![Page 1: Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green](https://reader035.vdocuments.mx/reader035/viewer/2022062412/5a4d1ada7f8b9ab0599747ec/html5/thumbnails/1.jpg)
Update on Assessment of the Major Causes of Dust-Resultant Haze in the
WRAP
Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green
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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
![Page 3: Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green](https://reader035.vdocuments.mx/reader035/viewer/2022062412/5a4d1ada7f8b9ab0599747ec/html5/thumbnails/3.jpg)
Asian Dust Event Arizona Regional Dust Event
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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?
![Page 5: Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green](https://reader035.vdocuments.mx/reader035/viewer/2022062412/5a4d1ada7f8b9ab0599747ec/html5/thumbnails/5.jpg)
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
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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
![Page 7: Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green](https://reader035.vdocuments.mx/reader035/viewer/2022062412/5a4d1ada7f8b9ab0599747ec/html5/thumbnails/7.jpg)
!(
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!(
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!(
!(
!(
!(
!(
!(
!(WHPE1
WHIT1
SAPE1
SACR1
SAAN1
PEFO1
MEVE1
GUMO1
GICL1
CHIR1
BOAP1
BAND1
BALD1
Salt CreekWilderness
Yellow dots are ISH met sitesRed dots are IMPROVE sites
Salt Creek Example4/8/00-12/31/02 IMPROVE dataUsing Roswell AirportASOS winds
![Page 8: Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green](https://reader035.vdocuments.mx/reader035/viewer/2022062412/5a4d1ada7f8b9ab0599747ec/html5/thumbnails/8.jpg)
Roswell Airport
Wilderness Area Dairies, feedlots,
goat and sheepranches are the major nearbyagricultural sources
farms
farms
farms
grasslands
grasslands
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#S
#S
#S
#S#S
Bosque del ApacheWA
Salt Creek WAWhite Mountain WA
Guadalupe Mountains NP
San AndresNational Wildlife Refuge
White Sands
Carlsbad Caverns NP
Samalayuca Dunes
40 0 40 80 MilesN
Dust sources in southern NM
More playas
Ag lands
Ag lands
Ag lands
Ag lands
Yellow shaded areas are locations of potential dust sources
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Salt Creek
0
2
4
6
8
10
12
14
16
18
0 10 20 30 40 50
Max Gust (mph)
Fine
Soi
l Con
c (µ
g/m
3 )
Hourly maximum gust during the sample dayfor all IMPROVE samples
(knots)
Gust not reported for these points
![Page 11: Update on Assessment of the Major Causes of Dust-Resultant Haze in the WRAP Vic Etyemezian, Jin Xu, Dave Dubois, and Mark Green](https://reader035.vdocuments.mx/reader035/viewer/2022062412/5a4d1ada7f8b9ab0599747ec/html5/thumbnails/11.jpg)
Salt Creek
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
0 5 10 15 20 25 30 35 40
Max Hourly Wind Speed (mph)
Fine
Soi
l Con
c (µ
g/m
3 )
Maximum hourly wind speed during the sample dayfor all IMPROVE samples
(knots)
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Salt Creek
0
2
4
6
8
10
12
14
16
18
0 5 10 15 20 25
Mean 24-hour Wind Speed (mph)
Fine
Soi
l Con
c (µ
g/m
3 )
Mean 24-hour wind speed during the sample dayfor all IMPROVE samples
(knots)
Can see somewhat of a threshold here
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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
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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
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Comparison of aerosol properties on April 29, 1998 and the averages of year 1998 and 2001
Date Sites Al/Ca Al/Si Ca/Si Fe/Si K/Fe CM/Soil4/29/1998 Worst Case Day Sites-17 Average 2.1 0.52 0.25 0.29 0.59 2.11
STDEV 0.3 0.06 0.03 0.04 0.07 0.94
Year 2001 worst dust days Average 1.4 0.31 0.22 0.27 0.67 7.10
Year 2002 worst dust days Average 1.7 0.43 0.25 0.25 0.72 16.02
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.
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Asian Dust Signatures
• Al/Ca = 2.1 + 0.3• K/Fe = 0.59 + 0.07• Al/Si = 0.52 + 0.06
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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
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Number of sites with Score > 2000 in the Year 2001
0
5
10
15
20
25
30
2001
0314
2001
0419
2001
0501
2001
0516
2001
0603
2001
0615
2001
0627
2001
0718
2001
0730
2001
0823
2001
0904
2001
0922
2001
1004
2001
1025
2001
1115
4/16/2002
5/10/2001
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Figure 3. Satellite images in China (top) and US(Bottom) on April 6 and April 16, 2001, respectively.
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• On April 7, The NRL dust model shows the dust cloud emerging from the Gobi desert
• A week later, part of the dust cloud passes over N.America toward the Atlantic
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Figure 7. NRL aerosol model outputs on May 10 2001
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Number of sites with Score > 2000 in the Year 2001
0
4
8
2002
0117
2002
0321
2002
0426
2002
0520
2002
0613
2002
0707
2002
0731
2002
0827
2002
0926
2002
1107
4/26/2002
5/8-11/2002
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Figure 8. NRL aerosol model outputs on April 26 2002
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