There is strong evidence that this tiny critter causes asthma. Dust mite.
Causes of Dust. Data Analysis
description
Transcript of Causes of Dust. Data Analysis
![Page 1: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/1.jpg)
Causes of Dust. Data Analysis
Ilias Kavouras, Vic Etyemezian, Dave DuBois, Mark Green, Marc Pitchford, Jin Xu
Division of Atmospheric Sciences, Desert Research Institute
![Page 2: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/2.jpg)
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
![Page 3: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/3.jpg)
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
![Page 4: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/4.jpg)
“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
![Page 5: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/5.jpg)
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
![Page 6: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/6.jpg)
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
![Page 7: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/7.jpg)
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
![Page 8: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/8.jpg)
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)
![Page 9: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/9.jpg)
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
![Page 10: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/10.jpg)
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
![Page 11: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/11.jpg)
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
![Page 12: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/12.jpg)
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
![Page 13: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/13.jpg)
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
![Page 14: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/14.jpg)
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
![Page 15: Causes of Dust. Data Analysis](https://reader030.fdocuments.net/reader030/viewer/2022013103/56813ffd550346895dab2b66/html5/thumbnails/15.jpg)
Date: May 15, 2003
X: Worst day
+: Worst dust day
O: Meteorological data available