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Prepared by: Ilias Kavouras, Vic Etyemezian, Dave DuBois, Mark Green, Marc Pitchford, Jin Xu

Preliminary assessment of the principal causes of dust-resultant haze at IMPROVE sites in the Western United States. Prepared by: Ilias Kavouras, Vic Etyemezian, Dave DuBois, Mark Green, Marc Pitchford, Jin Xu Division of Atmospheric Sciences, Desert Research Institute.

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Prepared by: Ilias Kavouras, Vic Etyemezian, Dave DuBois, Mark Green, Marc Pitchford, Jin Xu

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  1. Preliminary assessment of the principal causes of dust-resultant haze at IMPROVE sites in the Western United States Prepared by: Ilias Kavouras, Vic Etyemezian, Dave DuBois, Mark Green, Marc Pitchford, Jin Xu Division of Atmospheric Sciences, Desert Research Institute Prepared for: Western Regional Air Partnership, Dust Emissions Joint Forum Tempe, 11/15/2005

  2. Outline • Introduction – scope of the study • Development of Empirical/Heuristic Approach (EHA) • Description of tools • Integration into GIS • Interpretation of EHA outcomes • Case studies • Results • Seasonal and spatial variation of dust sources • Future studies

  3. Scope of the study Identify events resulting in duston worst dust days in WRAP (worst dust day: A 20% worst-case visibility day when dust was the largest contributor) • Long-range transport (Intercontinental) • Windblown events • Upwind transport • Undetermined sources (2) and (3) sources/events were further identified as Local (1 site affected) or Regional (multiple sites affected in the area)

  4. Methodology • Developed an empirical/heuristic approach (EHA) using • Elemental concentration ratios • Multivariate linear regression analysis • Air mass backward trajectories; • Land use/land cover data for US; • USGS Wind erodibility group (soil erosion metric) for US • Use of the EHA to assign worst dust days into a source/event • Integrate all tools into a geographical information system (GIS) • Develop a set of guideline criteria • Generate maps for each worst dust day

  5. Asian Dust April 29,1998: 17 of the WRAP IMPROVE monitoring sites had 20% worst dust days.

  6. Asian Dust Score (ADS) Zx/y is the Z-score for the ratio X/Y; Ux/y is the uncertainty of the ratio X/Y x/yday is the ratio of X/Y during a given day; x/yref is the reference ratio of X/Y estimated based on April 29,1998 event ; σ(x/y) is the standard deviation of reference X/Y ratios and; Ux and Uy are the measurement uncertainties for elements X and Y

  7. ADS interpretation Reference Asian Dust Ratio (based on 04/29/1998 episode): Al/Ca = 2.1 + 0.3; K/Fe = 0.59 + 0.07; Al/Si = 0.52 + 0.06

  8. Multivariate linear regression analysis to estimate Locally-generated Windblown Dust (LWD) ymis the measured dust mass concentration on a given site and date; yp is the dust concentration estimated by a linear combination of independent variables that describe the wind conditions; b1, b2,……., bk are the regression coefficients of the independent variables; x1, x2,……., xk are the values of independent variables that describe the wind conditions; a is the intercept which corresponds to yp when x1, x2,……., xk are equal to 0 and; ε is the residual error - the difference between the ym and yp

  9. 1-h Central Meteorological Database RAWS CASTNET AZDEQ ISH NPS NASA ( 24-h ( Σ Database development • Days with precipitation for more than 10h • Precipitation occurred after 12:00 p.m. 1-h Modified Central Meteorological Database

  10. MLRAnalysis 24-h Modified Central Meteorological Database 24-h “Dust” Database IMPROVE data 2001-2003 • Regression analysis • Only wind conditions groups corresponding to wind speed higher than 14 miles/hour • Least-squares method • Forward, backward and stepwise variable screening methods • Regression coefficients significant at p-value < 0.10 or 0.15 • Regression coefficients with Variance infiltration factor VIF > 10 were rejected • Null hypothesis (H: μ1= μ2=..... = μk= 0) for both regression coefficients and model was investigated • Only measurements with LWD – 2 standard error  0 were considered

  11. IMPROVE sites where dust/wind relationship exists

  12. Overall: 129 IMPROVE sites 71 sites with available meteorological data 43 sites with statistically significant MLRA results 41 sites with reliable Local Windblown Dust (LWD) results

  13. Example: Regression coefficients by quadrant Although regression coefficients can be used to predict the dependent variable using a set of independent variables, it provides no information about the relative contribution of each independent variable because independent variables means and variances were not considered. The standardized z-score coefficients were estimated, thus all independents variables have mean value of 0 and standard deviation of 1. The standardized regression coefficients (β1, β2,......, βn) provide enough evidence of the relative contribution of the independent variables.

