Particulate Matter and Acid Deposition Modeling by keralaguest


									Particulate Matter and Acid Deposition Modeling for
the Southern Appalachian Mountains Initiative
James Boylan, James Wilkinson, Yueh-Jiun Yang, Talat Odman and Armistead
Georgia Institute of Technology, 200 Bobby Dodd Way, Atlanta, GA 30332-0512

As part of the Southern Appalachians Mountains Initiative (SAMI), the effects of
emissions controls on air quality are being assessed using an integrated,
“one-atmosphere” modeling approach. The modeling system consists of RAMS for
meteorology, EMS-95 for emissions and the Urban-to-Regional Multiscale (URM)
model for transport and chemistry. With this system, the evolution of primary and
secondary gas-phase and aerosol-phase pollutants, can be followed and both PM10
and PM2.5 can be simulated. EMS-95 has the ability to inventory size- and species-
resolved aerosol emissions. URM, as applied to this project, includes a wet
deposition module, an aerosol module, and a direct sensitivity analysis module, which
simultaneously yields three-dimensional sensitivities of pollutant concentrations and
deposition fluxes to emissions. Nine episodes (each seven to ten days in duration)
will be simulated over the eastern United States using a fine grid of 12 km in and
around the SAMI states, and successively coarser grids, up to 192 km near the
boundaries of the domain. The results are used to develop seasonal and annual air
quality metrics and their response to emission controls, for an assessment of the
visibility and acid deposition in the region. In this paper, URM is evaluated for its
ability to accurately simulate PM2.5 concentrations, as well as, the deposition of acids
in four episodes. When compared to data from IMPROVE sites, the model predicted
concentrations for most fine aerosol species with less than 50 percent normalized
error. The wet deposition amounts are in good agreement with the NADP
observations when the high spatial and temporal variability of precipitation is
factored into the performance metrics. These results suggest that the modeling
system can be confidently used for assessing control strategy impacts. In this study,
the domain has been divided into eight source regions, and using the direct sensitivity
analysis feature of URM, the relationships between the aerosol levels in the southern
Appalachians and the emissions from various regions have been studied. SO2
emission reductions from different regions displayed very different levels of impact at
various sites within the SAMI region.

Studies that have been conducted in national parks and national forest wilderness
areas of the southern Appalachian Mountains have documented adverse air pollution
effects on visibility, streams, soil, and vegetation. Although it is known that air
pollution levels which currently affect park and wilderness resources come from
existing sources of pollution - large and small, mobile and stationary, near and
distant, the relative contribution of each source type to the regional air pollution
problem is not well quantified. The 1990 Clean Air Act Amendments (CAAA)
required major reductions in airborne pollutants. Although the reductions are
expected to produce air quality improvements, there is uncertainty whether the results
will be enough to protect and preserve the ecosystems and natural resources of the
southern Appalachians, especially in Class I areas.
        The Urban-to-Regional Multiscale (URM) model5 has been applied to the
SAMI modeling domain to characterize the air pollution formation processes that
affect air quality in the southern Appalachian Mountains. The URM model results
will be used to assess emission control strategies designed to reduce atmospheric
pollutants in the Class I areas.
        The URM model is a three-dimensional Eulerian photochemical model that
accounts for the transport and chemical transformation of pollutants in the
atmosphere. The URM model uses a finite element, variable mesh transport scheme6.
The gas-phase reaction kinetics are calculated using the SAPRC chemical
mechanism2. This mechanism has been updated to account for a more accurate
isoprene chemistry among other reactions. URM uses variable size grids in its
horizontal domain to effectively capture the details of pollution dynamics without
being computationally intensive. The URM model has been expanded to include
aerosol dynamics and wet deposition scavenging processes. The ISORROPIA
aerosol module4, the Reactive Scavenging Module1 for acid deposition, and the Direct
Decoupled Method9 for gas and aerosol sensitivities have been incorporated into the
URM framework to produce an integrated, “one atmosphere” airshed model. The
URM model and its setup for this project are discussed in more detail in Odman et

