Evaluation of 3-D Regional Particulate Models: Measurement Needs for Inorganic Species

1. Robin L. Dennis Atmospheric Sciences Modeling Division Air Resources Laboratory, NOAA/US EPA Research Triangle Park, NC 27711 European Monitoring and Evaluation Program (EMEP) Workshop on Particulate Matter Measurement and Modeling April 20-23, 2004 New Orleans, Louisiana, USA Evaluation of 3-D RegionalParticulate Models:Measurement Needs for Inorganic Species

2. Acknowledgements Shawn Roselle and Shaocai Yu provided valuable assistance in making the CMAQ runs and conducting analyses. Preliminary Supersite data were provided by Spyros Pandis (Pittsburgh) and Jay Turner (St. Louis). Data were provided by the SEARCH and the Atlanta Supersite Programs. Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. Robin Dennis is on assignment to the National Exposure Research Laboratory, U.S. Environmental Protection Agency

3. Policy Issues for the Inorganic System* • Are we getting the right mix of inorganic fine particles • What degree of nitrate replacement for sulfate will there be • Which is more effective: NOX control or NH3 control • What are other consequences (e.g., acidity, O3, SO42-, SOA) • What happens to character as well as mass • What is degree of acidity of aerosols in general and sulfate • What is the effectiveness of urban-oriented controls • New insights from special measurement campaigns * Courtesy of John Bachmann, EPA

4. PRIMARY EMISSIONS VOC CO SO2 NO OH O3 HO2 RO2 hv O3 OH H2O2 O3 Fe NO2 NO3 O3 OH N2O5 NH3 HNO3 H2SO4 H2O Heterogeneous Gas Phase Fine Particles NO3 PMfine SO4 PMfine

5. Evaluation Thrusts(Focus of the testing and the talk) • System Setup • Budgets • Gas/Particle Partitioning • Current state of the gas/aerosol system • Response Dynamics • Move to future states of the gas/aerosol system

6. Log10 Annual Area Source Ammonia Emissions Summer: August 1999 Winter: January 2002 CMAQ @ 32-km resolution CMAQ @ 36-km resolution 3 Sites: Pittsburgh, PA St. Louis, MO Atlanta, GA

7. System Setup(budgets) • SOX emissions • Sulfate production and losses • NOX emissions • Total-Nitrate (HNO3+aNO3-) production and losses • Day (gas) versus nighttime (heterogeneous) pathway differences • NHX (NH3+NH4+) emissions and losses • Getting NHX right is important (dilemma: official or best for model?) • Seasonal • Daily • Diurnal • Meteorological inputs • Boundary layer height (sensible/latent heat); vertical mixing rates

8. Atlanta: HNO3(average diurnal cycle) Urban Suburban • HNO3 concentrations significantly reduced with updated CMAQ • Must turn off all production from N2O5 to get down to observed levels of HNO3 • Daytime over-production of HNO3 is also an issue (winter photochemistry) • These are more winter than summer issues

9. Inverse modeling against monthly NH4 wet concentrations was used to define seasonality of NH3 emissions

10. Having the correct NH3 seasonality was critical to getting surface NHx right

11. Atlanta: NHX The CMAQ NHX predictions track the synoptic signal quite well, but they do not track the measured diurnal pattern

12. Atlanta: NHX Atlanta: NO3- Diurnal biases in NHX show up as biases in aerosol nitrate, especially in the early morning.

13. We see a pattern of early evening overprediction at urban and rural Sites. We believe the PBL is collapsing pre-maturely. Urban NOY Rural NOY

14. Measurements to Support EvaluationSystem Setup • Inert/slowly reacting “primary” specie (check meteorology) • EC will do; also NOY and CO • Temperature (soil moisture) • SO42- • NOY, HNO3 (O3, NOX[= NO + true-NO2]to examine O3 production) • Total-Nitrate (total because looking at budgets) • NHX (total because looking at budgets) • Wet deposition, rainfall amounts (dry deposition)

15. Gas/Particle Partitioning(Current conditions) • Equilibrium dynamics • Model errors affecting the partitioning • Temperature • Measurement errors affecting the testing of equilibrium module • Other conditions than assumed in the model • Non-equilibrium dynamics • External instead of internal mixture • Non-equilibrium pathways • Coarse particle interactions • Loss pathways

16. Differences in predictions of NO3- with observed and modeled (MM5) temperatures at the Pittsburgh site in January 2002. Differences are greatest at the higher temperatures.

17. Random error sensitivity aerosol NO3- (ug/m3) Base case aerosol NO3- (ug/m3) • Gaussian random error (1σ=15% to mimic measurement error) superimposed • on inputs of SO42- and NHX causes a large uncertainty in the prediction of NO3-. • The error in NHX has a larger impact than the error in SO42-.

