## Appendix A

WRAP Regional Summary Report

The “Western Regional Air Partnership Regional Haze Rule Reasonable Progress

Summary Report” contains analyse s for 15 states in the western region of the United

States and is available at: http://www.wrapair2.org/RHRPR.aspx

. The Summary

Report section for California can be downloaded from the same website, as Section 6.3,

under the State Summaries (and State Specific Appendices):

Californi a ( pdf ) (1.1 MB, 39 pages), Appendix C ( pdf ) ( 2.6 MB, 154 pages.)

### This page intentionally left blank.

## Appendix B

Emission Inventory

2013 Almanac

### This page intentionally left blank.

### B

- 1 NOx Emissions (TPD)

Statewide 2000 2005 2010 2015 2020

Electric Utilities 60.6 26.8 23.1 25.3 26.8

Cogeneration 28.7 18.1 15.9 17.2 20.6

Oil And Gas Production (Combustion) 24.7 14.4 10.2 9 8.4

Petroleum Refining (Combustion) 46.9 25.6 20.2 19.7 19.6

Manufacturing And Industrial 145.2 80.3 63.1 65.3 64.9

Food And Agricultural Processing 42 36.3 28.3 15.3 9.5

Service And Commercial 72.1 49.6 46.5 45.8 47.2

Other (Fuel Combustion) 20.1 17.2 12.3 14.7 13.5

Sewage Treatment 0.4 0.4 0.4 0.5 0.5

Landfills 0.6 1 1 1.1 1.2

Incinerators 1.7 2.1 2.6 2.7 2.9

Soil Remediation 0.1 0.1 0.1 0.1 0.1

Other (Waste Disposal) 0.1 0 0 0 0

Laundering 0 0 0 0 0

Degreasing 0 0 0 0 0

Coatings And Related Process Solvents 0.1 0.1 0 0.1 0.1

Printing 0 0 0 0 0

Adhesives And Sealants 0 0 0 0 0

Other (Cleaning And Surface Coatings) 0.1 0.1 0.1 0.2 0.2

Oil And Gas Production 3.6 2.7 2.2 2 1.8

Petroleum Refining 9.9 5.4 13.8 2.3 2.3

Petroleum Marketing 0.7 0.2 0.2 0.3 0.3

Other (Petroleum Production And Marketing) 0 0 0 0 0

Chemical 1.8 2 1.7 1.9 2

Food And Agriculture 0.7 0.2 0.1 0.2 0.2

Mineral Processes 101.2 103.3 54.4 57 61.9

Metal Processes 1.5 1.1 1 0.7 0.7

Wood And Paper 3.8 2.5 1 1 1.1

Glass And Related Products 13.7 10.1 5.8 4.1 4.4

Electronics 0 0 0 0 0

Other (Industrial Processes) 3.4 2.7 9.1 1.1 1.4

Consumer Products 0 0 0 0 0

Architectural Coatings And Related Process Solvents 0 0 0 0 0

Pesticides/Fertilizers 0 0 0 0 0

Asphalt Paving / Roofing 0 0 0 0 0

Residential Fuel Combustion 80.6 68.1 59.9 59.3 59

Farming Operations 0 0 0 0 0

Construction And Demolition 0 0 0 0 0

### B

- 2 NOx Emissions (TPD) (continued)

Statewide 2000 2005 2010 2015 2020

Paved Road Dust 0 0 0 0 0

Unpaved Road Dust 0 0 0.1 0 0

Fugitive Windblown Dust 0 0 0 0 0

Structural and Automobile Fire s 0.2 0.2 0.2 0.2 0.2

Managed Burning And Disposal 15 16.6 14.7 14.6 14.6

Cooking 0 0 0 0 0

Other (Miscellaneous Processes) 0 0 0 0 0

Light Duty Passenger (Lda) 463.7 249.8 157.4 83.7 50.5

Light Duty Trucks

- 1 (Ldt1) 166.8 66.9 39.2 26.6 17.6

Light Duty Trucks

- 2 (Ldt2) 265.3 148.1 101 56.9 32.2

Medium Duty Trucks (Mdv) 117 127.2 107.1 75.5 52.2

Light Heavy Duty Gas Trucks

- 1 (Lhdv1) 28.1 49.6 47.9 41 34.4

Light Heavy Duty Gas Trucks

- 2 (Lhdv2) 9.7 5.6 4.2 3.3 2.6

Medium Heavy Duty Gas Trucks (Mhdv) 18.3 12 10.5 6.5 4.1

Heavy Heavy Duty Gas Trucks (Hhdv) 11.3 4 3.9 3.5 3.2

Light Heavy Duty Diesel Trucks

- 1 (Lhdv1) 24.6 98 102.1 74.7 53.6

Light Heavy Duty Diesel Trucks

- 2 (Lhdv2) 23.7 27.1 25.8 18.9 13.8

Medium Heavy Duty Diesel Trucks (Mhdv) 161.7 162.7 100.6 64.7 32.2

Heavy Heavy Duty Diesel Trucks (Hhdv) 706.3 728.3 432 286.2 192.6

Motorcycles (Mcy) 4.4 7.5 9.1 9.1 9.5

Heavy Duty Diesel Urban Buses (Ub) 44.2 35.7 34.2 30.2 26.5

Heavy Duty Gas Urban Buses (Ub) 2.8 2.2 2.2 2 1.9

School Buses

- Gas (Sbg) 1 0.8 0.7 0.5 0.5

School Buses

- Diesel (Sbd) 10.8 10 7.3 6.8 5.9

Other Buses

- Gas (Obg) 2.7 4.4 3.5 2.7 1.9

Other Buses

- Motor Coach

- Diesel (Obc) 12.4 12.3 8.2 5.5 2.8

All Other Buses

- Diesel (Obd) 7.5 7.6 5.1 4 2.1

Motor Homes (Mh) 14.6 8.9 8 5.9 4.5

Aircraft 42.6 45.8 53.2 57.7 64.1

Trains 198.8 157.4 95.6 111.3 109.7

Ships And Commercial Boats 0.2 0.1 0.1 0.1 0.1

Ocean Going Vessels 170.5 218.2 196.1 236.2 224.9

Commercial Harbor Craft 83.4 74.3 63.6 44.9 36.5

Recreational Boats 16.6 20.5 19.5 18.6 18.4

Off

- Road Recreational Vehicles 0.4 1.5 1.4 2.7 3

Off

- Road Equipment 339.5 307.3 188.7 163.9 126.9

Farm Equipment 154 132.5 106.9 81.4 59.7

Fuel Storage And Handling 0 0 0 0 0



### Total (TPD) 3782 3214 2323 1887 1553

### B

- 3 SOx Emissions (TPD)

Statewide 2000 2005 2010 2015 2020

Electric Utilities 5.4 4.1 5 5.1 5.5

Cogeneration 1.8 1.2 1.1 1.2 1.4

Oil And Gas Production (Combustion) 7.4 1.3 2.1 0.6 0.6

Petroleum Refining (Combustion) 12.8 11.9 18.9 8.9 8.1

Manufacturing And Industrial 9.1 6.3 7.6 8.7 8.4

Food And Agricultural Processing 1.1 0.7 0.6 0.4 0.4

Service And Commercial 3 2.5 2.8 3 3.1

Other (Fuel Combustion) 0.4 0.9 0.4 0.4 0.4

Sewage Treatment 0.2 0.2 0.2 0.3 0.3

Landfills 0.2 0.5 0.5 0.5 0.5

Incinerators 0.1 0.3 0.5 0.6 0.6

Soil Remediation 0 0 0 0 0

Other (Waste Disposal) 0 0 0 0 0

Laundering 0 0 0 0 0

Degreasing 0 0 0 0 0

Coatings And Related Process Solvents 0 0 0 0 0

Printing 0 0 0 0 0

Adhesives And Sealants 0 0 0 0 0

Other (Cleaning And Surface Coatings) 0 0 0 0.1 0.1

Oil And Gas Production 0.1 0.1 0.1 0.1 0.1

Petroleum Refining 56.6 40.1 5 4.7 4.2

Petroleum Marketing 0 0 0 0 0

Other (Petroleum Production And Marketing) 0 0 0 0 0

Chemical 2.9 3.4 2.6 2.6 3

Food And Agriculture 1.8 0.3 0.5 0.5 0.6

Mineral Processes 20.7 18.9 12.4 13.3 14.9

Metal Processes 0.2 0 0.1 0.1 0.1

Wood And Paper 0.7 0 0 0 0

Glass And Related Products 6.6 2.9 2.5 1.8 1.9

Electronics 0 0 0 0 0

Other (Industrial Processes) 0.8 1 1.4 0.5 0.7

Consumer Products 0 0 0 0 0

Architectural Coatings And Related Process Solvents 0 0 0 0 0

Pesticides/Fertilizers 0 0 0 0 0

Asphalt Paving / Roofing 0 0 0 0 0

Residential Fuel Combustion 5.4 3.8 3.4 2.7 2.7

Farming Operations 0 0 0 0 0

Construction And Demolition 0 0 0 0 0

### B

- 4 SOx Emissions (TPD) (continued)

