1. Causes of Dust. Data Analysis Ilias Kavouras, Vic Etyemezian, Dave DuBois, Mark Green, Marc Pitchford, Jin Xu Division of Atmospheric Sciences, Desert Research Institute

2. Scope and methodology Scope: identify and quantify sources of airborne dust –Local and regional windblown dust –Long-range transported dust (e.g. Asia) –Wildfire-related dust –Other unknown sources Approach: Analysis of IMPROVE network and meteorological data –Chemical fingerprints of dust (e.g. Asian, wildfire-related) –Multivariate statistical analysis of Dust concentrations, wind speed/direction and precipitation

3. Database development CASTNETAZDEQ NPSISH RAWS NASA Central Meteorological Database Days with precipitation for more than 12h or precipitation occurred after 12:00 p.m. Modified Central Meteorological Database Grouped in 16 categories according to wind speed/direction WS1=0-14, WS2=14-20, WS3=20-26, WS4>26 mph WD1A=315-45, WD2A=45-135, WD3A=135-225, WD4A=225-315 WD1B=0-90, WD2B=90-180, WD3B=180-270, WD4B=270-360 “Dust” Meteorological Database IMPROVE database “Dust” Database

4. “Model” Database Regression coefficients Sensitivity analysis GPS data Maps for each day “Dust” event YES/NO Meteo-data YES/NO Precipitation YES/NO When? 0-12 or 12-24 IMPROVE-data YES/NO “Worst” day YES/NO “Worst dust” day YES/NO

5. Statistical analysis – Multi-linear regression analysis Measurement inter-correlations: Durbin-Watson test: mostly higher than 1.4 Tolerance: higher than 0.80 Linear regression was done using three methods: Forward selection: One component is added (if p> [set value], rejected) Backward selection: One component is removed if p> [set value] Stepwise selection: One component is added; those with p > [set value] are eliminated

6. Statistical analysis – Criteria development Significance level: 0.100 or 0.150 or higher Valid prediction: C predicted – E predicted > 0 or P 0.05,Measured

7. Monthly variation of model – “dust” days

8. AGTI0DOME0MELA173SAGU80TRIN0 BADL388GICL62MEVE26SAPE57ULBE10 BALD140GRBA0MOHO0SAWE123WEMI117 BAND93GRCA0MONT0SAWT8WHIT644 BIBE149GRSA145NOAB0SEKI0WHRI127 BLIS254GUMO367PASA2SIAN62WICA0 BOAP27HAVO0PHOE0SIME0YELL0 BRCA302HILL191PINN0SNPA0YOSE0 BRLA0HOOV19PORE0SPOK24ZION46 CANY96IKBA56PUSO16STAR1 CHIR19JOSH6QUVA123SYCA0 CORI341KALM129ROMO0TCRC0 CRMO577LABE12SACR407THIS0 DENA0LAVO0SAGA0THRO116 DEVA390LOST69SAGO0TONT3 Dust days per site (based on regression analysis)

9. 1. Salt Creek – descriptive statistics Monitoring period: 01/01/01 – 12/31/03 IMPROVE database completeness: 93.2% Meteorological database completeness: 82.4% All days (n=309)80% Worst daysWorst dusty days MeanSt. ErrorMinimumMaximumCountMeanCountMean Bext30.83.874.60123.126855.581162.78 Dust_mass13.00.69.1698.336823.311162.92 MeanSt. ErrorMaximumMinimum A_0.1007.421.22122.460.19 A_0.1507.651.21122.890.19 B_0.1007.080.9584.180.17 B_0.1507.080.9584.180.17 Predicted dust mass Measured dust mass

10. 1. Salt Creek – Regression coefficients

11. 1. Salt Creek – Predicted vs. Measured Dust A-groups B-groups Worst dust days: 7 / 4

12. 2. Bandelier Nat. Mon.– descriptive statistics Monitoring period: 01/01/01 – 12/31/03 IMPROVE database completeness: 92.6% Meteorological database completeness: 76.4% All days (n=309)80% Worst daysWorst dusty days MeanSt. ErrorMinimumMaximumCountMeanCountMean Bext16.130.163.6485.766428.30430.30 Dust_mass4.050.110.1030.66686.80424.40 MeanSt. ErrorMaximumMinimum A_0.1002.910.7230.600.14 A_0.1502.910.7230.600.14 B_0.1003.841.3216.260.19 B_0.1503.841.3216.260.19 Predicted dust mass Measured dust mass

13. 2. Bandelier Nat. Mon. – Regression coefficients

14. 2. Bandelier Nat. – Predicted vs. Measured Dust A-groups B-groups Worst dust days: 3 / 1

15. Date: May 15, 2003 X : Worst day + : Worst dust day O: Meteorological data available