The aerosol module is capable of simulating concentrations of all major primary and
secondary components of atmospheric PM. There are three groups of aerosol species
that are considered in the aerosol routine. These include inert species, inorganic
equilibrium species, and organic species. The inert species include magnesium,
potassium, calcium, elemental carbon, and an “other PM” group which includes all
other inert PM species. The inorganic equilibrium species include sulfate, nitrate,
ammonium, sodium, chloride, and hydrogen ion. The organic aerosols are
represented by a lumped species which is the sum of numerous condensable organics
resulting from the oxidation of organic gases.
        A sectional approach is used for characterization of the continuous aerosol
size distribution by using four size bins: smaller than 0.156 µm, 0.156 - 0.625 µm,
0.625 - 2.5 µm, and 2.5 - 10.0 µm. The module simulates mass transfer and particle
growth occurring between the gaseous and aerosol species during condensation and
evaporation. The effects of nucleation and coagulation are ignored. The
ISORROPIA algorithm is used to model inorganic atmospheric aerosols.
ISORROPIA is a computationally efficient and rigorous thermodynamic model that
predicts the physical state and composition of the sodium-ammonium-chloride-
sulfate-nitrate-water aerosol system. The aerosol particles are assumed to be
internally mixed, meaning that all particles of the same size have the same
composition. The possible species for each phase are:
     Gas Phase: NH3, HNO3, HCl, H2O
     Liquid Phase: NH4+, Na+, H+, Cl-, NO3-, SO42-, HSO4-, OH-, H2O
     Solid Phase: (NH4)2SO4, NH4HSO4, (NH4)3H(SO4)2, NH4NO3, NH4Cl, NaCl,
                   NaHSO4, Na2SO4, H2SO4

The ISORROPIA mechanism contains fifteen equilibrium reactions that are solved
for in conjunction with their equilibrium constants4.
        The production of condensable organic species from oxidation of gaseous
organic compounds is based on the organic yields reported by Pandis et al.8 The
formation of condensable organic aerosol species is done in the chemistry module,
followed by the distribution of condensed organic aerosols to the four size bins in the
aerosol routine. Also, an algorithm to simulate particle deposition and gravitational
settling for particles of various sizes has been added. Inputs to the aerosol module
include temperature, relative humidity, air density, and gas and aerosol
concentrations. Outputs from the module are the updated equilibrium concentrations
for the gas (HNO3, NH3, and HCl) and the aerosol species.

The Reactive Scavenging Module (RSM) uses synoptic scale temperature and
precipitation rates to simulate a field of representative clouds which are defined by
scavenging rates, water profiles, and wind fields. The module simulates the time
dependent chemical kinetic interaction of these clouds with the gas and aerosol
species, as well as, simulating vertical convective transport within a column of air1.
Scavenging processes within the module include gas, aerosol, and microphysical
scavenging. Gas scavenging can occur in cloud water (equilibrium process based on
species solubilities), rain water (mass transfer rates are calculated for most species),
and snow (limited to nitric acid). Aerosol scavenging is treated by nucleation and by
inertial impaction processes. Microphysical scavenging refers to processes that not
only transfer water from one water category to another, but also transfer the water-
bound pollutants.
         There are a number of input parameters that are required to be passed from
URM to the RSM module, including temperatures and pressures for all vertical layers
at each computational node. There are five additional meteorological parameters that
are required to be passed: (1) total area averaged precipitation rate of convective and
stratiform clouds, (2) fraction of precipitation associated with convective rain, (3)
fraction of grid area covered by convective storms, (4) cloud top height of convective
storm, and (5) cloud top height of stratiform storm.
         The other required inputs are the gas and aerosol concentration profiles for the
species that will be scavenged and/or transported via the convective clouds. The
scavenged species are sulfur dioxide, aerosol sulfate, ozone, nitric acid, aerosol
nitrate, hydrogen peroxide, ammonia, ammonium aerosol, and soluble crustals (Mg
and Ca). Other gas and aerosol species are passed into the RSM module where
vertical convective transport is simulated. Output from the module includes updated
concentration profiles for all the species affected by scavenging and convective cloud
transport, in addition to wet deposition mass fluxes for SO2, SO4, NO3, H2O2, NH4,
Mg, Ca, K, and H-ion.