Statewide 2000 2005 2010 2015 2020

Paved Road Dust 0 0 0 0 0

Unpaved Road Dust 0 0 0 0 0

Fugitive Windblown Dust 0 0 0 0 0

Structur al and Automobile Fires 0 0 0 0 0

Managed Burning And Disposal 3.6 3.6 3.5 3.6 3.6

Cooking 0 0 0 0 0

Other (Miscellaneous Processes) 0 0 0 0 0

Light Duty Passenger (Lda) 2.7 1.8 1.9 2 1.8

Light Duty Trucks

- 1 (Ldt1) 0.7 0.3 0.3 0.3 0.3

Light Duty Trucks

- 2 (Ldt2) 1.4 0.9 0.9 1 0.9

Medium Duty Trucks (Mdv) 0.6 1 1 1 1

Light Heavy Duty Gas Trucks

- 1 (Lhdv1) 0.1 0.2 0.3 0.3 0.3

Light Heavy Duty Gas Trucks

- 2 (Lhdv2) 0 0 0 0 0

Medium Heavy Duty Gas Trucks (Mhdv) 0 0 0 0 0

Heavy Heavy Duty Gas Trucks (Hhdv) 0 0 0 0 0

Light Heavy Duty Diesel Trucks

- 1 (Lhdv1) 0.2 0.7 0.1 0.1 0.1

Light Heavy Duty Diesel Trucks

- 2 (Lhdv2) 0.2 0.2 0 0 0

Medium Heavy Duty Diesel Trucks (Mhdv) 1.2 1.4 0.1 0.1 0.2

Heavy Heavy Duty Diesel Trucks (Hhdv) 4.9 5.8 0.6 0.7 0.8

Motorcycles (Mcy) 0 0 0 0 0

Heavy Duty Diesel Urban Buses (Ub) 0.4 0.4 0 0 0

Heavy Duty Gas Urban Buses (Ub) 0 0 0 0 0

School Buses

- Gas (Sbg) 0 0 0 0 0

School Buses

- Diesel (Sbd) 0.1 0.1 0 0 0

Other Buses

- Gas (Obg) 0 0 0 0 0

Other Buses

- Motor Coach

- Diesel (Obc) 0.1 0.1 0 0 0

All Other Buses

- Diesel (Obd) 0.1 0.1 0 0 0

Motor Homes (Mh) 0.1 0 0 0 0

Aircraft 3 3.2 3.5 3.8 4.2

Trains 8.4 7.5 0.6 0.1 0.1

Ships And Commercial Boats 0 0 0 0 0

Ocean Going Vessels 120.9 155.1 41.6 8.1 10.2

Commercial Harbor Craft 0.9 0.9 0.4 0.4 0.4

Recreational Boats 0 0 0 0 0

Off

- Road Recreational Vehicles 0 0.1 0 0.1 0.1

Off

- Road Equipment 1.4 1.6 0.2 0.2 0.2

Farm Equipment 1 0.9 0.1 0.1 0.1

Fuel Storage And Handling 0 0 0 0 0



### Total (TPD) 289.3 286.3 122.8 78 81.9

### B

- 5 VOC Emissions (TPD)

Statewide 2000 2005 2010 2015 2020

Electric Utilities 4.8 3.2 2.4 2.4 2.4

Cogeneration 3 1.8 1.7 2.1 2.5

Oil And Gas Production (Combustion) 2.9 2.6 2.6 2.3 2.1

Petroleum Refining (Combustion) 1.8 2.9 2.6 3 3

Manufacturing And Industrial 11.7 6.6 7.5 7.8 8.3

Food And Agricultural Processing 5.1 4.9 3.8 2.8 2.3

Service And Commercial 5.6 4 4.9 5.2 5.4

Other (Fuel Combustion) 2.2 1.4 1.1 1 0.9

Sewage Treatment 0.6 0.7 1.1 0.7 0.8

Landfills 13.7 10.6 11.4 12.3 13.1

Incinerators 0.2 3.2 1.3 1.5 1.5

Soil Remediation 0.3 0.5 0.4 0.4 0.4

Other (Waste Disposal) 22.6 27.6 27.6 24.8 26.3

Laundering 1.6 2.2 2.1 1.8 1.9

Degreasing 64.1 32.5 33.6 38.1 43

Coatings And Related Process Solvents 90.8 67.3 57.4 64.9 71.6

Printing 24 18.8 17.4 18.8 20.1

Adhesives And Sealants 26.4 19.9 19.8 20.5 21.3

Other (Cleaning And Surface Coatings) 6.2 5.2 7.2 8.1 8.9

Oil And Gas Production 58.6 42.1 37.3 34.3 31

Petroleum Refining 38.4 12.3 16.4 11.9 11.9

Petroleum Marketing 109.4 80.6 80.1 81.8 84.5

Other (Petroleum Production And Marketing) 0.4 0.3 15.9 0.3 0.3

Chemical 22.6 18.4 15.8 17.1 20.2

Food And Agriculture 15.2 16.4 17.7 18.8 20.6

Mineral Processes 6.8 6 4.1 4.2 4.7

Metal Processes 1.3 0.5 0.4 0.4 0.4

Wood And Paper 3.6 2.9 2.5 2.5 2.7

Glass And Related Products 0.2 0 0 0 0

Electronics 0.9 0.4 0.3 0.3 0.3

Other (Industrial Processes) 19.2 19.9 20.6 10.5 12.5

Consumer Products 255.2 230.4 213 202.2 210.6

Architectural Coatings And Related Process Solvents 119.7 104.6 87.7 76.4 79.8

Pesticides/Fertilizers 59.1 47.1 36.8 42 41.9

Asphalt Paving / Roofing 27 28.4 29 30.6 31.9

Residential Fuel Combustion 95.4 71.5 57.5 57.8 57.8

Farming Operations 174.8 177 178.9 149.7 155.5

Construction And Demolition 0 0 0 0 0

### B

- 6 VOC Emissions (TPD) (continued)

Statewide 2000 2005 2010 2015 2020

Paved Road Dust 0 0 0 0 0

Unpaved Road Dust 0 0 0.3 0 0

Fugitive Windblown Dust 0 0 0 0 0

Structur al and Automobile Fires 0.6 0.7 0.7 0.7 0.7

Managed Burning And Disposal 43.2 45.1 42.6 42.6 42.5

Cooking 6 6.3 6.7 7.1 7.6

Other (Miscellaneous Processes) 1.9 1.9 1.9 1.9 1.9

Light Duty Passenger (Lda) 500.8 283.9 191.8 90.1 51.9

Light Duty Trucks

- 1 (Ldt1) 165.8 74.5 52.2 31 21.5

Light Duty Trucks

- 2 (Ldt2) 160.5 93.7 72.8 43.5 29.4

Medium Duty Trucks (Mdv) 59 63.8 62.8 49.2 41.5

Light Heavy Duty Gas Trucks

- 1 (Lhdv1) 16.5 30.2 29.2 21 17.3

Light Heavy Duty Gas Trucks

- 2 (Lhdv2) 12.5 5.3 2.9 1.6 1.1

Medium Heavy Duty Gas Trucks (Mhdv) 20 10.4 7.6 3.6 2

Heavy Heavy Duty Gas Trucks (Hhdv) 8 2.6 1.9 0.8 0.4

Light Heavy Duty Diesel Trucks

- 1 (Lhdv1) 0.7 3.2 4 3.3 2.7

Light Heavy Duty Diesel Trucks

- 2 (Lhdv2) 0.7 0.9 0.9 0.8 0.7

Medium Heavy Duty Diesel Trucks (Mhdv) 8 7.9 5.1 2.8 1.8

Heavy Heavy Duty Diesel Trucks (Hhdv) 37.1 38.6 23.7 13.8 14.3

Motorcycles (Mcy) 48.5 48.4 41.5 31.6 31.1

Heavy Duty Diesel Urban Buses (Ub) 1.5 1.4 1.3 1.2 1.1

Heavy Duty Gas Urban Buses (Ub) 1.8 1.3 1.1 0.9 0.9

School Buses

- Gas (Sbg) 1.5 1.1 0.7 0.5 0.3

School Buses

- Diesel (Sbd) 0.8 0.7 0.5 0.1 0.1

Other Buses

- Gas (Obg) 2.2 2.2 1.5 1.1 0.9

Other Buses

- Motor Coach

- Diesel (Obc) 0.6 0.6 0.4 0.2 0.2

All Other Buses

- Diesel (Obd) 0.5 0.5 0.3 0.1 0.1

Motor Homes (Mh) 8.3 3.8 2.5 1.5 0.9

Aircraft 26.5 26.2 27.8 30.3 33.8

Trains 11.5 12.2 8.3 7.6 6.1

Ships And Commercial Boats 0 0 0 0 0

Ocean Going Vessels 6.9 8.8 8.3 11.2 14.5

Commercial Harbor Craft 5.3 5.1 4.8 4.3 4.2

Recreational Boats 143.8 126.5 107.1 87.5 72.4

Off

- Road Recreational Vehicles 30.1 39.8 36.6 32.2 31.5

Off

- Road Equipment 195.4 177.4 132.3 110.9 100.1

Farm Equipment 30.1 26.2 20.7 14.9 10.7

Fuel Storage And Handling 50.3 35.7 20.5 14.9 12.3



### Total (TPD) 2902.3 2261.6 1943.2 1624.1 1560.9

### B

- 7 PM2.5 Emissions (TPD)

