envair dri alpine geophysics

Understanding Relationships Between Changes in Ambient Ozone and Precursor Concentrations and

Changes in VOC and NOx Emissions from 1990 to 2004 in Central California

Envair

DRI

Alpine Geophysics

May 25, 2006

2

Today’s Meeting – Progress Report

Data acquisition and evaluation Met classification results (version 2) Ambient AQ trends Next steps Feedback?

3

Data Acquisition and Evaluation

4

Emissions Inventories

Obtained ARB monthly historical emissions Have checked completeness & readability Status: ready for re-aggregation into county or

greater total VOC, NOx, CO

5

Ambient AQ Data

Hourly gas-phase (O3, NO, NOx, CO, HC)– Completeness– Rounding conventions

VOC: PAMS, ARB, SJVAQS (1990) – No combination of previous VOC validation provides

complete spatial & temporal coverage– Start with EPA archives and validate data

6

Meteorological Data

Upper air: Oakland soundings 4 am & 4 pm Pressure gradients: San Francisco to Medford,

Reno, and Fresno Surface wind speed and direction (applied QA

steps recommended by BAAQMD)

7

Surface Met Data QA Issues Identified by BAAQMD

Site locations Time zone Zero values vs. missing data Sudden increases or decreases in WS, T Long periods of constant WS, WD, or T Incorrect flag values Redundant data

8

Network

N CD C

C IM IS

BAAQ M D

Sites w ith 10 + years of M eteorological Data (daily w indspeed and w ind direction)

Surface MeteorologicalStations

9

Met Classification Methods

10

Met Classification Approach

Goal is to stratify days, not to adjust ozone Stratifications related to transport directions Primary clustering method is based on met

variables related to transport Secondary method is spatial correlation of

peak ozone (e.g., DRI CCOS clustering algorithm, BAAQMD analysis)

11

Met Classification Details

K-means clustering Use all days May through October each year Standardized variables 850 mb T, height, u and v wind vector components Pressure gradients SFO to Medford, Reno, Fresno Surface u and v wind vector components, 53

stations Limited to 12 cluster types

12

Classification Results

13

Summary of Findings

Met classifications yield clusters having differences in transport direction or distance

Different clusters exhibit different mean concentrations of air pollutants

Pollutant trends at a site may but do not always differ among clusters

Clusters account for about 30 – 40% of ozone variance

14

Four Met Types Dominate Jul - Oct

15

Which Clusters Have Highest Ozone?

Clusters 1, 3, 5, 6 (summer and fall) Sometimes cluster 9 (Sep and Oct) In southern SJV, also clusters 2, 7, 8, 11

(occur most frequently May and June)

16

Daily 8-Hour Peak Ozone, 1990-2004Livermore

Meteorological Type (cluster number)

17

Daily 8-Hour Peak Ozone, 1990-2004Folsom


18

Daily 8-Hour Peak Ozone, 1990-2004Parlier


19

Daily 8-Hour Peak Ozone, 1990-2004Arvin


20

Do clusters exhibit differences in transport direction and distance?… most easily seen using plots ofU and V wind vector components(these show direction of origin)

21

Directional Differences

22


23


24


25

Mean Directional Differences

26

850 mb Directional Differences

27


28

Main Finding of Cluster Analysis

Clusters exhibit differences in transportdirections or distances. The differencesare more pronounced at some sites than others.

29

Can Clusters Be Improved?

Maybe -

Direct comparisons of met types (clusters) to groupings determined from wind speed and direction at eachsite suggest room for improvement.

30

Calm<1m/s

NE1-2m/s

SE1-2m/s

SW1-2m/s

NW1-2m/s

NE>2m/s

SE>2m/s

SW>2m/s

NW>2m/s

Chabot

83 8 1 12 34 3 2 5

6 41 12

12 64 109 44 55

4 7 4 16

1 3

16 5

1 5

1 2 21 4

36 24 20 1 3 5

1 42 10

1 4 2 10 11 1 4

1 4 2 8

1

2

3

4

5

6

7

8

9

10

11

12

Direction/Windspeed

Clu

ster

2

100

62 5

31 126

2

166 23

31

Sacramento

212 1 13 27 41 6 1

5 157

148 26 207 19 14 131

16 1 10 7 25

22 16 286

6 5 59 30 319

1 3 18 233

20 5 85 23 7 82 12

24 9 55 1 99

7 4 9 1 5 69 2

1 62

2 1 38

1

3

4

5

6

7

8

9

10

11

12

Direction/Windspeed

Clu

ster

Calm<1m/s

NE1-2m/s

SE1-2m/s

SW1-2m/s

NW1-2m/s

NE>2m/s

SE>2m/s

NW>2m/s

32 5

12

SW>2m/s

21

32

Fresno

290 3 1

1 2 4 71 59

346 1 2 2 186 5

38 1 15 5 13 3

79 2 7 196

25 1 1 226 1 159

3 18 2 231

2 30 198

77 1 1 90 15

3 1 12 79

39 1 13 6

1 40

1

3

4

5

6

7

8

9

10

11

12

Direction/Windspeed

Clu

ster

Calm<1m/s

NE1-2m/s

SE1-2m/s

SW1-2m/s

NW1-2m/s

NE>2m/s

SE>2m/s

SW>2m/s

NW>2m/s

302

6

3

50

33

Preliminary Trends Assessment

34

Trends Methods

Split subregional top 60 days by cluster For each site and each cluster, compute linear

regression of AQ metric against day Determine regression slope and SE

35

Do trends differ among clusters?

