SUPPLEMENTARY MATERIAL
DUST EMISSION MODELING FOR THE WESTERN BORDER REGION OF
MEXICO AND THE UNITED STATES
Johana M. Carmona,1 Ana Y. Vanoye,1 Fabian Lozano,2 and Alberto Mendoza1.
1
Department of Chemical Engineering, 2 Center for Environmental Quality
Content:
1. Emission versus ambient air concentrations at one location
Figure S1. Map of Higley Air Quality site (ID: 04-013-4006) located at Gilbert, AZ
urban center.
Figure S2. Predicted PM10 emission rates (base case scenario) versus observed
PM10 concentrations during the entire episode at Higley Air Quality site, AZ.
Figure S3. Predicted versus observed PM10 concentration in the base case
scenario and sensitivity tests for January 8th, 2006 at Higley Air Quality site, AZ.
2. Dataset references
3. Acknowledgment
1. Emission versus ambient air concentrations at one location
The Higley Air Quality site (ID: 04-013-4006) was selected for this analysis. The
site represents both urban and rural emissions in a semi arid region. The Higley
station is located in Gilbert, AZ (33º 18' 38.664" N -111º 43' 21.179" W) in the
Maricopa county, southeast of Phoenix, within the Phoenix metropolitan area
(Figure S1).
Figure S1. Map of Higley Air Quality site (ID: 04-013-4006) located at Gilbert, AZ
urban center.
The simulated hourly dust emissions (g km−2) were compared against surface
hourly PM10 concentrations (µg m−3) data reported by the U.S. Environmental
Protection Agency (EPA) (see section 4 in this SM document for data references).
Simulated dust emissions were extracted from one grid cell of the modeling
domain, with the same location of the monitoring EPA site in Gilbert, AZ (33º 18'
38.664" N -111º 43' 21.179" W).
As a result of this comparison, a correlation (r2) of 48% was obtained for the entire
modeling period using the base case results. p-values suggest that the observed
concentration could be a predictor of the simulated emissions in this location;
however, other issues including variation in aerosol type, sources contributions and
transport dynamics must be completely understood before establishing a
relationship between emissions and concentrations. Additionally, the time series of
PM10 concentrations along with time series of simulated PM10 emissions were
analyzed. Figure S2 illustrates a similar temporal behavior between predicted
emissions and observed concentration. Results indicate that, considering the 4-km
domain, the model was able to replicate with reasonable accuracy events of high
PM10 concentrations. In some periods, few or no dust emissions were predicted;
this could mean that the threshold wind velocities necessary to re-suspend dust
were not reached and the dust model was not able to reproduce the events.
Figure S2. Predicted PM10 emission rates (base case scenario) versus observed
PM10 concentrations during the entire episode at Higley Air Quality site, AZ.
The wind erosion model was subject to three sensitivity tests based on perturbation
of the input surface parameters: soil density (SD), plastic pressure (PP), Leaf Area
Index (LAI) and fraction of vegetation cover. The sensitivity analysis was performed
following a “brute-force” approach: the wind erosion model was run changing one
parameter of interest at a time.
In the base case scenario, the statistical majority value contained in each
computational cell was selected for estimating LAI and FPAR. In addition, the
average value was selected in order to test the sensitivity of the estimated
emissions to this choice. Satellite data were used to estimate this values (see
references)
In the base case soil density and plastic pressure were given the values of 1,000
kg m−3 and 20,250 N m−2, respectively. In the sensitivity analysis were tested with
1,700 kg m3 and 50,000 N m2, respectively. These last values were the highest
values reported in NRCS (2005) for the soil types found in the United States
domain (see section 2 in this SM document for data references).
We then compared the simulated dust emissions (g km−2) on hourly basis, with
surface hourly PM10 concentrations (µg m−3) data. As a result of this comparison, a
correlation (r2) of 78% was obtained for January 8th in the base case. Additionally,
the time series of PM10 concentrations along with time series of simulated PM10
emissions were plotted for the base case and sensibility scenarios. Figure S3
illustrates a similar temporal behavior between predicted emissions and observed
concentration in all scenarios. When dust emissions increased, usually PM
concentration increases. Overall, we concluded that the model output was more
sensitive to changes in soil parameters (soil density and plastic pressure) than to
changes in land surface data (Leaf Area Index and Fraction of Vegetation Cover).
However, it can be observed in Figure S3 that for the specific site selected, the
peak emission rate varied the least under the soil density case.
Variable
PM10
Base Case
Density
Pressure
LAI/FVEG
100
90
80
70
300
60
50
200
40
30
100
20
PM10 predicted (g km-2)
PM10 observed (ug m-3)
400
10
0
0
2
4
6
8
10
12 14
Hour
16
18
20
22
24
Figure S3. Predicted versus observed PM10 concentration in the base case
scenario and sensitivity tests for January 8th, 2006 at Higley Air Quality site, AZ.
2. Dataset references
Pedological map for Mexico side is available at http://www.conabio.gob.mx/informacion/gis/
Soil
map
and
Database
for
US
side
are
avaliable.at
http://datagateway.nrcs.usda.gov/
Raw
data
(sample
data
as
reported)
are
available
at
http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html#Raw
Satellite MOD15A2 products used for estimating Leaf Area Index and vegetation
coverage were:
MCD15A2.A2006001.h08v05.005.2008091210144.hdf
MCD15A2.A2006001.h08v06.005.2008091211621.hdf
MCD15A2.A2006001.h09v05.005.2008091211540.hdf
3. Acknowledgment
Authors would like to thank to the MODIS Land Science Team and the EPA team
for their contributions to the production data sets using in this research.