Statewide 2000 2005 2010 2015 2020

Electric Utilities 6.9 5.6 5.1 4.8 5

Cogeneration 3.7 2.9 3 2.6 3.2

Oil And Gas Production (Combustion) 1.9 1.8 2.1 1.9 1.7

Petroleum Refining (Combustion) 4 3.4 3.4 1.8 1.8

Manufacturing And Industrial 8.9 5.9 5.9 4.8 4.8

Food And Agricultural Processing 2.4 2.2 1.7 1.3 1.1

Service And Commercial 5.1 3.9 4.8 4.5 4.7

Other (Fuel Combustion) 4.6 3.6 0.7 0.6 0.5

Sewage Treatment 0 0 0 0 0

Landfills 0.3 3.1 0.3 0.5 0.5

Incinerators 0.1 0.3 0.2 0.1 0.1

Soil Remediation 0 0 0 0 0

Other (Waste Disposal) 0.1 0.1 0 0 0.1

Laundering 0 0 0 0 0

Degreasing 0 0 0 0.1 0.1

Coatings And Related Process Solvents 1.1 1.8 1.7 1.8 2

Printing 0 0.2 0.2 0.2 0.2

Adhesives And Sealants 0 0 0 0 0

Other (Cleaning And Surface Coatings) 0 0 0.1 0.8 0.8

Oil And Gas Production 0.1 0.1 0 0 0

Petroleum Refining 2 3.3 3 1.5 1.5

Petroleum Marketing 0.1 0 0 0 0

Other (Petroleum Production And Marketing) 0 0 0 0 0

Chemical 3.1 1.9 1.6 1 1.1

Food And Agriculture 4 3.6 3.3 3.5 3.9

Mineral Processes 24.1 26.1 22.7 22.9 24.8

Metal Processes 1 0.8 0.7 0.4 0.4

Wood And Paper 9.5 9.1 6.6 7.3 8.1

Glass And Related Products 1.4 1.8 0.6 0.5 0.5

Electronics 0 0 0 0 0

Other (Industrial Processes) 7.4 9.2 13.9 1.5 1.7

Consumer Products 0 0 0 0 0

Architectural Coatings And Related Process Solvents 0 0 0 0 0

Pesticides/Fertilizers 0 0 0 0 0

Asphalt Paving / Roofing 0 0 0 0 0

Residential Fuel Combustion 96 71.2 56.4 55.5 55.5

Farming Operations 28.5 21.8 21.4 21.1 20.9

Construction And Demolition 15.4 15.3 16.2 17.9 20.3

### B

- 8 PM2.5 Emissions (TPD) (continued)

Statewide 2000 2005 2010 2015 2020

Paved Road Dust 23.9 24.3 26.1 24.8 25.4

Unpaved Road Dust 27.8 27.9 25.5 27.1 27.1

Fugitive Windblown Dust 175.3 67.2 49 48.8 48.5

Structural and Automobile Fires 1 1.1 1.1 1.1 1.2

Managed Burning And Disposal 51.4 53.7 50.6 50.4 50.3

Cooking 25.8 26.5 28.1 29.8 31.6

Other (Miscellaneous Processes) 0.3 0.3 0.3 0.3 0.3

Light Duty Passenger (Lda) 15.5 12.3 11.4 11 11.2

Light Duty Trucks

- 1 (Ldt1) 3.5 1.9 1.7 1.6 1.5

Light Duty Trucks

- 2 (Ldt2) 5.3 4.4 4.1 4 4.1

Medium Duty Trucks (Mdv) 2 3.5 3.4 3.3 3.4

Light Heavy Duty Gas Trucks

- 1 (Lhdv1) 0.3 0.7 0.7 0.7 0.7

Light Heavy Duty Gas Trucks

- 2 (Lhdv2) 0.1 0.1 0.1 0.1 0.1

Medium Heavy Duty Gas Trucks (Mhdv) 0.1 0.1 0.1 0.1 0.1

Heavy Heavy Duty Gas Trucks (Hhdv) 0 0 0 0 0

Light Heavy Duty Diesel Trucks

- 1 (Lhdv1) 0.4 1.3 1.5 1.3 1.1

Light Heavy Duty Diesel Trucks

- 2 (Lhdv2) 0.4 0.4 0.4 0.4 0.3

Medium Heavy Duty Diesel Trucks (Mhdv) 6.3 6.4 4.2 2.4 1.6

Heavy Heavy Duty Diesel Trucks (Hhdv) 23.7 24.6 17.4 6.2 5.5

Motorcycles (Mcy) 0.1 0.1 0.1 0.1 0.1

Heavy Duty Diesel Urban Buses (Ub) 1.4 1.2 1.2 1.2 1.2

Heavy Duty Gas Urban Buses (Ub) 0 0 0 0 0

School Buses

- Gas (Sbg) 0 0 0 0 0

School Buses

- Diesel (Sbd) 0.7 0.6 0.5 0.2 0.2

Other Buses

- Gas (Obg) 0 0 0 0 0

Other Buses

- Motor Coach

- Diesel (Obc) 0.4 0.4 0.3 0.1 0.1

All Other Buses

- Diesel (Obd) 0.4 0.4 0.2 0.1 0.1

Motor Homes (Mh) 0.2 0.2 0.2 0.1 0.1

Aircraft 7.8 7.5 7.7 8.2 8.9

Trains 4.2 4.4 2.7 2.9 2.5

Ships And Commercial Boats 0 0 0 0 0

Ocean Going Vessels 15.2 19.5 7.1 4.1 5.3

Commercial Harbor Craft 3.4 3.1 2.6 1.7 1.2

Recreational Boats 6.2 5.9 4.9 4 3.3

Off

- Road Recreational Vehicles 0.2 0.3 0.3 0.3 0.3

Off

- Road Equipment 16.9 16.9 11.7 10 7.8

Farm Equipment 8.5 7.6 6.1 4.5 3.3

Fuel Storage And Handling 0 0 0 0 0



### Total (TPD) 660.4 523.8 446.9 409.8 413.7

## Appendix C

Deciview Record (2000

- 2012)

### This page intentionally left blank.

### C

- 1 Table C

- 1

Worst Days Deciview Record *

IMPROVE

Monitor Baseline

Average

(2000

- 2004) 2005 2006 2007 2008 2009 2010 2011 2012** 2018

RPGs

TRIN* 17.4 na 19.7 13.9 23.1 12.7 12.4 13.9 17.1 16.4

LABE 15.1 13.5 14.4 12.2 16.9 13.9 10.4 11.7 15.9 14.4

LAVO 14.1 12.4 14.1 14.1 22.4 17.1 12.8 11.7 14.3 13.3

BLIS 12.6 12.0 12.5 14.8 16.8 12.0 10.0 11.2 11.0 12.3

HOOV 12.9 10.4 10.5 12.6 15.5 12.0 8.8 8.6 9.8 12.5

YOSE 17.6 15.7 15.9 17.7 18.6 16.5 12.8 14.4 13.9 16.7

KAIS* 15.5 15.2 na 16.2 17.4 14.1 13.1 13.7 12.8 14.9

SEQU 25.4 23.1 23.4 24.7 24.7 21.0 20.1 21.2 20.6 22.7

DOME* 19.4 na na 20.9 19.3 17.6 16.7 17.1 17.0 18.1

REDW* 18.5 18.1 20.8 18.2 19.8 18.9 na 17.2 16.5 17.8

PORE 22.8 22.3 22.0 22.4 22.1 21.3 22 20.2 20.1 21.3

PINN 18.5 18.1 17.9 18.3 19.8 17.8 15.1 16.4 15.6 16.7

RAFA 18.8 18.3 20.2 20.6 19.6 17 16.6 16.5 15.7 17.3

SAGA* 19.9 19.5 17.4 17.4 17.9 na na na 14.3 17.4

SAGO 22.2 21.5 19.9 21.5 20.2 19.2 16.7 15.9 16.1 19.9

AGTI* 23.5 21.2 na 22.0 21.0 19.4 18.4 18.1 17.8 21.6

JOSH 19.6 19.4 18.1 18.1 16.7 16.8 14.7 14.2 14.9 17.9



### Table C

- 2

Best Days Deciview Record *

IMPROVE

Monitor Baseline

Average

(2000

- 2004) 2005 2006 2007 2008 2009 2010 2011 2012**

TRIN* 3.4 na 2.1 3.9 4.0 2.6 1.9 2.8 2.2

LABE 3.2 2.9 2.4 3.6 2.6 2.5 2.5 3.1 2.3

LAVO 2.7 2.5 2.2 2.9 2.8 2.4 1.6 2.1 1.8

BLIS 2.5 2.1 2.0 2.7 2.2 1.9 1.6 2.1 1.5

HOOV 1.4 1.1 1.3 1.7 1.2 1.1 0.9 1.3 0.9

YOSE 3.4 3.2 3.4 3.9 2.0 2.3 1.8 2.7 2.8

KAIS* 2.3 1.2 na 1.8 1.4 1.8 1.1 1.5 1.0

SEQU 8.8 7.2 7.7 9.2 9.3 6.2 5.7 7.5 6.8

DOME* 5.1 na na 5.7 4.9 4.8 3.9 5.6 3.7

REDW* 6.1 5.7 5.9 6.0 5.4 4.9 na 6.7 4.7

PORE 10.5 9.8 9.6 8.9 9.5 7.8 8.0 8.8 6.9

PINN 8.9 8.0 7.9 8.5 8.4 7.2 7.2 7.6 7.0

RAFA 6.4 5.0 5.2 6.8 5.9 4.6 4.2 4.5 4.3

SAGA* 4.8 4.3 4.4 5.4 3.7 na na na 2.7

SAGO 5.4 4.1 5.0 5.5 4.4 3.6 3.0 3.3 3.5

AGTI* 9.6 6.9 na 9.0 7.5 6.3 6.8 6.0 6.7

JOSH 6.1 5.5 5.7 5.7 5.1 4.4 4.5 4.3 4.2

* Complete data was not available for the years marked “na.”