  14. Example: Standardized regression coefficients by quadrant Badlands National Park, SD Bosque del Apache, NM

  15. LWD vs. Total Measured Dust

  16. #WDD with predicted LWD / total WDD (number shows the total number of WDD)

  17. Mean LWD / total TMD (number shows the mean dust conc for WDD)

  18. LWD and TMD for reliable MLRA results

  19. Air masses backward trajectories • NOAA HYSPLIT trajectory model • For all sites and worst dust days: • Duration:48-h and 192-h • Frequency: Every 3 hours (8:00, 14:00 and 20:00) • Resolution: 1 hour • Starting heights: 10, 500 and 1500 m.a.g.l. • Trajectory speed (km/h) = distance between two trajectory points • 0 – 14 miles/hour • 14 – 20 miles/hour • > 20 miles/hour

  20. Trajectories Trajectory endpoint at 8:00 a.m. (CST) 0.00 < speed < 14.00 mph " 14.00 < speed < 20.00 mph " speed > 20.00 mph " Trajectory endpoint at 2:00 p.m. (CST) ! 0.00 < speed < 14.00 mph ! 14.00 < speed < 20.00 mph speed > 20.00 mph ! Trajectory endpoint at 8:00 p.m. (CST) # 0.00 < speed < 14.00 mph # 14.00 < speed < 20.00 mph # speed > 20.00 mph Air masses backward trajectories

  21. Land use / Land cover National Land Cover Dataset 1992 (NLCD 1992) Landsat Thematic Mapper satellite data (U. S. Geophysical Survey and U. S. Environmental Protection Agency) Resolution: 30 meters 21 classes of land cover (Anderson Land Cover Classification) Data were obtained from: http://landcover.usgs.gov

  22. Land use / Land cover

  23. Land use / Land cover: 3 categories

  24. Wind Erodibility Group (WEG) • Indicator of susceptibility to wind erosion based on: • soil texture, • organic matter content, • effervescence due to carbonate reaction with HCl, • rock and para-rock fragment content • minerology. • Soil moisture and the presence of frozen soil also influence soil blowing. • . The range of valid entries for wind erodibility group data is 1, 2, 3, 4, 4L, 5, 6, 7, and 8 . Source: US Department of Agriculture. National Resources Conservation Services National Soil Survey Handbook: Soil Properties and Qualities (Part 618) Data were obtained from: http://water.usgs.gov/GIS/dsdl/muid.e00.gz

  25. Wind Erodibility Group (WEG)

  26. Combination of Land Use / Land Cover and Wind Erodibility Group layers Human influenced layer X = Grass- and shrub-lands layer Forest s and wetlands layer

  27. Background layer for GIS analysis

  28. Trajectories Land use and wind erosion Forests & wetlands Low erodibility based on soil texture Trajectory endpoint at 8:00 a.m. (CST) 0.00 < speed < 14.00 mph " High erodibility based on soil texture 14.00 < speed < 20.00 mph " speed > 20.00 mph " Shrubland and grassland areas Trajectory endpoint at 2:00 p.m. (CST) Low erodibility based on soil texture ! 0.00 < speed < 14.00 mph ! 14.00 < speed < 20.00 mph High erodibility based on soil texture speed > 20.00 mph ! Trajectory endpoint at 8:00 p.m. (CST) Representation of multiple linear regression of wind conditions vs. total measured dust available for this site day Human-induced areas B # 0.00 < speed < 14.00 mph Precipitation occurred at the site Low erodibility based on soil texture # 14.00 < speed < 20.00 mph IMPROVE site with a valid sample ! # speed > 20.00 mph Ï IMPROVE site IMPROVE site without a valid sample High erodibility based on soil texture Local windblown dust (only shown for worst dust days § No Met data Asian Dust Score (only shown for worst dust days) ADS < 750 ! LWD/TMD = 0.00 3 3 750 < ADS < 1500 LWD/TMD < 0.25 ! 3 0.25 < LWD/TMD < 0.50 ! ADS > 1500 ! 0.50 < LWD/TMD < 1.00 ! LWD/TMD > 1.00

  29. Low/moderate erodible forest areas Low/moderate erodible human-influenced areas IMPROVE site without a valid sample Precipitation occurred at this IMPROVE site Moderate speed 8:00 trajectory over low/moderate erodible shrubland areas IMPROVE sites with a valid sample but not a worst dust day High speed 14:00 trajectory for WICA over moderate/high erodible shrubland areas that are more than 24 hours away from the site The ADS is higher than 1500 and no LWD was calculated because of no meteorological data Moderate/high speed trajectories upwind of the site The ADS is higher than 1500 and the LWD/TMD is lower than 0.25 Low speed trajectories at and near the site Moderate/high WEG shrubland areas No ADS and LWD/TWD were calculated because meteorological and chemical data were not available The ADS is not calculated due to absence of reliable chemistry data and the LWD/TMD is between 0.50 and 1.00 Moderate speed 20:00 trajectory over Mexico The ADS is higher than 1500 and the LWD/TMD is 0.00

  30. Asian dust event

  31. Asian dust example (***** for NOAB, SIAN, YELL and ZION)

  32. Windblown dust event

  33. Windblown dust example (*****)

  34. Upwind transport example (*****)

  35. Regional windblown or upwind transport events

  36. Regional event example: R1: ZION and BRCA; R2: MEVE and WEMI

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