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

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

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 AGU2010 Vienna 2-7 May 2010

ATMOSPHERIC Fe PROCESSING AGU2010 Vienna 2-7 May 2010

Impacts of dust minerals Solar radiation forcing Different behavior of minerals Hematite – high absorption in visible part Carbonates – small absorption in thermal part AGU2010 Vienna 2-7 May 2010

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

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 AGU2010 Vienna 2-7 May 2010

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

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

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) AGU2010 Vienna 2-7 May 2010

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

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

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 AGU2010 Vienna 2-7 May 2010

Relative contributions of CLAY minerals Claquin et al, (1999) mineralogy table Relative contributions of CLAY minerals Relative contributions of SILT minerals AGU2010 Vienna 2-7 May 2010

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. 2001 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

Global 1km data base of minerals (contd.) Final step – find effective mineral fractions: Effective fractions for minerals (1km global grid) AGU2010 Vienna 2-7 May 2010

Tegen et al. (2002) table modified with Shirazi et al. (2001) AGU2010 Vienna 2-7 May 2010

Clay soils Illite Kaolinite AGU2010 Vienna 2-7 May 2010

Clay soils Smectite Calcite AGU2010 Vienna 2-7 May 2010

Clay soils Quartz AGU2010 Vienna 2-7 May 2010

Silt soils Quartz Feldspar AGU2010 Vienna 2-7 May 2010

Silt soils Calcite Hematite AGU2010 Vienna 2-7 May 2010

Fe-carrying clay fractions illite+kaolinite+smectite Fe-carrying silt fractions feldspar+hematite AGU2010 Vienna 2-7 May 2010

1km data on minerals archived in DEM-like tiles Data sub-sets: 10 minerals x 27 tiles AGU2010 Vienna 2-7 May 2010

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 AGU2010 Vienna 2-7 May 2010

DREAM DUST/IRON GOVERNING EQUATIONS Number of equations: N (bins) x 10 (minerals) AGU2010 Vienna 2-7 May 2010

Fe – ocean fertilizer Bloom of Trichodesmium Canary Islands August 2004 (Ramos et al., 2008) Fe solubility (% ); 22 July 2004 12UTC Surface dust concentration (μg m-3 ) 24 July 2004 12UTC Fe solubility (% ); 24 July 2004 12UTC AGU2010 Vienna 2-7 May 2010

Canary Islands Fe – ocean fertilizer Soluble Fe (ng m-3 ) 21-31 July 2004 Fe Solubility (%) 21-31 July 2004 Dust concentration (μg m-3) (Ramos et al, 2008) AGU2010 Vienna 2-7 May 2010

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

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