** D ata made available February 28, 2014 is outside the Mid

- Course Review time frame but is shown

here for continuity

, and to illustrate further progress.

### C

- 2 Table C

- 3

Statewide 2018 Reasonable Progress Goal Summary using 2012 Data

IMPROVE

Monitor California Class I Area(s) Best Days

Baseline

(dv) Best Days

(2008

- 2012)

(dv) Visibility

Change

(dv) Worst Days

Baseline (dv) Worst Days

(2008

- 2012)

(dv) Visibility

Change

(dv) 2018

RPG

(dv) Progress to

2018 RPG

by 2012

NORTHERN CALIFORNIA

TRIN Marble Mountain W.

Yolla Bolly

- Middle Eel W. 3.4 2.7 0.7 17.4 15.8 1.6 16.4 160%

LABE Lava Beds N.M.

South Warner W. 3.2 2.6 0.6 15.1 13.7 1.4 14.4 200%

LAVO Lassen Volcanic N.P.

Caribou W.

Thousand Lakes W. 2.7 2.1 0.6 14.1 15.6

- 1.5 13.3

- 188%

SIERRA CALIFORNIA

BLIS Desolation W.

Mokelumne W. 2.5 1.9 0.6 12.6 12.2 0.4 12.3 133%

HOOV Hoover W. 1.4 1.1 0.3 12.9 10.9 2 12.5 500%

YOSE Yosemite N.P.

Emigrant W. 3.4 2.3 1.1 17.6 15.2 2.4 16.7 267%

KAIS Ansel Adams W.

Kaiser W.

John Muir W. 2.3 1.4 0.9 15.5 14.2 1.3 14.9 217%

SEQU Sequoia N.P.

Kings Canyon N.P. 8.8 7.1 1.7 25.4 21.5 3.9 22.7 144%

DOME Dome Lands W. 5.1 4.6 0.5 19.4 17.5 1.9 18.1 146%

COASTAL CALIFORNIA

REDW Redwood N.P. 6.1 5.4 0.7 18.5 18.1

(missing 2010) 0.4 17.8 57%

PORE Point Reyes N.S. 10.5 8.2 2.3 22.8 21.2 1.6 21.3 107%

PINN Pinnacles W.

Ventana W. 8.9 7.5 1.4 18.5 16.9 1.6 16.7 89%

RAFA San Rafael W. 6.4 4.7 1.7 18.8 17.1 1.7 17.3 113%

SOUTHERN CALIFORNIA

SAGA San Gabriel W.

Cucamonga W. 4.8 3.2 1.6 19.9 16.1

(2008,2012 only) 3.8 17.4 152%

SAGO San Gorgonio W.

San Jacinto W. 5.4 3.6 1.8 22.2 17.6 4.6 19.9 200%

AGTI Agua Tibia W. 9.6 6.7 2.9 23.5 18.9 4.6 21.6 242%

JOSH Joshua Tree N.P. 6.1 4.5 1.6 19.6 15.4 4.2 17.9 247%

W = Wilderness N.M. = National Monument N.P. = National Park N.S. = National Seashore

## Appendix D

Technical Analyses of Factors Impeding Progress

### This page intentionally left blank.

### D

- 1 Purpose of Focused Technical Analysis

The annual deciview levels in California are trending downward over the long

- term, as

measured by the IMPROVE monitors

, indicat ing diminishing light extinction from haze

causing pollutants in ambient air. Episodic events, such as wildfires and dust storms,

cause short

- term increases in the concentrations of these pollutants. These events are

predominately natural in origin, but can be significant. Some of the episodic wildfire

emissions are substantial enough to skew an annual average, and influence the five

year average trend. There are other activities, beyond the regulatory jurisdiction of the

State of California, which also influence the deciview levels at some sites. The following

Case Studies analyze the data from three monitoring sites to explain ho w these events

and activities impede short

- term progress in otherwise long

- term visibility

improvements.

ARB examined five

- year deciview average trends at the IMPROVE monitors

representing California’s Class 1 Areas and sele cted the three IMPROVE monitors that

did not show short

- term progress i n the reporting period ending with 2011 IMPROVE

monitoring data. I ndividually, the BLIS, LAVO, and REDW monitors have collected data

for over twenty years and show long

- term visibility improvements. The ARB emissio ns

inventory shows continuous reductions in emissions from the RH Plan control strategy,

which should assure improved visibility. Closer examination of the monitoring data

comprising the five

- year averages through 2011, shows how reasonable progress from

the reductions in anthropogenic emissions within California’s jurisdiction is sometimes

masked by uncontrollable factors

. These uncontrollable factors a re pollution from

natural events and man

- made emissions beyond the regulatory jurisdiction of individual

states.

IMPROVE monitoring data for 2012 was posted to the WRAP

- TSS website, on

February 28, 2014. See http://vista.cira.colostate.edu/tss/Results/HazePlanning.aspx to

use the interactive Haze Planning tool and view the monitoring data and recent five

- year

deciview averages.

### D

- 2 Case Study: BLIS

Wildfire Impacts

O rganic carbon (OMC) is the primary driver of haze on Worst Days at BLIS. Figure D

- 1

shows that i n every year in the past decade, except 2010, OM C has contributed more

than any other species to light extinction on Worst Days. The years with higher OMC

extinction correlate with wildfire smoke impacts. Notably, i n 2010, when no significant

wildfires affected BLIS, the OMC contribution to extinction was less than natural

Rayleigh gas scattering. Rayleigh scattering consists mainly of light scattering fro m

atmospheric gases that are smaller in diameter than the wavelength of incoming light.

Additional light scattering and light absorption by larger particulate matter are what adds

to haze. Gaps in data in 2004 meant that neither Best nor Worst Days avera ges could

be calculated that year.

Figure D

- 1

Species Driving Light Extin c tion at BLIS on Worst Days

Figure D

- 2 shows the relationship between OMC light extinction and deciview for each

sampling day at BLIS since 1990. Although there are wildfire imp act days in almost

every year in the past decade, the magnitude of natural wildfire smoke impacts at BLIS

is most obvious in 2008. Fifty

- seven named wildfires were burning at the same time in

California, consuming more than one million acres of primarily forested natural areas.

The American River Complex wildfire (20,500 acres) was the largest of the six named

fires within 50 miles of the BLIS monitor during June through August. Figure D

- 2 shows

that in 2008, daily OMC extinction and deciview levels reac hed the highest levels in 21 0 10 20 30 40 50 60

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Inverse Megameters 

### BLIS: WORST Days

- Annual Average Species Extinction

Rayleigh

OMC Extinction

SO4 Extinction

NO3 Extinction

EC Extinction

CM Extinction

Soil Extinction

SeaSalt Extinction

Note: There was insufficient data to calculate the Worst Days average in 2004.

### D

- 3 years of recordkeeping at BLIS. The five worst deciview days in 2008 had OMC

extinction values 5

- 33 times the average levels for that time of year. Those five days

alone caused a 3.0 deciview increase in the Worst Days annual average for 2008.

Figure D

- 2

Historical Record of OM C Extinction and Deciview Levels at BLIS

Table D

- 1 shows how the Worst Days annual averages pattern for OMC light extinction

(green column) corresponds with that for deciviews (last column) in the past decade.

The slopes of the trend line s from 2000

- 2011

, for each species contribution

, are shown

on the bottom line

. The slopes indicate that almost all species are trending downward

in their contribution to light extinction (haze) since 2000. The increasing trend in light

extinction due to sea salt was not analyzed further because its contribution to light

extinction is negligible, and the source is natural. E lemental carbon (EC) is occasionally

elevated from wildfire smoke, but the annual average Worst Days EC extinction level is

decreasing over the long term due to the strict regulation of diese l

- fuel combustion

emissions, which also generate EC. The dramatic decrease in nitrate extinction is

### D

- 4 attributed to NOx source controls. The high OMC light extinction in 2008 correlates with

the Lightning Strike Complex Fires.