The URM modeling domain covers the eastern half of the United States. The
multiscale grid dimensions correspond to 192, 96, 48, 24, and 12 km. We have
placed the fine 12 km grid over the southern Appalachian Mountains and the adjacent
areas that are expected to most directly influence the air quality in the region of
interest. The coarse grid is placed over the boundary cells and in areas that are not
expected to significantly contribute to the air quality in the southern Appalachian
Mountains. The domain height is 12,867 m and is divided into seven vertical grid
layers. The thickness of each layer from the ground to the top of the domain are:
19 m, 43 m, 432 m, 999 m, 1779 m, 3588 m, and 6007 m. The use of finer resolution
near the surface of the domain, as compared to the coarser resolution aloft, allows the
steeper concentration gradients that typically exist in the near-surface troposphere and
the evolution of the mixing depths during the day to be captured. Also, the SAMI
modeling domain has been divided up into sub-domains in order to perform
sensitivity analysis. Figure 1 shows the URM modeling grid along with the eight
sub-domains that will be considered in assessing emission reductions.

               Figure 1: SAMI modeling domain and sensitivity sub-domains
A total of nine episodes will be used to develop seasonal and annual air quality
metrics to assess visibility and acid deposition problems in the region. This paper
focuses on four episodes: July 11 - 19, 1995; May 24 - 29, 1995; May 11 - 17, 1993;
and March 23 - 31, 1993. A comprehensive set of statistical calculations has been
performed to determine the ability of the model to accurately estimate ambient
aerosol concentrations and acid deposition mass fluxes. Among the statistical
measures examined are mean bias, normalized bias, mean error, and normalized error.
The statistical calculations were done using the Modeling Analysis and Plotting
System (MAPS) package3. The results will be summarized below.

Aerosol Performance
Aerosol performance was evaluated by comparing modeling results to observations
taken from the Interagency Monitoring of Protected Visual Environments
(IMPROVE ) monitoring network. The species that were compared include fine (<
2.5 m) sulfate, fine nitrate, fine ammonium, fine elemental carbon, fine organic
carbon, fine soils (crustals), total PM2.5, and total PM10. There are eighteen
IMPROVE monitoring sites in the modeling domain. However, five of those sites are
located near or on the boundary of the modeling domain and are easily influenced by
the boundary conditions. Therefore, only observations from the remaining thirteen
stations are used to determine the aerosol model performance. Table 1 lists the
resolution of the URM grid cells containing these stations. IMPROVE measurements
are taken twice each week (Wednesday and Saturday) and are reported as a twenty-
four hour average concentration. The four nearest nodes to each IMPROVE station
are distance weighted and used to determine the aerosol concentration at each
monitoring site.
Table 2 shows a summary of the normalized mean error for all the stations in the
SAMI states for each day that IMPROVE measurements were available throughout
the four modeled episodes. The normalized mean error (NME) is calculated as:

                                                cie  cio
                             NME                            100%
                                   N     i 1

        e                                                                    o
where ci is the model-estimated 24-hour aerosol concentration at station i, ci is the
observed 24-hour aerosol concentration at station i, and N equals the number of
estimate-observation pairs drawn from all valid monitoring station data on the
simulation day of interest.

        Table 1: URM grid resolution for IMPROVE stations.
          IMPROVE STATION                      Grid cell size (km)
          Brigantine (NJ)                              48
          Dolly Sods/Otter Creek (WV)                  12
          Great Smoky Mountains (TN)                      12
          Jefferson/James River Face (VA)                 12
          Lye Brook (VT)                                  96
          Mammoth Cave (KY)                               24
          Okefenokee (GA)                                 96
          Cape Romain (SC)                                48
          Shenandoah (VA)                                 12
          Shining Rock (NC)                               12
          Sipsy (AL)                                      12
          Upper Buffalo (AR)                              96
          Washington, D.C.                                24

