three SOA models differ in: the total amounts of SVOC and SOA the gas/particle partitioning the relative amounts of anthropogenic and biogenic SOA (Pun et al., ES&T, 37, 3647, 2003)

Reconciliation of CTM and receptor modeling results Refinement of the modeling results using additional information leads to better agreement among the different models ( Pitchford et al., BRAVO report, vista.cira.colostate.edu/improve, 2004 ) Contribution of various source areas (Mexico, Texas, eastern U.S. and western U.S.) to sulfate in Big Bend National Park, Texas

Response of PM to changes in precursors (adapted from Pandis, )

Effect of decreased precursor emissions on PM concentrations (  g/m 3 ) Sulfate Nitrate Organics SO 2 NO x VOC (Seigneur, AIChEJ, 51, 355, 2005)

Evaluating model response A satisfactory operational evaluation does not imply that a model will predict the correct response to changes in precursors emissions There is a need to conduct a diagnostic/mechanistic evaluation to ensure that the model predicts the correct chemical regimes Indicator species can be used to evaluate the model’s ability to predict chemical regimes

Major chemical regimes Sulfate –SO 2 vs. oxidant-limited Ammonium nitrate –NH 3 vs. HNO 3 -limited Organics –Primary vs. secondary –Biogenic vs. anthropogenic Oxidants (O 3 & H 2 O 2 ) –NO x vs. VOC-limited

Example of indicator species Sensitivity of O 3 formation to VOC & NO x H 2 O 2 / (HNO 3 + Nitrate) as an indicator Low values: VOC sensitive High values: NO x sensitive O3O3 NONO 2 HNO 3 OH HO 2 H2O2H2O2 VOC

Example of indicator species Sensitivity of nitrate formation to NH 3 & HNO 3 Excess NH 3 as an indicator Low values: NH 3 sensitive High values: HNO 3 sensitive Ammonium nitrate HNO 3 NH 3 Ammonium sulfate

Qualitative estimates of uncertainties (adapted from Seigneur & Moran, )

Possible topics for improving PM model performance Model inputs –Emission inventories (ammonia, primary PM, etc.) –Transport processes (e.g., vertical mixing) –Assimilation of cloud and precipitation data –Boundary conditions from global models Model formulation –Advanced plume-in-grid treatment for point sources –SOA formation –Heterogeneous chemistry –Deposition velocities