Title: Shaocai Yu*, Robin Dennis* , Brian Eder* , Shawn Roselle* ,
1Can the thermodynamic model and 3-D air quality
model predict the aerosol NO3- reasonably?
- 4. Conclusion
- ISORROPIA and AIM have been evaluated by
comparing the modeled partitioning of TNO3 and
TNH4 between gas and aerosol phases with
observations. At the Atlanta site, both models
could reproduce most of observed NH4 and
HNO3within a factor of 2. However, both models
cannot reproduce most of observed NO3- and NH3
within factor of 2. At the Clinton site, both
models performed a little better on NO3- than at
the Atlanta site. There are different reasons for
the model unable to reproduce NO3- reasonably.
Sensitivity tests show that both models have
similar responses in the predicted aerosol NO3-
to the possible errors in SO42- and TNH4. This
analysis indicates that errors in TNH4 are more
critical than errors in SO42- to prediction of
NO3-. Regardless, the 3-D model performance on
SO42- and TNH4 needs to be quit good and better
than current daily performance, before the 3-D
air quality model can predict aerosol NO3-
reasonably although it can predict TNO3
reasonably.
- 1. INTRODUCTION
- Studying the behaviors of nitrate is one of most
intriguing aspects of atmospheric aerosols
because particulate nitrate concentrations depend
not only on the amount of nitric acid, but also
on availability of ammonia, sulfate
concentrations, temperature and relative
humidity. Particulate nitrate is produced
partially or predominantly from the equilibrium
reaction between two gas-phase species, HNO3 and
NH3. Several inorganic thermodynamic models have
been developed to partition the semi-volatile
species between gas and aerosol phases during the
past two decades. It has been postulated and
confirmed by ambient measurements that the
sulfate/nitrate/ammonium aerosol constituents
should be in thermodynamic equilibrium with the
local gas phase (Nenes et al., 1999 Ansari and
Pandis, 2000 Moya et al., 2001). It is still one
of the most challenging tasks to partition the
semi-volatile inorganic aerosol components
between the gas and aerosol phases correctly,
especially when the thermodynamic models are
incorporated in a 3-D air quality model. - 2. MODELING AEROSOL NITRATE THERMODYNAMICS AND
OBSERVATIONAL DATASETS - 2.1. Thermodynamic Models
- SO42-, TNH4 (NH4 NH3) and TNO3 (NO3-HNO3 ) as
input to - ISORROPIA the optimal solution of the
thermodynamic equations and precalculated tables,
whenever possible, to speed up (Nenes et al.,
1999). - AIM2-Model II (Clegg et al., 1998) a
theoretically complete and accurate phase
equilibrium model not apply any simplifying
assumptions. - 2.2. Observational Dataset
- At the Atlanta site PM2.5 SO42-, NO3-, and NH4
were measured with a 5-minute sampling (8/18 to
9/1, 1999) (Weber et al., 2003). NH3 (g) and
HNO3 (g) were measured with a time resolution of
15 and 9 minutes, respectively. Temperature and
RH were determined. Total 325 data points. - At the Clinton Horticultural Crop Research
Station, NC, PM2.5 NH4, NO3- and SO42-, and gas
NH3 and HNO3 were measured with 12-hour
resolution (1/20 to 11/2, 1999). Temperature and
RH were provided by State Climate Office of NC.
Shaocai Yu, Robin Dennis, Brian Eder, Shawn
Roselle, Athanasios Nenes, John
Walker Atmospheric Sciences Modeling
Division, National Exposure Research
Laboratory, Air Pollution Prevention and
Control Division, National Risk Management
Research Laboratory, U.S. EPA, NC 27711
Schools of Earth and Atmospheric Sciences and
Chemical and Biomolecular Engineering Georgia
Institute of Technology, Atlanta Georgia 30332
On assignment from the National Oceanic and
Atmospheric Administration, U.S. Department of
Commerce
- 3. Results and discussions
- 3.1. Test of thermodynamic models with
observational data - Figure 1 and Table 1 for Atlanta site 94 and
96 of the NH4 predictions are within a factor
of 1.5 for ISORROPIA and AIM2, respectively.
