Detailed Documentation for Fugitive Dust and Ammonia ... · 2000 Ag LP and Hvst PM10.xls Output...

California Air Resources Board 04/03/2003 Planning & Technical Support Division

Detailed Documentation for Fugitive Dust and Ammonia Emission Inventory Changes for the SJVU APCD Particulate Matter SIP Overview The table that follows lists the internal working memos used to develop and document changes to the fugitive dust and ammonia components of the San Joaquin Valley’s PM10 emission inventory. These documents, combined with dozens of meetings with the SJV air district staff and stakeholders, served as the basis for many of the emission inventory improvements for the 2003 PM10 SIP. Following the table, each document is provided.

April 2003 Planning and Technical Support Division California Air Resources Board

Fugitive Dust and Ammonia Emission Inventory Documentation for the SJV 2003 SIP Revision


ARB/phg 4/3/03 EI DOCS SUMMARY.DOC 2

Summary of Working Documents Used for the San Joaquin Valley

1999 Base Year PM10 Emission Inventory for Fugitive Dust and Ammonia

File Name Description Date Land Prep & Harvest EF Selection 12_2002.doc Selection of PM10 Emission Factors

for Agricultural Harvest and Land Preparation Questions and answers regarding assignment of the UCD emission factors to crop activities Does not include refinements to harvest emission factors to assign EF to all crops. Attach detailed LP & harvest emissions spreadsheet page: Crop Specific LandPrep & Harvest Ems SJV.xls

December 3, 2002

Land Prep & Harvest EF Selection 10_2002.doc Selection of PM10 Emission Factors for Agricultural Harvest Activities (also includes land prep assignments) Questions and answers regarding assignment of the UCD emission factors to crop activities

September 4, 2002

Land Prep & Harvest EF Selection 9_2002.doc Selection of PM10 Emission Factors for Agricultural Harvest and Land Preparation

September 4, 2002

Land Prep & Harvest EF Issues 8_2002.doc Assigning UC Davis Agricultural Emission Factors to Land Preparation & Harvest Activities Initial questions for assigning UCD Efs to crop activities.

August 23, 2002

Harvest EF Documentation12_2002.doc Methodology for Assigning PM10 Emission Factors for California Agriculture Harvest Activities − Final summary of harvest EF assumptions − Does not include final table showing all crop

assignments (attach Excel data from sheet: Crop Ranking & Harvest AnalysisRev2.xls / no- attach to land prep & EF selection document instead.

December 3, 2002



File Name Description Date Harvest EF Documentation11_2002.doc Methodology for Assigning PM10 Emission Factors

for California Agriculture Harvest Activities − Final summary of harvest EF assumptions − Does not include final table showing all crop

assignments (attach Excel data from sheet: Crop Ranking & Harvest AnalysisRev2.xls / no- attach to land prep & EF selection document instead.

November 4, 2002

Harvest EF Proposal 10_2002.doc Initial Methodology For Assigning PM10 Emission Factors For California Agriculture Harvest Activities. Initial proposal with attached Excel EF assignments (see .pdf)

October 18, 2002

Ag Emissions Summary Field&Cattle.doc Update of Agricultural PM10 Emissions for Harvest, Land Preparation, and Confined Cattle Operations − Changes in harvest & land prep emissions − Bar charts showing changes by crops − Charts showing changes by county − Discussion of Efs for cattle

September 19, 2002

EFAssignToDistrictCassel.doc Prepared by UCD to discuss almond Efs Also provides discussion and Efs for land planing activities

July 12, 2002

Agricultural Emission Factors.doc Summary of UCD emission factors and assignment of crops to respective emission factors

May 14, 2002

onfarm_travel_questionsARBcomment.doc Sierra Research questionnaire with ARB comments for on-farm unpaved road travel

September 2002

Beef & Dairy PM EF Selection9_2002.doc Selection of PM10 Emission Factors for Feedlot and Dairy

Operations (Initial write-up) August 30, 2002

Beef & Dairy PM EF Selection12_2002.doc Selection of PM10 Emission Factors for Feedlot and Dairy Operations (Final write-up)

November 23, 2002

Beef & Dairy PM Estimate.doc Initial scoping estimates of PM10 emissions from beef and dairy operations. Rough estimates of beef and dairy PM using different scenarios. Compares emissions with other sources.

August 13, 2002



File Name Description Date Crop Ranking & Harvest AnalysisRev2.xls Table which provides final harvest emission factor assignments

for the SJV and statewide. Provides initial emissions summaries and ranking. Follows previous document, Methodology for Assigning PM10 Emission Factors for California Agriculture Harvest Activities.

November 2002

Crop Specific LandPrep & Harvest Ems SJV.xls Shows crops, composite emission factors, emissions, and assumptions for harvest and land preparation emissions. Includes notes, documentation, base emission factors, and temporal profiles.

November 13, 2002

2000 Ag LP and Hvst PM10.xls Output provided following first document in this table, Selection of PM10 Emission Factors for Harvest and Land Preparation. Prepared by Hong Yu. Shows crops, composite emission factors, emissions, and assumptions for harvest and land preparation emissions. See Crop Specific LandPrep & Harvest Ems SJV.xls for fully documented spreadsheet (this just includes part of one page of full sheet.)

November 13, 2002

Ag EI Change Summary 2_2003.ppt Improvements to Particulate Matter Agricultural Emission Estimates. PowerPoint presentation summarizing changes to agricultural emission inventory.

February 25, 2002

Ag Harvest Methodology4_2003.pdf ARB full methodology for estimating harvest emissions (draft)

January 2003

Ag LandPrep Methodology4_2003.pdf ARB full methodology for estimating land preparation emissions (draft)

January 2003

Unpaved Ag1999 Mar_26_2003.xls Spreadsheet showing assignments of available agricultural crop VMT data to each commodity.

March 2003

PavedRoadEmsDraft7_31_2000.pdf Documentation and analysis of the main update to paved road dust emissions. Future refinements were made which slightly altered the emissions.

July 2002

Analysis of AP42 Paved_Unpaved.doc Brief analysis of the differences between the ARB methodologies and the EPA methodologies for paved and unpaved road dust.

July 2002

NH3 Environ Draft Final Report.pdf Ammonia emission inventory documentation. Contact ARB for document (32 pages).

December 2002



File Name Description Date NH3 Environ Final Workplan All.pdf Assumptions and documentation used for ammonia

emission inventory development. Contact ARB for document (63 pages).

Prepared by: Patrick Gaffney California Air Resources Board Planning and Technical Support Division [email protected] 916-322-7303

California Air Resources Board Planning & Technical Support Division

Land Prep & Harvest EF Selection 12_2002.doc 1

MEMORANDUM

TO: SJV PM10 SIP Emission Inventory Group FROM: Patrick Gaffney, ARB DATE: December 3, 2002 SUBJECT: Selection of PM10 Emission Factors

for Agricultural Harvest and Land Preparation Objectives: This document provides the emission factors and

assumptions used for computing agricultural harvest and land preparation emissions for California. The following information is provided: 1) Harvest - Summarize the geologic PM10 emission factors

used for almond, cotton, and wheat harvesting based on California specific data.

2) Land Preparation - Assign the most appropriate geologic PM10 emission factors for agricultural land preparation using California emission factors.

For both land preparation and harvesting, activity specific California emission factors are used to replace the generic national emission factors used in the past.

Approach: In a previous ARB technical summary1, the available geologic

PM10 emission factors for harvest and land preparation were summarized and discussed. To help identify the most appropriate PM emission factors for harvest and land preparation, this initial summary was used as a basis for a teleconference with regulators, researchers, and industry representatives2. Considering the lack of comprehensive agricultural emission factor data, the majority of the teleconference focused on developing consensus on the “best-fit” emission factors. The selected factors are based on a combination of direct scientific applicability, as well as general experience and observations about the relative emissions potential of various operations.

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Harvest Emission Factors:

For the crops show, Table 1 below summarizes the harvest emission factors3 to be used for California emission estimates. Discussion of each item in the table is provided below. Note that for many harvest operations, crop specific emission factors are not available. Therefore, the emission factors shown in Table 1 were used to assign harvest emissions to all other California crops. This approach is discussed in a separate ARB technical paper4. In the past, emissions for crops without specific emission factors were set to zero.

Table 1. Harvest Emission Factors.

Emission Factor3 Agricultural Harvest Operation (lbs PM10/acre)

Cotton Cotton Picking 1.7 Cotton Stalk Cutting 1.7

Cotton Total 3.4

Almond Almond Shaking 0.37 Almond Sweeping 3.7 1st Almond Pickup 2nd Almond Pickup 36.7

Almond Total 40.8

Wheat Combining 5.8

Harvest Emission Factor Questions & Decisions:

1) Is the updated cotton harvest EF acceptable? - It is based on more tests and more recent tests. - But, picking and cutting EFs are the same value (based on separate tests) for the updated factor. Is this reasonable? It was decided by the group that the updated UC Davis emission factor of 3.4 lbs PM10/acre is appropriate for the combination of cotton picking and stalk cutting. It was mentioned that the tests were performed under various typical conditions. Some tests were performed at dusk, which could lead to higher than average emission readings.

2) Is the most recent almond shaking EF of 3.7 lbs/acre acceptable? Is the magnitude reasonable in comparison to the almond harvest other operations? After extended discussion, the answer to this question was no. There were too few tests performed and the

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emissions do not seem consistent with observed PM levels. Based on subjective judgment, it was agreed to use a value of 0.37 for the shaking emission EF, which is one-hundredth of the pickup emissions. This value will be updated as additional information becomes available.

3) Is the updated almond sweeping factor acceptable and a reasonable relative magnitude (13.1 lbs PM10/acre) compared to other operations? Generally, the old factors are considered invalid because plume profiling was not performed. Again, the answer to this question was no. All of the tests were performed within the canopy, which is not representative of emissions leaving the orchard. In addition, observation shows that sweeping is substantially less dusty than pickup operations. Based on subjective judgment, it was agreed to use a value of 3.7 for the sweeping emission EF, which is one-tenth of the pickup emissions. This value will be updated as additional information becomes available

4) Is the almond pickup emission factor of 36.7 lbs PM10/acre acceptable? It is in reasonable agreement with the older emission factor of 32.3. In this case, the answer was yes, this factor seems reasonable, it is based upon tests performed outside the canopy, and it is in reasonable agreement with earlier tests. Therefore, a total of 36.7 lbs PM10/acre will be used for the complete almond pickup operation. [Note 12/3/02: Some recent PM sampling comparisons by UCD and Texas A&M indicate that it may be justified to reduce the UCD almond harvest emission factor by approximately 50%. More analysis is being performed to resolve this issue, and the emissions may be updated accordingly.]

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Land Preparation Emission Factors:

Table 2 below summarizes the new emission factors selected for land preparation activities for cotton and wheat. In the next section, these emission factors are applied to similar activities for other crops. (Previously, a single emission factor of approximately 4 lbs PM10/acre-pass was assigned to all land preparation activities.) In addition to the EFs below, UCD also has field test data from 70 to 90 additional land preparation tests that have not been analyzed. If the resources become available to analyze these data, the emission factors shown below will be updated to reflect this new information.

Table 2. Land Preparation Emission Factors.

Emission Factor3 Land Preparation Activity (lbs PM10/acre-pass)

Root cutting 0.3 Discing, Tilling, Chiseling 1.2 Ripping, Subsoiling 4.6 Land Planing & Floating5 12.5 Weeding 0.8

Land Preparation Emission Factor Questions and Decisions:

1) Is the new root cutting emission factor acceptable? In the past, this operation was assigned ~4/lbs PM10/acre. Yes. The testing performed was specific to cotton

2) Is the new discing emission factor of 1.2 acceptable? In the last SIP this value was assigned a value of 4. Yes. The value is based on several representative tests for cotton and wheat, and is of a reasonable magnitude.

3) Based on UCD analysis5 (T. Cassel, 7/12/2002), the chiseling emission factor is based on very dry, but operationally valid conditions following June/July wheat harvest. It is suggested that rather than use the non-representative, worst case conditions, that an average of the discing and chiseling emission factors be used for typical chiseling. This value is 2.9 lbs PM10/acre-pass. Should the average value be used for chiseling? If not, what is the alternative? No. The average value is not acceptable because it is observed that chiseling and tilling operations are typically less dusty than discing operations, so using a larger value is not appropriate. Further, Terry also clarified that the tested operation listed as chiseling in the UCD report more closely represents ripping or subsoiling, in which a small

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number of long shanks are used to work the soil. Therefore, it was decided to apply the UCD discing emission factor of 1.2, to tilling and chiseling. Ripping and subsoiling will be assigned to the UCD ‘chiseling’ emission factor of 4.6.

4) Based on UCD analysis5 (T. Cassel, 7/12/2002), new emission rates were provided for summer land planing and floating. The average emissions are 12.5 lbs PM10/acre-pass which is based on 3 different sites and 23 tests. Is this factor acceptable? The average EFs for each site are comparable. Although testing occurred during the summer, it is typical to perform planing/floating during relatively dry conditions, so it is likely that the factors are representative. Some were surprised about the magnitude of the planing and floating emission factor, but the data appear to be valid. Tests were performed for garbanzo, tomato, and wheat under typical moisture conditions. It was decided to use the UCD factor for land planing and floating. ARB will provide a summary of the emissions by operation to ensure that the factor does not produce unrealistic emission estimates.

5) As part of typical land maintenance, land used for field crops is periodically planed or leveled to remove high spots. Because these operations are not performed annually, they are not reflected in the default crop calendars. How often does land planing and leveling occur? In discussions with agricultural experts, it was estimated that land planing and leveling are, on average, performed once every five years for field and row crops. The emission factor assigned to these operations is 12.5 lbs PM10/acre-pass. Planing and leveling are not routinely performed for orchard or vine crops, so the land maintenance operations are not applied to these crops.

6) Currently the ARB does not include cultivation operations in our emission estimates. Because of the lack of information, it is suggested that we retain this approach. In addition, cultivation typically does not occur in the SJV during the time of the year with elevated PM levels. The weeding emission factor will not be used. In discussions with the group, it was decided that the weeding factor is applicable to various land preparation activities such as listing and rolling, so it will be used for those operations. There is not sufficient information to assign cultivation emissions. It is expected that these emissions will be relatively small and occur during the growing season, when PM levels are typically not substantially elevated.

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Assigning Emission Factors to Land Preparation Operations:

There are a limited number of emission factors available and a large number of agricultural land preparation operations. Table 3 summarizes the activity specific emission factor assignments agreed upon by the agricultural stakeholder group. The available emission factor choices are shown in the previous table and include root cutting (0.3 lbs PM10/acre-pass), discing (1.2), ripping (4.6), land planing/floating (12.5), and weeding (0.8). The emission factor used in the previous 1997 PM10 SIP for all land preparation operations was approximately 4 lbs PM10/acre-pass.

Table 3. Land Preparation Emission Factor Assignments

Land Preparation Operation

Emissions Category

Emission Factor (lbs PM10/ acre-pass)

List Weeding 0.8 List & Fertilize Weeding 0.8 Listing Weeding 0.8 Roll Weeding 0.8 Spring Tooth Weeding 0.8 Bed Preparation Weeding 0.8 Seed Bed Preparation Weeding 0.8 Shape Beds Weeding 0.8 Shape Beds & Roll Weeding 0.8 Shaping Weeding 0.8 Terrace Weeding 0.8 Chisel Discing 1.2 Plow Discing 1.2 Mulch Beds Discing 1.2 Disc Discing 1.2 Disc & Furrow-out Discing 1.2 Disc & Roll Discing 1.2 Finish Disc Discing 1.2 Harrow Disc Discing 1.2 Post Burn/Harvest Disc Discing 1.2 Stubble Disc Discing 1.2 Unspecified Operation Discing 1.2 Land Preparation, Gen. Discing 1.2 Subsoil Ripping 4.6 Subsoil-deep chisel Ripping 4.6 Float Land planing 12.5 3 Wheel Plane Land planing 12.5 Land Plane Land planing 12.5 Laser Level Land planing 12.5 Level Land planing 12.5 Level (new vineyard) Land planing 12.5 Plane Land planing 12.5

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Conclusions: Using the above emission factors and assumptions, the ARB

estimated statewide emissions by county. Using the current assumptions, the land preparation emissions in the SJV decreased by about 62% (34,000 tons per year to 13,000 tons per year). Harvest related emissions increased by about 75%, from 7,600 tons per year to 13,300 tons per year. In total, the land preparation and harvest PM10 emissions estimates decreased by about 37%. The attached table provides crop specific emission estimates and assumptions for the San Joaquin Valley, as well as emission factor and temporal profile summaries. The updated emissions using these new data are an important step forward in better representing agricultural PM10 emissions, especially considering that the prior default value of 4 lbs of PM10 per acre-pass was applied to every land preparation operation in California, and harvest emission factors were only assigned to four crops.

References: 1Assigning UC Davis Agricultural Emission Factors to Land

Preparation & Harvest Activities, P. Gaffney, California Air Resources Board, 8/23/2002 2Teleconference 8/29/2002. Dave Jones (SJVUAPCD), Patia Siong (SJVUAPCD), Stephen Shaw (SJVUAPCD), Terry Cassel (UCD), Paul Martin (Western United Dairymen), Roger Isom (California Cotton Ginners and Growers Association), Cynthia Corey (California Farm Bureau), Gene Beach (Almond Hullers and Processors Association), George Bluhm (CDFA), Matt Summers (CDFA), Patrick Gaffney (ARB). With additional discussion at the 10/28/2002 SJV Agricultural Technical Advisory Group regarding land maintenance. 3Sources and Sinks of PM10 in the San Joaquin Valley, Interim Report. Flocchini, R.G., James, T.A., et. al., August 10, 2002. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1. 4Methodology for Assigning PM10 Emission Factors for California Agriculture Harvest Activities, P. Gaffney, California Air Resources Board, 11/04/2002 5Terry Cassel, Informal write-up for SJV Ag Tech Committee, Evaluation of ARB application of UCD emission factors, July 12, 2002

Contacts: Patrick Gaffney, Air Resources Board [email protected] (916) 322-7303 Hong Yu, Air Resources Board [email protected]

California Air Resources BoardPlanning Technical Support Division

DRAFT December 3, 2002

Estimates of PM10 Fugitive Dust Emissions from Agricultural Land Preparation and Harvestingin the San Joaquin Valley for 2000

Air Basin Crop Name

Land Preparation Harvest

Total Land Preparation and Harvest

Land Prep. (lbs/acre-pass)

