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Dust Impacts on the 20% Worst Visibility Days

Vic Etyemezian,

David Dubois,

Mark Green,

and

Jin Xu

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Improve Sites in 1997 (black) and 2002 (all)

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SPECIES FORMULA ASSUMPTIONS POSSIBLE SOURCES

SULFATE 4.125[S] All elemental S is from sulfate. All

sulfate is from ammonium sulfate. Fossil fuel combustion

NITRATE 1.29[NO3] Denuder efficiency is close to 100%. All nitrate is from ammonium nitrate.

Industrial and automobile emissions, organic decomposition

Organic Mass by Carbon (OMC) 1.4 * OC Average organic molecule is 70%

carbon.

Biomass burning, automobile emissions, fossil fuel combustion, gas-to-particle conversion of hydrocarbons

Light absorbing Carbon (LAC)

EC1+EC2+EC3-OP (see definitions below)

Incomplete combustion of fossil and biomass fuels

SOIL (fine soil) 2.2[Al]+2.49[Si]+1.63[Ca] +2.42[Fe]+1.94[Ti]

[Soil K]=0.6[Fe]. FeO and Fe2O3 are equally abundant. A factor of 1.16 is used for MgO, Na2O, H2O, CO2.

Desert dust, construction, road Dust

CM (coarse mass) [PM10] - [PM2.5] Consists only of insoluble soil particles Crushing or grinding operations, dust from paved or unpaved roads

Bext = 3F(RH)[Sulfate] + 3F(RH)[Nitrate] + 4[OMC] + 10[LAC] + 1[Soil] + 0.6[CM]+

10 (Rayleigh Gas Scattering)

Reconstructed Light Extinction Coefficients

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20% Worst Days

• After sorting the reconstructed light extinction coefficient values of site X in year Y from lowest to highest, the days with light extinction coefficients above the 80th percentile value are considered 20% worst days in terms of visibility.

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For This Presentation

• “Dust” = Coarse Mass (CM) + Fine Soil (FS)

• “Visibility extinction due to dust” is portion of Bext that is due to CM + FS

• Unless otherwise stated, data shown for 1997-2002

• Unless otherwise stated, Bext does NOT include Rayleigh scattering

• Some sites have longer record than others

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Sources of “Dust” – CM+FS• Regional Windblown• Local Windblown• Road Dust• Construction• Mining• Agriculture• Asian Origin• African Origin• Organic debris• Wildfires• Volcanoes• Sea spray• Other

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Fractional Contribution of Dust to Aerosol Extinction For All Worst Days

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Absolute Contribution of Dust to Aerosol Extinction For All Worst Days (Mm-1)

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Fraction of Worst Days When Dust Contributed 15% or more to Aerosol Extinction

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Fraction of Worst Days When Dust Contributed more to Aerosol Extinction than Any Other Component

(NO3, SO4, OMC, LAC)

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Q1 Q2

Q3 Q4

Seasonal Patterns:

Fraction of worst days when dust was principal component of extinction in each quarter.

LegendSummary_Dust_Seasonal.Q1

0.000000 - 0.100000

0.100001 - 0.200000

0.200001 - 0.300000

0.300001 - 0.400000

0.400001 - 0.500000

0.500001 - 1.000000

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Example 1:Regional Windblown Dust Event

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Regional Windblown Dust 4/26/02

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Regional Windblown Dust 4/26/02

