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– 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

Database development

CASTNET AZDEQ

NPSISH

RAWS

NASA

Central Meteorological

Database

Days with precipitation for more than 12h or precipitation occurred after 12:00 p.m.

Modified Central

Meteorological Database

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” Meteorological

Database

IMPROVE database

“Dust” Database

“Dust” Database

“Model” Database

Regression coefficientsSensitivity analysis

GPS data

Maps for each day

“Dust” eventYES/NO

Meteo-dataYES/NO

PrecipitationYES/NOWhen?

0-12 or 12-24

IMPROVE-dataYES/NO

“Worst” dayYES/NO

“Worst dust” dayYES/NO

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

Statistical analysis – Criteria development

• Significance level: 0.100 or 0.150 or higher

• Valid prediction: Cpredicted – Epredicted > 0 or P0.05,Measured

-40 -20 0 20 40 60 80 100

-40

-20

0

20

40

60

80

100

y=0.330451xr = 0.67946

Pre

dic

ted d

ust m

ass

Measured dust mass

y=0.45061x-3.06857r = 0.67946

0

100

200

300

400

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10 11 12

Month

"Dust"

days

0

2

4

6

8

10

12

14

"D

ust"

days/s

ite

Dust days

Dust days/site

Monthly variation of model – “dust” days

AGTI 0 DOME 0 MELA 173 SAGU 80 TRIN 0

BADL 388 GICL 62 MEVE 26 SAPE 57 ULBE 10

BALD 140 GRBA 0 MOHO 0 SAWE 123 WEMI 117

BAND 93 GRCA 0 MONT 0 SAWT 8 WHIT 644

BIBE 149 GRSA 145 NOAB 0 SEKI 0 WHRI 127

BLIS 254 GUMO 367 PASA 2 SIAN 62 WICA 0

BOAP 27 HAVO 0 PHOE 0 SIME 0 YELL 0

BRCA 302 HILL 191 PINN 0 SNPA 0 YOSE 0

BRLA 0 HOOV 19 PORE 0 SPOK 24 ZION 46

CANY 96 IKBA 56 PUSO 16 STAR 1

CHIR 19 JOSH 6 QUVA 123 SYCA 0

CORI 341 KALM 129 ROMO 0 TCRC 0

CRMO 577 LABE 12 SACR 407 THIS 0

DENA 0 LAVO 0 SAGA 0 THRO 116

DEVA 390 LOST 69 SAGO 0 TONT 3

Dust days per site (based on regression analysis)

1. Salt Creek – descriptive statistics

Monitoring period: 01/01/01 – 12/31/03IMPROVE database completeness: 93.2%Meteorological database completeness: 82.4%

All days (n=309) 80% Worst days

Worst dusty days

Mean St. Error

Minimum

Maximum

Count Mean Count

Mean

Bext 30.83 .87 4.60 123.12 68 55.58 11 62.78

Dust_mass

13.00 .69 .16 98.33 68 23.31 11 62.92

Mean St. Error Maximum Minimum

A_0.100 7.42 1.22 122.46 0.19

A_0.150 7.65 1.21 122.89 0.19

B_0.100 7.08 0.95 84.18 0.17

B_0.150 7.08 0.95 84.18 0.17

Predicted dust mass

Measured dust mass

0

45

90

135

180

225

270

315

-10-505

1015202530

-10-505

1015202530

p < 0.010 p < 0.015 0

45

90

135

180

225

270

315

-10

0

10

20

30

-10

0

10

20

30

p < 0.010 p < 0.015

1. Salt Creek – Regression coefficients

1. Salt Creek – Predicted vs. Measured Dust

0 20 40 60 80 100 120 1400

20

40

60

80

100

120

140

p < 0.150y=0.6934x-0.11695r=0.8483

p < 0.100y=0.6934x-0.11695r=0.8483

p < 0.150y=0.65877x+1.59276r=0.8339

Pre

dic

ted d

ust m

ass

p < 0.100y=0.65434x+1.64241r=0.8302

0 20 40 60 80 100 120 1400

20

40

60

80

100

120

140

0 20 40 60 80 100 120 1400

20

40

60

80

100

120

140

Measured dust mass

0 20 40 60 80 100 120 1400

20

40

60

80

100

120

140

A-groups

B-groups

Worst dust days:7 / 4

2. Bandelier Nat. Mon.– descriptive statistics

Monitoring period: 01/01/01 – 12/31/03IMPROVE database completeness: 92.6%Meteorological database completeness: 76.4%

All days (n=309) 80% Worst days

Worst dusty days

Mean St. Error

Minimum

Maximum

Count Mean Count

Mean

Bext 16.13 0.16 3.64 85.76 64 28.30 4 30.30

Dust_mass

4.05 0.11 0.10 30.66 68 6.80 4 24.40

Mean St. Error Maximum Minimum

A_0.100 2.91 0.72 30.60 0.14

A_0.150 2.91 0.72 30.60 0.14

B_0.100 3.84 1.32 16.26 0.19

B_0.150 3.84 1.32 16.26 0.19

Predicted dust mass

Measured dust mass

0

45

90

135

180

225

270

315

-10-505

1015202530

-10-505

1015202530

p < 0.010 p < 0.015 0

45

90

135

180

225

270

315

-10

0

10

20

30

-10

0

10

20

30

p < 0.010 p < 0.015

2. Bandelier Nat. Mon. – Regression coefficients

0 10 20 30 40 50 600

10

20

30

40

50

60

p < 0.150y=0.70626x-0.89583r=0.67015

p < 0.100y=0.70626x-0.89583r=0.67015

p < 0.150y=0.64446x-0.63899r=0.69638

Pre

dic

ted d

ust m

ass

p < 0.100y=0.66455x-0.63924r=0.69265

0 10 20 30 40 50 600

10

20

30

40

50

60

0 10 20 30 40 50 600

10

20

30

40

50

60

Measured dust mass

0 10 20 30 40 50 600

10

20

30

40

50

60

2. Bandelier Nat. – Predicted vs. Measured DustA-groups

B-groups

Worst dust days:3 / 1

Date: May 15, 2003

X: Worst day

+: Worst dust day

O: Meteorological data available