Harvest (lbs/acre) Acres Acre

PassesCrop Calendar

ProfileHarvest EF Base Factor

Harvest EF Division Factor

SJV ALMOND HULLS 0.00 0.00 Almonds Almonds 1SJV ALMONDS ALL 604.1 7881.8 8,485.9 12.50 40.77 386,646 96,661 Almonds Almonds 1SJV APPLES ALL 0.6 0.7 1.3 1.20 0.08 17,151 1,029 Citrus Cotton 40SJV APRICOTS ALL 0.5 0.6 1.2 1.19 0.08 15,098 906 Citrus Cotton 40SJV ASPARAGUS UNSPECIFIED 74.6 22.1 96.7 1.78 1.69 26,190 83,808 Melon Cotton 2SJV AVOCADOS ALL 0.0 0.0 0.1 1.03 0.09 646 39 Citrus Cotton 40SJV BARLEY FEED 7.6 11.9 19.5 3.09 5.80 4,100 4,920 Wheat Wheat 1SJV BARLEY UNSPECIFIED 36.6 57.4 94.0 3.08 5.80 19,784 23,741 Wheat Wheat 1SJV BEANS BLACKEYE (PEAS) 48.1 10.5 58.7 1.48 1.68 12,500 65,000 DryBeans Cotton 2SJV BEANS DRY EDIBLE UNSPEC. 148.3 32.5 180.8 1.48 1.68 38,520 200,303 DryBeans Cotton 2SJV BEANS FRESH UNSPECIFIED 29.8 13.0 42.8 1.48 3.37 7,740 40,248 DryBeans Cotton 1SJV BEANS GARBANZO 1.2 0.3 1.5 1.48 1.68 322 1,674 Garbanzo Cotton 2SJV BEANS KIDNEY RED 18.9 4.1 23.0 1.48 1.69 4,900 25,480 DryBeans Cotton 2SJV BEANS LIMA BABY DRY 16.1 3.5 19.7 1.48 1.68 4,190 21,788 DryBeans Cotton 2SJV BEANS LIMA GREEN 8.9 2.0 10.8 1.48 1.69 2,309 12,007 DryBeans Cotton 2SJV BEANS LIMA LG. DRY 37.3 8.2 45.4 1.48 1.68 9,677 50,320 DryBeans Cotton 2SJV BEANS LIMA UNSPECIFIED 35.0 7.7 42.7 1.48 1.69 9,100 47,320 DryBeans Cotton 2SJV BEANS SEED 10.6 2.3 12.9 1.48 1.68 2,758 14,342 DryBeans Cotton 2SJV BEANS SNAP FRESH MARKET 4.0 0.1 4.1 1.48 0.17 1,036 5,387 DryBeans Cotton 20SJV BERRIES STRAWBERRIES UNSPEC 3.0 0.0 3.0 1.78 0.08 1,047 3,350 Melon Cotton 40SJV BIOMASS ORCHARD 0.0 0.00 0.00 Almonds Cotton 40SJV BROCCOLI FRESH MARKET 5.8 0.1 5.8 1.63 0.09 1,356 7,051 Vegetables Cotton 40SJV BROCCOLI PROCESSING 3.3 0.0 3.3 1.64 0.08 766 3,983 Vegetables Cotton 40SJV BROCCOLI UNSPECIFIED 47.9 0.5 48.3 1.63 0.08 11,260 58,552 Vegetables Cotton 40SJV CAULIFLOWER FRESH MARKET 1.6 0.0 1.6 1.64 0.11 373 1,940 Vegetables Cotton 40SJV CAULIFLOWER UNSPECIFIED 9.7 0.1 9.8 1.63 0.09 2,290 11,908 Vegetables Cotton 40SJV CHERRIES SWEET 0.7 0.8 1.4 1.19 0.08 18,193 1,092 Citrus Cotton 40SJV CITRUS BY-PRODUCTS MISC. 0.0 0.00 0.00 Citrus Cotton 40SJV CITRUS UNSPECIFIED 0.0 0.1 0.1 1.11 0.08 1,205 72 Citrus Cotton 40SJV CORN GRAIN 320.7 78.3 399.0 1.64 1.68 92,950 390,390 Corn Cotton 2SJV CORN SILAGE 1097.5 26.8 1,124.3 1.64 0.17 318,121 1,336,108 Corn Cotton 20SJV CORN SWEET ALL 36.7 0.5 37.1 1.64 0.08 10,634 44,661 Corn Cotton 40SJV COTTON LINT PIMA 939.1 355.6 1,294.7 1.44 3.37 211,037 1,308,432 Cotton Cotton 1SJV COTTON LINT UNSPECIFIED 420.0 159.1 579.1 1.44 3.37 94,391 585,224 Cotton Cotton 1SJV COTTON LINT UPLAND 2282.0 864.1 3,146.1 1.44 3.37 512,804 3,179,382 Cotton Cotton 1SJV COTTON SEED PLANTING 169.0 64.0 233.0 1.44 3.37 37,979 235,470 Cotton Cotton 1SJV COTTONSEED 0.0 0.00 0.00 Cotton Cotton 1SJV CUCUMBERS 11.1 0.1 11.2 1.63 0.08 2,601 13,525 Vegetables Cotton 40SJV EGGPLANT ALL 3.9 0.0 3.9 1.64 0.09 910 4,732 Vegetables Cotton 40SJV FIELD CROPS SEED MISC. 35.4 0.9 36.3 1.64 0.17 10,267 43,121 Corn Cotton 20SJV FIELD CROPS UNSPECIFIED 347.0 8.5 355.5 1.64 0.17 100,582 422,444 Corn Cotton 20SJV FIGS DRIED 0.4 12.2 12.7 1.20 2.04 11,987 719 Citrus Almonds 20SJV FRUITS & NUTS UNSPECIFIED 0.5 0.6 1.1 1.19 0.08 13,703 822 Citrus Cotton 40SJV GARLIC ALL 68.6 17.8 86.4 1.55 1.68 21,121 88,709 Garlic Cotton 2SJV GRAPEFRUIT ALL 0.1 0.1 0.2 1.24 0.08 2,695 162 Citrus Cotton 40SJV GRAPES RAISIN 251.1 16.3 267.4 1.15 0.17 192,980 437,487 Grapes-Raisin Cotton 20

Emissions (tons PM10/year) PM10 Emission Factors Activity Data Land Prep. & Harvest Assignments

Crop Specific LandPrep & Harvest Ems SJV.xls, hyu and pgaffney DRAFT 1



Air Basin Crop Name





PassesCrop Calendar




SJV GRAPES TABLE 31.5 6.4 37.9 1.51 0.17 75,895 41,742 Grapes-Table Cotton 20SJV GRAPES UNSPECIFIED 10.4 1.2 11.5 1.48 0.17 13,800 14,035 Grapes-Wine Cotton 20SJV GRAPES WINE 217.3 24.4 241.7 1.48 0.17 289,235 294,152 Grapes-Wine Cotton 20SJV HAY ALFALFA 1030.2 0.0 1,030.2 2.76 0.00 515,095 746,888 Alfalfa Zero 1SJV HAY GRAIN 114.1 48.0 162.1 2.76 1.68 57,024 82,685 Alfalfa Cotton 2SJV HAY GREEN CHOP 45.2 0.0 45.2 2.76 0.00 22,600 32,770 Alfalfa Zero 1SJV HAY OTHER UNSPECIFIED 76.7 32.3 109.0 2.76 1.69 38,357 55,618 Alfalfa Cotton 2SJV HAY SUDAN 18.4 0.0 18.4 2.76 0.00 9,204 13,346 Alfalfa Zero 1SJV KIWIFRUIT 0.1 0.1 0.2 1.22 0.08 2,464 148 Citrus Cotton 40SJV LEMONS ALL 0.3 0.3 0.5 1.22 0.09 6,808 408 Citrus Cotton 40SJV LETTUCE BULK SALAD PRODS. 4.6 0.0 4.6 2.44 0.08 796 3,739 Lettuce Cotton 40SJV LETTUCE HEAD 117.4 0.9 118.3 2.45 0.08 20,421 95,980 Lettuce Cotton 40SJV LETTUCE LEAF 27.6 0.2 27.8 2.45 0.08 4,800 22,560 Lettuce Cotton 40SJV MELONS CANTALOUPE 117.2 1.7 118.9 1.78 0.08 41,120 131,585 Melon Cotton 40SJV MELONS HONEYDEW 12.7 0.2 12.9 1.78 0.09 4,460 14,272 Melon Cotton 40SJV MELONS UNSPECIFIED 13.5 0.2 13.7 1.78 0.08 4,750 15,201 Melon Cotton 40SJV MELONS WATERMELON 19.9 0.3 20.2 1.78 0.08 6,979 22,333 Melon Cotton 40SJV NECTARINES 1.2 1.4 2.5 1.20 0.08 32,375 1,942 Citrus Cotton 40SJV NURSERY TURF 0.0 0.0 0.0 0.00 0.00 932 0 No Land Prep. Zero 1SJV OATS GRAIN 5.0 7.9 13.0 3.08 5.80 2,727 3,272 Wheat Wheat 1SJV OLIVES 0.7 0.9 1.6 1.20 0.08 20,211 1,213 Citrus Cotton 40SJV ONIONS 74.7 19.4 94.0 1.55 1.68 22,980 96,514 Onions Cotton 2SJV ORANGES NAVEL 4.1 4.8 8.8 1.20 0.08 112,958 6,777 Citrus Cotton 40SJV ORANGES UNSPECIFIED 0.1 0.2 0.3 1.22 0.08 3,830 230 Citrus Cotton 40SJV ORANGES VALENCIA 1.5 1.8 3.3 1.20 0.08 42,173 2,530 Citrus Cotton 40SJV PASTURE FORAGE MISC. 0.0 0.0 0.0 0.00 0.00 45,596 0 No Land Prep. Zero 1SJV PASTURE IRRIGATED 0.0 0.0 0.0 0.00 0.00 318,781 0 No Land Prep. Zero 1SJV PASTURE RANGE 0.0 0.0 0.0 0.00 0.00 4,389,225 0 No Land Prep. Zero 1SJV PEACHES CLINGSTONE 0.6 0.7 1.4 1.20 0.08 17,252 1,035 Citrus Cotton 40SJV PEACHES FREESTONE 1.2 1.4 2.6 1.20 0.08 32,736 1,964 Citrus Cotton 40SJV PEACHES UNSPECIFIED 0.0 0.1 0.1 1.21 0.09 1,103 66 Citrus Cotton 40SJV PEARS UNSPECIFIED 0.1 0.1 0.2 1.27 0.08 2,371 142 Citrus Cotton 40SJV PEAS GREEN PROCESSING 12.2 0.3 12.4 1.48 0.17 3,157 16,416 DryBeans Cotton 20SJV PECANS 1.4 1.8 3.2 12.47 4.08 898 225 Almonds Almonds 10SJV PEPPERS BELL 31.0 0.3 31.2 1.40 0.08 6,132 44,150 Tomatoes Cotton 40SJV PERSIMMONS 0.1 0.1 0.1 1.28 0.08 1,303 78 Citrus Cotton 40SJV PISTACHIOS 100.9 131.6 232.5 12.50 4.08 64,551 16,138 Almonds Almonds 10SJV PLUMS 1.3 1.6 2.9 1.20 0.08 36,960 2,218 Citrus Cotton 40SJV PLUMS DRIED 0.5 0.6 1.1 1.19 0.08 14,534 872 Citrus Cotton 40SJV POMEGRANATES 0.2 0.2 0.3 1.19 0.09 4,205 252 Citrus Cotton 40SJV POTATOES IRISH ALL 194.7 14.4 209.1 4.38 1.69 17,075 88,791 Sugar Beets Cotton 2SJV POTATOES SEED 11.8 0.9 12.7 4.38 1.68 1,035 5,382 Sugar Beets Cotton 2SJV POTATOES SWEET 118.4 8.8 127.2 4.38 1.68 10,386 54,007 Sugar Beets Cotton 2SJV PUMPKINS 13.0 0.4 13.4 1.78 0.17 4,550 14,560 Melon Cotton 20SJV QUINCE 0.0 0.0 0.0 1.56 0.09 213 13 Citrus Cotton 40SJV RICE MILLING 188.1 15.8 203.9 3.51 1.68 18,806 107,194 Rice Cotton 2SJV SAFFLOWER 37.2 48.0 85.2 2.05 5.80 16,542 36,393 Safflower Wheat 1SJV SEED ALFALFA 67.7 0.0 67.7 2.76 0.00 33,854 49,088 Alfalfa Zero 1SJV SEED OTHER (NO FLOWERS) 4.2 0.2 4.3 2.76 0.17 2,080 3,016 Alfalfa Cotton 20SJV SEED VEG & VINECROP 21.5 0.4 21.9 1.63 0.17 5,054 26,282 Vegetables Cotton 20SJV SILAGE 379.7 17.3 397.0 3.08 0.17 205,237 246,284 Wheat Cotton 20




Air Basin Crop Name





PassesCrop Calendar




SJV SORGHUM GRAIN 0.4 0.6 1.0 3.08 5.78 211 253 Wheat Wheat 1SJV SPINACH UNSPECIFIED 16.5 0.1 16.6 2.45 0.08 2,870 13,489 Lettuce Cotton 40SJV SQUASH 3.0 0.1 3.1 1.78 0.17 1,040 3,328 Melon Cotton 20SJV STRAW 0.0 0.00 0.00 Alfalfa Wheat 1SJV SUGAR BEETS 598.5 44.2 642.7 4.38 1.69 52,497 272,986 Sugar Beets Cotton 2SJV TANGELOS 0.0 0.0 0.1 1.33 0.08 1,000 60 Citrus Cotton 40SJV TANGERINES & MANDARINS 0.1 0.1 0.2 1.15 0.08 2,604 156 Citrus Cotton 40SJV TOMATOES CHERRY 0.9 0.0 0.9 1.41 0.12 170 1,224 Tomatoes Cotton 40SJV TOMATOES FRESH MARKET 160.8 1.3 162.2 1.40 0.08 31,847 229,300 Tomatoes Cotton 40SJV TOMATOES PROCESSING 960.0 16.0 976.0 1.40 0.17 190,101 1,368,727 Tomatoes Cotton 20SJV VEGETABLES ORIENTAL ALL 5.7 0.1 5.8 1.64 0.09 1,350 7,020 Vegetables Cotton 40SJV VEGETABLES UNSPECIFIED 235.7 4.7 240.3 1.63 0.17 55,449 288,335 Vegetables Cotton 20SJV WALNUTS ENGLISH 172.8 2254.0 2,426.8 12.50 40.77 110,573 27,643 Almonds Almonds 1SJV WHEAT ALL 598.4 938.0 1,536.4 3.08 5.80 323,461 388,153 Wheat Wheat 1SJV WHEAT SEED 7.8 12.2 19.9 3.08 5.80 4,191 5,029 Wheat Wheat 1

Land Prep Harvest Total Land Prep Harvest Acres Acre PassSJV TOTAL ALL CROPS 13,028 13,333 26,361 2.71 2.77 9,616,913 13,894,524

NOTES:

Generala) Only the San Joaquin Valley Air Basin part of Kern county is included in the emission estimates.b) The crop names are based on the names used in the CDFA annual crop summary for the year 2000.c) The emissions shown are for the year 2000, based on the most recent CDFA acreage data. These values were backcasted to develop the 1999 emission estimates.

Emissionsd) Land preparation geologic dust emissions are emissions resulting from discing, ripping, land planing, weeding, and other operations.e) Harvest geologic dust emissions are emissions resulting from crop harvesting. Emission estimates are based on scaling emission measurements from cotton, wheat, and almond harvesting emission factors.

Emission Factorsf) The Land Preparation emission factors shown are a composite, which are based on a combination of the emissions from the types and number of land preparation operations performed for each crop. These assignments are based upon the Crop Calendar Profile shown in the Land Prep. & Harvest Assignments category. The base emission factors are shown below.g) The Harvest emission factor is based on scaling the measured emissions from harvesting cotton, wheat, or almonds. Each crop is assigned a Base Factor and a Division factor to scale the emissions appropriately to each crop. These assignments are shown in the Land Prep. & Harvest Assignments category. The base harvest emission factor are shown below.

Activity Datah) Acres shown are the total number of acres grown within the San Joaquin Valley Air Basin. This acreage is from the annual statewide summary reports prepared by CDFA, and represents the year 2000. Acres are used in computing harvest emissions.i) Acre-Passes are the number of acres for each crop, multiplied by the annual number of land preparation operations for the crop. The number of land preparation operations is based on information in the crop calendar profile. Acre passes are used in computing land preparation emissions.

Averages (lbs/acre)PM10 Emission Totals (tons/year) Activity Totals




Air Basin Crop Name





PassesCrop Calendar




Land Prep. & Harvest Assignmentsj) The Crop Calendar Profile is the crop calendar that was assigned to the crop for emission estimation purposes. For each crop, the calendar includes what operations are performed, how many times they are performed, and when they are performed.k) the Harvest EF Base Factor is the base emission factor used to compute the harvest emissions. The three possible selections are cotton, almonds, or wheat. For other crops, variants of these factors were assigned to compute harvesting emissions.l) The Harvest EF Division Factor is used to divide the Harvest EF Base Factor to scale the base factor appropriately for the crops that do not have specific emission factors.

Base Emission Factors for Land Preparation Activities Base Emission Factors for Harvest Activities

Land Preparation Operation Emissions Category

Emission Factor

(lbs PM10/ acre-pass)

List Weeding 0.8List & Fertilize Weeding 0.8Listing Weeding 0.8Roll Weeding 0.8Spring Tooth Weeding 0.8Bed Preparation Weeding 0.8Seed Bed Preparation Weeding 0.8Shape Beds Weeding 0.8Shape Beds & Roll Weeding 0.8Shaping Weeding 0.8Terrace Weeding 0.8Chisel Discing 1.2Plow Discing 1.2Mulch Beds Discing 1.2Disc Discing 1.2Disc & Furrow-out Discing 1.2Disc & Roll Discing 1.2Finish Disc Discing 1.2Harrow Disc Discing 1.2Post Burn/Harvest Disc Discing 1.2Stubble Disc Discing 1.2Unspecified Operation Discing 1.2Land Preparation, Gen. Discing 1.2Subsoil Ripping 4.6Subsoil-deep chisel Ripping 4.6Float Land planing 12.53 Wheel Plane Land planing 12.5Land Plane Land planing 12.5 Prepared by:Laser Level Land planing 12.5 Hong Yu ([email protected])Level Land planing 12.5 Patrick Gaffney ([email protected])Level (new vineyard) Land planing 12.5 California Air Resources BoardPlane Land planing 12.5 November 13, 2002

Agricultural Harvest Operation

Emission Factor (lbs PM10/acre)

Cotton

Wheat

3.440.85.8

Almonds

Average SJV Temporal Profiles (monthly percent)JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC5.12 4.82 9.02 1.50 1.86 1.24 1.61 1.98 1.39 8.79 31.71 30.970.07 0.07 0.14 0.14 0.14 5.10 5.24 0.37 37.47 43.96 7.22 0.07

Land PreparationHarvest


DRAFT – Stakeholder Use Only / Do Not Release 9/4/2002

Preliminary Analysis Only DRAFT Do Not Cite or Quote

Selection of PM10 Emission Factors for Agricultural Harvest Activities Objectives: California specific emission factors are available for cotton,

almond, and wheat harvesting. Develop an approach using existing data to assign harvest emission factors to the majority of crop acreage in the San Joaquin Valley.

Approach: In a previous ARB technical summary1, the available

emission factors for harvest and land preparation were summarized and discussed. To help identify the most appropriate PM emission factors for harvest and land preparation, this initial summary was used as a basis for a teleconference with regulators, researchers, and industry representatives2. Considering the lack of comprehensive agricultural emission factor data, the majority of the teleconference focused on developing consensus on the “best-fit” emission factors. The selected factors are based on a combination of direct scientific applicability, as well as general experience and observations about the relative emissions potential of various operations. It was also agreed that emissions computed using the new emission factor assignments will be provided to for review prior to their inclusion into the SJV PM SIP.


The table below summarizes the selected harvest emission factors recommended for use in the SJV PM10 SIP. Discussion of each selection is provided below.



Cotton Total 3.4


Almond Total 40.8

Wheat Combining 5.8



Harvest Emission Factor Questions & Decisions: 1) Is the updated cotton harvest EF acceptable?

- It is based on more tests and more recent tests. - But, picking and cutting EFs are the same value (based on separate tests) for the updated factor. Is this reasonable? It was decided by the group that the updated UC Davis emission factor of 3.4 .lbs PM10/acre is appropriate for the combination of cotton picking and stalk cutting. It was mentioned that the tests were performed under various typical conditions. Some tests were performed at dusk, which could lead to higher than average emission readings.

2) Is the most recent almond shaking EF of 3.7 lbs/acre acceptable? Is the magnitude reasonable in comparison to the almond harvest other operations? After extended discussion, the answer to this question was no. There were too few tests performed and the emissions do not seem consistent with observed PM levels. Based on subjective judgment, it was agreed to use a value of 0.37 for the shaking emission EF, which is one-hundredth of the pickup emissions. This value will be updated as additional information becomes available.


4) Is the almond pickup emission factor of 36.7 lbs PM10/acre acceptable? It is in reasonable agreement with the older emission factor of 32.3. In this case, the answer was yes, this factor seems reasonable, it is based upon tests performed outside the canopy, and it is in reasonable agreement with earlier tests. Therefore, a total of 36.7 lbs PM10/acre will be used for the complete almond pickup operation.

For many harvest operations emission factors are not available. In these cases, these emissions will continue



to be zero as they have been historically. Fortunately, harvest emission rates are available for the highest acreage crop (cotton), and for one of the highest PM10 producing harvest activities (almond harvest).


The table below summarizes the new emission factors selected for land preparation activities for cotton and wheat. In the next section, these emission factors are applied to similar activities for other crops. In addition to the EFs below, UCD also has field test data from 70 to 90 additional land preparation tests that have not been analyzed. If the resources become available to analyze these data, the emission factors shown below will be updated to reflect this new information.






3) Based on Terry’s (UCD) 7/12/2002 analysis, the chiseling emission factor is based on very dry, but operationally valid conditions following June/July wheat harvest. It is suggested that rather than use the non-representative, worst case conditions, that an average of the discing and chiseling emission factors be used for typical chiseling. This value is 2.9 lbs PM10/acre-pass. Should the average value be used for chiseling? If not, what is the alternative? No. The average value is not acceptable because it is observed that chiseling and tilling operations are typically less dusty than discing operations, so using a larger value is not appropriate.



Further, Terry also clarified that the tested operation listed as chiseling in the UCD report more closely represents ripping or subsoiling, in which a small number of long shanks are used to work the soil. Therefore, it was decided to apply the UCD discing emission factor of 1.2, to tilling and chiseling. Ripping and subsoiling will be assigned to the UCD ‘chiseling’ emission factor of 4.6.