36

CO Trends

37

Daily Morning CO – All Sites

38

Fremont

-140

-120

-100

-80

-60

-40

-20

0

20

40

Cluster 1 Cluster 2 Cluster 3 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster 11

Mo

rnin

g C

O (

pp

bv/

year

)

Error bars = 2 SE of slope

39

Sacramento - T St.

-140

-120

-100

-80

-60

-40

-20

0

20

40


Mo

rnin

g C

O (

pp

bv/

year

)


40

Fresno - Drummond

-140

-120

-100

-80

-60

-40

-20

0

20

40


Mo

rnin

g C

O (

pp

bv/

year

)


41

Bakersfield

-140

-120

-100

-80

-60

-40

-20

0

20

40


Mo

rnin

g C

O (

pp

bv/

year

)


42

Sacramento and Northern Sierra Foothills - Cluster 1

-120

-100

-80

-60

-40

-20

0

20

40

60

80

Sacramento-TStreet

Elk Grove-Bruceville

Road

NorthHighlands-

Blackfoot Way

Sacramento-Del Paso

Manor

Roseville-NSunrise Blvd

Folsom-Natoma Street

Auburn-Dewitt-CAvenue

Grass Valley-Litton Building

Placerville-Gold Nugget

Way

Mo

rnin

g C

O (

pp

bv/

year

)


43


-120

-100

-80

-60

-40

-20

0

20

40

60

80

Sacramento-TStreet


Road

NorthHighlands-

Blackfoot Way

Sacramento-Del Paso

Manor






Way

Mo

rnin

g C

O (

pp

bv/

year

)


44


-120

-100

-80

-60

-40

-20

0

20

40

60

80

Sacramento-TStreet


Road

NorthHighlands-

Blackfoot Way

Sacramento-Del Paso

Manor






Way

Mo

rnin

g C

O (

pp

bv/

year

)


45


-120

-100

-80

-60

-40

-20

0

20

40

60

80

Sacramento-TStreet


Road

NorthHighlands-

Blackfoot Way

Sacramento-Del Paso

Manor






Way

Mo

rnin

g C

O (

pp

bv/

year

)


46


-120

-100

-80

-60

-40

-20

0

20

40

60

80

Sacramento-TStreet

Elk Grove-Bruceville Road

NorthHighlands-

Blackfoot Way

Sacramento-DelPaso Manor


Folsom-NatomaStreet


Placerville-GoldNugget Way

Mo

rnin

g C

O (

pp

bv/

year

)


47

NOx Trends

48

Daily Morning NOx – All Sites

49

Fremont

-10

-8

-6

-4

-2

0

2

4


Mo

rnin

g N

Ox

(pp

bv/

year

)


50

Sacramento - T St.

-10

-8

-6

-4

-2

0

2

4


Mo

rnin

g N

Ox

(pp

bv/

year

)


51

Fresno - Drummond

-10

-8

-6

-4

-2

0

2

4


Mo

rnin

g N

Ox

(pp

bv/

year

)


52

Bakersfield

-10

-8

-6

-4

-2

0

2

4


Mo

rnin

g N

Ox

(pp

bv/

year

)


53

Ozone Trends

54

Daily Peak 8-Hour Ozone – All Sites

55

Livermore

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2


Pea

k 8-

ho

ur

Ozo

ne

(pp

bv/

year

)


56

Folsom

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2


Pea

k 8-

ho

ur

Ozo

ne

(pp

bv/

year

)


57

Parlier

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2


Pea

k 8-

ho

ur

Ozo

ne

(pp

bv/

year

)


58

Arvin

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2


Pea

k 8-

ho

ur

Ozo

ne

(pp

bv/

year

)


59

What Have We Learned So Far?

Met clusters ~30 – 40% of ozone variability Clusters exhibit differences in mean transport

directions and distances. Might be able to improve distinctions among clusters.

Trends vary among clusters & sites Hypothesis: variations of ambient trends are a

function of spatial variations of emission trends

60

Next Steps –

Compare ambient to emission trends – county level or groups of counties

Draft Phase I report & Phase II plan – July 1 Task 4 meeting – ? Final Phase I report & Phase II plan If Phase II approved, compare ambient trends to

emission trends occurring in the upwind areas of influence (need to grid emission estimates)

envair dri alpine geophysics

Documents

hour peak ozone

ambient ozone

highest ozone

ozone variance

fallsometimes cluster

distancedifferent clusters

octwhich clusters

v wind vector componentsthese