         Typically, the largest portion of fine PM consists of sulfate. Sulfate is
produced by the gas phase reaction of SO2 with OH or heterogeneously by reacting
SO2 with H2O2 and/or ozone when a rain, cloud, or fog droplet is present. The mean
normalized mean error is calculated by averaging the normalized mean errors for each
of the 10 IMPROVE days that measurements were taken. These values are reported
for each species in Table 2. The mean normalized mean error for sulfate is less than
45%. Figure 2 shows a scatter plot of observed versus modeled sulfate concentrations
for all the IMPROVE stations in the domain for each episode. Aerosol nitrate is
formed by the condensation of nitric acid into the aqueous or salt complex form. Its
concentration depends on the amount of gas-phase nitric acid, ammonia, and sulfate
that is available. The nitrate concentrations are usually low (less than 1 g/m3);
therefore, the normalized error can be very high. Ammonium usually presents
              Sulfate                             Nitrate                            Ammonium
             Norm. Error                        Norm. Error                          Norm. Error
  Date          (%)                  Date           (%)                  Date           (%)
 7/12/95       16.60                7/12/95       331.12                7/12/95        23.97
 7/15/95       14.54                7/15/95       133.81                7/15/95        33.14
 7/19/95       41.33                7/19/95        73.05                7/19/95        42.80
 5/24/95       51.34                5/24/95       238.15                5/24/95        31.51
 5/27/95       31.74                5/27/95       128.08                5/27/95        17.28
 5/12/93       46.10                5/12/93       193.39                5/12/93        42.51
 5/15/93       32.24                5/15/93      1092.96                5/15/93        31.82
 3/24/93       27.94                3/24/93       412.42                3/24/93        33.83
 3/27/93       67.55                3/27/93        88.12                3/27/93        58.39
 3/31/93       107.60               3/31/93       261.20                3/31/93        125.88
 MEAN          43.70                MEAN          295.23                MEAN           44.11

              Organics                         Elem. Carbon                             Soils
             Norm. Error                        Norm. Error                          Norm. Error
  Date          (%)                  Date           (%)                  Date            (%)
 7/12/95       61.71                7/12/95        37.22                7/12/95        212.36
 7/15/95       67.64                7/15/95        42.28                7/15/95        104.91
 7/19/95       46.89                7/19/95        58.20                7/19/95        321.54
 5/24/95       51.90                5/24/95        28.17                5/24/95        89.42
 5/27/95       38.92                5/27/95        26.43                5/27/95        229.66
 5/12/93       39.50                5/12/93        73.95                5/12/93        176.63
 5/15/93       57.83                5/15/93        38.17                5/15/93        114.63
 3/24/93       24.38                3/24/93        39.89                3/24/93        703.68
 3/27/93       59.36                3/27/93        74.19                3/27/93        744.38
 3/31/93       48.13                3/31/93        49.13                3/31/93        239.58
 MEAN          49.63                MEAN           46.76                MEAN           293.68

              PM_2.5                              PM_10                              PM_Coarse
             Norm. Error                        Norm. Error                          Norm. Error
  Date          (%)                  Date           (%)                  Date           (%)
 7/12/95       35.34                7/12/95        26.88                7/12/95        122.11
 7/15/95       32.50                7/15/95        26.80                7/15/95        60.17
 7/19/95       28.51                7/19/95        34.30                7/19/95        104.70
 5/24/95       41.01                5/24/95        27.26                5/24/95        43.44
 5/27/95       10.67                5/27/95        37.53                5/27/95        202.34
 5/12/93       35.02                5/12/93        37.76                5/12/93        85.58
 5/15/93       22.99                5/15/93        21.84                5/15/93        54.42
 3/24/93       22.52                3/24/93        62.50                3/24/93        283.30
 3/27/93       66.20                3/27/93        60.89                3/27/93        128.55
 3/31/93       109.75               3/31/93        85.68                3/31/93        131.28
 MEAN          40.45                MEAN           42.14                MEAN           121.59
 Table 2: Aerosol mean normalized errors for July '95, May '95, May '93, and March
                                  '93 episodes.

itself in the form of ammonium sulfate ((NH4)2SO4), ammonium bisulfate
(NH4HSO4), ammonium nitrate (NH4NO3), and/or mixed salts. The ammonium
concentration primarily depends on the amount of sulfate and gas-phase ammonia
that is available. There are no direct measurements of ammonium at the IMPROVE
stations. Therefore, for the purpose of ammonium performance evaluation, it has
been assumed that the sulfate and nitrate are completely neutralized with NH4. The
mean normalized mean error for the ammonium aerosol is similar to that of sulfate
(i.e. less than 45%).
         The amount of organic aerosols formed in the atmosphere is determined by
using measured organic aerosol yields. Almost all of the model predictions are biased
low. This can possibly be attributed to an under prediction in the organic aerosol
yields that were used or a deficiency in the emission inventory. The mean normalized
mean error is still less than 50%. Elemental carbon (EC) is an inert primary emission
species. The observations for EC are typically very low (around 0.5 g/m3). The
model results match well with observations and have an average mean error of less
than 0.25 g/m3. The aerosol species that are lumped into “soils” consist of calcium
and “other” PM. The predictions typically are biased high. There are high
uncertainties in the emission inventories for these crustal species, which may lead to
the overprediction.
         In order to determine PM2.5, all the aerosol species for the first 3 size bins
were summed together. To determine the PM10 concentrations, the coarse aerosol
fractions (2.5 – 10.0 m) were added to the PM2.5 concentrations. There is good
agreement between model predictions and observation (typically less than 40%
normalized mean error) for most of the episode days, except for the March 1993
episode. This larger discrepancy in the March 1993 episode is mainly due to the high
errors associated with the sulfate, ammonium, and soils.