4) Based on Terry’s 7/12/2002 analysis, new emission rates were provided for summer land planing and floating. The average emissions are 12.5 lbs PM10/acre-pass which is based on 3 different sites and 23 tests. Is this factor acceptable? The average EFs for each site are comparable. Although testing occurred during the summer, it is typical to perform planing/floating during relatively dry conditions, so it is likely that the factors are representative. Some were surprised about the magnitude of the planing and floating emission factor, but the data appear to be valid. Tests were performed for garbanzo, tomato, and wheat under typical moisture conditions. It was decided to use the UCD factor for land planing and floating. ARB will provide a summary of the emissions by operation to ensure that the factor does not produce unrealistic emission estimates.

5) Currently the ARB does not include cultivation operations in our emission estimates. Because of the lack of information, it is suggested that we retain this approach. In addition, cultivation typically does not occur in the SJV during the time of the year with elevated PM levels. The weeding emission factor will not be used. In discussions with the group, it was decided that the weeding factor is applicable to various land preparation activities such as listing and rolling, so it will be used for those operations.

Assigning Emission Factors to Operations:

There are a limited number of emission factors and a large number of agricultural land preparation operations. The table following summarizes the emission factor assignments agreed upon by the group during our teleconference. The available emission factor choices are shown in the previous table and include root cutting (0.3 lbs PM10/acre-pass), discing (1.2), ripping (4.6), land planing/floating (12.5), and weeding (0.8). The emission factor used in the 1997 PM10 SIP for all land preparation operations was approximately 4 lbs PM10/acre-pass.



Land Preparation Emission Factor Assignments Land Preparation Operation

Emissions Category




will estimate the SJV harvest and land preparation emissions. These emissions will be provided by crop and by operation for stakeholder review. It is also important that EPA and environmental group representatives be included in reviewing the emissions and underlying assumptions. The updated emissions using these new data is an important step forward in better representing agricultural PM10 emissions, especially considering that the prior default value of 4 lbs of PM10 per acre-pass was applied to every land preparation operation in California.




Preparation & Harvest Activities, P. Gaffney, California Air Resources Board, 8/23/2002 2Teleconference 8/29/2002. Dave Jones (SJVUAPCD), Patia Siong (SJVUAPCD), Stephen Shaw (SJVUAPCD), Terry Cassel (UCD), Paul Martin (Western United Dairymen), Roger Isom (California Cotton Ginners and Growers Association), Cynthia Corey (California Farm Bureau), Gene Beach (Almond Hullers and Processors Association), George Bluhm (CDFA), Matt Summers (CDFA), Patrick Gaffney (ARB). 3Sources and Sinks of PM10 in the San Joaquin Valley, Interim Report. Flocchini, R.G., James, T.A., et. al., August 10, 2002. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1. 4Terry Cassel, Informal write-up for SJV Ag Tech Committee, Evaluation of ARB application of UCD emission factors, July 12, 2002




Selection of PM10 Emission Factors for Agricultural Harvest and Land Preparation Objectives: Identify the most appropriate emission factors for estimating

the PM10 component of fugitive dust emissions from agricultural harvesting and land preparation operations within California.

Approach: In a previous ARB technical summary1, the available

emission factors for harvest and land preparation were summarized and discussed. To help identify the most appropriate PM emission factors for harvest and land preparation, this initial summary was used as a basis for a teleconference with regulators, researchers, and industry representatives2. Considering the lack of comprehensive agricultural emission factor data, the majority of the teleconference focused on developing consensus on the “best-fit” emission factors. The selected factors are based on a combination of direct scientific applicability, as well as general experience and observations about the relative emissions potential of various operations. It was also agreed that emissions computed using the new emission factor assignments will be provided to for review prior to their inclusion into the SJV PM SIP.


The table below summarizes the selected harvest emission factors recommended for use in the SJV PM10 SIP. Discussion of each selection is provided below.



Cotton Total 3.4


Almond Total 40.8

Wheat Combining 5.8



Harvest Emission Factor Questions & Decisions: 1) Is the updated cotton harvest EF acceptable?

- It is based on more tests and more recent tests. - But, picking and cutting EFs are the same value (based on separate tests) for the updated factor. Is this reasonable? It was decided by the group that the updated UC Davis emission factor of 3.4 .lbs PM10/acre is appropriate for the combination of cotton picking and stalk cutting. It was mentioned that the tests were performed under various typical conditions. Some tests were performed at dusk, which could lead to higher than average emission readings.

2) Is the most recent almond shaking EF of 3.7 lbs/acre acceptable? Is the magnitude reasonable in comparison to the almond harvest other operations? After extended discussion, the answer to this question was no. There were too few tests performed and the emissions do not seem consistent with observed PM levels. Based on subjective judgment, it was agreed to use a value of 0.37 for the shaking emission EF, which is one-hundredth of the pickup emissions. This value will be updated as additional information becomes available.


4) Is the almond pickup emission factor of 36.7 lbs PM10/acre acceptable? It is in reasonable agreement with the older emission factor of 32.3. In this case, the answer was yes, this factor seems reasonable, it is based upon tests performed outside the canopy, and it is in reasonable agreement with earlier tests. Therefore, a total of 36.7 lbs PM10/acre will be used for the complete almond pickup operation.

For many harvest operations emission factors are not available. In these cases, these emissions will continue



to be zero as they have been historically. Fortunately, harvest emission rates are available for the highest acreage crop (cotton), and for one of the highest PM10 producing harvest activities (almond harvest).


The table below summarizes the new emission factors selected for land preparation activities for cotton and wheat. In the next section, these emission factors are applied to similar activities for other crops. In addition to the EFs below, UCD also has field test data from 70 to 90 additional land preparation tests that have not been analyzed. If the resources become available to analyze these data, the emission factors shown below will be updated to reflect this new information.






3) Based on Terry’s (UCD) 7/12/2002 analysis, the chiseling emission factor is based on very dry, but operationally valid conditions following June/July wheat harvest. It is suggested that rather than use the non-representative, worst case conditions, that an average of the discing and chiseling emission factors be used for typical chiseling. This value is 2.9 lbs PM10/acre-pass. Should the average value be used for chiseling? If not, what is the alternative? No. The average value is not acceptable because it is observed that chiseling and tilling operations are typically less dusty than discing operations, so using a larger value is not appropriate.



Further, Terry also clarified that the tested operation listed as chiseling in the UCD report more closely represents ripping or subsoiling, in which a small number of long shanks are used to work the soil. Therefore, it was decided to apply the UCD discing emission factor of 1.2, to tilling and chiseling. Ripping and subsoiling will be assigned to the UCD ‘chiseling’ emission factor of 4.6.

4) Based on Terry’s 7/12/2002 analysis, new emission rates were provided for summer land planing and floating. The average emissions are 12.5 lbs PM10/acre-pass which is based on 3 different sites and 23 tests. Is this factor acceptable? The average EFs for each site are comparable. Although testing occurred during the summer, it is typical to perform planing/floating during relatively dry conditions, so it is likely that the factors are representative. Some were surprised about the magnitude of the planing and floating emission factor, but the data appear to be valid. Tests were performed for garbanzo, tomato, and wheat under typical moisture conditions. It was decided to use the UCD factor for land planing and floating. ARB will provide a summary of the emissions by operation to ensure that the factor does not produce unrealistic emission estimates.

5) Currently the ARB does not include cultivation operations in our emission estimates. Because of the lack of information, it is suggested that we retain this approach. In addition, cultivation typically does not occur in the SJV during the time of the year with elevated PM levels. The weeding emission factor will not be used. In discussions with the group, it was decided that the weeding factor is applicable to various land preparation activities such as listing and rolling, so it will be used for those operations.

Assigning Emission Factors to Operations:

There are a limited number of emission factors and a large number of agricultural land preparation operations. The table following summarizes the emission factor assignments agreed upon by the group during our teleconference. The available emission factor choices are shown in the previous table and include root cutting (0.3 lbs PM10/acre-pass), discing (1.2), ripping (4.6), land planing/floating (12.5), and weeding (0.8). The emission factor used in the 1997 PM10 SIP for all land preparation operations was approximately 4 lbs PM10/acre-pass.



Land Preparation Emission Factor Assignments Land Preparation Operation

Emissions Category




will estimate the SJV harvest and land preparation emissions. These emissions will be provided by crop and by operation for stakeholder review. It is also important that EPA and environmental group representatives be included in reviewing the emissions and underlying assumptions. The updated emissions using these new data is an important step forward in better representing agricultural PM10 emissions, especially considering that the prior default value of 4 lbs of PM10 per acre-pass was applied to every land preparation operation in California.




Preparation & Harvest Activities, P. Gaffney, California Air Resources Board, 8/23/2002 2Teleconference 8/29/2002. Dave Jones (SJVUAPCD), Patia Siong (SJVUAPCD), Stephen Shaw (SJVUAPCD), Terry Cassel (UCD), Paul Martin (Western United Dairymen), Roger Isom (California Cotton Ginners and Growers Association), Cynthia Corey (California Farm Bureau), Gene Beach (Almond Hullers and Processors Association), George Bluhm (CDFA), Matt Summers (CDFA), Patrick Gaffney (ARB). 3Sources and Sinks of PM10 in the San Joaquin Valley, Interim Report. Flocchini, R.G., James, T.A., et. al., August 10, 2002. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1. 4Terry Cassel, Informal write-up for SJV Ag Tech Committee, Evaluation of ARB application of UCD emission factors, July 12, 2002


DRAFT – Internal Use Only – Do Not Release 8/23/2002


Assigning UC Davis Agricultural Emission Factors to Land Preparation & Harvest Activities Objectives UC Davis has recently published particulate matter emission

factors for a variety of agricultural activities1. These factors need to be assigned to specific crop activities to replace the existing EPA AP-42 emission factor. This paper summarizes the issues and decisions that need to be made in applying the new factors to the PM10 SIP emission estimates.

Harvest Emission Factors

The table below summarizes the existing emission factors used for harvesting and the new emission factors in the UCD report. There are some dramatic differences between the old and new values. The cotton harvest estimates triple in size and the almond harvest estimates double.

Old2 New1 Agricultural Operation lbs PM10/acre lbs PM10/acre mg/m2

Cotton Cotton Picking 0.42 1.7 190 Cotton Stalk Cutting 0.7 1.7 188

Cotton Total 1.12 3.4

Almond Almond Shaking 0.13 14.9 / 3.7* 1670 / 412*Almond Sweeping 1.76 13.1 1466 1st Almond Pickup 12.8 2nd Almond Pickup 19.5 36.7 4106

Almond Total 34.2 64.7 / 53.5*

Wheat Combining 5.8 650

* Updated value from 12 July 2002 analysis by Terry Cassel, UC Davis. Harvest Emission Factor Questions:

1) Is the updated cotton harvest EF acceptable? - It is based on more tests and more recent tests. - But, picking and cutting EFs are the same value (based on separate tests) for the updated factor. Is this reasonable?

2) Is the most recent almond shaking EF of 3.7 lbs/acre acceptable? Is the magnitude reasonable in comparison to the almond harvest other operations?

3) Is the updated almond sweeping factor acceptable and a reasonable relative magnitude (13.1 lbs PM10/acre) compared to other operations? Generally,



the old factors are considered invalid because plume profiling was not performed.

4) Is the almond pickup emission factor of 36.7 lbs PM10/acre acceptable? It is in reasonable agreement with the older emission factor of 32.3.

Land Preparation Emission Factors

The table below summarizes the existing and new emission factors for land preparation activities for cotton and wheat. It is proposed that these emission factors be applied to other similar activities for other crops. In addition to the EFs below, UCD also has test data from 70 to 90 additional land preparation tests, but the resources are unavailable to analyze the data (see table 6.3 of the UCD report). As additional information becomes available, the emission factors shown will be updated. Below are two sets of questions. The first set is to clarify the acceptability of the emission factors. The second questions are to help assign the limited number of emission factors to the full array of agricultural activities.

Old3 New1 Cotton / Wheat Tests lbs/acre-pass lbs/acre-pass mg/m2

Root cutting 0.3 33 Discing & Chiseling

Discing 1.2 134.2 Chiseling (dry) 4.6 512.2

Average (disc & chisel) 2.9 Land Planing & Floating4 12.5 1397 Weeding

≈4.0 default for all

operations

0.8 89.2 Land Preparation Emission Factor Questions:

1) Is the new root cutting emission factor acceptable? In the past, this operation was assigned ~4/lbs PM10/acre.

2) Is the new discing emission factor acceptable? 3) Based on Terry’s (UCD) 7/12/2002 analysis, the

chiseling emission factor is based on very dry, but operationally valid conditions following June/July wheat harvest. It is suggested that rather than use the non-representative, worst case conditions, that an average of the discing and chiseling emission factors be used for typical chiseling. This value is 2.9 lbs



PM10/acre-pass. Should the average value be used for chiseling? If not, what is the alternative?

4) Based on Terry’s 7/12/2002 analysis, new emission rates are provided for summer land planing and floating. Based on 3 different sites and 23 tests, the average emissions are 12.5 lbs PM10/acre-pass. Is this factor acceptable? The average EFs for each site are comparable. Although testing occurred during the summer, it is typical to perform planing/floating during relatively dry conditions, so it is likely that the factors are representative.

5) Currently the ARB does not include cultivation operations in our emission estimates. Because of the lack of information, it is suggested that we retain this approach. In addition, cultivation typically does not occur in the SJV during the time of the year with elevated PM levels. The weeding emission factor will not be used.

Assigning Emission Factors to Operations

There are a limited number of emission factors and a large number of agricultural land preparation operations. The table below is an initial assignment by ARB staff. Help is needed in assigning the available factors to the most similar operations. The available emission factor choices are shown in the previous table and include root cutting (0.3 lbs PM10/acre-pass), discing (1.2), chiseling (4.6), average of discing and chiseling (2.9), land planing/floating (12.5). The emission factor used in the 1997 PM10 SIP for all land preparation operations was approximately 4 lbs PM10/acre-pass. Emission Factor Assignment Questions: Are the emission factor assignments in the following table correct? If not, please provide suggested best-fit assignments.



Land Preparation Operation

Emissions Category


Assignment Okay? (Y/N) Suggestion?

Chisel Average 2.9 Plow Average 2.9 Spring Tooth Average 2.9 Subsoil Average 2.9 Subsoil-deep chisel Average 2.9 Disc Disc 1.2 Disc & Furrow-out Disc 1.2 Disc & Roll Disc 1.2 Finish Disc Disc 1.2 Float Disc 1.2 Harrow Disc Disc 1.2 Post Burn/Harvest Disc Disc 1.2 Stubble Disc Disc 1.2 3 Wheel Plane Land plane 12.5 Bed Preparation Land plane 12.5 Land Plane Land plane 12.5 Laser Level Land plane 12.5 Level Land plane 12.5 Level (new vineyard) Land plane 12.5 Plane Land plane 12.5 Unspecified Operation Average 2.9 Land Preparation, Gen. Average 2.9 List Average 2.9 List & Fertilize Average 2.9 Listing Average 2.9 Mulch Beds Average 2.9 Roll Disc 1.2 Seed Bed Preparation Disc 1.2 Shape Beds Disc 1.2 Shape Beds & Roll Disc 1.2 Shaping Disc 1.2 Sulfur Dusting Any Ideas? 0? Terrace Average 2.9

Conclusions The above questions need to be resolved so that appropriate

emission rates can be assigned to update the PM10 estimates for land preparation and harvest activities. This information will be incorporated into the SJV PM10 SIP. Although some emission rates may not be completely definitive, they are likely a positive forward step from the previous default of 4 lbs PM10 per acre-pass for all crops and activities within California.



References: 1Sources and Sinks of PM10 in the San Joaquin Valley, Interim

Report. Flocchini, R.G., James, T.A., et. al., August 10, 2002. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1. 2UCD 1995, Evaluation of Fugitive PM10 Emissions from Cotton Production, Annual Report, 1995” 3Compilation of Air Pollutant Emission Factors, Volume 1, Fourth Edition, AP-42. U.S. EPA, September 1985. Section 11.2.2 4Terry Cassel, Informal write-up for SJV Ag Tech Committee, Evaluation of ARB application of UCD emission factors, July 12, 2002



Harvest EF Documentation12_2002.doc 1

MEMORANDUM

TO: SJV PM10 SIP Emission Inventory Group FROM: Patrick Gaffney, ARB DATE: December 3, 2002 SUBJECT: Methodology for Assigning PM10 Emission Factors

for California Agriculture Harvest Activities Objective: Develop a method to assign PM10 geologic dust harvest PM

emission factors to all California crops. In the past, geologic PM harvest emissions were only computed for four crops and the remaining crops were assigned zero harvest emissions.

Approach: UC Davis researchers, under contract to the USDA,

measured geologic dust particulate matter emissions from harvesting cotton, almonds, and wheat in California. These factors are shown below1. Using the available emission factors as a baseline, harvest emission factors were also approximated for the other California crops. The scaling approach used to assign the three available PM emission factors to the dozens of California crops is highly subjective. ARB staff provided the initial scaling assignments, which were then refined by members of the agricultural community2. Members of the SJV Agricultural Technical Advisory Group concluded that it was more realistic to include rough approximations of PM harvest emissions than to set the values to zero, as has been done historically. Table 1. California Harvest Emission Factors

Harvest Emission Factors1 Agricultural Harvest

Operation (lbs PM10/acre)


Cotton Total 3.4 Almond

Almond Shaking 0.37 Almond Sweeping 3.7 Almond Pickup 36.7

Almond Total 40.8 Wheat

Combining 5.8

2

Assumptions: The emission rate assumptions were assigned to reflect the

relative geologic PM generation potential of various harvest practices. This broad approach is a first attempt to estimate these emissions. Table 2 below provides examples of some of the emission factor assignments. Emission factor assumptions and emissions estimates for all SJV crops are provided in the detailed emissions table attached to the ARB memo, “Selection of PM10 Emission Factors for Agricultural Harvest and Land Preparation,” December 2002.3 Table 2. Example Harvest Emission Factor Assumptions

Crop AssumptionHarvest Emission

Factor (lbs PM10/acre)

Cotton Cotton 3.4 Vine Cotton/20 0.17 Tomato Cotton/20 0.17 Fruit Trees Cotton/40 0.085 Corn Wheat/2 2.8 Alfalfa Zero 0.0 Walnuts Almonds 40.8 Sugar beets, onions, potatoes

Cotton/2

2.7

As additional harvest emissions data are collected, they will be incorporated into the methodology and the emissions estimates will be recalculated. [Note 12/3/02: Some recent PM sampling comparisons by UCD and Texas A&M indicate that it may be justified to reduce the UCD almond harvest emission factor by approximately 50%. More analysis is being performed to resolve this issue, and the emissions may be updated accordingly.]

Diesel and gasoline exhaust pipe emissions from tractors and other equipment used in harvesting are computed separately as part of the ARB’s off-road mobile source emissions model, and are not included in these estimates.

Results: Using the approach shown, approximately 77% of the

geologic harvest PM10 emissions are from the three crops with measured emission factors. If walnuts are included (using the almond emission factor) this value increases to 94% of the total harvest emissions. Roughly, the SJV PM10 harvest emissions from cotton are estimated as 1400 tons PM10/year, wheat is 900 tons/year, almonds are 7900 tons/year, and walnuts are 2200 tons/year,

3

for a total of about 12,400 tons/year for these four crops. All of the remaining crops contribute an additional 930 tons/year of PM10 in the SJV, for a grand total of approximately 13,330 tons/year (36.5 tons/day) of PM10 from SJV crop harvest activities. Crop specific emission details are provided in Reference 3. As mentioned, the assumptions included in this method will need refinement as additional information become available. However, the method shown will be used for the statewide 1999 harvest emissions inventory update.

References: 1 Sources and Sinks of PM10 in the San Joaquin Valley, Interim Report. Flocchini, R.G., James, T.A., et. al., August 10, 2002. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1.

2 SJV Agricultural Technical Advisory Group, 11/28/2002. Also personal communications with Roger Isom (Cotton Incorporated) and Manuel Cunha (Nisei Farmers League) on 11/29/2002 based on discussions with growers during 11/28/2002.

3 Selection of PM10 Emission Factors for Agricultural Harvest and

Land Preparation, Memorandum; California Air Resources Board; Patrick Gaffney, December 3, 2002

Contact: Patrick Gaffney, Air Resources Board [email protected] (916) 322-7303


1

MEMORANDUM

TO: SJV PM10 SIP Emission Inventory Group FROM: Patrick Gaffney, ARB DATE: November 4, 2002 SUBJECT: Methodology for Assigning PM10 Emission Factors

for California Agriculture Harvest Activities Objective: Develop a method to assign PM10 geologic dust harvest PM

emission factors to all California crops. In the past, geologic harvest PM emissions were only computed for four crops and the remaining crops were assigned zero harvest emissions.