Percent of BextLocation Site_Name Bext (Mm-1) State Soil% CM/% Dust% CM/SoilBAND1 Bandelier National Monument 33.0 NM 11.4% 49.0% 60.4% 4.3BOAP1 Bosque del Apache 74.9 NM 13.2% 61.7% 74.9% 4.7CHIR1 Chiricahua National Monument 42.7 AZ 11.3% 70.5% 81.8% 6.2GICL1 Gila Wilderness 22.0 NM 17.4% 48.1% 65.5% 2.8MEVE1 Mesa Verde National Park 28.1 CO 21.4% 48.7% 70.1% 2.3PEFO1 Petrified Forest National Park 31.3 AZ 14.3% 64.6% 79.0% 4.5QUVA1 Queen Valley 49.5 AZ 17.0% 66.6% 83.5% 3.9SACR1 Salt Creek 47.1 NM 2.3% 17.1% 19.4% 7.5SAPE1 San Pedro Parks 21.2 NM 14.8% 32.0% 46.8% 2.2SAWE1 Saguaro West 47.7 AZ 15.7% 66.3% 82.0% 4.2WEMI1 Weminuche Wilderness 20.2 CO 13.2% 41.9% 55.1% 3.2WHIT1 White Mountain 84.3 NM 11.1% 45.6% 56.7% 4.1

Min 20.2 2.3% 17.1% 19.4% 2.2Max 84.3 21.4% 70.5% 83.5% 7.5

Average 41.8 13.6% 51.0% 64.6% 4.2

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Surface Weather 4/26/04 ~ 5:00 PM Local time

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NRL Model Prediction (WestPhal & Co) ~ 5:00 PM Local time

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Supplemental Met and PM Data

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0

50

100

150

200

250

4/2

3/0

20

:00

4/2

4/0

20

:00

4/2

5/0

20

:00

4/2

6/0

20

:00

4/2

7/0

20

:00

4/2

8/0

20

:00

4/2

9/0

20

:00

4/3

0/0

20

:00

Time

PM

10

ug

/m3

0

2

4

6

8

10

12

Win

d S

pee

d (

m/s

)

PM10 from PimaCounty

Wind Speed (m/s)from CASTNETChiricahua site

Pretty sure this is windblown dust!

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Example 2: Asian Dust

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2001 Asian Dust Episode (4/16)

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Asian Dust

• During the spring season, the desert regions in Mongolia and China are massive sources of mineral aerosols

• Aerosol particles emitted from the Northwest desert region of China may have a significant influence over Eastern Asia, the Northern Pacific and even as far away as North America

• Recent work suggests that the frequency of dust storms in China has increased in the last few decades

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The Asians Don’t Like It Either

Winds in excess of 60 mph can suspend enormous amounts of dust from a very large region

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2001 Asian Dust Episode (4/16)

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2001 Asian Dust Episode (4/16)

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Properties of Asian Dust

• Average CM:FS

– All 2001 WRAP Worst days caused by dust (except 4/16/01): 4.6

– All WRAP sites when 4/16/01 was worst day: 0.93

• Average K:Fe

– 2001 WRAP sites average: 0.91

– 4/16/01 worst day sites: 0.5

• Average Al:Si

– 2001 WRAP sites average: 0.2

– 4/16/01 worst day sites: 0.5

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Example 3:Local Windblown Dust?

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Single-site dust in Montana 7/27/01

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Example of hazagons of confirmed fires in NV, WA, OR, ID

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Surface weather ~ 5:00 PM Local Time

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EPA AIRS Air Quality Monitors in Adjacent Counties

0

50

100

150

200

250

7/2

4/0

1 0

:00

7/2

5/0

1 0

:00

7/2

6/0

1 0

:00

7/2

7/0

1 0

:00

7/2

8/0

1 0

:00

7/2

9/0

1 0

:00

7/3

0/0

1 0

:00

7/3

1/0

1 0

:00

AIR

S P

M1

0 m

on

ito

r (u

g/m

3)

0

5

10

15

20

25

30

Win

d S

pe

ed

Gu

sts

at

RA

WS

Sta

tio

n (

mp

h)

Missoula

Lewis & Clark

Lake

Wind gusts (mph)

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Moral of the Story: For this case nothing jumps out immediately as a

convincing “most likely” cause of the dust haze in Montruse

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Another Resort: Chemistry

Example Jarbridge WA Cross-Correlation Plots

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Some Things to Think About

• Should this Type of Analysis be done for every 20% worst day at every site in WRAP?