Table D

- 1

Annual Averages for Worst Days

Light Extinction (in lnverse Megameters) and Deciviews at BLIS

Year* Sulfate

Extinction Nitrate

Extinction Organic

Carbon

Extinction Elemental

Carbon

Extinction Fine Soil

Extinction Coarse

Mass

Extinction Sea Salt

Extinction Rayleigh

Gas

Extinction Deciview

2000 5.29 3.55 9.5 2.8 0.47 1.51 0 9 11.6

2001 4.91 2.74 15.78 3.48 1.91 3.21 0.01 9 13.4

2002 4.56 2.15 17.36 3.26 1.04 1.91 0 9 13.4

2003 5.54 1.06 13.57 2.42 0.62 1.78 0.06 9 12.2

2005 6.79 1.77 11.4 3.12 0.51 1.86 0.03 9 12

2006 6.34 1.38 14.42 2.81 0.86 2.42 0.05 9 12.5

2007 5.79 3.67 22.75 3.88 0.77 2.17 0.11 9 14.8

2008 6.3 2.08 49.24 6.29 0.94 2.25 0.08 9 16.8

2009 4.86 1.69 13.54 2.42 0.69 1.97 0.07 9 12

2010 4.52 2.31 7 1.58 1.09 2.13 0.09 9 10

2011 4.89 1.19 12.51 2.49 0.4 1.99 0.18 9 11.2

SLOPE

- 0.0 1

- 0.0 9 0.4 5

- 0.0 2

- 0.03

- 0.003 0.01 constant

- 0.05

*Insufficient sampling points for cal c ulating Worst D ays averages for 2004

.



### Although the annual Worst Days deciview average trends downwards, further analysis

shows that elevated OMC extinction on Worst Days in 2008 subsequently impeded the

rate of progress measured by the 5

- year rolling average. If the top five Worst Days

occurring during the Lightning Strike Complex Fires are excluded, recalculation of the

2008 OM C light extinction average becomes 17.0 inverse megameters, and the slope of

the trendline for OMC becomes

- 0.01 (decreasing trend.) If those same top five Worst

Days are not included in the Worst Days annual average by deciviews, the 2008

deciview level would be 13.8 dv. As shown in Table D

- 2, the 5

- year rolling average

changes to a decreasing trendline.

Table D

- 2

BLIS without Wildfire D ays

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Slope

Deciview 11.6 13.4 13.4 12.2 12 12.5 14.8 16.8 12 10 11.2

- 0.05

5

- year Rolling

Average 12.7 12.8 12.5 12.9 14.0 13.6 13.2 13.0 0.11

Recalculated

2008 De c iview 11.6 13.4 13.4 12.2 12 12.5 14.8 13.8 12 10 11.2

- 0.10

Recal c ulated

5

- year Rolling

Average 12.7 12.8 12.5 12.9 13.3 13.0 12.6 12.4

- 0.01

### D

- 5 Annual deciview values and the forecast trendlines to 2018 (dashed line) are plotted in

Figure D

- 3 to show progress to the 2018 RPG. The trendlines are regression lines that

display a straight

- line trend based on available data. While they ca nnot forecast a long

term glide path, they illustrate the sensitivity of trends to single point changes. The top

graph shows how the 5

- year rolling average for Worst Days would change with the

substitutions made for 2008. In the absence of large wildfire impacts, visibility at BLIS is

improving, even though the 2008 data dampens progress for the 5

- year rolling average

from 2008 through 2011. Wildfire smoke also led to several Worst Days in 2007 at

BLIS. If those days are also removed from annual average calculations, the rolling

averages for 2007 through 2011 improve even more, as shown in the lower graph.

Figure D

- 3

BLIS: Worst Days Averages with Wildfire Adjustments

0 2 4 6 8 10 12 14 16 182000200120022003200420052006200720082009201020112012201320142015201620172018Deciviews 2018 RPG

Annual Worst Days

Average (no

substitution)

5-Year Rolling

Average

5-Year rolling

average with 2008

substitution

Linear (Annual

Worst Days Average

(no substitution))

0 2 4 6 8 10 12 14 16 182000200120022003200420052006200720082009201020112012201320142015201620172018Deciviews 2018 RPG

Annual Worst Days

Average without

Wildfire Days

5-Year Rolling

Average without

Wildfire Days

Linear (Annual Worst

Days Average

without Wildfire

Days)

Linear (5-Year

Rolling Average

without Wildfire

Days)

### D

- 6 The BLIS monitor is located in El Dorado County . It represents the Desolation

Wilderness in Placer and El Dorado Counties and the Mokelumne Wilderness in Alpine,

Amador and Calaveras counties. A quick examination of the emissions inventories for

the se surrounding counties further i llustrates the impact of wildfires. Total organic gas

(TOG) emissions are used as a surrogate for OMC because they include the building

blocks for organic aerosol molecules, in the absence of a precise inventory of dire ctly

emitted organic aerosols.

The local emissions inventory of Reactive Organic Gases (R OG) f rom anthropogenic

sources in these counties is decreasing

. The 10

- year average of natural R OG sources

(predominately from biogenic emissions ) is included for comparison. Emissions from

natural sources make up more than 50 percent of the R OG inventory. Figure D

- 4

shows that despite natural source emissions , anthropogenic source control strategies

are effective ly reducing precursor emissions. The summary result is that the annual

Worst Days deci view average at BLIS is trending downward , despite the progress

impeded by the impacts of uncontrollable sources on visibility.

Figure D

- 4

Local Inventory Changes O ver Time

0 20 40 60 80 100 120 140 160

2000 2005 2008 2010 2015 2020Tons Per Day

Year 

### ROG Emissions Inventory for 5 Counties Surrounding BLIS

Placer

El Dorado

Calaveras

Amador

Alpine

5-County Natural ROG

(10-Year Average)

5-County Total

Anthropogenic ROG

### D

- 7 Impact of 2012 data for BLIS

The 2012 Worst Days deciview average for BLIS is 11.0 dv. The five

- year Worst Days

Rolling Average for the years 2008

- 2012 at BLIS is 12.2 dv. Both of these deciview

values are below 12 .3 dv, the 2018 RPG for BLIS. The y ears after 2008 were low

wildfire impact years at BLIS, as indicated by the OC extinction levels on the Worst

Days from 2008 through 2012, shown below in Figure D

- 5 . The single high OMC

extinction days in 2011 and 2012 contribute to high deciview days, but the remaining

days have deciview levels slightly lower than those prior to 20 08 , shown in Figure D

- 2.

Figure D

- 5

Recent OMC Extinction and Deciview Levels at BLIS on Worst Days (2008

- 2012)

### D

- 8 Case Study: LAVO

Wildfire Impacts

Data has been collected at the LAVO monitoring site since 198 8 . Comparing pre

- 2000

data for all sampling days with that from 2000

- 2011 shows that OMC contributions to

total light extinction increased 60 percent while other contributions decreased slightly on

average. The net effect is a very slight decrease in the decadal deciview average, f or

all monitored days. Figure D

- 6 shows light extinction for all sampling days over two

decades. The Best Days annual average is improving, as is the average for all

Sampling Days. The following analysis shows that wildfire smoke, rather than

anthropoge nic sources, are skewing long

- term progress on Worst Days.

Figure D

- 6

Changes in Light Extinction at LAVO

– All Sampling Days

Wildfire Smoke and OMC Extinction

Worst Days occur most frequently June through September at LAVO. Elevated organic

mass extinction tracks with the occurrence of Worst Days. OMC extinction at LAVO can

be about 40 percent of total extinction on Worst Days. No Best Days occurred in July or

August from 2000 through 2011, when organic mass extinction contributions are

normally at their highest. The fire season begins in May or June and can run through

November, depending on moisture conditions. There are almost no residences having

the potential to generate woodsmoke near LAVO, which is why there is very low orga nic

matter extinction in winter months. This pattern is the basis for suggesting that wildfire

smoke alone can skew the annual deciview levels and mask progress in reducing

anthropogenic emissions. 38%

26% 13%

8% 8% 5% 2%

<0.5% LAVO

- LIGHT EXTINCTION 1988

- 1999

Rayleigh

OMC Extinction

SO4 Extinction

CM Extinction

EC Extinction

NO3 Extinction

Soil Extinction

SeaSalt Extinction

Total Extinction Average: 25.96 Inverse Megameters 31%

42% 12% 4% 6% 4% 1% <0.5%

LAVO

- LIGHT EXTINCTION 2000

- 2011

Rayleigh

OMC Extinction

SO4 Extinction

CM Extinction

EC Extinction

NO3 Extinction

Soil Extinction

SeaSalt Extinction

Total Extinction Average: 25.35 Inverse Megameters

### D

- 9 The long

- term Worst Days deciview average for 1989 throu gh 2011 is 14.3 dv, higher

than the 2000

- 2004 baseline average of 14.1 dv. In the recent years of 2002, 2008, and

2009, both averages were exceeded. In those specific years, large wildfires occurred in

southern Oregon or northern California with wildfire smoke impacting the monitor.

Every other year since 2000 was equal to or below the baseline Worst D ay average

(14.1dv.) Figure D

- 7 shows the particle species and Rayleigh contribution to light

extinction on Worst Days since 2000, demonstrating the influ ence of OMC contributions

to haze.