Approach: Particulate matter harvest emission factors for geologic dust

have been measured in California for cotton, almonds, and wheat by UC Davis, under contract to the USDA. These factors are shown below. Using the available emission factors as a baseline, harvest emission factors were approximated for other California crops. The scaling approach used to assign the three available PM emission factors to the dozens of California crops is highly subjective. Initial scaling assignments were made by ARB staff, which were then refined by members of the agricultural community. Members of the SJV Agricultural Technical Advisory Group concluded that it was more realistic to include rough approximations of PM harvest emissions than to set the values to zero, as has been done historically. Table 1. California Harvest Emission Factors

Harvest Emission Factors1 Agricultural Harvest



Cotton Total 3.4 Almond

Almond Shaking 0.37 Almond Sweeping 3.7 Almond Pickup 36.7

Almond Total 40.8 Wheat

Combining 5.8


2

Assumptions: The emission rate assumptions were assigned to reflect the

relative geologic PM generation potential of various harvest practices. This broad approach is a first attempt to estimate these emissions. Table 2 below provides examples of some of the emission factor assignments. Assumptions for all crops are provided on the attached table. Table 2. Example Harvest Emission Factor Assumptions

Crop AssumptionHarvest Emission


Cotton Cotton 3.4 Vine Cotton/20 0.17 Tomato Cotton/20 0.17 Fruit Trees Cotton/40 0.085 Corn Wheat/2 2.8 Alfalfa Zero 0.0 Walnuts Almonds 40.8 Sugar beets, onions, potatoes

Cotton/2

2.7

As additional harvest emissions data are collected, they will be incorporated into the methodology and the emissions estimates will be recalculated. Diesel and gasoline exhaust pipe emissions from tractors and other equipment used in harvesting are computed separately as part of the ARB’s off-road mobile source emissions model.

Results: Using the approach shown, approximately 78% of the

geologic harvest PM10 emissions are from the three crops with measured emission factors. If walnuts are included (using the almond emission factor) this value increases to 94% of the total harvest emissions. Roughly, the SJV PM10 harvest emissions from cotton are estimated as 1500 tons PM10/year, wheat is 1100 tons/year, almonds are 8400 tons/year, and walnuts are 2300 tons/year, for a total of about 13,300 tons/year. All of the remaining crops contribute an additional 850 tons/year of PM10 in the SJV. This is an increase of approximately 6%, or a total of approximately 14,150 tons/year (39.7 tons/day) of PM10 from SJV harvest activities. As mentioned, the assumptions included in this method will need refinement as additional information are available. However, the method shown will be used for the 1999 SJV emission inventory update.


3

References: 1Sources and Sinks of PM10 in the San Joaquin Valley, Interim Report. Flocchini, R.G., James, T.A., et. al., August 10, 2002. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1.


DRAFT November 4, 2002

Assignment of Geologic PM10 Harvest Emission Factors for the SJV

SJV Crop Acreage Summary (sorted by Acreage) SJV Harvest Emission Factor Assumptions and Emissions

CDFA Crops (some consolidation performed)

Sum Of CDFA SJV Harvested

Acres

% non Pasture

total acreage

Running Total

Crop Harvest Category*

Harvest EF Category

Harvest EF Assumption

(lbs PM10/acre)

Emissions Estimate

(tons PM10/year)

% of Emissions

Running Total

COTTON UPLAND+PIMA+UNSPEC 880,976 17% 17% Field Mech Cotton 3.4 1497.7 10.6% 11%HAY ALFALFA+SEED ALFALFA+HAY GREEN 612,829 12% 29% Field Mech Zero 0 0.0 0.0% 11%GRAPES WINE+RASIN+TABLE 585,581 11% 40% Vine Cotton/20 0.17 49.8 0.4% 11%ALMONDS ALL 413,069 8% 48% Tree Nut Almonds 40.8 8426.6 59.6% 71%WHEAT ALL+SEED+BARLEY+BARLEY FEED 379,120 7% 56% Field Mech Wheat 5.8 1099.4 7.8% 78%CORN SILAGE 318,121 6% 62% Field Mech Cotton/20 0.17 27.0 0.2% 79%SILAGE 220,437 4% 66% Field Mech Cotton/20 0.17 18.7 0.1% 79%TOMATOES PROCESSING 192,469 4% 70% Field Mech Cotton/20 0.17 16.4 0.1% 79%ORANGES NAVEL 121,476 2% 72% Tree Fruit Cotton/40 0.085 5.2 0.0% 79%WALNUTS ENGLISH 111,039 2% 75% Tree Nut Almonds 40.8 2265.2 16.0% 95%FIELD CROPS UNSPECIFIED 103,942 2% 77% Field Mech Cotton/20 0.17 8.8 0.1% 95%HAY GRAIN + HAY OTHER 103,381 2% 79% Field Mech Cotton/2 1.7 87.9 0.6% 95%GRAIN CORN 92,950 2% 80% Field Mech Cotton/2 1.7 79.0 0.6% 96%PISTACHIOS 73,856 1% 82% Tree Nut Almonds/10 4.08 150.7 1.1% 97%VEGETABLES UNSPECIFIED 68,281 1% 83% Veg Mech Cotton/20 0.17 5.8 0.0% 97%SUGAR BEETS 54,155 1% 84% Field Mech Cotton/2 1.7 46.0 0.3% 97%ORANGES VALENCIA 45,010 1% 85% Tree Fruit Cotton/40 0.085 1.9 0.0% 98%COTTON SEED 44,347 1% 86% Field Mech Cotton 3.4 75.4 0.5% 98%MELONS CANTALOUPE 41,546 1% 87% Fruit Hand Cotton/40 0.085 1.8 0.0% 98%BEANS DRY EDIBLE UNSPEC. 39,499 1% 88% Field Mech Cotton/2 1.7 33.6 0.2% 98%PLUMS 37,656 1% 88% Tree Fruit Cotton/40 0.085 1.6 0.0% 98%TOMATOES FRESH 32,884 1% 89% Hand Cotton/40 0.085 1.4 0.0% 98%NECTARINES 32,780 1% 90% Tree Fruit Cotton/40 0.085 1.4 0.0% 98%PEACHES FREESTONE 32,736 1% 90% Tree Fruit Cotton/40 0.085 1.4 0.0% 98%ASPARAGUS UNSPECIFIED 26,190 1% 91% Field Mech Cotton/2 1.7 22.3 0.2% 98%BEANS LIMA LG+UNSPEC+BABY+GREEN 25,276 0% 91% Field Mech Cotton/2 1.7 21.5 0.2% 99%ONIONS 25,050 0% 92% Field Mech Cotton/2 1.7 21.3 0.2% 99%POTATOES IRISH ALL 23,600 0% 92% Field Mech Cotton/2 1.7 20.1 0.1% 99%GARLIC ALL 22,590 0% 93% Field Mech Cotton/2 1.7 19.2 0.1% 99%LETTUCE HEAD 20,610 0% 93% Veg Hand Cotton/40 0.085 0.9 0.0% 99%OLIVES 20,445 0% 93% Tree Fruit Cotton/40 0.085 0.9 0.0% 99%RICE MILLING 18,806 0% 94% Field Mech Cotton/2 1.7 16.0 0.1% 99%APPLES ALL 18,448 0% 94% Tree Fruit Cotton/40 0.085 0.8 0.0% 99%CHERRIES SWEET 18,360 0% 94% Tree Fruit Cotton/40 0.085 0.8 0.0% 99%SAFFLOWER 18,203 0% 95% Field Mech Wheat 5.8 52.8 0.4% 100%PEACHES CLINGSTONE 17,252 0% 95% Tree Fruit Cotton/40 0.085 0.7 0.0% 100%APRICOTS ALL 15,319 0% 95% Tree Fruit Cotton/40 0.085 0.7 0.0% 100%PLUMS DRIED 14,534 0% 96% Tree Fruit Cotton/40 0.085 0.6 0.0% 100%FRUITS & NUTS UNSPECIFIED 14,470 0% 96% Tree Nut Cotton/40 0.085 0.6 0.0% 100%GRAPES UNSPECIFIED 13,800 0% 96% Vine Cotton/20 0.17 1.2 0.0% 100%BEANS BLACKEYE (PEAS) 12,500 0% 96% Field Mech Cotton/2 1.7 10.6 0.1% 100%FIGS DRIED 11,987 0% 97% Tree Fruit Almond/20 2.04 12.2 0.1% 100%FIELD CROPS SEED MISC. 11,307 0% 97% Field Mech Cotton/20 0.17 1.0 0.0% 100%BROCCOLI UNSPECIFIED 11,260 0% 97% Veg Hand Cotton/40 0.085 0.5 0.0% 100%CORN SWEET ALL 10,816 0% 97% Veg Hand Cotton/40 0.085 0.5 0.0% 100%POTATOES SWEET 10,386 0% 98% Field Mech Cotton/2 1.7 8.8 0.1% 100%HAY SUDAN 9,204 0% 98% Field Mech Zero 0 0.0 0.0% 100%

California Air Resources Board, pgaffney DRAFT

DRAFT November 4, 2002

MELONS WATERMELON 7,907 0% 98% Fruit Hand Cotton/40 0.085 0.3 0.0% 100%LEMONS ALL 7,892 0% 98% Tree Fruit Cotton/40 0.085 0.3 0.0% 100%BEANS FRESH UNSPECIFIED 7,740 0% 98% Vine Cotton/20 0.017 0.1 0.0% 100%PEPPERS BELL 6,836 0% 98% Veg Hand Cotton/40 0.085 0.3 0.0% 100%SEED VEG & VINECROP 5,227 0% 98% Veg Hand Cotton/40 0.085 0.2 0.0% 100%MELONS UNSPECIFIED 4,920 0% 99% Fruit Hand Cotton/40 0.085 0.2 0.0% 100%BEANS KIDNEY RED 4,900 0% 99% Field Mech Cotton/2 1.7 4.2 0.0% 100%LETTUCE LEAF 4,800 0% 99% Veg Hand Cotton/40 0.085 0.2 0.0% 100%PUMPKINS 4,550 0% 99% Field Mech Cotton/20 0.17 0.4 0.0% 100%MELONS HONEYDEW 4,460 0% 99% Fruit Hand Cotton/40 0.085 0.2 0.0% 100%POMEGRANATES 4,205 0% 99% Tree Fruit Cotton/40 0.085 0.2 0.0% 100%ORANGES UNSPECIFIED 3,830 0% 99% Tree Fruit Cotton/40 0.085 0.2 0.0% 100%PEAS GREEN PROCESSING 3,157 0% 99% Vine Cotton/20 0.17 0.3 0.0% 100%GRAPEFRUIT ALL 3,035 0% 99% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%SPINACH UNSPECIFIED 2,870 0% 99% Veg Hand Cotton/40 0.085 0.1 0.0% 100%BEANS SEED 2,758 0% 99% Field Mech Cotton/2 1.7 2.3 0.0% 100%OATS GRAIN 2,727 0% 99% Field Mech Wheat 5.8 7.9 0.1% 100%TANGERINES & MANDARINS 2,604 0% 99% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%CUCUMBERS 2,601 0% 99% Veg Hand Cotton/40 0.085 0.1 0.0% 100%KIWIFRUIT 2,552 0% 99% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%PEARS UNSPECIFIED 2,371 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%CAULIFLOWER UNSPECIFIED 2,290 0% 100% Veg Hand Cotton/40 0.085 0.1 0.0% 100%SEED OTHER (NO FLOWERS) 2,080 0% 100% Field Mech Cotton/20 0.17 0.2 0.0% 100%PEACHES UNSPECIFIED 1,622 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%TANGELOS 1,470 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%NURSERY TURF 1,370 0% 100% Field Mech Zero 0 0.0 0.0% 100%BROCCOLI FRESH MARKET 1,356 0% 100% Veg Hand Cotton/40 0.085 0.1 0.0% 100%VEGETABLES ORIENTAL ALL 1,350 0% 100% Veg Hand Cotton/40 0.085 0.1 0.0% 100%PERSIMMONS 1,303 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%CITRUS UNSPECIFIED 1,205 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%LETTUCE BULK SALAD PRODS. 1,170 0% 100% Veg Hand Cotton/40 0.085 0.0 0.0% 100%BERRIES STRAWBERRIES UNSPEC 1,047 0% 100% Fruit Hand Cotton/40 0.085 0.0 0.0% 100%SQUASH 1,040 0% 100% Veg Hand Cotton/20 0.17 0.1 0.0% 100%BEANS SNAP FRESH MARKET 1,036 0% 100% Vine Cotton/20 0.17 0.1 0.0% 100%POTATOES SEED 1,035 0% 100% Veg Mech Cotton/2 1.7 0.9 0.0% 100%EGGPLANT ALL 910 0% 100% Veg Hand Cotton/40 0.085 0.0 0.0% 100%PECANS 898 0% 100% Tree Nut Almond/10 4.08 1.8 0.0% 100%BROCCOLI PROCESSING 766 0% 100% Veg Mech Cotton/40 0.085 0.0 0.0% 100%AVOCADOS ALL 646 0% 100% Tree Fruit Cotton/40 0.085 0.0 0.0% 100%CAULIFLOWER FRESH MARKET 373 0% 100% Veg Hand Cotton/40 0.085 0.0 0.0% 100%BEANS GARBANZO 322 0% 100% Field Mech Cotton/2 1.7 0.3 0.0% 100%QUINCE 213 0% 100% Tree Fruit Cotton/40 0.085 0.0 0.0% 100%SORGHUM GRAIN 211 0% 100% Field Mech Wheat 5.8 0.6 0.0% 100%TOMATOES CHERRY 170 0% 100% Veg Hand Cotton/40 0.085 0.0 0.0% 100%

TOTAL ACRES 5,144,378 100% Total PM10 14,139 100.0%Total Acres Total PM10 (tpy)

*Mech = Mechanically Harvested 13289 Cotton, Almond, Hand = Hand Harvested Wheat, Walnut

850 Total - (C,A,W,Walnut)Prepared by Patrick Gaffney with Assistance from Roger Isom, Manuel Cunha, and others 6% Percent ChangeContact Information:California Air Resources BoardPlanning and Technical Support [email protected], 916-322-7303Last Revised 11/4/2002


DRAFT – Internal Use 10/18/2002


Initial Methodology for Assigning PM10 Emission Factors for California Agriculture Harvest Activities Objective: Credible California PM10 emission rates are available for the

harvesting of cotton, almonds, and wheat. This document provides an initial approach for assigning harvesting emissions for crops grown in California that do not have emission factors.

Approach: The table below summarizes the currently available harvest

emission factors. Other factors provided in out of publication EPA documents are not appropriate for California.

Harvest Emission Factor3 Agricultural Harvest



Cotton Total 3.4

Almond Almond Shaking 0.37 Almond Sweeping 3.7 Almond Pickup 36.7

Almond Total 40.8

Wheat Combining 5.8

Ten crops make up nearly 80% of the total non-pasture agricultural acreage in the San Joaquin Valley. Cotton, almonds, and wheat are about one-third of the total SJV acreage, so measured emission factors are available for a significant amount of acreage. The proposed approach is to use the available California emission factors and scale these factors to roughly represent the possible PM10 emissions from harvesting other crops. Because appropriate scientific information is not available, the scaling is purely subjective, based on rough guesses by regulatory and agricultural representatives. Using this approach with initial assumptions, approximately 73% of the harvest PM10 emissions are from the three crops with measured emission factors. If walnuts are included (using the almond emission factor) this value increases to 88% of the total emissions.

DRAFT – Internal Use 10/18/2002


Assumptions: All assumptions are a first estimate, provided to stimulate

discussion. The assumptions were somewhat arbitrarily, but were assigned to attempt to reflect the relative PM generation potential of various harvest practices. All assumptions are provided on the attached table, but a few examples include the following:

Crop Assumption EF (lbs PM10/acre) Cotton Cotton 3.4 Vine Cotton/20 0.17 Tomato Cotton/20 0.17 Fruit Trees Cotton/40 0.085 Corn Wheat/2 2.8 Sugar beets, onions, potatoes

Cotton/2

2.7

As shown, the assumed emission factors are scaled from existing emission factors. As other crop specific information becomes available, it will be substituted for the rough assumptions provided.

Next Steps: The assumptions in the attached table need to be refined as necessary. When these are complete, the harvest emissions will be calculated using this new information. Including the harvest emission estimates for crops without specific emission factors increased the emission estimates from about 11,000 tons/year to 15,000 tons per year, or about a 38% increase.

Contact: Patrick Gaffney, Air Resources Board

[email protected] (916) 322-7303

DRAFT October 18, 2002

Analysis of SJV Harvest Emissions Using Emission Factor Assumptions

SJV Crop Acreage Summary SJV Harvest Emission Factor Assumptions and Emissions

CDFA Crops (some consolidation performed)

Sum Of CDFA SJV Harvested

Acres

% non Pasture

total acreage

Running Total

Crop Harvest

Category*Harvest EF Category

Harvest EF Assumption

(lbs PM10/acre)

Emissions Estimate

(tons/year)% of

EmissionsRunning

TotalCOTTON UPLAND+PIMA+SEED+UNSPEC 925323 18% 18% Field Mech Cotton 3.4 1573.0 10.4% 10%HAY ALFALFA+HAY+GRAIN&SEED ALFALFA 716210 14% 32% Field Mech Zero 0 0.0 0.0% 10%GRAPES WINE+RASIN+WINE 585581 11% 43% Vine Cotton/20 0.17 49.8 0.3% 11%ALMONDS ALL 413069 8% 51% Tree Nut Almonds 40.8 8426.6 55.5% 66%CORN SILAGE+GRAIN CORN 411071 8% 59% Field Mech Wheat/2 2.8 575.5 3.8% 70%WHEAT ALL+SEED+BARLEY+BARLEY FEED 379120 7% 67% Field Mech Wheat 5.8 1099.4 7.2% 77%TOMATOES PROCESSING+FRESH 225353 4% 71% Field Mech Cotton/20 0.17 19.2 0.1% 77%SILAGE 220437 4% 75% Field Mech Wheat 5.8 639.3 4.2% 82%ORANGES NAVEL 121476 2% 78% Tree Fruit Cotton/40 0.085 5.2 0.0% 82%WALNUTS ENGLISH 111039 2% 80% Tree Nut Almonds 40.8 2265.2 14.9% 97%FIELD CROPS UNSPECIFIED 103942 2% 82% Field Mech Wheat/2 2.8 145.5 1.0% 98%PISTACHIOS 73856 1% 83% Tree Nut Almonds/10 4.1 151.4 1.0% 99%VEGETABLES UNSPECIFIED 68281 1% 85% Veg Mech Cotton/20 0.17 5.8 0.0% 99%SUGAR BEETS 54155 1% 86% Field Mech Cotton/2 1.7 46.0 0.3% 99%ORANGES VALENCIA 45010 1% 87% Tree Fruit Cotton/40 0.085 1.9 0.0% 99%MELONS CANTALOUPE 41546 1% 87% Field Mech Cotton/40 0.085 1.8 0.0% 99%BEANS DRY EDIBLE UNSPEC. 39499 1% 88% Vine Cotton/40 0.085 1.7 0.0% 99%PLUMS 37656 1% 89% Tree Fruit Cotton/40 0.085 1.6 0.0% 99%NECTARINES 32780 1% 90% Tree Fruit Cotton/40 0.085 1.4 0.0% 99%PEACHES FREESTONE 32736 1% 90% Tree Fruit Cotton/40 0.085 1.4 0.0% 99%ASPARAGUS UNSPECIFIED 26190 1% 91% Field Mech Zero 0 0.0 0.0% 99%ONIONS 25050 0% 91% Field Mech Cotton/2 1.7 21.3 0.1% 99%POTATOES IRISH ALL 23600 0% 92% Field Mech Cotton/2 1.7 20.1 0.1% 99%GARLIC ALL 22590 0% 92% Field Mech Cotton/2 1.7 19.2 0.1% 99%LETTUCE HEAD 20610 0% 92% Field Hand Cotton/40 0.085 0.9 0.0% 99%OLIVES 20445 0% 93% Tree Fruit Cotton/40 0.085 0.9 0.0% 99%RICE MILLING 18806 0% 93% Field Mech Cotton/20 0.17 1.6 0.0% 99%APPLES ALL 18448 0% 94% Tree Fruit Cotton/40 0.085 0.8 0.0% 99%CHERRIES SWEET 18360 0% 94% Tree Fruit Cotton/40 0.085 0.8 0.0% 99%SAFFLOWER 18203 0% 94% Field Mech Wheat 5.8 52.8 0.3% 100%PEACHES CLINGSTONE 17252 0% 95% Tree Fruit Cotton/40 0.085 0.7 0.0% 100%APRICOTS ALL 15319 0% 95% Tree Fruit Cotton/40 0.085 0.7 0.0% 100%PLUMS DRIED 14534 0% 95% Tree Fruit Cotton/40 0.085 0.6 0.0% 100%FRUITS & NUTS UNSPECIFIED 14470 0% 95% Tree Nut Cotton/40 0.085 0.6 0.0% 100%GRAPES UNSPECIFIED 13800 0% 96% Vine Cotton/20 0.17 1.2 0.0% 100%BEANS BLACKEYE (PEAS) 12500 0% 96% Vine Cotton/40 0.085 0.5 0.0% 100%FIGS DRIED 11987 0% 96% Tree Fruit Cotton/40 0.085 0.5 0.0% 100%FIELD CROPS SEED MISC. 11307 0% 96% Field Mech Cotton/2 1.7 9.6 0.1% 100%BROCCOLI UNSPECIFIED 11260 0% 97% Veg Hand Cotton/40 0.085 0.5 0.0% 100%CORN SWEET ALL 10816 0% 97% Field Mech Wheat/2 2.8 15.1 0.1% 100%POTATOES SWEET 10386 0% 97% Field Mech Cotton/2 1.7 8.8 0.1% 100%BEANS LIMA LG. DRY 9677 0% 97% Vine Cotton/40 0.085 0.4 0.0% 100%HAY SUDAN 9204 0% 97% Field Mech Zero 0 0.0 0.0% 100%


