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

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 NO SO2 NH3 NO2 HNO3 H2SO4 O3 NO3 PMfine SO4 N2O5 VOCCO NO
SO2
NH3
NO2
HNO3
H2SO4 O3
Gas Phase
Fine Particles hv OH
HO2
RO2
NO3
PMfine
SO4
H2O2 Fe
N2O5
H2O
Heterogeneous

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 Winter: January 2002
32-km resolution 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 Evaluation System 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 Differences are
greatest at the higher temperatures.

17. Gaussian random error (1σ=15% to mimic measurement error) superimposed
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 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 Evaluation Gas/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 * SO42-
GR = =
Total Nitrate HNO3(g) + NO3-(p)
GR > => HNO3 limiting
0 < GR < => NH3 limiting
GR < => NH3 severely limiting (can’t form NH4NO3)
Calculated in Molar Units

24. The modeled and observed Gas Ratios are reasonably consistent
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 Evaluation Response 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 NO (Total-Nitrate as 2nd choice)
NH3 and NH (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 NO SO2 NH3 NO2 HNO3 H2SO4 O3 NO3 PMfine SO4 PMcoarse
VOC CO NO
SO2
NH3
NO2
HNO3
H2SO4 O3
Gas Phase
Fine Particles hv OH
HO2
RO2
NO3
PMfine
SO4
H2O2 Fe
PMcoarse
Coarse Particles
N2O5
H2O
Heterogeneous

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

33. Same behavior of HNO3 overprediction is observed at Pittsburgh.
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. The CMAQ O3 Release is best even though it has biases.
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. The CMAQ O3 Release is worst (02 CMAQ Release would be much worse).
St Louis
The CMAQ O3 Release is worst (02 CMAQ Release would be much worse).
CMAQ with Zero N2O5 is closest to Observations