• OR is there a semi-systematic approach that can be used instead of brute force method?

• What types of information can we expect to learn?

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Should This Analysis Be Done for Every Worst Day at Every WRAP

Site?

• # of 20% Worst Site-Days in WRAP Region– Between 1997 and 2003*: 6,839– Between 2001 and 2003* : 5,838– Between 2001 and 2003* AND

• Dust significant contributor (>15% of Bext): 2,392

• Dust principal contributor (greatest Bext): 899

• *2003 Data Available ~ October, 2004

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Should “Episode Analysis” Be Done for Each of the 899 Cases

– Analyst can research and document 2 – 4 cases a day

– OR ~ 1 – 2 labor years - $$$$.– Not clear that this will result in a useful

explanation of “dust” for every case– Will have to be repeated in the future – if

desired

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Can a semi-systematic, less ambitious, method be used?

• Look closely at a subset of worst days with dust as a dominant source

• Find commonalities among “like” events and differences between “unlike” events

• Use a set of criteria to place all remaining worst days into one of several categories according to “most likely source type”

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How can this be done?

• Local and Regional Windblown dust1. For each site, identify a nearby meteorological

station that can provide reasonably representative wind speed data

2. Look at Wind Speed vs. Coarse Mass to estimate a threshold value for windblown dust at that site

3. Check if on a particular worst day with dust as dominant haze component

a. threshold value is exceeded b. ratio of Coarse Mass to Fine Soil above a predetermined

value

4. If so, categorize as Windblown Dust

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Determining Threshold Wind Speed for Windblown Dust

0

100

200

300

400

500

600

700

800

900

0 2 4 6 8 10 12

Chiricahua Average WS (M/S)

PIM

A P

M1

0 (

ug

/m3

)

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Windblown Dust

• 1 site affected – “Local Windblown” dust

• Multiple sites affected – “Regional Windblown”

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Asian Dust

• Tendency to have large regional influence• Compare CM:FS ratio to predetermined value

(nominally 1 or less means long-range transport)• Inspect chemical signature (K/FE, Al/Si)• Identify a possible corresponding Asian Dust

Event (E.g. Using NRL model)• Inspect air mass trajectories• If all points to Asian origin then “Asian Dust”

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Other Sources

• Construction: Unless close to monitor, likely infrequent and mixed with urban plume. Difficult to identify unless well-documented

• Road Dust: Same as Construction. If from urban source, urban air quality monitors might help. Signature of exhaust might help.

• Mining: Is there a mine within one day’s transport of site? Do trajectories show this as possible? Can chemistry be used as a tracer

• Agriculture: Could be substantial, depending on season. Difficult to confirm individual event occurrence. E.g. “Did Farm X harvest almond on Date Y?”

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Other Sources

• African Dust: Can use same approach as Asian dust, though probably very infrequent cause of worst day

• Organic debris: Can be related to agriculture. Probably seasonal. Probably shows different CM:FS ratio than windblown

• Wildfires, Volcanoes: Were there any wildfires or volcano eruptions nearby? Ratios of FS:Organic, K:FS, CM:FS can help.

• Sea Spray: Probably impacts coastal sites (if any in WRAP). Na and Cl content and ratio of CM:FS can help

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The “Other” Category

• For some cases, multiple sets of criteria would be met– Depending on # of such cases, inspect individually,

try to find supplemental information• For some cases, no set of criteria is met

satisfactorily– These will go into “Other” category. Can Happen

when:• Inadequate met data• Multiple sources in comparable quantities• Criteria set incorrectly• Unforeseen/undocumented source• Just Because

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Summary of Method

• Consider only 2001-2003 20% worst days in WRAP at sites where dust (CM+FS) is dominant haze constituent

• Inspect subset of those days for useful trends to include known days for impacts from major source types

• As much as reasonable, place each worst day into category based on defined criteria. # of categories determined by how well the criteria can be defined