DRAFT October 18, 2002

BEANS LIMA UNSPECIFIED 9100 0% 98% Vine Cotton/40 0.085 0.4 0.0% 100%MELONS WATERMELON 7907 0% 98% Field Hand Cotton/40 0.085 0.3 0.0% 100%LEMONS ALL 7892 0% 98% Tree Fruit Cotton/40 0.085 0.3 0.0% 100%BEANS FRESH UNSPECIFIED 7740 0% 98% Vine Cotton/40 0.085 0.3 0.0% 100%PEPPERS BELL 6836 0% 98% Veg Hand Cotton/40 0.085 0.3 0.0% 100%SEED VEG & VINECROP 5227 0% 98% Veg Hand Cotton/40 0.085 0.2 0.0% 100%MELONS UNSPECIFIED 4920 0% 98% Field Mech Cotton/20 0.17 0.4 0.0% 100%BEANS KIDNEY RED 4900 0% 99% Vine Cotton/40 0.085 0.2 0.0% 100%LETTUCE LEAF 4800 0% 99% Veg Hand Cotton/40 0.085 0.2 0.0% 100%PUMPKINS 4550 0% 99% Field Mech Cotton/20 0.17 0.4 0.0% 100%MELONS HONEYDEW 4460 0% 99% Field Mech Cotton/20 0.17 0.4 0.0% 100%POMEGRANATES 4205 0% 99% Tree Fruit Cotton/40 0.085 0.2 0.0% 100%BEANS LIMA BABY DRY 4190 0% 99% Vine Cotton/40 0.085 0.2 0.0% 100%ORANGES UNSPECIFIED 3830 0% 99% Tree Fruit Cotton/40 0.085 0.2 0.0% 100%PEAS GREEN PROCESSING 3157 0% 99% Vine Cotton/40 0.085 0.1 0.0% 100%GRAPEFRUIT ALL 3035 0% 99% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%SPINACH UNSPECIFIED 2870 0% 99% Veg Hand Cotton/40 0.085 0.1 0.0% 100%BEANS SEED 2758 0% 99% Vine Cotton/40 0.085 0.1 0.0% 100%OATS GRAIN 2727 0% 99% Field Mech Zero 0 0.0 0.0% 100%TANGERINES & MANDARINS 2604 0% 99% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%CUCUMBERS 2601 0% 99% Veg Hand Cotton/40 0.085 0.1 0.0% 100%KIWIFRUIT 2552 0% 99% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%PEARS UNSPECIFIED 2371 0% 99% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%BEANS LIMA GREEN 2309 0% 100% Vine Cotton/40 0.085 0.1 0.0% 100%CAULIFLOWER UNSPECIFIED 2290 0% 100% Veg Hand Cotton/40 0.085 0.1 0.0% 100%SEED OTHER (NO FLOWERS) 2080 0% 100% Veg Hand Cotton/40 0.085 0.1 0.0% 100%PEACHES UNSPECIFIED 1622 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%TANGELOS 1470 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%NURSERY TURF 1370 0% 100% Field Mech Zero 0 0.0 0.0% 100%BROCCOLI FRESH MARKET 1356 0% 100% Veg Hand Cotton/40 0.085 0.1 0.0% 100%VEGETABLES ORIENTAL ALL 1350 0% 100% Veg Hand Cotton/40 0.085 0.1 0.0% 100%PERSIMMONS 1303 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%CITRUS UNSPECIFIED 1205 0% 100% Tree Fruit Cotton/40 0.085 0.1 0.0% 100%LETTUCE BULK SALAD PRODS. 1170 0% 100% Veg Hand Cotton/40 0.085 0.0 0.0% 100%BERRIES STRAWBERRIES UNSPEC 1047 0% 100% Fruit Hand Cotton/40 0.085 0.0 0.0% 100%SQUASH 1040 0% 100% Field Mech Cotton/20 0.17 0.1 0.0% 100%BEANS SNAP FRESH MARKET 1036 0% 100% Vine Cotton/40 0.085 0.0 0.0% 100%POTATOES SEED 1035 0% 100% Veg Mech Cotton/2 1.7 0.9 0.0% 100%EGGPLANT ALL 910 0% 100% Veg Hand Cotton/40 0.085 0.0 0.0% 100%PECANS 898 0% 100% Tree Nut Cotton/40 0.085 0.0 0.0% 100%BROCCOLI PROCESSING 766 0% 100% Veg Mech Cotton/40 0.085 0.0 0.0% 100%AVOCADOS ALL 646 0% 100% Tree Fruit Cotton/40 0.085 0.0 0.0% 100%CAULIFLOWER FRESH MARKET 373 0% 100% Veg Hand Cotton/40 0.085 0.0 0.0% 100%BEANS GARBANZO 322 0% 100% Vine Cotton/40 0.085 0.0 0.0% 100%QUINCE 213 0% 100% Tree Fruit Cotton/40 0.085 0.0 0.0% 100%SORGHUM GRAIN 211 0% 100% Field Mech Wheat 5.8 0.6 0.0% 100%TOMATOES CHERRY 170 0% 100% Veg Hand Cotton/40 0.085 0.0 0.0% 100%

5144378 1 15177 100.0%Total Acres Total PM10

*Mech = Mechanically Harvested Hand = Hand Harvested


Winston H. Hickox Agency Secretary

California Environmental Protection Agency

Printed on Recycled Paper

Air Resources Board Alan C. Lloyd, Ph.D.

Chairman 1001 I Street • P.O. Box 2815 • Sacramento, California 95812 • www.arb.ca.gov

Gray DavisGovernor

MEMORANDUM

TO: SJV Ag Tech Group FROM: Patrick Gaffney DATE: September 19, 2002 SUBJECT: Update of Agricultural PM10 Emissions for Harvest, Land Preparation,

and Confined Cattle Operations We have completed preliminary SJV estimates of PM10 emissions from agricultural land preparation and harvest activities based on the most current emission factors (UCD) and emission factor assignments determined by a subgroup of the SJV Ag Tech group. In summary, using the new data, land preparation PM10 emission estimates change from about 34,000 tons/year to 9,000 tons/year (a 74% reduction from current values). However, if land maintenance is added, which assumes land leveling every 4 years (more on this later), the updated land preparation PM10 emissions are approximately 14,000 tons/year (a 59% overall reduction). Harvest PM10 emission estimates change from 7,600 tons/year to 12,400 tons/year (a 64% increase). Figure 1 illustrates these emission changes for the San Joaquin Valley. The net change in the agricultural field operations emission estimates is - 49% if land maintenance is not included, and - 37% if land maintenance is included in the estimates. Figure 2 shows the contributions of land preparation and harvest emissions for each county for both 1993 and 2000. There are two major issues remaining that need to be resolved:

1) Should the land maintenance emissions be included? For the 1997 SIP, based on grower comments we assumed that all field crop acreage (non-orchard, non-vine) was leveled once every 4 years. This activity is not included in the crop calendars. Because the land leveling emission factor is fairly large (12.5 lbs PM10/acre-pass), it does bump up the emission significantly (from 9,000 to 14,000 tons PM10/year). If used, it will also be necessary to determine how to temporally apportion the land maintenance emissions.

2) There is some concern that harvest emissions are only included for almonds, cotton, and wheat, because these are the only crops with harvest emission factors. Harvest emissions are not included for any other crops. However, for land preparation, we assign existing emission factors to similar activities to

2

perform a complete emission estimate. Should we make similar assignments for some of the large acreage crops for harvesting?

For your viewing pleasure, I have also added some additional graphics if you want to see what crops are the largest contributors (the Figure 3 series) and the detailed differences in the old versus new emission estimates (the Figure 4 series). Moving on to cattle, for dairy PM10 emissions we selected an emission factor of 5 lbs PM10/1000 head/day. This factor is based on extensive testing at a single dairy in Texas. It provides a decent ‘gut-feeling’ number when compared with the UCD feedlot number of 29 lbs PM10/1000 head/day, but several people have commented that the 5-pound number seems quite low, so let’s discuss this. Background documents describe many of the assumptions used to develop the data described in this memo. The documents are:

Land Prep & Harvest EF Issues 8_2002.doc Land Prep & Harvest EF Selection 9_2002.doc Beef & Dairy PM Estimate.doc Beef & Dairy PM EF Selection.doc

The documents are available either from me at [email protected], 916-322-7303, or through Patia with the district.

Thanks for looking this over and it will be good to hear your comments at the Ag Tech meeting on the 23rd.

3

Figure 1. 1993 Versus Updated 2000 PM10 Land Preparation and Harvest Emissions Estimates.

1993 vs 2000 Land Preparation and HarvestPM10 Emission Estimates

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Includes land leveling every 4 years for f ield crops. 59% reduction from 1993

74% reduction from 1993

64% increase from 1993

Figure 2. 1993 Versus Updated 2000 PM10 County Land Preparation and Harvest Emissions Estimates.

1993 vs 2000 Land Preparation and Harvest PM10 Emission Estimates

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DRAFT Internal Review Only Figures 3a, 3b, 3c. Monthly Land Preparation and Harvest PM10 Emissions

APR MAY JUN JUL AUG SEP OCT NOV DEC0 0 0 0 0 0 381 381 3810 0 0 0 0 0 0 628 6280 0 0 0 0 0 9 9 90 0 0 0 0 0 0 239 2390 0 0 0 0 0 0 1,187 1,1870 0 0 0 0 0 0 0 00 0 0 0 0 1 1 1 00 0 0 0 13 13 13 13 05 28 28 23 100 23 45 0 0

17 0 0 0 0 0 13 17 014 62 62 49 49 49 109 0 00 0 0 747 0 0 747 0 00 0 0 0 0 0 0 43 430 0 0 0 82 82 82 82 82

2,794 2,794 0 0 0 0 59 59 590 0 0 0 0 0 0 51 51

160 160 160 160 160 160 160 160 1604 4 4 4 4 4 4 4 40 0 0 0 0 0 361 361 361

90 90 90 90 90 90 90 90 900 0 0 0 0 0 0 281 281

APR MAY JUN JUL AUG SEP OCT NOV DEC0 0 0 0 0 7,391 7,391 0 0 14,7820 0 0 0 0 0 777 777 0 1,5530 0 838 838 0 0 0 0 0 1,675

Monthly PM10 Emissions from Land Preparation and Harvesting

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JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

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thWheat (harvest)Cotton (harvest)Almonds (harvest)WheatVegetablesTomatoesSunflowerSugar BeetsSafflowerRiceOnionsMelonLettuceGrapes-WineGrapes-TableGrapes-RaisinGarlicGarbanzoDryBeansCottonCornCitrusAlmonds

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California Air Resources Board DRAFT

DRAFT Internal Review OnlyFigures 4a, 4b, 4c. Breakdown of Current and Revised PM10 Emissions for Land Preparation and Harvest.

Updated and 1993 CEIDARS Land Prep PM10 Emissions

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California Air Resources Board DRAFT

12 July 2002 Evaluation of ARB application of UCD emission factors

Emission factors obtained from the UCD Interim Report to USDA, 2001, were assigned to harvest and land preparation operations by ARB in a draft document dated 6/24/02. These assignments raised several questions about the validity of specific experimental data and the applicability of those data to the categories listed. In this document we address the following questions:

• In the reported almond harvesting emission factors, the shaking and sweeping emissions are larger than expected relative to the pickup. Also, shaking emissions are larger than expected relative to sweeping. What are the circumstances surrounding the collection of these data that may have caused this unexpected result?

• In the reported land preparation emission factors, the chiseling emissions are larger than expected relative to discing. What are the circumstances causing this result?

• In the assignment of land preparation emission factors to the land preparation operations enumerated in the 1996 crop calendar, there is a need for an emission factor for land planning. Is there data to provide a factor for the land plane?

Almond harvest operations were monitored in 1994 and in 1995. All emission factors released in 1995 (“Evaluation of Fugitive PM10 Emissions from Cotton Production, Annual Report, 1995” as cited in the draft emission factor assignments) were computed from concentrations measured at a single height in a simple box model using a plume height estimated by visual observation to be 4 meters. These data are presented in Table 5.1 and figure 5.5 in the interim report. Data collected in 1995 included concentrations measured at multiple heights. These data were used to calculate emission factors using 3 models (Table 5.2 in the Interim report, reproduced here as Table 1).

Log Integration Block Integration Simple Box Model

Almond Shaking 1670±1303 (4) 1647±1074 (4) 823±558 (4)

Almond Sweeping 1466±1042 (2) 1935±1403 (2) 1087±631 (2)

Almond Nut Pickup 4467±5830 (7) 3233±1956 (7) 1201±647 (8)Table 1: 1995 Almond harvest emission factors for all valid tests. When the Interim report was written, emission factors were recalculated from these data using the specific methods defined in the peer reviewed publication (Holmen et al., 2000). The tests of almond pickup operation were screened to find only those conducted outside the canopy, as tests performed within the canopy were judged to overestimate the emission from the orchard as a unit. Those emission factors are presented in Table 5.3 in the Interim report and were averaged for inclusion to Table 6.1 in the Interim report. That average emission factor for almond nut pickup is the “new” factor in the draft ARB

emission factor assignment, 4106.19 mg/m2. It can be seen that this factor is very similar to the factor initially calculated using the log integration method for all valid tests (Table 1). Evaluation of the very small number of valid shaking and sweeping tests indicated that a recalculated average emission factor for these operations would be difficult to validate, so the previously reported emission factors (Table 1) were judged to be the best available and those were included in Table 6.1 of the Interim report and thus to the draft ARB emission factor assignments. Subsequent examination of the relevant almond shaking and sweeping tests yielded the following observations (Table 2):

Table 2: Inventory of almond shaking and sweeping tests from Appendix B of the Interim report and emission factors were computable. Most of the shaking and sweeping tests included in the average emission factors reported were conducted within the canopy. Thus, those data are judged to be non-representative of emissions from the orchard as a unit. A new average using only tests conducted within the canopy for shaking will give an emission factor of 412.5 mg/m2 or about 0.25 lbs/acre. Unfortunately, there were no valid sweeping tests outside the canopy. Of the two valid tests, one was conducted without the blowers on. We cannot recommend a PM10 emission factor for almond sweeping operations from the data currently available. Chiseling, also referred to as ripping in our database, was monitored following the wheat harvest in June and July. The emission factor in the Interim report (Table 6.1, Figure 6.7) for chiseling represents the driest possible soil conditions (about 2%) and should be considered a worst-case scenario. In photographs and field observation we judge the entrainment of soil by the tracks of the Caterpillar tractor used in this operation to be sufficient to cause the observed emission factor. The emission factor measured during

Shaking teststest # E.F. (mg/m2) comment95-038 only 9 m sample valid95-039 only 9 m sample valid95-048 only 1 and 3 m samples valid95-049 increasing concentration with height, plume not adequately defined95-050 wind direction inappropriate95-066 only 1 and 3 m samples valid95-067 360 valid test95-068 465 valid test95-069 1268 in canopy95-070 912 in canopy95-071 no valid downwind data95-072 2922 in canopy95-073 only 9 m sample valid95-074 wind direction inappropriate

Sweeping tests95-051 only 1 and 3 m samples valid95-052 only 1 and 3 m samples valid95-057 2324 in canopy95-058 598 in canopy, sweeper blowers not on95-059 wind direction inappropriate

one of the tests (97-050) when the relative humidity was above 40% indicates that emissions for this operation could be substantially lower when chiseling is conducted later in the year, as when following a cotton crop. Both land planning and floating were monitored in 1999. Emission factors computed from those data were not included in the Interim report, but Krystyna Trzepla-Nabaglo and Dr. Flocchini presented some of the data in a paper last month and the results are available now. The land planning tests were conducted in September on two fields, one following garbonzos and one following tomatoes. Floating was monitored on a third field in June and July following wheat harvest. The averaged result of these tests is presented in Table 3.

Table 3: Summary of land planning and floating tests, PM10 emission factors computed as kg/km2 (equal to mg/m2). These data indicate that, for land planning and floating operations conducted in the summer months, there is not a significant difference between emissions from the two operations. Therefore, we suggest a composite average PM10 emission factor for both operations of 1397 mg/m2. Since these operations were monitored exclusively in the summer season, it is reasonable to expect a significantly lower emission factor for colder, wetter conditions. Additional measurements will be necessary to quantify such an effect. We are currently restricted by the available data to describing this “worst case” condition. Please address any questions to Teresa Cassel email [email protected], phone 530/218-5690.

COMMODITY PRACTICE OPERATION # OF TESTS AVE. EF(KG/KM2) std dev (mg/m2) SEASONGarbonzo Land Preparation Land Planing 7 1703.77 1041.96 Sep.Tomatoes Land Preparation Land Planing 7 1228.60 1317.59 SeptemberWheat Land Preparation Floating 9 1257.62 1166.62 June/July

DRAFT 5/14/2002

Harvesting Emission Factors (UCD studies) Questions: 1. Which set of emission factors should be used? 2. Should we assign emission factor to other crops? How?

Old1 New2 Agricultural Operation lbs/acre lbs/acre mg/m2

Cotton Cotton Picking 0.42 1.7 189.8 Cotton Stalk Cutting 0.7 1.7 188.17

Cotton Total 1.12 3.4

Almond Almond Shaking 0.13 14.9 1670 Almond Sweeping 1.76 13.1 1466 1st Almond Pickup 12.83 2nd Almond Pickup 19.48 36.7 4106.19

Almond Total 34.2 64.7

Wheat Combining 5.8 649.85

Old emission factors were based on 1994 field samples, most of which were made at three meters above ground. New emission factors were the results of 1995, 1996, and 1998 field tests, which were collected at three different heights of each sample location. Land Preparation Emission Factors (UCD Studies)

Old3 New2 cotton, wheat lbs/acre-pass lbs/acre-pass mg/m2 Root cutting 0.3 33 Discing & discing

Discing 1.2 134.24Chiseling 4.6 512.2 Average 2.9

Weeding

≈4.5 (k•4.8•s0.6)

0.8 89.21 1 UCD 1995, “Evaluation of Fugitive PM10 Emissions from Cotton Production, Annual Report, 1995” 2 UCD 2001, “Interim Report: Sources and sinks of PM10 in the San Joaquin Valley”. 3 EPA AP-42 emission factor, currently used by ARB.

DRAFT 5/14/2002

Draft of assigning emission factors to land preparation operations • All the land preparation operations are summarized into four preliminary

categories: Land Plane, Disc, Chisel, and Land Preparation (general). • Discing and Chiseling have available emission factors in the UCD report. • Assuming the process of land plane is similar to chiseling, we assign 4.6

lbs/acre-pass to the land plane. • Land Preparation (general) takes the average emission factor. Land Preparation (from Crop Calendar, 1996)

Preliminary category

Preliminary Emission Factor (lbs/acre-pass)

Chisel Chisel 4.6 Plow Chisel 4.6 Spring Tooth Chisel 4.6 Subsoil Chisel 4.6 Subsoil-deep chisel Chisel 4.6 Disc Disc 1.2 Disc & Furrow-out Disc 1.2 Disc & Roll Disc 1.2 Finish Disc Disc 1.2 Float Disc 1.2 Harrow Disc Disc 1.2 Post Burn/Harvest Disc Disc 1.2 Stubble Disc Disc 1.2 3 Wheel Plane Land plane 4.6 Bed Preparation Land plane 4.6 Land Plane Land plane 4.6 Laser Level Land plane 4.6 Level Land plane 4.6 Level (new vineyard) Land plane 4.6 Plane Land plane 4.6 Blank Land Preparation 2.9 Land Preparation Land Preparation 2.9 List Land Preparation 2.9 List & Fertilize Land Preparation 2.9 Listing Land Preparation 2.9 Mulch Beds Land Preparation 2.9 Roll Land Preparation 2.9 Seed Bed Preparation Land Preparation 2.9 Shape Beds Land Preparation 2.9 Shape Beds & Roll Land Preparation 2.9 Shaping Land Preparation 2.9 Sulfur Dusting Land Preparation 2.9 Terrace Land Preparation 2.9

DRAFT 5/14/2002

Cultivation Weeding? Should we attempt to include cultivation emissions? Would probably use the UCD ‘Weeding’ emission factor if the answer is yes. Crop Calendars Crop calendars should to be revised to reflect the most current practices. Prepared for SJV Ag Tech group comment by: Hong Yu California Air Resources Board 916-3234887 [email protected]

Survey of On-Farm Travel Activity General Information Q1 In terms of acreage, what is the principal crop grown on the farm?

