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Causes of Dust. Data Analysis

Causes of Dust. Data Analysis. Ilias Kavouras, Vic Etyemezian, Dave DuBois, Mark Green, Marc Pitchford, Jin Xu Division of Atmospheric Sciences, Desert Research Institute. Scope and methodology. Scope: identify and quantify sources of airborne dust Local and regional windblown dust

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Causes of Dust. Data Analysis

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  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 RAWS Days with precipitation for more than 12h or precipitation occurred after 12:00 p.m. CASTNET AZDEQ Central Meteorological Database Modified Central Meteorological Database ISH NPS NASA 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” Database “Dust” Meteorological Database IMPROVE database

  4. Sensitivity analysis Regression coefficients “Dust” Database “Model” Database GPS data Maps for each day IMPROVE-data YES/NO Meteo-data YES/NO “Dust” event YES/NO Precipitation YES/NO When? 0-12 or 12-24 “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: Cpredicted– Epredicted > 0 or P0.05,Measured

  7. Monthly variation of model – “dust” days

  8. 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% Measured dust mass Predicted dust mass

  10. 1. Salt Creek – Regression coefficients

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

  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% Measured dust mass Predicted dust mass

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

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

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

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