HNO3 86 (ISORROPIA) and 87 (AIM2) within a
factor of 1.5. However, both models cannot
reproduce most of observed aerosol NO3- and gas
NH3 ( NO3- 32 (ISORROPIA) and 48 (AIM2)
within a factor of 2, and NH3 25 (ISORROPIA)
and 51 (AIM2) within a factor of 2. - Figures 2 and 3 overprediction are associated
with low temperature (T), high RH and
sulfate-poor conditions (TNH4/SO42-gt2.0)
underpredictions are associated with high T, low
RH and sulfate-rich (TNH4/SO42-lt2.0). - Figure 4 both models reproduced observed NH3
concentrations very well (95 (ISORROPIA) and 97
(AIM2) within a factor of 1.5) at Clinton site. - Figure 5 Most of the cases at the Clinton site
are representative of very sulfate-poor
conditions. - The possible reasons for poor performance
- (1) a dynamic instead of an equilibrium model
may be more suitable for these cases. Moya et
al., (2001) a dynamic instead of an equilibrium
model was good for the cases with high T and low
RH values. - (2) Models are not able to accurately simulate
such cases for the conditions encountered Ansari
and Pandis (2000) metastable state assumption
predicted 11 higher of NO3- than stable state
assumption. - (3) Other ions (such as Na, Cl-, Ca2 and
Mg2) significant contributions to aerosol
components and their effects not considered
Coarse mode effects. - (4) Other mechanisms such as absorption by
carbonaceous aerosol instead of thermodynamic
equilibrium produce aerosol NO3- Middlebrook et
al. (2002) results at Atlanta site. - (5) There are significant errors in observations
of other important aerosol components (such as
SO42-) and TNH4. See section 3.3
- 3.2. Situation about simulations of SO42-,TNH4,
NO3- by 3-D air quality models - Table 2 and Figures 6 and 7 for CMAQ
reproduced 46-79 and 68-94 of SO42- within a
factor of 1.5 and 2, respectively reproduced
39-72 and 61-86 of NH4 within a factor of 1.5
and 2, respectively reproduced 11-31 and 17-51
of NO3- within a factor of 1.5 and 2,
respectively much lower than TNO3 (34-59 and
66-78 within a factor of 1.5 and 2,
respectively). - 3.3. Effects of errors in SO42-, TNH4, T and RH
on predicting aerosol NO3- - Test dataset of total 163 data points both
ISRROPIA and AIM2 predict the existence of
aerosol NO3-, based on observational data at the
Atlanta site. - Base-case results The prediction results of each
thermodynamic model for the test dataset before
introduced errors. - Figure 8 and Table 3
- both ISORROPIA and AIM2 have similar responses in
the predicted NO3- to the possible errors in
SO42- and TNH4 NO3- predictions by ISORROPIA are
modestly more sensitive to the errors in SO42-
and TNH4 than those by AIM2. - Conditions with -50 errors in TNH4 including
cases 5, 6 and 7, both ISORROPIA and AIM
underpredict almost all NO3- by more than a
factor of 2. - Conditions with 50 error in SO42- (case 1), or
50 error in TNH4 (case 2), or 50 error in
SO42- and 50 error in TNH4 (case 8), both model
cannot reproduce most of NO3- within a factor of
2 (percentagelt40). - Conditions with the case 3 (50 errors in both
SO42- and TNH4) and case 4 (-50 errors in
SO42-) relative less effects on the prediction
of NO3-. This is because of compensation error
from both SO42- and TNH4. - Figure 9 and Table 3 Responses of the NO3
predictions are less sensitive to errors in T and
somewhat less sensitive to errors in RH. ?20
errors in T and RH result in both models not
being able to reproduce most of NO3- within a
factor of 1.5 (percentagelt42)
Acknowledgements The authors wish to thank other
members at ASMD of EPA for their contributions to
the 2003 release version of EPA Models-3/CMAQ
during the development and evaluation. This work
has been subjected to US Environmental Protection
Agency peer review and approved for publication.
Mention of trade names or commercial products
does not constitute endorsement or recommendation
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