1. Cereal Crops (Rice, Wheat, BarelyBarley) 2. Vegetables (Tomatoes, Onions, Garlic, Lettuce) 3. General Row Crops (Field Corn, Lettuce, Cotton) 4. Grapes 5. Citrus Fruit (Orange, Lemon, Grapefruit, Lime, Tangerine) 6. Deciduous Fruit (Apples, Peaches, Apricots, Nectarines) 7. Walnuts 8. Almonds 9. Other – (please specify) 10. Don’t Know 11. Refuse to Participate

Q2Q3 What County is your farm located in? Q3 Q2 For the crop selected above, Wwhat is the acreage of your

average acre size perlargest farm? I tried to get them to not go with averages at our meeting because it creates

too much vagueness. In the STI questions, they had the farmer answer based on just their largest farm (Question Q1 in their report). I think this is a better approach.

0 – 40 40 – 80 80 – 160 160 – 320 320 – 640

over 640 (please specify) Q5 For the farm above, hHow many miles do you travel on unpaved

roads per farm (I think the ‘per farm’ creates too much ambiguity) on the farm per year?

0 – 20 20 – 40 40 – 80 over 80 (please specify)

2

Q6 What percent of annual yearly unpaved road farm travel occurs during harvest season? (may be unclear what we mean by ‘the peak’; my wording assumes harvest is peak, is that correct?)unpaved miles of travel are at the peak? Please split your answer between the following vehicle categories. If for example if 1030% of your unpaved road travel on the farm occurs during harvest, at the peak you would split that value between the two categories, with the total equaling 30%. them.

Licensed vehicles ____ Implements of Husbandry ____

3

Survey of Farm Travel Activity (cont.) Q7 How many total days are included in your answer to Question 6? Q8 What is the maximum number of trips made on your peak harvest

day? Think of a trip as entering or leaving a field.

Licensed vehicles ____ Implements of Husbandry ____


Beef & Dairy PM EF Selection12_2002.doc 1

MEMORANDUM

TO: SJV PM10 SIP Emission Inventory Group FROM: Patrick Gaffney, ARB DATE: December 3, 2002 SUBJECT: Selection of PM10 Emission Factors

for Feedlot and Dairy Operations Objectives: Identify the most appropriate emission factors for estimating

the PM10 component of fugitive dust emissions from cattle feedlot and dairy operations. These factors will replace the generic and undocumented emission factors used in past emission estimates that only included feedlot PM10 emissions. Due to lack of information, PM10 estimates are not being performed for other livestock at this time.

Approach: In a previous ARB technical summary1, cattle feedlot and

dairy emission factors were discussed, and emissions calculated. This initial summary was used as a basis for a discussion with regulators, researchers, and industry representatives2 to help identify the most appropriate PM emission factors for feedlot and dairy cattle.

Data: Table 1 summarizes the available emission factor candidates

for beef and dairy operation PM10 fugitive dust emissions. Table 1. Feedlot and Dairy Emission Factors

Category Emission Factor (lbs PM10/1000 head/day) Source

Feedlot 135 EPA3, 1985 Feedlot 29 UCD4, 2001 Feedlot 19 Texas A&M5, 2002 Dairy 90 UCD4, 2001 Dairy 4.4 Texas A&M5, 2002

Discussion: It was determined the EPA feedlot PM10 estimate did not

include adequate testing or documentation to use for feedlot emission estimates. The UCD feedlot emission estimate was based on a reasonable number of samples and accepted testing methods were used. The Texas A&M PM10 feedlot estimate is provided for comparison, but the original Texas A&M test report for feedlots has not been evaluated. For dairies, the UCD PM10 estimate was based on limited

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and non-representative sampling, and was performed primarily for method development, so it cannot be used for emission estimates. The Texas A&M dairy PM10 estimate is based on 12 separate tests using comprehensive sampling and analysis methods. In selecting emission factors, is important to recognize that the selections are flexible. As better information becomes available, it will be used to update emission estimates. The selections below are based on current “best available science”. Feedlot PM Emission Factor Selection The ARB will use the average value of 29 lbs PM10/1000 head/day for feedlots. This value is based on the work performed by UC Davis researchers4 and is assumed to be more representative of California conditions than the Texas A&M emission factor. Dairy PM Emission Factor Selection The Texas A&M dairy emission factor5 of 4.4 lbs PM10/1000 head/day was selected as the base dairy emission factor. To make the Texas emission factor more California specific, it was multiplied by a scaling factor. The scaling factor was based on the ratio of the California feedlot emission factor to the Texas feedlot emission factor. This ratio is 29:19, which converts to a multiplier of 1.53 and produces a dairy emission factor of 6.72 lbs PM10/1000 head/day for California dairies. Additional Assumptions Feedlot population estimates were based on information from California Department of Food and Agriculture (CDFA), which was correlated with data provided by the California Cattlemen’s Association. In computing population for the San Joaquin Valley Air Basin portion of Kern county, it was estimated that 53% of the county feedlot population is in the SJV Air Basin. Dairy population estimates were based on information from CDFA. The calf and heifer populations were not included in the PM10 emission estimate, which excludes about 40% of the overall dairy cattle population. Calves and heifers were excluded because they may not be located at dairies and their handling and behavior is different than the producing animals. This assumption may warrant further discussion. For making the Kern county air basin split, it was estimated that 70% of the Kern county dairy population is in the SJV Air

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Basin. Emissions were computed as PM10, and then converted to Total Suspended Particulate (TSP) for entry into the ARB’s emissions database. For this conversion, PM10 = TSP * 0.4818.

Results and Conclusions:

The emission factor of 29 lbs PM10/1000 head/day was selected to estimate the fugitive dust emissions for California feedlots. An emission factor of 6.73 lbs PM10/1000 head/day was selected to estimate the fugitive dust emissions for California dairies. The resulting emission estimates are shown in Table 2. Although far from perfect, these values are based on more recent and more comprehensive testing any previous estimates. Of course, as additional information becomes available, it will be used to update the emission estimates. Table 2. Fugitive PM10 Emissions for Dairies and Feedlots in the San Joaquin Valley Air Basin (tons per year) Dairy Cows Feedlot Cattle Head, 1999* PM10 (tpy) Head, 1999 PM10 (tpy) Fresno 86,150 105.1 95,524 503.3 Kern 39,227 47.9 15,046 79.3 Kings 124,890 152.4 0 0.0 Madera 36,027 44.0 16,312 85.9 Merced 191,005 233.1 0 0.0 San Joaquin 93,044 113.5 0 0.0 Stanislaus 152,344 185.9 59,309 312.5 Tulare 333,941 407.5 56,516 297.8 Entire SJV 1,056,628 1,289.2 242,707 1,278.8

* Does not include dairy calves or heifers

References: 1Initial Scoping Estimates of PM10 Emissions from Beef and Dairy

Operations, P. Gaffney, California Air Resources Board, 8/13/2002 2Teleconference 8/29/2002. Dave Jones (SJVUAPCD), Patia Siong (SJVUAPCD), Stephen Shaw (SJVUAPCD), Terry Cassel (UCD), Paul Martin (Western United Dairymen), Roger Isom (California Cotton Ginners and Growers Association), Cynthia Corey (California Farm Bureau), Gene Beach (Almond Hullers and Processors Association), George Bluhm (CDFA), Matt Summers (CDFA), Patrick Gaffney (ARB). Also, Frank Mitloehner in separate conversation with P. Gaffney. Further discussion also occurred at the 10/28/2002 meeting of the SJV Agricultural Technical Advisory Group and informally at some

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meetings of the SJV Ag Tech Dairy Subgroup. 3Compilation of Air Pollutant Emission Factors, Volume 1, Fourth Edition, AP-42. U.S. EPA, September 1985. Section 6.15 Beef Cattle Feedlots, Table 6.15-1. 4Sources and Sinks of PM10 in the San Joaquin Valley, Interim Report. Flocchini, R.G., James, T.A., et. al., August 10, 2001. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1. 5Preliminary PM10 Emission Factor for Freestall Dairies, Goodrich, L.B., Parnell, C.B., Mukhtar, S., Lacey, R.E., Shaw, B.W., Department of Biological and Agricultural Engineering, Texas A&M University, Paper presented to the 2002 ASAE Annual International Meeting, July 2002

Contact: Patrick Gaffney, Air Resources Board [email protected] (916) 322-7303 November 13, 2002



Selection of PM10 Emission Factors for Feedlot and Dairy Operations Objectives: Identify the most appropriate emission factors for estimating

the PM10 component of fugitive dust emissions from cattle feedlot and dairy operations.

Approach: In a previous ARB technical summary1, cattle feedlot and

dairy emission factors were discussed, and emissions calculated. This initial summary was used as a basis for a discussion with regulators, researchers, and industry representatives2 to help identify the most appropriate PM emission factors for feedlot and dairy cattle.

Data: Only the following three emission factors were included in the

initial ARB technical summary.

Category Emission Factor (lbs PM10/1000 head/day) Source

Feedlot 135 EPA3, 1985 Dairy 90 UCD4, 2001 Feedlot 29 UCD4, 2001

Discussion: It was readily determined that none of the provided emission

factors were satisfactory. The EPA feedlot value is considered very rough and is based on a report from 1977. The researchers from UCD are opposed to using their emissions rate data for emission estimates due to the limited nature of the testing. In selecting emission factors, is important to recognize that the selections are flexible, and as better information becomes available, it will be used to update emission estimates. The selections below are based on current ‘best available science’. Dairy PM Emissions George Bluhm (CDFA), in conversations with Texas A&M University, discovered that researchers there are completing a dairy PM sampling study in which 220 samples were taken at a 2000 head freestall dairy with lagoons. Some additional analysis is needed to finalize an emissions rate, but the initial estimate shows 5 lbs PM10/1000 head/day, which is substantially lower than the other available dairy emission factor (i.e., 90 from UCD).



Because of the robustness of the testing and similarities between Texas and California dairy operations, it was agreed by the group to initially use the value of 5 lbs for the dairy PM10 emission estimates. However, in a subsequent conversation with Frank Mitloehner (UCD), he suggested that this number seems to be rather low. At present, the group agreed that the available Texas A&M emission factor be used for the initial dairy PM10 emission estimates for the SIP. George has agreed to work with Texas A&M to get the necessary information. There are two important ramifications to this decision: 1) If the low emission factor is used, any controls applied will have a lower emission reduction benefit; 2) Based on Frank’s comments, it is possible that additional testing will show higher emissions, requiring an increase in future emission inventory estimates. Feedlot PM Emissions George has also agreed to check with Texas regarding information they may have on feedlot PM emissions. Therefore, a decision was not yet made regarding emission factor use. As a placeholder, the ARB will use the UCD value of 29 lbs PM10/1000 head/day. This will be used for initial scoping estimates while additional information is being obtained.

Conclusions: At this time, a PM10 emission factor of approximately

5 lbs PM10/1000 head/day will be used for dairy operations. This is based on 220 dairy tests performed at Texas A&M. For feedlots, a value of 29 lbs PM10/1000 head/day will be used, which is based on 26 tests by UCD. Although far from perfect, these values are based on more recent and more comprehensive testing than the current EPA default feedlot emission factor of 135. Of course, as additional information becomes available, it will be used to update the emission estimates. An important next step in incorporating these emission factors into the PM10 SIP will be to discuss them with EPA and environmental groups.

References: 1Initial Scoping Estimates of PM10 Emissions from Beef and Dairy

Operations, P. Gaffney, California Air Resources Board, 8/13/2002



2Teleconference 8/29/2002. Dave Jones (SJVUAPCD), Patia Siong (SJVUAPCD), Stephen Shaw (SJVUAPCD), Terry Cassel (UCD), Paul Martin (Western United Dairymen), Roger Isom (California Cotton Ginners and Growers Association), Cynthia Corey (California Farm Bureau), Gene Beach (Almond Hullers and Processors Association), George Bluhm (CDFA), Matt Summers (CDFA), Patrick Gaffney (ARB). Also, Frank Mitloehner in separate conversation with P. Gaffney. 3Compilation of Air Pollutant Emission Factors, Volume 1, Fourth Edition, AP-42. U.S. EPA, September 1985. Section 6.15 Beef Cattle Feedlots, Table 6.15-1. 4Sources and Sinks of PM10 in the San Joaquin Valley, Interim Report. Flocchini, R.G., James, T.A., et. al., August 10, 2002. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1.

Contact: Patrick Gaffney, Air Resources Board [email protected] (916) 322-7303 August 30, 2002



Initial Scoping Estimates of PM10 Emissions from Beef and Dairy Operations Objectives: Perform a rough estimate of the PM10 emissions from beef

and dairy operations in the SJV. Compare estimates using various available emission factors. The results will only be used to provide an approximation of the potential significance of the emissions.

Method and Data:

The method consists of multiplying the number of animals by available emission factors. To expedite the calculations, existing ARB beef and dairy cattle populations computed for 1996 were used. Emission factors used are the historical feedlot emission factor from EPA1, and recent estimates by UC Davis2. In addition, various scenarios were evaluated in which emission factors were reduced in attempts to more realistically represent dairy emissions.

Results: Table 1 shows the estimated emissions first using the EPA

emission factor, which is 135 lbs PM10 per 1000 head per day. As a ‘what-if’ scenario, emissions were also computed assuming that dairy emissions are 50% of the livestock emissions. Using the UC Davis emission factors, which are 90 lbs PM10/1000 head/day for dairies, and 29 lbs PM10/1000 head/day for feedlots, three emission scenarios were run. The first scenario uses the emission factors as published, the second uses the beef emission factor for all animals, and the third scenario uses one-half of the beef emission factor for dairies, while using the beef factor for beef animals. Table 1. Beef and Dairy PM10 Rough Estimates

Emissions

(tons PM10/day) Beef Dairy Total SJV 1996 Population 453,527 1,783,959 2,237,486 EPA EF Emissions 30.6 120.3 151 EPA EF w/50% dairy 30.6 60.2 91 UCD EF Emissions 6.5 80.7 87 UCD, Beef EF for all 6.5 25.8 32 UCD, Beef EF w/50% dairy 6.5 12.9 19



For comparison, Table 2 shows the emissions from various

sources included in the 2001 emissions inventory. Even based on the lowest emission estimates for beef and dairy, the emissions are large enough to warrant additional analysis and refinement of the estimates. Table 2. PM10 Annual Emission Estimates for 2001

Comparison Emissions PM10 (tons/day) Beef & Dairy (very preliminary) 19 - 91 Fireplaces & Woodstoves 12 Land Preparation & Harvest 111 Construction 66 Paved Road Dust 115 Unpaved Road Dust 51 Prescribed & Ag Burning 40 All Mobile Sources 16 All Stationary Sources 27 Total All Sources 167

Conclusions: Based on this rough approximation of PM10 emissions from

dairies and feedlots in the San Joaquin Valley, it appears that further refinement of these emissions estimates are warranted. The most important information that is needed is credible and representative emission factor information. The EPA feedlot emission factor is based on limited testing performed in 1977. The UC Davis emission factors are based on testing performed in the San Joaquin Valley, but it is counterintuitive that the measured feedlot emissions are substantially smaller than the dairy emissions. This possible discrepancy needs to be resolved In addition, other sources of emission factors may be available which should be investigated, such as from work performed at Texas A&M University.

References: 1Compilation of Air Pollutant Emission Factors, Volume 1, Fourth

Edition, AP-42. U.S. EPA, September 1985. Section 6.15 Beef Cattle Feedlots, Table 6.15-1. 2Sources and Sinks of PM10 in the San Joaquin Valley, Interim Report. Flocchini, R.G., James, T.A., et. al., August 10, 2002. Air Quality Group, Crocker Nuclear Laboratory, University of California, Davis. Table 6.1.


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SECTION 7.5 AGRICULTURAL HARVEST OPERATIONS

(Revised January 2003)

METHODS AND SOURCES The activities used to harvest agricultural commodities entrain soil and plant material into the air. These emissions may simply be due to the vehicles traveling over the soil, or via the mechanical processing of the plant material and underlying soil, or, as in the case of almonds, via the actual blowing or sweeping of the crop to remove waste materials and position it for pickup. Although at the time of this update, harvest particulate matter emission factors measured in California are only available for cotton, almonds, and wheat, all other crops are assigned emission factor by scaling from these measured emission factors. The attached Table 1 shows the total particulate matter and PM10 fraction of the harvest emissions estimates for these crops. As additional measured harvest emission factors for more crops are available, they will be incorporated into this methodology. Particulate emissions from harvest operations are computed by multiplying an emission factor by an activity factor. Agricultural harvest particulate dust emissions are estimated for all crops in each county in California using the following equation:

Emissionscrop = Emission Factorcrop x Acres Harvestedcrop

The individual crop emissions for each county are summed to produce the county and statewide total particulate matter and PM10 fraction of the harvest emission estimates. For harvesting, the emission factors are based on measurements performed by UC Davis 1, and harvested acreage is based on 2000 summary data from the California Department of Food and Agriculture (CDFA) 2. The remainder of this section discusses the emission factors and acreage in more detail. Emission Factor. The emission factors used to estimate the PM10 dust emissions from agricultural harvesting are from a study performed by UC Davis 1 under contract to the USDA and their subsequent supplementary data analysis 3. PM10 emissions were measured during 1994 to 1998 harvest operations. The emission factors are shown below in Table A. Using the available emission factors as a baseline, harvest emission factors were approximated for other California crops in consultation with agricultural experts.

EMISSION INVENTORY SOURCE CATEGORY Miscellaneous Processes / Farming Operations EMISSION INVENTORY CODES (CES CODES) AND DESCRIPTION 620-615-5400-0000 (83980) Agricultural Harvest Operations

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Table A. California Harvest Emission Factors Agricultural Harvest Operation

Emission Factor (lbs PM10/acre)

Cotton Cotton Picking 1.7 Cotton Stalk Cutting 1.7 Cotton Total 3.4 Almond Almond Shaking 0.37 Almond Sweeping 3.7 1stAlmond Pickup 36.7 2nd Almond Pickup Almond Total 40.8 Wheat Combining 5.8

The emission rate assumptions were assigned to reflect the relative geologic PM10 generation potential of various harvest practices. Table B below provides examples of some of the emission factor assignments. Assumptions for all crops are provided in the attached Table 2.

Table B. Example Harvest Emission Factor Assumptions Crop Assumption

Harvest Emission


Cotton Cotton 3.4 Vine Cotton/20 0.17 Tomato Cotton/20 0.17 Fruit Trees Cotton/40 0.085 Corn Wheat/2 2.8 Alfalfa Zero 0.0 Walnuts Almonds 40.8 Sugar beets, onions, potatoes Cotton/2 2.7

Unlike the soil preparations activities (e.g., discing, tilling, etc.) harvest operations tend to be fairly unique for each crop. Because of this, harvest emission factors generally combine all of the operations that go into harvesting a commodity into a single factor that includes emissions from all of the relevant operations. Because of this, acre-passes, which are used in estimating emissions from soil preparation operations, are not needed for harvesting. Although the UCD study shows trends in PM10 emission factors with environmental conditions such as relative humidity and soil moisture, the incorporation of environmental factors are still under exploration. If it is needed and there is an appropriate way to incorporate the environmental factor effects in the future, this methodology will be updated. The UC Davis researchers directly measured PM10 emissions. Because the ARB’s databases store TSP (total suspended particulate) emissions, in order to get TSP, the PM10 emissions are multiplied by 2.2, which is the ARB’s soil size speciation value for agricultural tilling dust.