Acid Deposition Performance
Wet acid deposition performance was evaluated by comparing modeling results to
observations taken from the National Atmospheric Deposition Program (NADP)
monitoring network. The species that were compared include sulfate, nitrate,
ammonium, hydrogen ion, and crustal cations (Mg and Ca). NADP measurements
are taken once each week (Tuesday) and the concentrations and precipitation are
reported as a 7-day cumulative. Dividing the measured concentration by the
precipitation results in depositions with units of mass flux. Since there is a large
spatial variation in precipitation and the RAMS model results do not match observed
precipitation at exact locations, the NADP observations are compared to the best (i.e.,
closest to the observation) model result within a 30 km radius from the monitoring
site. There are eighty-three NADP monitoring sites in the modeling domain.
However, since wet deposition is very localized, only data from the fourteen stations
in the 12 km grids are used to determine model performance. These stations are listed
in Table 3.

             Table 3: NADP stations falling into the 12-km URM grid
   STATION                                         COUNTY                     STATE
   Sand Mountain Exp. Station                      De Kalb                      AL
   Georgia Station                                 Pike                        GA
   Lilley Cornett Woods                            Letcher                     KY
   Coweeta                                         Macon                       NC
   Piedmont Research Station                       Rowan                       NC
   Mt. Mitchell                                    Yancey                      NC
   Walker Branch Watershed                         Anderson                     TN
   Wilburn Chapel                                  Giles                        TN
    Great Smokey Mountains National Park –            Sevier                     TN
    Charlottesville                                   Albemarle                 VA
    Horton’s Station                                  Giles                     VA
    Shenandoah National Park                          Madison                   VA
    Babcock State Park                                Fayette                   WV
    Parsons                                           Tucker                    WV
        Sulfate and nitrate are two of the most important wet deposition species. They
both show low normalized mean error. The average mass fluxes and normalized mean
errors for the four wet deposition episodes are located in Table 4. The mean
normalized mean errors for sulfate and nitrate are 35.4% and 31.8%, respectively.
Figure 3 shows a scatter plot of observed versus modeled wet sulfate deposition mass
fluxes for all the NADP stations in the 12 km grid for each episode. The correlation
between modeled and observed sulfate mass fluxes are very good, except for the May
1995 episode which shows an overprediction.

 Table 4: Wet deposition performance for July '95, May '95, May '93, and March '93
                     Sulfate                                                Nitrate
                    Mass Flux    Norm. Error                               Mass Flux     Norm. Error
                    (mg/m2)         (%)                                    (mg/m2)          (%)
July 11-18, 1995      64.49        26.97               July 11-18, 1995     34.03          27.52
May 23-30, 1995       42.56        79.34               May 23-30, 1995      29.29          42.80
May 11-18, 1993      113.81        14.73               May 11-18, 1993      55.66          44.65
March 23-30, 1993     83.40        20.57               March 23-30, 1993    56.21          12.26
       MEAN           76.07        35.40                      MEAN          43.80          31.81

                    Ammonium                                                H-ion
                     Mass Flux   Norm. Error                               Mass Flux     Norm. Error
                     (mg/m2)        (%)                                    (mg/m2)          (%)
July 11-18, 1995       7.78        160.72              July 11-18, 1995      1.36          46.92
May 23-30, 1995        6.79        264.14              May 23-30, 1995       0.85          13.87
May 11-18, 1993       15.01        83.56               May 11-18, 1993       1.93          16.55
March 23-30, 1993      7.70        484.68              March 23-30, 1993     1.97          64.37
       MEAN            9.32        248.28                     MEAN           1.53          35.43