18. Figure 3. Results from Monte Carlo simulations performed for selected periods in July 2001 and January 2002. Error bars extend to the 5th and 95th percentiles of the cumulative distribution function associated with each prediction. The shaded area bounds the interval between the 5th and 95th percentiles of the observed aerosol nitrate cumulative distribution functions, although concentrations below zero are not shown.(courtesy Spyros Pandis)

19. Figure 5.Simulations for July 9 and 21 assuming that particles are • internally mixed liquid aerosols, and • an external mixture of crystallized ammonium sulfate and wet acidic aerosols when the relative humidity is below 40%.(courtesy Spyros Pandis)

20. August ’99 Atlanta: NO3- While there appears to be a daytime under-prediction of NO3- by the model, single-particle mass spec measurements (Lee, Murphy, et al.) show the nitrate was not associated with ammonium (i.e., not the standard equilibrium pathway).

21. Measurements to Support EvaluationGas/Particle Partitioning • SO42- • HNO3 and NO3- • NH3 and NH4+ • Base cations (coarse and fine) • Single particle mass spectrometer particle composition information • Coarse particles (chemical composition by size) • T, RH • Good characterization of measurement error; best accuracy and precision possible (10%)

22. Response Dynamics • Gas Ratio (Excess Ammonia), modified Gas Ratio • Hourly • Daily • Degree of neutralization • Gas/Particle fractions

23. Gas Ratio(per S. Pandis) Free Ammonia NHX - 2 * SO42- GR = ---------------------- = ------------------------------ Total Nitrate HNO3(g) + NO3-(p) GR > 1 => HNO3 limiting 0 < GR < 1 => NH3 limiting GR < 0 => NH3 severely limiting (can’t form NH4NO3) Calculated in Molar Units

24. The modeled and observed Gas Ratios are reasonably consistent. The major excursions are mostly associated with plume(1) and wet deposition events(3).

25. A fair amount of the hourly Gas Ratio comparison information is able to be captured by daily Gas Ratio comparisons, although interpretation is most insightful and reliable at the hourly time resolution.

26. Gas Ratio comparisons can vary considerable across space due to differences in model biases. At Pittsburgh Total-Nitrate and NHX are both biased high. At St. Louis Total-Nitrate is high and NHX is biased low.

27. Measurements to Support EvaluationResponse Dynamics • SO42- • Minimally Total-Nitrate, but prefer HNO3 and NO3- • Minimally NHX, but prefer NH3 and NH4+ • Temperature (for interpretation) • Precipitation (for interpretation) • Wet deposition (for interpretation)

28. Summary of Evaluation Measurement Needs • Critical Suite (continuous) • SO2 and SO42- • HNO3 and NO3- (Total-Nitrate as 2nd choice) • NH3 and NH4+ (NHX as 2nd choice) • Base cations (coarse and fine) and other anions (e.g., Cl and Br) • Inert/slowly reacting “primary” specie (check meteorology) • EC will do; also NOY and CO • T, RH, Precipitation, WD • Wet deposition (Daily; Weekly as 2nd choice) • Additions for a Full Set (continuous) • NOY, HNO3, O3, NOX (=NO+true-NO2), H2O2, PAN (Ox. capacity; O3 prod’n) • Coarse particles (chemical composition by size) • Single particle mass spectrometer particle composition data • Good characterization of measurement error; best accuracy and precision possible (goal: 10%) • Dry deposition flux (direct as possible) of gases and particles • Satellite sites for sub-grid variability studies

29. Summary of Eval. Measurement Needs (cont.) • Hourly vs. Daily perspective (need continuous) • Now: Several continuous, Many/most daily sites with critical suite • Future: Most sites with critical suite of continuous measurements • Summer vs. Winter perspective • Winter needs to get equal experiment time for special intensives • Eventually the entire year needs to be covered • Regional/Rural vs. Local/Urban • Harmonize techniques. If separate networks, then need to have careful intercomparisons • Degree of comparability needs to be established • Wet and Dry Deposition perspective • Wet: collocation; daily most useful but pragmatism may say longer • Dry: issue of carrying out special measurement programs - particles • Subgrid Variability • Attacked through select measurement clusters (annual) • Variability of budgets (setup), species partitioning, Gas Ratio

30. Extra Slides

31. PRIMARY EMISSIONS VOC CO SO2 NO OH O3 HO2 RO2 hv O3 OH H2O2 O3 Fe NO2 NO3 O3 OH N2O5 NH3 HNO3 H2SO4 H2O Heterogeneous Gas Phase Fine Particles NO3 PMfine SO4 PMfine Coarse Particles NO3 PMcoarse SO4 PMcoarse

32. Suburban Atlanta: HNO3(average diurnal cycle) • Daytime over-production of HNO3 is also an issue

33. Pittsburgh: Winter Atlanta: Summer • Same behavior of HNO3 overprediction is observed at Pittsburgh. • The overprediction of HNO3 appears relatively smaller in summer • (no daytime issue)than in winter. Winter may have bigger issues.

35. Pittsburgh Full Period The CMAQ O3 Release is best even though it has biases. CMAQ with Zero N2O5 is not as good even though its total-Nitrate looks best.

36. St Louis The CMAQ O3 Release is worst (02 CMAQ Release would be much worse). CMAQ with Zero N2O5 is closest to Observations