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Acres. The acreage data used for estimating harvest emissions are from the California Department of Food and Agriculture’s (CDFA) summary of crop acreage harvested in 2000. The acreage data, compiled from individual county agricultural commissioner reports, were subdivided by county and crop type for the entire state. Complete listings of individual county crop acreage are provided in the land preparation background document. Crop Calendar. Harvesting is performed at very specific times each year, so crop calendar data, which tells when harvest activities occur, is important. To get the best estimates possible, staff of the ARB met with producers of the various commodities to gather the most realistic and current information available on when harvesting occurs. Focusing on the largest acreage crops, we were able to gather updated information for about 90% of California’s crop acreage. For the crops that were not explicitly updated, we either applied an updated crop profile from a similar crop, or used one of the existing ARB profiles. Using these data, we created detailed temporal profiles that help to indicate when PM emissions from harvesting may be highest. The background document for soil preparation operations includes detailed calendars for each crop. ASSUMPTIONS 1. The current harvest emission factors assume that for each crop, harvesting produces the same

level emissions under all conditions for all equipment. 2. The emission factors for crops other than almonds, cotton, and wheat were assigned to reflect

the relative geologic PM10 generation potential of various harvest practices. 3. Crop calendar data collected for San Joaquin Valley crops and practices were extrapolated to

the same crops in the remainder of the state. TEMPORAL ACTIVITY Temporal activity for harvesting is derived by summing, for each county, the monthly emissions from all crops. For each crop, the monthly emissions were calculated based on its monthly profile, which reflects the percentage of harvesting activities occurs in that month. Below is an example of the monthly profile for almonds, cotton, and wheat. Because the crop composite differs by county, the monthly profiles for each county are different from each other. An example of some composite county monthly profiles is shown below in Tables C-1 through C-3. Table 3 lists the composite temporal data for each county. The background document provides details on how the monthly temporal profiles were developed. Table C-1. Temporal profile

CES Hours Days Weeks 47332 24 7 52

Table C-2. Monthly Profile of Crops Crops JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECAlmonds 0 0 0 0 0 0 0 0 50 50 0 0 Cotton 0 0 0 0 0 0 0 0 0 50 50 0 Wheat 0 0 0 0 0 50 50 0 0 0 0 0 Table C-3. County Harvest Profile Composite County JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECFresno 0.1 0.1 0.2 0.2 0.1 5.6 5.9 0.8 30.7 42.8 13.6 0.1

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COMMENTS AND RECOMMENDATIONS The scaling approach used to assign the three available PM emission factors to the dozens of California crops is highly subjective. Initial scaling assignments were made by ARB staff, then they were refined by members of the agricultural community. Members of the SJV Agricultural Technical Advisory Group concluded that it was more realistic at this time to include rough approximations of PM harvest emissions than to set the values to zero, as has been done historically. As additional harvest emissions data are collected, they will be incorporated into the methodology and the emissions estimates will be recalculated. CHANGES IN METHOD AND EMISSION ESTIMATES There were significant improvements to the land preparation emissions estimates for this update. These include: • Incorporation of new crop specific emission factors; • Approximating emission factors for all crops by scaling measured emission factors. • Use of updated 2000 crop acreage data from the California Department of Food and

Agriculture. These changes produced an estimated emissions increase of about 80% from the previous 1993 emission inventory estimates for agricultural harvest operations. The change was predominately due to increases in the base emission factors, and the inclusion of all harvested crops, many of which were previously set to zero emissions. SUGGESTED GROWTH SURROGATES Growth in this category is based on the crop acreage projection estimated by the Department of Water Resources. The growth varies by regions. SAMPLE CALCULATIONS To estimate PM10 emissions from agricultural harvest operations, the following method is used: Step 1: Acreage. The acres harvested for a few of the crops in Fresno county are shown in the

‘Acres’ column of the table. These data are available from the county agricultural commissioner annual reports or the CDFA. The 2000 acreage data are summarized in the agricultural land preparation background document.

Step 2: Crop specified Emission Factor. Using the data in Table 2, assign the appropriate

emission factor for each crop.

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Step 3: Calculate Crop PM10 emissions. Multiply the acres for each crop by the appropriate

emission factor, then divide by 2000 lbs/ton to compute annual tons of PM10 emissions. Emissions = (Acres x Emission Factor) / 2000

Step 4: County total emissions. Sum the emissions for each crop to compute the total available

PM10 emissions from harvest operations.

Table D. Estimating Harvest Operation Emissions in Fresno County

Crop Acres Emission Factor (lbs PM10/acre)

PM10 Emissions (tons PM10/yr)

Almonds All 57350 40.77 1169.1 Barley Feed 4100 5.8 11.9 … … … … Total 1189319 2088.7

REFERENCES 1. Flocchini, R.G., James, T.A., et al. Sources and Sinks of PM10 in the San Joaquin

Valley (Interim Report), a study for United States Department of Agriculture Special Research Grants Program. Contract Nos. 94-33825-0383 and 98-38825-6063. August 10, 2001.

2. California Agricultural Statistics Service. 2000 acreage extracted from agricultural

commissioner’s reports. Sacramento, CA. 3. Terry Cassel. Informal write-up for SJV Ag Tech Committee, Evaluation of ARB

application of UCD emission factors, July 12, 2002. 4. Gaffney, P.H. Methodology for Assigning PM10 Emission Factors for California

Agriculture Harvest Activities, Memorandum to SJV PM10 SIP Emission Inventory Group. December 2002.

5. Yu, Hong. Agricultural Harvest: Geologic Particulate Matter Emission Estimates,

Background Document. California Air Resources Board. December 2002. UPDATED BY Hong Yu, Patrick Gaffney January 2003

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TABLE 1 2000 Agricultural harvest PM10 and TSP emissions

EIC: 620-615-5400-0000; CES: 85980; Activity: Acres

Air Basin

County ID #

County Acres PM10 Emissions (tpy)

TSP Emissions (tpy)

GBV 2 ALPINE 0 0 0 14 INYO 4,660 0.9 2.0 26 MONO 10,935 1.6 3.5

LC 17 LAKE 18,457 103.9 228.7 LT 9 EL DORADO 396 0.6 1.3

31 PLACER 2,233 4.0 8.7 MC 3 AMADOR 6,196 11.1 24.5

5 CALAVERAS 1,880 14.6 32.0 9 EL DORADO 2,908 4.3 9.4 22 MARIPOSA 1,239 0.1 0.2 29 NEVADA 303 0.0 0.1 31 PLACER 13,624 24.1 53.1 32 PLUMAS 12,950 6.5 14.4 46 SIERRA 4,500 2.5 5.6 55 TUOLUMNE 770 0.6 1.4

MD 15 KERN 280,668 792.0 1,743.3 19 LOS ANGELES 13,790 2.7 5.9 33 RIVERSIDE 68,678 28.3 62.3 36 SAN 11,171 1.0 2.3

NC 8 DEL NORTE 2,440 2.1 4.5 12 HUMBOLDT 1,600 0.9 2.0 23 MENDOCINO 16,301 1.2 2.7 49 SONOMA 36,726 10.4 22.9 53 TRINITY 175 0.1 0.3

NCC 27 MONTEREY 319,356 52.5 115.5 35 SAN BENITO 56,726 81.1 178.5 44 SANTA CRUZ 19,652 1.4 3.1

NEP 18 LASSEN 76,895 43.1 95.0 25 MODOC 363,716 72.8 160.3 47 SISKIYOU 103,879 108.8 239.5

SC 19 LOS ANGELES 9,194 1.8 4.0 30 ORANGE 11,328 1.4 3.2 33 RIVERSIDE 84,186 34.7 76.4 36 SAN 27,349 2.5 5.6

SCC 40 SAN LUIS OBISPO 109,106 129.9 285.9 42 SANTA BARBARA 115,638 16.7 36.8 56 VENTURA 99,157 7.7 16.9

SD 37 SAN DIEGO 58,006 8.8 19.4 SF 1 ALAMEDA 8,789 3.2 7.0

7 CONTRA COSTA 31,480 41.2 90.8 21 MARIN 6,336 3.2 7.0 28 NAPA 33,436 7.0 15.3 38 SAN FRANCISCO 0.0 41 SAN MATEO 3,897 1.9 4.1 43 SANTA CLARA 21,268 13.5 29.6 48 SOLANO 32,379 66.7 146.9 49 SONOMA 23,481 6.6 14.6

SJV 10 FRESNO 1,189,319 2,088.7 4,597.7 15 KERN 596,420 1,682.9 3,704.5 16 KINGS 573,639 889.8 1,958.6 20 MADERA 302,610 1,146.7 2,524.1 24 MERCED 503,793 1,920.6 4,227.7 39 SAN JOAQUIN 548,940 1,927.4 4,242.6 50 STANISLAUS 386,435 2,376.8 5,231.8 54 TULARE 761,224 1,288.1 2,835.3

SS 13 IMPERIAL 589,996 279.1 614.3 33 RIVERSIDE 68,678 28.3 62.3

SV 4 BUTTE 198,477 1,212.1 2,668.1 6 COLUSA 292,410 792.6 1,744.6 11 GLENN 224,816 861.2 1,895.7 31 PLACER 6,477 11.5 25.2 34 SACRAMENTO 132,613 117.4 258.4 45 SHASTA 26,401 30.7 67.5 48 SOLANO 129,514 266.9 587.6 51 SUTTER 262,736 643.4 1,416.3 52 TEHAMA 52,769 391.7 862.1 57 YOLO 325,147 573.3 1,261.9 58 YUBA 74,329 249.0 548.2

State Totals 9,374,598 20,498.3 45,120.7

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PM Fraction: PM10 = TSP X 0.4543 (TSP Emissions = PM10 / 0.4543) TABLE 2

Summary of Crop Emission Factor Assumption

CDFA Crop Code

CDFA Crop Description Crop Profile Assumption Emission Factor (lbs PM10/acre)

101999 WHEAT ALL Wheat Wheat/1 5.8 104999 RYE FOR GRAIN Wheat Wheat/1 5.8 106199 RICE, FOR MILLING Rice Cotton/2 1.68 106269 FIELD CROP BY PRODUCTS Cotton Cotton/20 0.17 108999 FOOD GRAINS, MISC Corn Cotton/2 1.68 111559 CORN, WHITE Corn Cotton/40 0.08 111991 CORN FOR GRAIN Corn Cotton/2 1.68 111992 CORN FOR SILAGE Corn Cotton/20 0.17 112999 OATS FOR GRAIN Wheat Wheat/1 5.8 113994 BARLEY, MALTING Wheat Wheat/1 5.8 113995 BARLEY, FEED Wheat Wheat/1 5.8 113999 BARLEY, UNSPECIFIED Wheat Wheat/1 5.8 114991 SORGHUM, GRAIN Wheat Wheat/1 5.8 121219 COTTON LINT, UPLAND Cotton Cotton/1 3.37 121229 COTTON LINT, PIMA Cotton Cotton/1 3.37 121299 COTTON LINT, UNSPEC Cotton Cotton/1 3.37 132999 SUGAR BEETS Sugar Beets Cotton/2 1.68 151999 COTTONSEED Cotton Cotton/1 3.37 153999 PEANUTS, ALL Safflower Cotton/2 1.68 158269 SAFFLOWER Safflower Wheat/1 5.8 158316 SUNFLOWER SEED, PLANTING Corn Wheat/1 5.8 158319 SUNFLOWER SEED Corn Wheat/1 5.8 158499 JOJOBA Melon Cotton/40 0.08 161131 BEANS, LIMAS, LG. DRY DryBeans Cotton/2 1.68 161132 BEANS, LIMAS, BABY DRY DryBeans Cotton/2 1.68 161199 LIMA BEANS, UNSPECIFIED DryBeans Cotton/2 1.68 161717 BEANS, RED KIDNEY DryBeans Cotton/2 1.68 161721 BEANS, PINK DryBeans Cotton/2 1.68 161741 BEANS, BLACKEYE (PEAS) DryBeans Cotton/2 1.68 161742 BEANS, GARBANZO Garbanzo Cotton/2 1.68 162399 BEANS, FAVA DryBeans Cotton/2 1.68 163999 PEAS, DRY EDIBLE DryBeans Cotton/20 0.17 169999 BEANS,UNSPEC. DRY EDIBLE DryBeans Cotton/2 1.68 171019 SEED WHEAT Wheat Wheat/1 5.8 171049 SEED RYE Wheat Wheat/1 5.8 171069 SEED RICE Rice Cotton/2 1.68 171129 SEED OATS Wheat Wheat/1 5.8 171139 SEED BARLEY Wheat Wheat/1 5.8 171519 SEED, COTTON FOR PLANTING Cotton Cotton/1 3.37 171582 SEED, SAFFLOWER, PLANTING Safflower Wheat/1 5.8 171619 SEED BEANS DryBeans Cotton/2 1.68 171639 SEED PEAS DryBeans Cotton/20 0.17 171949 SEED, MISC FIELD CROP Corn Cotton/20 0.17 171959 SEED, VEG & VINECROP Vegetables Cotton/20 0.17 172119 SEED, ALFALFA Alfalfa Zero/1 0 172289 CLOVER, UNSPECIFIED SEED Alfalfa Zero/1 0 173079 SEED, BERMUDA GRASS Alfalfa Zero/1 0 173669 SEED, SUDAN GRASS Alfalfa Zero/1 0 173999 SEED, GRASS, UNSPECIFIED Alfalfa Zero/1 0 178999 SEED, OTHER (NO FLOWERS) Alfalfa Cotton/20 0.17 181999 HAY, ALFALFA Alfalfa Zero/1 0 188499 HAY, GRAIN Alfalfa Cotton/2 1.68 188799 HAY, WILD Alfalfa Cotton/2 1.68 188899 HAY, SUDAN Alfalfa Zero/1 0 188999 HAY, OTHER UNSPECIFIED Alfalfa Cotton/2 1.68 194599 PASTURE, IRRIGATED No Land Zero/1 0 194699 PASTURE, RANGE No Land Zero/1 0 194799 PASTURE, MISC. FORAGE No Land Zero/1 0 195199 SILAGE Wheat Cotton/20 0.17 195299 HAY, GREEN CHOP Alfalfa Zero/1 0 195399 STRAW Alfalfa Wheat/1 5.8 198199 RICE, WILD Rice Cotton/2 1.68 198999 FIELD CROPS, UNSPEC. Corn Cotton/20 0.17 201119 ORANGES, NAVEL Citrus Cotton/40 0.08 201519 ORANGES, VALENCIAS Citrus Cotton/40 0.08 201999 ORANGES, UNSPECIFIED Citrus Cotton/40 0.08 202999 GRAPEFRUIT, ALL Citrus Cotton/40 0.08 203999 TANGERINES & MANDARINS Citrus Cotton/40 0.08 204999 LEMONS, ALL Citrus Cotton/40 0.08 205999 LIMES, ALL Citrus Cotton/40 0.08

DRAFT

7.5-8

206999 TANGELOS Citrus Cotton/40 0.08 207999 KUMQUATS Citrus Cotton/40 0.08

TABLE 2 (continued) Summary of Crop Emission Factor Assumption

CDFA Crop Code


208059 CITRUS, MISC BY-PROD Citrus Cotton/40 0.08 209999 CITRUS, UNSPECIFIED Citrus Cotton/40 0.08 211999 APPLES, ALL Citrus Cotton/40 0.08 212199 PEACHES, FREESTONE Citrus Cotton/40 0.08 212399 PEACHES, CLINGSTONE Citrus Cotton/40 0.08 212999 PEACHES, UNSPECIFIED Citrus Cotton/40 0.08 213199 CHERRIES, SWEET Citrus Cotton/40 0.08 214199 PEARS, BARLETT Citrus Cotton/40 0.08 214899 PEARS, ASIAN Citrus Cotton/40 0.08 214999 PEARS, UNSPECIFIED Citrus Cotton/40 0.08 215199 PLUMS Citrus Cotton/40 0.08 215399 PLUMCOTS Citrus Cotton/40 0.08 215999 PRUNES, DRIED Citrus Cotton/40 0.08 216199 GRAPES, TABLE Grapes-Table Cotton/20 0.17 216299 GRAPES, WINE Grapes-Wine Cotton/20 0.17 216399 GRAPES, RAISIN Grapes-Raisin Cotton/20 0.17 216999 GRAPES, UNSPECIFIED Grapes-Wine Cotton/20 0.17 217999 APRICOTS, ALL Citrus Cotton/40 0.08 218199 NECTARINES Citrus Cotton/40 0.08 218299 PERSIMMONS Citrus Cotton/40 0.08 218399 POMEGRANATES Citrus Cotton/40 0.08 218499 QUINCE Citrus Cotton/40 0.08 218839 CHERIMOYAS Citrus Cotton/40 0.08 218889 ORCHARD BIOMASS Almonds Cotton/40 0.08 218899 FRUITS & NUTS, UNSPEC. Citrus Cotton/40 0.08 221999 AVOCADOS, ALL Citrus Cotton/40 0.08 224999 DATES Citrus Almonds/20 2.04 225999 FIGS, DRIED Citrus Almonds/20 2.04 226999 OLIVES Citrus Cotton/40 0.08 228019 GUAVAS Citrus Cotton/40 0.08 229999 KIWIFRUIT Citrus Cotton/40 0.08 230639 BERRIES, BLACKBERRIES Grapes-Table Cotton/40 0.08 230869 BERRIES, BOYSENBERRIES Grapes-Table Cotton/40 0.08 234799 BERRIES, LOGANBERRIES Grapes-Table Cotton/40 0.08 236199 BERRIES, RASPBERRIES Grapes-Table Cotton/40 0.08 237199 STRAWBERRIES, FRESH MKT Melon Cotton/40 0.08 237299 STRAWBERRIES, PROC Melon Cotton/40 0.08 237999 STRAWBERRIES, UNSPECIFIED Melon Cotton/40 0.08 239999 BERRIES, BUSH, UNSPECIFIED Grapes-Table Cotton/40 0.08 261999 ALMONDS, ALL Almonds Almonds/1 40.77 263999 WALNUTS, ENGLISH Almonds Almonds/1 40.77 264999 PECANS Almonds Almonds/10 4.08 265999 WALNUTS, BLACK Almonds Almonds/1 40.77 266999 CHESTNUTS Almonds Almonds/10 4.08 267999 MACADAMIA NUT Almonds Almonds/10 4.08 268079 PISTACHIOS Almonds Almonds/10 4.08 268099 ALMOND HULLS Almonds Almonds/1 40.77 301999 ARTICHOKES Melon Cotton/40 0.08 302199 ASPARAGUS, FRESH MKT Melon Cotton/2 1.68 302299 ASPARAGUS, PROC Melon Cotton/2 1.68 302999 ASPARAGUS, UNSPECIFIED Melon Cotton/2 1.68 303999 BEANS, GREEN LIMAS DryBeans Cotton/2 1.68 304199 BEANS, SNAP FR MKT DryBeans Cotton/20 0.17 304299 BEANS, SNAP PROC DryBeans Cotton/20 0.17 304399 BEANS FRESH UNSPECIFIED DryBeans Cotton/20 0.17 304999 BEANS, UNSPECIFIED SNAP DryBeans Cotton/20 0.17 305999 BEETS, GARDEN Sugar Beets Cotton/2 1.68 306999 RAPINI Sugar Beets Cotton/40 0.08 307189 BROCCOLI,FOOD SERV Vegetables Cotton/40 0.08 307199 BROCCOLI, FR MKT Vegetables Cotton/40 0.08 307299 BROCCOLI, PROC Vegetables Cotton/40 0.08 307919 BROCCOLI, UNSPECIFIED Vegetables Cotton/40 0.08 308999 BRUSSELS SPROUTS Melon Cotton/40 0.08 309999 CABBAGE, CH. & SPECIALTY Lettuce Cotton/40 0.08 310999 CABBAGE, HEAD Lettuce Cotton/40 0.08 313189 CARROTS, FOOD SERV Sugar Beets Cotton/20 0.17 313199 CARROTS, FR MKT Sugar Beets Cotton/20 0.17 313299 CARROTS, PROC Sugar Beets Cotton/20 0.17 313999 CARROTS, UNSPECIFIED Sugar Beets Cotton/20 0.17 314189 CAULIFLOWER, FOOD SERV Vegetables Cotton/40 0.08