Figure D

- 7

Relative Contribu tions to Total Light Extinction at LAVO

The charts in Figure D

- 8 compare t he Worst Days deciview levels for 2008 with the

contributions to light extinction on those days. The highest deciview values correlate

with the highest OMC contributions to light extinction, and coincide with the timing of the

2008 Lightning Strike Comple x fires. There is a 23

- deciview spread between the

minimum and maximum values for Worst Days in 2008, with the two highest values

almost two standard deviations from the average. OMC has the highest contribution to

light extinction in every Worst Day abo ve the Worst Day annual average, evidence of its

strength in controlling the twenty percent Worst Days average. 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Inverse Megameters LAVO: Contributions to Light Extinction on Worst Days

Rayleigh

Organic Carbon

Sulfate

Elemental Carbon

Nitrate

Coarse Mass

Fine Soil

Sea Salt

### D

- 10 Figure D

- 8

Comparison of Deciview Value and Causes of Light Extinction (2008)

Several very high deciview days also occur in 2009. Tw o in July are more than two

standard deviations from the average. They correspond with high OMC extinct ion days,

as shown in Figure D

- 9 . At that time, there were several wildfires close to the

southeastern corner of Shasta County, where the LAVO monitor is locate d , as shown in

Figure D

- 10 . As with 2008, all of the days abov e the annual average for Worst D ays

were driven by elevated OMC. 0 5 10 15 20 25 30 35 405/3/20085/10/20085/17/20085/24/20085/31/20086/7/20086/14/20086/21/20086/28/20087/5/20087/12/20087/19/20087/26/20088/2/20088/9/20088/16/20088/23/20088/30/20089/6/20089/13/20089/20/20089/27/200810/4/200810/11/200810/18/2008Deciviews 

### LAVO 2008 Worst Days (Deciviews)

Annual Deciview Worst Day Average (22.4 dv)

Daily Worst Day deciview

one standard deviation

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

5/3/2008 6/3/2008 7/3/2008 8/3/2008 9/3/2008 10/3/2008Percent Contribution to Light Extinction 

### LAVO 2008 Worst Days (Light Extinction)

Rayleigh Organic Carbon Sulfate Elemental Carbon Nitrate Coarse Mass Fine Soil Sea Salt

### D

- 11 Figure D

- 9

Comparison of Deciview Value and Causes of Light Extinction (2009)

The Table D

- 3 lists the dates of wildfires in counties close to LAVO, to compare with

deciview levels at the same time. There is fluctuation in the monitor values over the 0 5 10 15 20 25 30 354/28/20095/5/20095/12/20095/19/20095/26/20096/2/20096/9/20096/16/20096/23/20096/30/20097/7/20097/14/20097/21/20097/28/20098/4/20098/11/20098/18/20098/25/20099/1/20099/8/20099/15/20099/22/20099/29/200910/6/200910/13/200910/20/200910/27/200911/3/200911/10/2009Deciviews 

### LAVO 2009 Worst Days (Deciviews)

Annual Deciview Worst Day Average (17.1 dv) Daily Worst Day deciview

one standard deviation

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

4/28/2009 5/28/2009 6/28/2009 7/28/2009 8/28/2009 9/28/2009 10/28/2009Percent Contribution to Light Extinction 

### LAVO 2009 Worst Days (Light Extinction)

Rayleigh Organic Carbon Sulfate Elemental Carbon Nitrate Coarse Mass Fine Soil Sea Salt

### D

- 12 several weeks of active and smoldering fires, as the wind shifts and alters the tracks of

smoke plumes. 11



### Table D

- 3

Large Wildfires (>500 acres)

**Year Fire Name**  **Location & Acreage Dates** 

**2008** Lightning Strike

Complex See Figure 8 6/22 through 8/11/2008

**2009** Backbone Trinity Alps/Trinity County 7/7/09

**2009** Tennant Macdoel/Siskiyou County 7/19/09

**2009** Dodge Complex

SHU Lightning Lassen County, 1,601 acres

Shasta County, 17,623 acres 8/1 through 8/18, 2009

**2009** Hat Creek Complex Lassen/Shasta Counties, 11,269

acres 8/1 through 8/26, 2009

**2009** Day Lassen County 8/27/09

**2009** Silver Meadow Valley/Plumas County 9/19/09



### Figure D

- 10

Location of 2008 Wildfires near LAVO

11 

### http://www.arb.ca.gov/smp/wildfire/wildfire.htm LAVO

### D

- 13 There is a ten

- deciview spread between the highest ( 22.4 dv in 2008 ) and lowest

( 11.7dv in 2011 ) Worst Days annual averages in the past ten years at LAVO. The large

v ariation is shown in Figure D

- 11 . The two most recent years, 2010 and 2011, are

actually below the 2018 RPG of 13.3 dv, in contrast with the two highest values in the

two prior years. Both 2008 and 2009 are in five

- year averaging periods for several

consecutive years, flattening the linear forecast trendline for the five

- year rolling

average shown as the dashed blue line in Figure D

- 11 . They also flatten the linear

forecast trendline for the annual Worst Days deciview average, shown as the solid

brown line. As previously noted, these trendlines do not forecast a long

- term glide path,

but they do illustrate the sensitivity of the short

- term slope to single year point values.

Figure D

- 11

Forecast Trend Lines for LAVO

If the two wildfire

- impacted years of 2008 and 2009 are excluded from the Worst Days

annual average s, the linear forecast trendline (dashed red line) approaches the 2018

RPG. Otherwise, the annual average trendline (so lid red line) is level. The solid blue

line shows the forecast trendline for the five

- year rolling average. If only the highest

smoke

- impacted days are removed from the calculation of the 2008 and 2009 Worst

Days average s (the new values for 2008 and 2009 are the orange triangles), the five

13.3

0 5 10 15 20 25

Baseline 2006 2008 2010 2012 2014 2016 2018Deciviews

2018 RPG (13.3 dv)

Annual Average

Annual Average Without 2008-2009 Wildfire Days

5-year Rolling Average

5-year Rolling Average without 2008-9 Wildfire Days

Linear (Annual Average)

Linear (Annual Average Without 2008-2009 Wildfire Days)

Linear (5-year Rolling Average)

Linear (5-year Rolling Average without 2008-9 Wildfire Days )

### D

- 14 year average begins to level out (dashed blue line). This demonstrates the impact of

outlier days from wildfire smoke on the rolling averages and forecasts. In 2010 and

2011, the annual averages for Worst Days wer e actually lower (better) than the 2018

RPG.

Reductions in the anthropogenic emissions inventory support a forecast of improved

vis ibility, as shown in Figure D

- 12

. Emissions of NOx, R OG, and SOx are all declining

in the four counties surrounding LAVO. PM is the only increasing category due

primarily to the formula predicting growth in road dust as vehicle miles traveled

increases with population growth. Coarse Mass and Fine Soil are major components of

road dust, but n either of these haze pollutants is a strong light extinguisher and both

have limited impact on the Worst Days averages.

Figure D

- 12

Local Inventory Changes over Time

0 50 100 150 200 250 300 350 400 450

2000 2005 2008 2010 2015 2020Tons Per Day

Year TOTAL 4-County

Natural ROG*

TOTAL 4-County

Anthropogenic ROG

Shasta Total

Anthropogenic ROG

Tehama Total

Anthropogenic ROG

Plumas Total

Anthropogenic ROG

Lassen Total

Anthropogenic ROG

*Natural Inventory is

multi

- year average.

### D

- 15 Impact of 2012 data for LAVO

The 2012 Worst Days deciview average for LAVO is 14.3 dv. The five

- year Worst Days

Rolling Average for the years 2008

- 2012 at LAVO is 15.6 dv. Both of these deciview

values are above 13.3 dv, the 2018 RPG for LAVO. While the Worst Days deciview

annual averages were decreasing each year since the high year of the 2008

- 2012

period, wildf ire smoke in 2012 reversed the trend. In that year, a lightning strike wildfire

started June 23 and burned more than 28,000 acres of predominately forested land

through the containment da te of August 22. As Figure D

- 13 below illustrates, the fire

was loc ated within 15 miles of the monitor.

Figure D

- 13

Reading Fire of 2012

Despite prevailing winds from the southwest, occasional easterly winds brought smoke

to the LAVO monitor location. As a result, seven of the 23 Worst Days in 2012 occurred

during that period. One of the days, August 19, 2012

, recorded 41.9 dv, the highest

deciview level measured since recordkeeping began in 1989. That value was nearly

twice the next highest deciview leve l measured in 2012 (21.6 dv) and the OMC mass LAVO Reading Fire Perimeter

Lassen Volcanic Park

### D

- 16 concentration that day was 12.3 times the average OMC mass on Worst Days that year.

If that single high deciview day value is removed, and the Worst Days average

recalculated for 2012, it would be 13.0 dv , low er than the 2018 RPG of 13.3 dv.

### D

- 17 Case Study: REDW

Imp acts from Pacific Offshore Shipping and Wildfire Smoke

Since 1988, when data was first collected at REDW, all the long

- term trends indicate

that visibility is improving. Figur e D

- 14 plots the Worst Days annual averages (brown),

the All Sampling Days annual averages (green), the Best Days annual averages (blue),

and the required rolling Worst Days 5

- year averages (black). Examining this data

further explains why visibilit y improvemen t appears to level off using the 2005

- 2009 and

2007

- 2011 five

- year averages. Figure D

- 12 shows the years 2006, 2008, and 2009

were the only years since 2000 above the long

- term Worst Days annual average

trendline (dashed brown line.) All three years skew the reported five

- year average in

Table 3 of the Progress Report and warrant further analysis.