                    Calcium                                                Magnesium
                    Mass Flux    Norm. Error                                Mass Flux    Norm. Error
                    (mg/m2)         (%)                                     (mg/m2)         (%)
July 11-18, 1995      2.45         31.48               July 11-18, 1995       0.30         30.66
May 23-30, 1995       2.30         81.74               May 23-30, 1995        0.42         144.67
May 11-18, 1993       4.94         10.96               May 11-18, 1993        0.79         59.05
March 23-30, 1993     2.13         191.81              March 23-30, 1993      0.46         419.17
       MEAN           2.95         79.00                      MEAN            0.49         163.39

       The ammonium wet deposition is biased high in all four episodes. The mean
normalized mean error is approximately 250%. The hydrogen ion deposition is
biased low in all four episodes with a mean normalized mean error of approximately
35%. Both the calcium and magnesium deposition fluxes are biased low in the July
1995 episode and biased high in the other three episodes. The mean normalized mean
errors for calcium and magnesium are 79% and 163%, respectively. Some of the
discrepancies between modeled and measured fluxes can be attributed to differences
in the modeled and observed precipitation rates.

                            Sulfate Wet Deposition

 Model (mg/m2)

                  100                                                                   May_1993


                        0                   50      100      150      200      250

                                            Observed (mg/m2)

                                  Fine Sulfate Aerosols

 Model ( g/m3)

                  15.00                                                                   March_1993
                  10.00                                                                   May_1993


                       0.00                 5.00     10.00    15.00    20.00    25.00

                                                 Observed ( g/m3)
Figure 2: Modeled vs. observed sulfate                                                                 Figure 3: Modeled vs. observed sulfate
          aerosol concentrations                                                                                  wet deposition mass

Using air quality models to calculate the sensitivity of gas and aerosol phase
concentrations and wet deposition fluxes to input and system parameters is important
in determining the most effective control strategies. In order to most efficiently and
accurately determine sensitivities, the Decoupled Direct Method (DDM) has been
integrated into the model. Using direct derivatives of the equations governing the
evolution of species concentrations, the local sensitivities to a variety of model
parameters and inputs are computed simultaneously with the species concentrations.

                                                                      GSM Daily Average Sulfate Sensitivity
                                                                       (10% Reduction in SO2 Emissions)
                            D Sulfate (%)

           Figure 4: Fine sulfate sensitivity to a 10 percent SO2 reduction.

Preliminary sensitivity runs were made for the July 2010 episode using the 1995
meteorological data with a 2010 emission inventory. Sensitivity coefficients were
computed each hour for all the grid cells in the domain. By examining sensitivities at
a specific station, we can determine the sub-domain from which emission reductions
would have the greatest effect. Figure 4 shows a 6-day stacked bar chart for daily
averaged sulfate sensitivities at the Great Smokey Mountains National Park. The
sulfate sensitivities represent the percent change in sulfate concentrations due to a
10% reduction in the west-inner sub-domain (WI), north-inner sub-domain (NI),
south-inner sub-domain (SI), east-inner sub-domain (WI), all outer sub-domains
(AO), and domain wide (sum of WI, NI, SI, EI, and AO). In the Great Smokey
Mountains National Park, it can be seen that different sub-domains can have varying
contributions to the overall reduction of sulfate from day-to-day depending on the
specific meteorology. For example, on July 11, a 10% reduction in domain-wide SO2
emissions will result in a 3% reduction in the sulfate concentration at GSM, with 2%
due to SO2 reductions in AO and 1% due to SO2 reductions in WI. It should be noted
that the sensitivity results reported above are just an example of the type of results
that are available. Since this is a work in progress and the data presented above is a
partial data set, any inferences that might be drawn could be significantly altered
when the rest of the data becomes available.

With the integration of the ISORROPIA aerosol module and the Reactive Scavenging
Module into the URM model, a “one atmosphere” model has been created which can
be used to predict aerosol concentrations and wet acid deposition mass fluxes for the
SAMI modeling domain. Typically, the mean normalized error for most of the
aerosol and wet deposition species is less than 50%. Through the use of sensitivity
analysis, control strategies can be developed to effectively reduce the impact of
aerosols and acid deposition on the southern Appalachian Mountain air quality.

This project is funded by the Southern Appalachian Mountains Initiative. The
authors thank the members of the SAMI Atmospheric Modeling Subcommittee for
their constructive criticism, innovative ideas and invaluable help throughout this

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Air quality, aerosols, wet deposition, modeling, SAMI, model performance,
sensitivity analysis

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