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Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP

Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP. Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green. Update. Categorized the events based on the spatial scale of the events (hazagon maps, spatial analysis) - Transcontinental events, Regional events, Local events

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Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP

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  1. Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green

  2. Update • Categorized the events based on the spatial scale of the events (hazagon maps, spatial analysis) - Transcontinental events, Regional events, Local events • Identified aerosol signatures of the transcontinental dust. Identified days/sites that are possibly influenced by Asian/African dust based on aerosol signatures, satellite images, NRL modeling results, back trajectories, etc. • Collected surface meteorology data and built database. • Linking each IMPROVE site with one or more weather stations • Studying relationship between meteorology data (WS, WD) and dust concentrations in all 20% worst dust days

  3. Arizona Regional Dust Event Asian Dust Event

  4. Dust Emission/Wind Correlations • Correlate surface wind measurements with IMPROVE dust concentrations • Look for relationships between high wind days and high fugitive dust across the IMPROVE network in the WRAP region • Questions to answer: • Can we extract wind speed thresholds for high wind events in the IMPROVE data set? • What wind metric is appropriate to flush out these events? • How can this help to distinguish high dust events that are local, regional, or large scale?

  5. Dust Emission/Wind Correlations • Choose wind measurements from nearby representative weather station(s) and rank them • What is representative? Take in consideration: • Intervening terrain • Nearby geographic features • Distance • Elevation differences between site and met station

  6. Dust Emission/Wind Correlations • Found reliable multiyear source of surface hourly meteorology for all of US • Based on Integrated Surface Hourly (ISH) database from the National Climatic Data Center • Built database of airport surface meteorology from 1999-2002 that we can query and develop a tool to do the comparison

  7. Salt Creek Example 4/8/00-12/31/02 IMPROVE data Using Roswell Airport ASOS winds Yellow dots are ISH met sites Red dots are IMPROVE sites Salt Creek Wilderness

  8. grasslands Wilderness Area farms Dairies, feedlots, goat and sheep ranches are the major nearby agricultural sources farms farms Roswell Airport grasslands

  9. Dust sources in southern NM Ag lands Ag lands Ag lands Ag lands Yellow shaded areas are locations of potential dust sources More playas

  10. Hourly maximum gust during the sample day for all IMPROVE samples Gust not reported for these points (knots)

  11. Maximum hourly wind speed during the sample day for all IMPROVE samples (knots)

  12. Mean 24-hour wind speed during the sample day for all IMPROVE samples Can see somewhat of a threshold here (knots)

  13. Dust Emission/Wind Correlations • Next step is to apply this exploratory method to many sites and flush out • Use coarse mass in addition to fine soil • Working on a code in Access to run query and generate plots of wind vs dust concentration • Coordinate with the spatial analysis to catalog events

  14. Asian Dust Analysis On April 19, 1998, a big Asian dust storm was generated over the Gobi Desert by springtime cold low pressure systems descending from the northwest. It crossed the Pasicic ocean, and subsided to the surface of the western United States around April 29

  15. Comparison of aerosol properties on April 29, 1998 and the averages of year 1998 and 2001 17 of the WARP IMPROVE monitoring sites were in 20% worst case days on April 29, 1998. The ratios of Al/Si, K/Fe, AL/Ca and CM/Soil are quite different to the average numbers.

  16. Asian Dust Signatures • Al/Ca = 2.1 + 0.3 • K/Fe = 0.59 + 0.07 • Al/Si = 0.52 + 0.06

  17. Rating Each Worst Dust Days • Score = 1 / (Zscore  %Uncertainty of Measured Ratio) Zscore = |Measured Ratio – Mean Asian Dust Ratio| / SQRT(Uncertainty of Measured Ratio^2 + Standard Deviation of Asian Dust Ratio^2) %Uncertainty of Measured Ratio = SQRT(%Uncertainty of A^2 + %Uncertainty of B^2) The higher the score, the higher the confidence that the measured ratios are close to the Asian dust ratios based on data from April 19, 1998 Asian dust storm

  18. Number of sites with Score > 2000 in the Year 2001 4/16/2002 5/10/2001

  19. Number of sites with Score > 2000 in the Year 2001 5/8-11/2002 4/26/2002

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