Figure D

- 14

Long

- Term Trends at REDW

If the long

- term 5

- y ear rolling average linear trend line (black dotted line) is extended to

2018, the 5

- year deciview average is predicted to be 17.3 dv. That is actually lower

than the RPG of 17.8 dv. The annual Worst Days averages are also trending downward

(brown dashed line) and could re ach 17.1 dv by 2018. The Best and Worst Days

annual averages are not available for 2010 due to incomplete data, however, the 0 5 10 15 20 25198819901992199419961998200020022004200620082010201320152017Deciviews WORST Days Annual

Average

Reasonable Progress

Goal (17.8 dv)

WORST Days 5-year

Rolling Average

Forecast for 5-year

Rolling Average

ALL Sampling Days

Annual Average

BEST Days Annual

Average

Linear (WORST Days

Annual Average)

Linear (WORST Days 5-

year Rolling Average)

Linear (ALL Sampling

Days Annual Average)

Linear (BEST Days

Annual Average)

### D

- 18 average of all avaliable data indicates that visibility was improving in 2010 also. The

following discussion examines what caused the averages for 2006, 2008, and 2009 to

be above the trendline.

Figure D

- 15 shows that sea salt, organic carbon, and sulfates contributed the most to

light extinction on the Worst Days in 2006, 2008, and 2009. The year 2007 is included

for comparison because the Worst Day deciview average is just under the long

- term

trendline for Worst Days. It is also one of the years in both the 2005

- 2009 and 2007

- 2011 five

- year averages. Seasonal contributions to light extinction by particles on the

Worst Days for all the years show that the summer months have the most Worst Days

at REDW. Th is is the time of year when organic mass and sulfate mass concentrations

are usually at their highest at all California IMPROVE monitors. Sea salt is not analyzed

further because it is a natural contributor to light extinction.

Figure D

- 15

Particle Contr ibutions to Light Extinction on Worst Days

### D

- 19 Figure D

- 16 shows the particle and natural Rayleigh gas scattering contributions to total

light extinction for the Worst Days annual averages at REDW. Rayleigh is a natural and

constant component of light extinction. Sea salt is a major natural component of haze

fo und in coastal areas; the concentrations vary daily with ocean breezes. Disregarding

sea salt and Rayleigh, sulfates are the strongest contributor to light extinction on Worst

Days at REDW in the most recent years. Elevated deciview levels also occur whe n OMC light extinction is elevated, as it was in 2006, 2008, and 2009. Nitrate

contributions are steadily declining, showing the effectivenes of California’s NOx control

measures over time. The three remaining constitutuents

– Coarse Mass, Elemental

Carb on, and Fine Soil

– have fluctuating contributions that are not significant enough in

light extinction to impair visibility improvement at this time.

Figure D

- 16

Comparison of Light Extinction and Recent Deciview Levels

Whether the annual Worst Days average is below or above the long

- term trend line

appears to depend on the relative contributions of sulfates and OMC to light extinction

in any given year. Trends in the emissions inventory for sources of SOx and reactive

organic gases (ROG) gases, as p recursors of sulfates and OMC, can be examined

further to explain the high years.

The REDW monitor is located at 244 meters above sea level, within one mile of the

Pacific Ocean, at the mouth of the Klamath River. The delta of the Klamath River is

undeve loped, limiting the anthropogenic sources nearest REDW to local two

- lane roads

to a scenic overlook and a few homes and buildings. The REDW monitor is exposed to

ocean fogs year

- round, offshore emissions from ocean

- going vessels, and smoke

drainage down t he Klamath River from inland wildfires during the dry season in the

mountains. Elevated humidity also supports the formation of nitrates at lower 0 5 10 15 20 25 30

2006 2007 2008 2009 2011Light Extinction in Inverse Megameters 

### REDW: Contributions to Worst Days Extinction and Deciview Averages

SeaSalt Extinction

SO4 Extinction

Rayleigh

OMC Extinction

NO3 Extinction

CM Extinction

EC Extinction

Soil Extinction

20.8 dv 18.2 dv 19.8 dv 18.9 dv 17.2 dv

Note: The 2010 Worst Days Average is not available. The RPG at REDW is 17.3 dv.

### D

- 20 temperatures in the winter, although relatively few Worst Days occur then at REDW.

Light extinction by sulfa tes, nitrates, and organic aerosols is heightened by elevated

relative humidity, another factor causing higher haze levels at coastal locations

nationwide.

The WRAP Summary Report shows an increase in sulfate extinction on Worst Days at

four monitors near the Pacific coast at the Oregon

- California border (REDW, TRIN,

KALM, and CRLA)

. This area has very few SOx

- emitting sources other than the

influence from offshore shipping emissions, compared to more populated areas along

the coast. The relative magnitu de of offshore shipping emissions has a greater impact

on this sparsely populated area. The location of shippi ng lanes is shown in Figure D

- 17

for comparison.

Figure D

- 17

Sulfate Extinction Trends Near Shipping Lanes

In the past decade traffic in these shipping lanes has increased dramatically in response

to changes in global production and shipment of goods from Asia to North American

markets. California has developed an inventory of emissions from ocean

- going vessel s (OGV) in shipping lanes within 100 nautical miles of the California coast. Since 2005,

California has also implemented several control measures aimed at reducing in

- port

emissions and in

- transit emissions for ships within 24 nautical miles of the Califo rnia

coastline. Vessels travelling outside the State’s jurisdictional control boundary may still

burn higher sulfur fuel until after 2012, when international agreements to reduce the

emissions from sulfur fuels begin scheduled implementation.

### D

- 21 Figure D

- 18 shows the increase in the relative magnitude of OGV SOx emissions within

one hundred miles offshore of Del Norte and Humboldt Counties, north and south of

REDW. The emissions are b ackcast and forecast, with growth and control factors

applied, using 2 008 as the base year. The emissions do not include the shipping

emissions offshore the Oregon coast, a little more than 30 miles north of the REDW

monitor, nor do they include emissions from Curry County in Oregon, the sparsely

populated county north of t he interstate border. The ocean

- going vessel emissions of

SOx along the California coast alone overwhelm the local anthropogenic sources of

SOx from the two coastal California counties surrounding REDW and containing

Redwood National Park.

Figure D

- 18

An thropogenic Emissions near REDW

Forecasted growth in Pacific shipping will continue to impact REDW. Continued

reductions of other anthropogenic sources of NOx and ROG will offset potential

increases in sulfate formation by lessening nitrate and OMC fo rmation. As evidence,

the Worst Days annual average for 2011 already achieves the 2018 RPG. The relative

reduction in OMC light extinction in 2011 occurred because there were fewer wildfire

smoke incidents impacting REDW.

As shown in Figure D

- 19

, wildfire smoke in the summer of 2008 also impacted REDW.

Beginning in late June and lasting into September, nighttime drainage of wildfire smoke

down the Klamath River was trapped in the mari ne layer near REDW. Figure D

- 19

illustrates how wildfire smoke results in high OMC contributions to light extinction. If just

the highest of the smoke

- related OMC

- driven Worst Days is removed from the

calculation of the annual average for 2008, the deciview level would be 19.3 dv. If the

three highest smoke days dur ing that time are removed, the Worst Days average would

be 18.6 dv. When the Worst Days average in a single year is skewed due to very high

wildfire smoke influence on a few days, both the annual average and the rolling

averages are also elevated by this natural source. These statistics mask progress 0 2 4 6 8 10 12 14 16

2000 2005 2010 2015 2020Tons Per Day 

### SOx Emissions Inventory for Del Norte and Humboldt Counties

Anthropogenic, non-OGV

Ocean-Going Vessels (OGV)

### D

- 22 made by reductions in anthropogenic emissions. The 2018 RPG at REDW is 17.8dv.

In 2011, the Worst Days average was 17.2 dv.

Figure D

- 19

Wildfire Smoke affects Deciview Calculation

0 5 10 15 20 25 302/6/082/20/083/5/083/19/084/2/084/16/084/30/085/14/085/28/086/11/086/25/087/9/087/23/088/6/088/20/089/3/089/17/0810/1/0810/15/0810/29/0811/12/0811/26/0812/10/08DECIVIEWS 

### REDW: 2008 Worst Days

Worst Days 2008 Worst Days Annual Average (19.8 dv) one standard deviation

0 20 40 60 80 100 120 140 1602/3/082/17/083/2/083/16/083/30/084/13/084/27/085/11/085/25/086/8/086/22/087/6/087/20/088/3/088/17/088/31/089/14/089/28/0810/12/0810/26/0811/9/0811/23/0812/7/08Light Extinction in Inverse Megameters

Sea Salt Sulfates OMC Rayleigh Nitrates Coarse Mass EC Fine Soil

### D

- 23 Impact of 2012 data for REDW

In 2011, and again in 2012, wildfire smoke impacts were not significant at REDW. The

Worst Days annual average was 17.2 dv in 2011, and even lower in 2012, at 16.5 dv.

Both of these values are below the 2018 RPG for REDW, 17.8 dv. Th e Worst Days

annual average could not be calculated for 2010, giving additional weight to the 2008

value in the most recent five

- year averaging period. The 2008 Worst Days annual

average skews the rolling 5

- year average through 2012, masking progress made by

reductions in anthropog enic emissions. Comparing the b aseline period a verage of

18.5 dv with the most recent five

- year average of 18.1 dv for 2008

- 2012, indicates that

visibility is improving at REDW, even if the rate of progress is slowed by high wil dfire

smoke days in the calculations for 2008. This improving visibility trend is shown by the

l inear trendlines in Figure D

- 20

.