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What Can We Learn? Pros and Cons of Method

• Cons:– Limited to worst days dominated by dust haze– Does not give “source apportionment” for any particular day– Some difficulties likely in determining source category for some

worst days• Pros:

– A mix of reasoning and brute force - optimize ratio of outcome to resources utilized

– Leverages many of the same tools currently used in Causes of Haze Assessment (COHA)

– Provides a first stab at a methodology that can be improved in the future

– Provides insight into knowledge gaps– Likely to result in accounting for the most frequent causes of

dust haze– Can be completed in 1 year or so

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Discussion?

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2001 Asian Dust Episode (4/16-4/19)

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Ratio of Coarse Mass to Fine Soil Extinction

Average Contributions of Major Checm ial Com ponnents to Light Extinction for 68 WRAP Sites (1997-2001 April Average)

Sulfate36%

Nitrate15%

OC20%

EC7%

FS7%

CM15%

Average Contributions of Major Checm ial Com ponnents to Light Extinction for 68 WRAP Sites (April 16, 2001)

Sulfate28%

Nitrate14%

OC14%

EC3%

FS21%

CM20%

Average for 68 WRAP sites in April over period 1997-2001

Average for 68 WRAP sites 4/16/2001

On 4/16/01, 45 of the 68 WRAP IMPROVE monitoring sites were in 20% worst case days of the year 2001. Up to 11 more were worst case days on 4/19/01.

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Other 2001 Worst Days at Mont1

1.813.4%8.6%4.8%65.820010510

3.512.1%9.4%2.7%49.120011001

4.58.4%6.9%1.5%48.420011004

2.430.9%21.8%9.2%47.620010817

4.412.0%9.8%2.2%44.920010814

3.310.4%7.9%2.4%44.820010928

4.121.7%17.4%4.2%40.120010925

8.02.6%2.3%0.3%39.920010113

6.72.8%2.4%0.4%39.120011115

11.512.9%11.9%1.0%38.320011109

2.152.3%35.4%16.8%32.220010829

2.632.2%23.2%9.0%29.920010820

1.420.2%11.7%8.6%28.720010507

2.025.5%16.9%8.6%28.020010913

4.617.0%14.0%3.0%26.820011007

3.556.8%44.1%12.7%26.820010727

3.451.4%39.7%11.7%26.720010724

1.936.4%24.1%12.4%26.720010823

2.120.3%13.8%6.5%26.420010513

2.946.2%34.5%11.7%25.820010904

7.21.9%1.6%0.2%24.920011025

2.514.5%10.4%4.2%24.520010519

3.917.5%13.9%3.6%24.320010525

CM%/Fine%Dust%CM/%Soil%BextDate

1.813.4%8.6%4.8%65.820010510

3.512.1%9.4%2.7%49.120011001

4.58.4%6.9%1.5%48.420011004

2.430.9%21.8%9.2%47.620010817

4.412.0%9.8%2.2%44.920010814

3.310.4%7.9%2.4%44.820010928

4.121.7%17.4%4.2%40.120010925

8.02.6%2.3%0.3%39.920010113

6.72.8%2.4%0.4%39.120011115

11.512.9%11.9%1.0%38.320011109

2.152.3%35.4%16.8%32.220010829

2.632.2%23.2%9.0%29.920010820

1.420.2%11.7%8.6%28.720010507

2.025.5%16.9%8.6%28.020010913

4.617.0%14.0%3.0%26.820011007

3.556.8%44.1%12.7%26.820010727

3.451.4%39.7%11.7%26.720010724

1.936.4%24.1%12.4%26.720010823

2.120.3%13.8%6.5%26.420010513

2.946.2%34.5%11.7%25.820010904

7.21.9%1.6%0.2%24.920011025

2.514.5%10.4%4.2%24.520010519

3.917.5%13.9%3.6%24.320010525

CM%/Fine%Dust%CM/%Soil%BextDate