1. Global distribution of minerals in arid soils
as lower boundary condition in dust models
Slobodan Nickovic(*)
Atmospheric Research and Environment Department
World Meteorological Organization (WMO)
Geneva, Switzerland
(*)This study has been performed out of the author’s office duties;
results shown here does not therefore necessarily reflect views of WMO
AGU2010 Vienna 2-7 May 2010

2. Minerals in dust Dust aerosol - a complex mixture of minerals
Major minerals in arid soils
Clays
Illite, Kaolinite, Smectite, Calcite, Quartz
Silts
Quartz, Feldspars, Calcite, Hematite, Gypsum
AGU2010 Vienna 2-7 May 2010

3. Impacts of dust minerals
Ocean bioproductivity
Input through dust deposition over open ocean
Major nutrients
Iron
Illite, Kaolinite, Smectite
iron limits the primary marine productivity
Silica
Quartz, Clay
controls growth of siliceous phytoplankton
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4. ATMOSPHERIC Fe PROCESSING
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5. Impacts of dust minerals
Solar radiation forcing
Different behavior of minerals
Hematite – high absorption in visible part
Carbonates – small absorption in thermal part
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6. Impacts of dust minerals (contd.)
Health and dust minerals
Major agents
Iron
Hypothesis: Fe as enhancement factor in meningitis outbreaks (Thompson, 2007, 2008)
Hypothesis: Fe could be readily mobilized in the lung once the particles are deposited on lung tissue (Prospero, 1999)
Silica
Crystalline silica listed as a human carcinogen by WHO
Silicosis: inflammation of the lung, leading to fibrosis, caused by foreign bodies, especially inhaled silica particles
AGU2010 Vienna 2-7 May 2010

7. Impacts of dust minerals (contd.)
Meningitis and Fe
Neisseria meningitidis increase rapidly in the presence of Fe
Potential dust mechanisms include:
Physical damage to epithelial cells in lung paths
Enhanced activation of bacteria through high Fe content
Thomson et al, 2007, Dust and epidemic meningitis in the Sahel: a
public health and operational research perspective
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8. Impacts of dust minerals (contd.) Pulmonary diseases and Fe
Fe can assist in damaging the epithelial pulmonary cells
Toxic reaction of lung tissue to dust particles is proportional to Fe solubility
Bio-reactivity depends on type of Fe in dust minerals
Centeno, J.A, 2009: Chemical and Pathology Studies of
Particulate Matter A Medical Geology Perspective
AGU2010 Vienna 2-7 May 2010

9. Impacts of dust minerals (contd.) Silica
Courtesy: Centeno, J.A, 2009: Chemical and Pathology Studies of
Particulate Matter A Medical Geology Perspective
AGU2010 Vienna 2-7 May 2010

10. Impacts of dust minerals (contd.)
Ice nucleation and dust
Different nucleation for different minerals
Key nucleation factors:
Mineralogy
Temperature
Relative humidity
RH (%)
T (oC)
(Zimmermann et al, 2006)
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11. Impacts of dust minerals (contd.)
Ice nucleation and dust (contd.)
Dust – excellent ice nucleation agent
observations
DREAM model
From: Klein et al., 2009, Saharan Dust and Ice Nuclei Over Central Europe
(submitted)
See also
EGU2010, Room 12 / Fri, 07 May, 13:30–17:0016:15–16:30
Klein at al., Seasonal variability of ice nuclei over Central Europe
AGU2010 Vienna 2-7 May 2010

12. Global 1km database of minerals
New 1-km dataset for global mineral distribution is developed
Data used:
USGS 1km global land cover
STASGO 1km soil textures
FAO 5’ soil types
Mineral distribution dataset provides fractions of 7 minerals as input in DREAM dust model
AGU2010 Vienna 2-7 May 2010

13. Global 1km data base of minerals (contd.)
Steps in developing new data base:
First step – find relative mineral fractions:
linking normalized (relative) mineral fractions CL and SL to 21 dust-productive soil types in each grid-point of the global FAO-UNESCO 5-min dataset
CL and SL are then interpolated from the global 5-min grid into the 30-sec grid
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14. Relative contributions of CLAY minerals
Claquin et al, (1999)
mineralogy table
Relative contributions of CLAY minerals
Relative contributions of SILT minerals
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15. Global 1km data base of minerals (contd.)
Second step - find effective fractions of clay and silt:
Selection of four populations: clay, silt, fine/medium sand and coarse sand for soil textures (e.g. Tegen et al. 2002)
12 soil textures based on 1km global USGS-STATSGO/FAO dataset
Linking populations to soil textures based on “soil triangle”
Shirazi et al extended “triangle” to “tetrahedron”
by adding rock as a population
Effective percentages of four soil populations
specified in 30sec global grid
AGU2010 Vienna 2-7 May 2010

16. Global 1km data base of minerals (contd.)
Final step – find effective mineral fractions:
Effective fractions for minerals (1km global grid)
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17. Tegen et al. (2002) table modified with Shirazi et al. (2001)
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18. Clay soils
Illite
Kaolinite
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19. Clay soils
Smectite
Calcite
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20. Clay soils
Quartz
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21. Silt soils
Quartz
Feldspar
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22. Silt soils
Calcite
Hematite
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23. Fe-carrying clay fractions illite+kaolinite+smectite
Fe-carrying silt fractions
feldspar+hematite
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24. 1km data on minerals archived in DEM-like tiles
Data sub-sets: 10 minerals x 27 tiles
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25. What are effective Fe fractions in considered Minerals?
Are Fe in every mineral equally soluble?
Minerals (grouped) Fe
(%)
Fe solubility (%)
(hydro)-oxide 65
0.005
Illite 4
1.5
Smectite 10
0.3
Kaolinite
0.24
4.26
Feldspar
0.34
2.5
More Fe content  Less Fe solubility !!
(useful for parameterization of Fe model solubility process)
From: Journet et al, 2008
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26. DREAM DUST/IRON GOVERNING EQUATIONS Number of equations:
N (bins) x 10 (minerals)
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27. Fe – ocean fertilizer Bloom of Trichodesmium Canary Islands
August 2004 (Ramos et al., 2008)
Fe solubility (% ); 22 July UTC
Surface dust concentration (μg m-3 )
24 July UTC
Fe solubility (% ); 24 July UTC
AGU2010 Vienna 2-7 May 2010

28. Canary Islands Fe – ocean fertilizer
Soluble Fe (ng m-3 ) July 2004
Fe Solubility (%) July 2004
Dust concentration
(μg m-3)
(Ramos et al, 2008)
AGU2010 Vienna 2-7 May 2010

29. Fe and Si – minerals affecting health
Examples of simulated Fe and Si surface concentrations
If daily predicted, such information could be a basis for predicting environmental conditions affecting dust health
Fe (μg m-3 ) 24 July 2004, 12UTC
Si (mg m-3 ) 24 July 2004, UTC
AGU2010 Vienna 2-7 May 2010

30. Conclusions
Developed high-resolution database for minerals in arid soils is useful information
for studying ocean productivity driven by mineral deposition
for parameterizing ice nucleation dependency on minerals
for predicting mineral fractions adversely affecting health
for improving modeling of radiation forcing dependent on dust mineral composition
THANK YOU
AGU2010 Vienna 2-7 May 2010