Figure D

- 20

Wildfire Smoke affects Deciview Calculation

0 5 10 15 20 252000200120022003200420052006200720082009201020112012201320142015201620172018DECIVIEWS 

### REDW: 2000

- 2012

2018 RPG

Annual Worst Day

Average

5-Year Rolling Average

Linear (Annual Worst

Day Average)

Linear (5-Year Rolling

Average)

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## Appendix E

Comments of Federal Land Management Agencies

with CARB Responses

### This page intentionally left blank.

### E

– 1 

## Comments and Responses to the Letter from the U.S. Department of

the Interior, National Park Service, dated March 27, 2014

1. Comment : “Please briefly discuss the major federal and state regulations

beyond those implemented specifically under CA’s Reg ional Haze Plan that

were included in CA’s reasonable progress goals, as these earlier

requirements, plus those listed in Table 1, appear to account for most of the

emissions reductions observed since 2000 in CA.”

Response : The RH plan includes a discussion of the regulations used to establish the

2018 RPGs. Th e 2018 RPGs were based on rules adopted by the end of the baseline

period, including those with quantifiable reductions scheduled for implementation after

2004 and bef ore 2018. The RH Plan also described the State strategy for continual

adoption and implementation of measures to reduce emissions, for which reductions

had not been quantified for the RH Plan modeling of the 20 18 RPGs. The Progress

Report lists new and m odified rules adopted 2005 through 2011, which clearly have

provided additional emission reduction benefits through 2020 that go beyond the

emission reductions included in the RH Plan

.

2. Comment : “Please clarify if CA’s Smoke Management Program meets the

e nhanced smoke management goals for states submitting plans under 40 CFR

51.309. Are Class I Areas identified as sensitive receptors in CA’s Smoke

Management Plan?”

Response : California elected to prepare its RH Plan pursuant to 40 CFR 51.308.

Californi a’s Smoke Management Program was established in State regulation at Title 17

California Code of Regulations sections 80100

- 80330. Class 1 Areas are listed and

defined in th e regulation and must be listed as sensitive smoke receptors in smoke

management p lans prepared by those conducting prescribed burns, with provisions

made to minimize smoke impacts. The Progress Report has been updated to clarify

that California’s Smoke Management Program identifies Class 1 areas as sensitive

receptors.

3. Comment : “Ple ase also provide a summary in tons/per year [for the

emissions inventory], since the conversion from tons per day to tons per year

likely differs for different emissions categories.”

Respons e : The California emissions inventory shown in the RH Progress Report is in

tons per day, using the annual average. To obtain the emissions in tons per year, each

daily value can be multiplied by 365. The Progress Report has been updated to include

a stat eme nt to calculate tons per year.

### E

– 2 4. Comment : “Please cite or include in Appendix A the Western Regional [Air]

Partnership Reasonable Progress Summary Report for California.”

Respons e : A citation and link has bee n added to Appendix A of the Progress Report

for the California Section of the WRAP Reasonable Progress Summary Report, in

addition to t he link for the entire report.

5. Comment : “Please add to the discussion of impacts from off shore shipping

that the North American Emissions Control Area inte rnational treaty limits

sulfur dioxide content in fuels for marine vessels operating within 200 nautical

miles of the US and Canadian coastlines, beginning in 2012.”

Response : Appendix D Case Study REDW includes a discussion of the impacts from

off shore shipping

. The benefits of the North American Emissions Control Area will

begin to be reflected in the data beginning in 2012. Since t he 2012 dat a became

available after the Progress Report was prepared, California did not include an

extensive analysis o f this data. The summary graph below shows that sulfate is being

reduced at all coastal sites with a sharp decline in 2012

.

0 0.5

1 1.5

2 2.5

3

BASELINE 2005 2006 2007 2008 2009 2010 2011 2012Mass (ug/m3)

YEAR 

### Sulfate Mass: Annual Average for All IMPROVE Sampling Days

REDW

PORE

PINN

RAFA

AQTI

### E

– 3 6. Comment: “CARB concludes that CA emissions reductions are sufficient to

meet 2018 Reasonable Progress Goals. Please add a table comparing current

emissions and current projections through 2020 to the 2018 emission

projections that were used to set the Reasonable Progress Goals.”

Response : California is constantly making improvements to its emissions inventor y

both in methodology and data used to forecast . These updates and refinements are

based on the best available information about changes in regulations, population,

business, vehicle and travel data, and control technology implementation for the base

year of an inv entory update. These updates can cause emissions to increase or

decrease relative to a previous inventory. Since the modeling that the RPGs were

based on looked at changes on a relative basis, comparing inventories on a relative

basis is most appropriate . When comparing the RH Plan inventories with the Progress

Plan inventories, Progress Plan emissions are being reduced at a greater rate for all

pollutants compared to the RH Plan. Based on this comparison, California concluded

that the emission reductio ns are sufficient to meet the 2018 RPGs.

7. Comment : “CARB asserts [that] CA emissions reductions are sufficient to

lessen CA’s impact, and specifically CA’s contributions to ammonium nitrate,

at neighboring Class 1 Areas. WRAP provided the western states with

particulate source apportionment analyses using the CAMx regional air quality

model for 2002 and 2018 inventories [including Crater Lake National Park in

Oregon and Grand Canyon National Park in Arizona.] We suggest that

[California] include such exa mple plots to support CARB’s demonstration.”

Response : The plots referenced in your comment have not been updated by the

WRAP since the RH Plan. As explained in Table 8.1 of the RH Plan, nitrates cause less

than 10 percent of light extinction (haze) on Worst Days, on average, at Crater Lake in

Oregon and at the Grand Canyon in Arizona. Nitrates are the second strongest

extinguishers of light when compared to the other haze pollutants; therefore reducing

their formation from NOx emissions is still an imp ortant visibility strategy. Due to new

control measures, California NOx emissions will continue to decline beyond the levels

included in the RH Plan.

### E

– 4 

## Comments and Responses to the Letter from the U.S. Department of

Agriculture, Forest Service, dated April 8, 2014

1. Comment : A review of the US EPA’s “Interim Air Quality Policy” and ARB’s

“Title 17” indicate that natural fires managed

- for

- resource

- benefits (those

meeting the objectives defined in Land/Resource Management Plans) to be

considered as prescribed burns whereas the “Exceptional Event Rule”

consider these as naturally ignited fires. We look forward to addressing this

discrepancy with CARB. The FLMs and the C alifornia Air Resources Board

(CARB) need to continue a dialogue in developin g strategies, before the

revision of the SIP in 2019, that can lead to less severe wildfires and thus

attain the reduction required on targeted “worst days”.

Response : ARB will continue this discussion at a policy level through the regu lar Air

and Land M anagers meetings of policy makers from the public land management and

air quality agencies in California. The technical issues relating to planned and

unplanned burns and air quality will continue to be discussed by the technical staff

participating in th e regular meetings of California’s Interagen cy Air and Smoke Council.

Both of these forums have operated for more than a decade to explore these issues,

develop protocols, and advance practicable policies.

### TRANSMITTED VIA ELECTRONIC MAIL

- NO HARDCOPY TO FOLLOW

N3615 (2350)

March 2 7 , 2014

Christine Suarez

- Murias

California Air Resources Board

1001 I Street

Sacramento, CA 95812

Dear Ms. Suarez

- Murias:

Thank you for the opportunity to review and comment on California’s Regional Haze Plan 2014

Progress Report. We agree with California Air Resources Board ( C A R B) ’s conclusion,

consistent with the periodic reporting requirements of 40 CFR 51.308(g)

, that th e emissions

reductions under California’s Regional Haze Plan and other CARB requirements are sufficient

for California and neighboring states to meet the 2018 Reasonable Progress Goals set in the

State’s Regional Haze plan. We also agree th at no further revision of the Regional Haze Plan is

needed at this time to meet the 2018 goals

.

We suggest some additions to t he draft Progress Report to better support California’s

demonstration:

 Section 2.1: Status of Emissions Control Strategies: Please briefly discuss the major

federal and state regulations beyond those implemented specifically under CA’s Regional

Haze Plan that were included in CA’s reasonable progress goals, as these earlier

requirements

, plus those listed in Table 1 , appear to a ccount for most of the emissions

reductions observed since 2000 in CA.

 Section 2.1: Please clarify if CA’s Smoke Management Program meets the enhanced

smoke management goals for states submitting plans under 40 C F R 51.309. Are Class I

areas identified as sensitive receptors in CA’s Smoke Management Plan? Section 1.3 is

cle ar that wildfire is a major uncontrollable source impacting visibility in Class I areas in

CA. A discussion of the possible role of prescribed fire in reducing wildfire smoke

impacts to visibility is beyond the scope of this progress review, but is a topi c for further

discussion in developing the regional haze plan due in 2018.

 Section 2.4 Emissions Inventory: CARB presents emissions in tons/day in Table 2 and

Figure 3 that indicate decreasing emissions from 2000 to 2020. Please also provide a 

# United States Department of the Interior

NATIONAL PARK SERVICE

Air Resources Division

P.O. Box 25287

Denver, CO 80225

- 0287

### 3

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