DRAFT

7.5-9

314199 CAULIFLOWER, FR MKT Vegetables Cotton/40 0.08 314299 CAULIFLOWER, PROC Vegetables Cotton/40 0.08

TABLE 2 (continued) Summary of Crop Emission Factor Assumption

CDFA Crop Code


314999 CAULIFLOWER, UNSPECIFIED Vegetables Cotton/40 0.08 316189 CELERY, FOOD SERV Lettuce Cotton/40 0.08 316199 CELERY, FR MKT Lettuce Cotton/40 0.08 316299 CELERY, PROC Lettuce Cotton/40 0.08 316999 CELERY, UNSPECIFIED Lettuce Cotton/40 0.08 318999 RADICCHIO Lettuce Cotton/40 0.08 320999 CHIVES Lettuce Cotton/40 0.08 322999 COLLARD GREENS Lettuce Cotton/40 0.08 323999 CORN, SWEET ALL Corn Cotton/40 0.08 325999 CUCUMBERS Vegetables Cotton/40 0.08 330999 EGGPLANT, ALL Vegetables Cotton/40 0.08 331999 ENDIVE, ALL Lettuce Cotton/40 0.08 332999 ESCAROLE, ALL Lettuce Cotton/40 0.08 333999 ANISE (FENNEL) Lettuce Cotton/2 1.68 335999 GARLIC, ALL Garlic Cotton/2 1.68 337999 KALE Lettuce Cotton/40 0.08 338999 KOHLRABI Lettuce Cotton/40 0.08 339196 LETTUCE, BULK SALAD PRODS. Lettuce Cotton/40 0.08 339999 LETTUCE, UNSPECIFIED Lettuce Cotton/40 0.08 340999 LETTUCE, HEAD Lettuce Cotton/40 0.08 341999 LETTUCE, ROMAINE Lettuce Cotton/40 0.08 342999 LETTUCE, LEAF Lettuce Cotton/40 0.08 343999 MELON, CANTALOUPE Melon Cotton/40 0.08 348999 MELON, HONEYDEW Melon Cotton/40 0.08 354299 MELON, UNSPECIFIED Melon Cotton/40 0.08 354999 MELON, WATER MELONS Melon Cotton/40 0.08 355999 MUSHROOMS No Land Prep. Zero/1 0 356999 MUSTARD Lettuce Cotton/40 0.08 357999 OKRA Lettuce Cotton/40 0.08 358999 ONIONS Onions Cotton/2 1.68 359999 PARSLEY Lettuce Cotton/40 0.08 361299 PEAS, GREEN, PROCESSING DryBeans Cotton/20 0.17 361999 PEAS, GREEN, UNSPECIFIED DryBeans Cotton/20 0.17 363999 PEPPERS, BELL Tomatoes Cotton/40 0.08 364999 PEPPERS, CHILI, HOT Tomatoes Cotton/40 0.08 366999 PUMPKINS Melon Cotton/20 0.17 367999 RADISHES Sugar Beets Cotton/40 0.08 368999 RHUBARB Lettuce Cotton/40 0.08 370999 RUTABAGAS Sugar Beets Cotton/2 1.68 372999 ONIONS, GREEN & SHALLOTS Onions Cotton/40 0.08 374189 SPINACH, FOOD SERV Lettuce Cotton/40 0.08 374199 SPINACH, FR MKT Lettuce Cotton/40 0.08 374299 SPINACH, PROC Lettuce Cotton/40 0.08 374999 SPINACH UNSPECIFIED Lettuce Cotton/40 0.08 375999 SQUASH Melon Cotton/20 0.17 376999 SWISSCHARD Lettuce Cotton/40 0.08 378199 TOMATOES, FRESH MARKET Tomatoes Cotton/40 0.08 378299 TOMATOES, PROCESSING Tomatoes Cotton/20 0.17 378999 TOMATOES, UNSPECIFIED Tomatoes Cotton/20 0.17 380999 TURNIPS, ALL Sugar Beets Cotton/2 1.68 381999 GREENS, TURNIP & MUSTARD Lettuce Cotton/40 0.08 387999 LEEKS Onions Cotton/40 0.08 391999 POTATOES, IRISH ALL Sugar Beets Cotton/2 1.68 392999 SWEET POTATOES Sugar Beets Cotton/2 1.68 393999 HORSERADISH Onions Cotton/40 0.08 394199 SALAD GREENS NEC Lettuce Cotton/40 0.08 394999 PEAS, EDIBLE POD (SNOW) DryBeans Cotton/20 0.17 395999 VEGETABLES, ORIENTAL, ALL Vegetables Cotton/40 0.08 396999 SPROUTS, ALFALFA & BEAN Lettuce Cotton/40 0.08 398199 CUCUMBERS, GREENHOUSE No Land Prep. Zero/1 0 398299 TOMATOES, GREENHOUSE No Land Prep. Zero/1 0 398399 TOMATOES, CHERRY Tomatoes Cotton/40 0.08 398499 TOMATILLO Tomatoes Cotton/40 0.08 398559 CILANTRO Lettuce Cotton/40 0.08 398599 SPICES AND HERBS Lettuce Cotton/40 0.08 398899 VEGETABLES, BABY Vegetables Cotton/40 0.08 398999 VEGETABLES, UNSPECIFIED Vegetables Cotton/20 0.17 832919 POTATOES SEED Sugar Beets Cotton/2 1.68 892999 NURSERY TURF No Land Prep. Zero/1 0

DRAFT

7.5-10

TABLE 3 Seasonal Profile for Agricultural Harvest Emissions

Air ID # County JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

GBV 2 ALPINE 14 INYO 0.001 0.001 0.136 0.136 0.136 0.145 0.145 0.145 0.145 0.010 0.001 0.001 26 MONO 0.005 0.005 0.119 0.119 0.119 0.147 0.147 0.147 0.147 0.033 0.005 0.005

LC 17 LAKE 0.000 0.000 0.006 0.006 0.006 0.006 0.006 0.006 0.482 0.476 0.003 0.000LT 9 EL DORADO 0.001 0.001 0.010 0.010 0.010 0.010 0.010 0.010 0.463 0.454 0.021 0.001

31 PLACER 0.000 0.000 0.014 0.014 0.014 0.014 0.014 0.014 0.409 0.395 0.112 0.000MC 3 AMADOR 0.000 0.000 0.013 0.013 0.013 0.013 0.013 0.013 0.454 0.441 0.026 0.000

5 CALAVERAS 0.000 0.000 0.002 0.002 0.002 0.002 0.002 0.002 0.493 0.490 0.003 0.000 9 EL DORADO 0.001 0.001 0.010 0.010 0.010 0.010 0.010 0.010 0.463 0.454 0.021 0.001 22 MARIPOSA 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.413 0.413 0.076 0.011 29 NEVADA 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 31 PLACER 0.000 0.000 0.014 0.014 0.014 0.014 0.014 0.014 0.409 0.395 0.112 0.000 32 PLUMAS 0.000 0.000 0.143 0.143 0.143 0.143 0.143 0.143 0.143 0.000 0.000 0.000 46 SIERRA 0.000 0.000 0.143 0.143 0.143 0.143 0.143 0.143 0.143 0.000 0.000 0.000 55 TUOLUMNE 0.000 0.000 0.143 0.143 0.143 0.143 0.143 0.143 0.143 0.000 0.000 0.000

MD 15 KERN 0.001 0.001 0.002 0.002 0.002 0.054 0.056 0.005 0.363 0.434 0.078 0.001 19 LOS ANGELES 0.015 0.015 0.068 0.068 0.066 0.153 0.153 0.153 0.162 0.112 0.019 0.015 33 RIVERSIDE 0.012 0.012 0.023 0.023 0.022 0.135 0.135 0.025 0.031 0.282 0.288 0.012 36 SAN 0.011 0.011 0.100 0.100 0.099 0.100 0.101 0.101 0.207 0.119 0.040 0.011

NC 8 DEL NORTE 0.000 0.000 0.143 0.143 0.143 0.143 0.143 0.143 0.143 0.000 0.000 0.000 12 HUMBOLDT 0.000 0.000 0.135 0.135 0.135 0.163 0.163 0.135 0.135 0.000 0.000 0.000 23 MENDOCINO 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.881 0.011 49 SONOMA 0.001 0.001 0.051 0.051 0.051 0.139 0.139 0.051 0.178 0.128 0.210 0.001 53 TRINITY 0.000 0.000 0.143 0.143 0.143 0.143 0.143 0.143 0.143 0.000 0.000 0.000

NCC 27 MONTEREY 0.008 0.008 0.044 0.044 0.013 0.200 0.206 0.046 0.135 0.163 0.126 0.008 35 SAN BENITO 0.000 0.000 0.029 0.029 0.027 0.099 0.100 0.031 0.352 0.327 0.005 0.000 44 SANTA CRUZ 0.022 0.022 0.077 0.077 0.039 0.185 0.185 0.081 0.081 0.059 0.150 0.022

NEP 18 LASSEN 0.000 0.000 0.109 0.109 0.109 0.211 0.211 0.109 0.119 0.011 0.011 0.000 25 MODOC 0.005 0.005 0.048 0.048 0.048 0.361 0.361 0.051 0.051 0.009 0.006 0.005 47 SISKIYOU 0.004 0.004 0.023 0.023 0.023 0.430 0.430 0.025 0.025 0.006 0.004 0.004

SC 19 LOS ANGELES 0.015 0.015 0.068 0.068 0.066 0.153 0.153 0.153 0.162 0.112 0.019 0.015 30 ORANGE 0.009 0.009 0.015 0.015 0.009 0.027 0.048 0.048 0.071 0.381 0.358 0.009 33 RIVERSIDE 0.012 0.012 0.023 0.023 0.022 0.135 0.135 0.025 0.031 0.282 0.288 0.012 36 SAN 0.011 0.011 0.100 0.100 0.099 0.100 0.101 0.101 0.207 0.119 0.040 0.011

SCC 40 SAN LUIS 0.001 0.001 0.017 0.017 0.016 0.252 0.261 0.025 0.209 0.187 0.015 0.001 42 SANTA 0.014 0.014 0.054 0.054 0.041 0.058 0.058 0.056 0.139 0.254 0.241 0.014 56 VENTURA 0.029 0.029 0.058 0.058 0.029 0.055 0.061 0.061 0.070 0.273 0.248 0.029

SD 37 SAN DIEGO 0.027 0.027 0.072 0.072 0.071 0.218 0.224 0.079 0.101 0.051 0.031 0.027SF 1 ALAMEDA 0.000 0.000 0.124 0.124 0.124 0.124 0.124 0.124 0.155 0.031 0.069 0.000

7 CONTRA COSTA 0.000 0.000 0.005 0.005 0.005 0.081 0.119 0.044 0.386 0.346 0.007 0.000 21 MARIN 0.000 0.000 0.132 0.132 0.132 0.167 0.167 0.132 0.132 0.000 0.003 0.000 28 NAPA 0.000 0.000 0.016 0.016 0.016 0.016 0.016 0.016 0.266 0.250 0.391 0.000 38 SAN FRANCISCO 41 SAN MATEO 0.002 0.002 0.054 0.054 0.054 0.294 0.294 0.062 0.062 0.059 0.061 0.002 43 SANTA CLARA 0.001 0.001 0.046 0.046 0.044 0.054 0.059 0.059 0.358 0.313 0.015 0.001 48 SOLANO 0.000 0.000 0.003 0.003 0.003 0.196 0.229 0.035 0.274 0.247 0.010 0.000 49 SONOMA 0.001 0.001 0.051 0.051 0.051 0.139 0.139 0.051 0.178 0.128 0.210 0.001

SJV 10 FRESNO 0.001 0.001 0.002 0.002 0.001 0.056 0.059 0.008 0.307 0.428 0.136 0.001 15 KERN 0.001 0.001 0.002 0.002 0.002 0.054 0.056 0.005 0.363 0.434 0.078 0.001 16 KINGS 0.000 0.000 0.001 0.001 0.001 0.168 0.168 0.001 0.108 0.330 0.223 0.000 20 MADERA 0.001 0.001 0.001 0.001 0.001 0.017 0.017 0.001 0.455 0.475 0.028 0.001 24 MERCED 0.001 0.001 0.002 0.002 0.002 0.017 0.017 0.003 0.446 0.475 0.033 0.001 39 SAN JOAQUIN 0.001 0.001 0.002 0.002 0.002 0.028 0.033 0.010 0.457 0.454 0.010 0.001 50 STANISLAUS 0.000 0.000 0.001 0.001 0.001 0.005 0.006 0.001 0.489 0.491 0.004 0.000 54 TULARE 0.001 0.001 0.001 0.001 0.001 0.063 0.063 0.001 0.374 0.431 0.063 0.001

SS 13 IMPERIAL 0.010 0.010 0.030 0.030 0.029 0.329 0.329 0.040 0.043 0.076 0.067 0.010 33 RIVERSIDE 0.012 0.012 0.023 0.023 0.022 0.135 0.135 0.025 0.031 0.282 0.288 0.012

SV 4 BUTTE 0.000 0.000 0.000 0.000 0.000 0.003 0.004 0.001 0.483 0.484 0.025 0.000 6 COLUSA 0.000 0.000 0.001 0.001 0.001 0.037 0.052 0.016 0.408 0.411 0.073 0.000 11 GLENN 0.000 0.000 0.001 0.001 0.001 0.030 0.032 0.002 0.446 0.450 0.036 0.000 31 PLACER 0.000 0.000 0.014 0.014 0.014 0.014 0.014 0.014 0.409 0.395 0.112 0.000 34 SACRAMENTO 0.002 0.002 0.009 0.009 0.009 0.222 0.284 0.071 0.206 0.137 0.044 0.002 45 SHASTA 0.000 0.000 0.059 0.059 0.059 0.083 0.083 0.059 0.316 0.258 0.025 0.000 48 SOLANO 0.000 0.000 0.003 0.003 0.003 0.196 0.229 0.035 0.274 0.247 0.010 0.000 51 SUTTER 0.000 0.000 0.001 0.001 0.001 0.025 0.050 0.026 0.427 0.407 0.059 0.000 52 TEHAMA 0.000 0.000 0.002 0.002 0.002 0.005 0.006 0.002 0.489 0.489 0.002 0.000 57 YOLO 0.000 0.000 0.002 0.002 0.002 0.111 0.155 0.046 0.348 0.308 0.026 0.000 58 YUBA 0.000 0.000 0.002 0.002 0.002 0.005 0.005 0.002 0.471 0.469 0.042 0.000

DRAFT

7.4-1

SECTION 7.4

AGRICULTURAL LAND PREPARATION

(Revised January 2003)

METHODS AND SOURCES The land preparation source category includes estimates of the airborne soil particulate emissions produced during the preparation of agricultural lands for planting and after-harvest activities. Operations included in this methodology are discing, shaping, chiseling, leveling, and other mechanical operations used to prepare the soil. Dust emissions are produced by the mechanical disturbance of the soil by the implement used and the tractor pulling it. Soil preparation activities tend to be performed in the early spring and fall months. Table 1 shows the estimated soil preparation particulate emissions for each California county. Particulate emissions from land preparation are computed by multiplying a crop specific emission factor by an activity factor. The crop specific emission factors are calculated using operation specific (i.e., discing or chiseling) emission factors developed by UC Davis researchers1, which are combined with the number of operations provided in the crop calendars. The activity factor is based on the harvested acreage of each crop for each county in the state. In addition, acre-passes are computed, which are the number of passes per acre that are typically needed to prepare a field for planting a particular crop. By combining the crop acreage and the operation specific emission factor, we estimate the particulate matter produced by agricultural land preparation operations. The particulate dust emissions from agricultural land preparation are estimated for each crop in each county in California using the following equation.

cropcropcrop Acres FactorEmissionEmissions ×= The crop emissions for each county are summed to produce the county and statewide particulate matter (PM) and PM10 emission estimates. The remainder of this section discusses each component of and related to the above equation. Acres. The acreage data used for estimating land preparation emissions are from the California Department of Food and Agriculture’s (CDFA) summary of crop acreage harvested in 2000.

EMISSION INVENTORY SOURCE CATEGORY Miscellaneous Processes / Farming Operations EMISSION INVENTORY CODES (CES CODES) AND DESCRIPTION 620-614-5400-0000 (47332) Agricultural Land Preparation

DRAFT

7.4-2

The acreage data are subdivided by county and crop type for the entire state, and are compiled from individual county agricultural commissioner reports. Acres for more than 200 crop commodities were reported by CDFA. Complete listings of individual county crop acreage are provided in the land preparation background document. Crop Calendars & Acre-Passes. Acre-passes are the total number of passes typically performed to prepare land for planting during a year. Acre-passes are used in computing crop specific emission factors for land preparation. These land preparation operations may occur following harvest or closer to planting, and can include discing, tilling, land leveling, and other operations. Each crop is different in the type of soil operations performed and when they occur. To get the best estimates available, staff of the ARB met with producers of the various commodities to gather the most realistic and current information available on agricultural practices. Focusing on the largest acreage crops, we were able to gather updated information for about 90 percent of California’s crop acreage. For the crops that were not explicitly updated, we either applied an updated crop profile from a similar crop, or used one of the existing ARB profiles. Table 2.a provides a listing of the land preparation operations of all crop profiles and their emission factors used in California. For updating acre-pass data, we also collected specific information on when agricultural operations occur. Using these data, it was possible to create detailed temporal profiles that help to indicate when PM emissions from land preparations may be highest. The more detailed background document includes detailed crop calendars for each crop with updated information. For all the acre-pass and crop calendar information, the farmers and other agricultural experts of the San Joaquin Valley were instrumental in helping us to update our crop information. The crop calendar consists of twenty representative crop profiles. To make better emission estimates for the over 200 crop commodities reported by CDFA, we assigned each crop to the profile with the highest similarity. The complete listings of individual crop commodities and the assigned profiles are provided in Table 3. Emission factor. The operation specific emission factors used to estimate the crop specific emission factor for agricultural land preparations were initially from a report of University of California at Davis and their subsequent supplementary data analysis 4. After discussions with regulators, researchers, and industry representatives, the emission factors were adjusted based on a combination of scientific applicability, general experience and observations. The initial emission factors were developed based on 1995-1998 test data measured in cotton and wheat fields in California. The operations tested include root cutting, discing, ripping and subsoiling, land planing and floating, and weeding, which are summarized in Table A below.

Table A. Land Preparation Operation Emission Factor Land Preparation Operations

Emission Factor (lbs PM10/acre-pass)

Root cutting 0.3 Discing, Tilling, Chiseling

1.2

Ripping, Subsoiling 4.6 Land Planing & Floating 12.5 Weeding 0.8

DRAFT

7.4-3

There are more than thirty different land preparation operations commonly used in California. With five emission factors available, the other operations were assigned “best-fit” factors based on similar potential emission levels. The assignment of emission factors for operations was based on the expertise and experience of regulators, researchers, and industry representatives. The complete list of land preparation operations and the assigned operation categories are provided in Table 2.b. For each crop, the emission factor is the sum of acre-pass weighted emission factor for each land preparation operation. Table 2.a provides the emission factors for each representative crops in the crop calendar. The figure below illustrates the entire emissions estimation process. ASSUMPTIONS 1. The land preparation emission factors for discing, tilling, etc., are assumed to produce the

same level of emissions, regardless of the crop type. 2. The land preparation emission factors do not change geographically for counties. 3. A limited number of emission factors are assigned to all land preparation activities. 4. Crop calendar data collected for San Joaquin crops and practices were extrapolated to the

same crops in the remainder of the State. Existing crop profiles were used for the small percentage of crops in which update information was not collected.

5. In addition to the activites provided in the crop calenders, it is also assumed that field and

row crop acreage receive a land planing pass once every five years. 6. UC Davis directly measured PM10 emissions. To compute TSP emissions, multiply the PM10

by 2.22, which is the ARB’s soil size speciation value for agricultural tilling dust. TEMPORAL ACTIVITY Temporal activity for harvesting is derived by summing, for each county, the monthly emissions from all crops. For each crop, the monthly emissions were calculated based on its monthly crop calendar profile, which reflects the percentage of harvesting activities that occurs in that month. Below is an example of the monthly profile for almonds, cotton, and wheat. Because the crop composite differs by county, the monthly profiles for counties are different. An example of

Operation specific Emission Factors (EF_opt)

Crop Calendar (Number of Passes)

Crop specific Emission Factor (EF_crop)

Crop specific Acres

Crop specific Emissions

Σ(EF_opt x Passes)

EF_crop x Acres

DRAFT

7.4-4

some composite county monthly profiles is shown below in Tables B-1 through B-3. Table 3 lists the composite temporal data for every county. The background document provides details on how the monthly temporal profiles were developed. Table B-1. Temporal Profiles

CES Hours Days Weeks 47332 24 7 52

Table B-2. Monthly Activity Profile of Selected Crops

Crops JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Almonds 0 0 0 0 0 0 0 0 0 0 50 50 Cotton 0 9 9 0 0 0 0 0 0 0 41 41 Grapes-wine 0 0 0 4 16 16 12 12 12 28 0 0 Table B.3 County Harvest Profile Composite

County JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Fresno 3 6 6 2 2 1 3 4 2 12 30 29 COMMENTS AND RECOMMENDATIONS Studies are ongoing by the University of California, Davis, to analyze field test data from 70 to 90 additional land preparation tests. As the UCD results become available, they will be incorporated to the emission estimation methodology. If possible, future updates could include county specific crop calendars and crop-pass information instead of being based on San Joaquin Valley practices. CHANGES IN METHOD AND EMISSION ESTIMATES There were significant improvements to the land preparation emissions estimates for this update. These include: • Incorporation of new operation specific land preparation emission factors; • Development of new crop specific emission factors; • Use of updated 2000 crop acreage data from the California Department of Food and

Agriculture. These changes produced an emissions reduction of about 50% from the previous 1997 published emission inventory estimates for agricultural land preparation. SUGGESTED GROWTH SURROGATES Growth in this category is based on the crop acreage projection estimated by the Department of Water Resources. The growth varies by regions. SAMPLE CALCULATIONS

DRAFT

7.4-5

The instructions and table below summarizes the data computations necessary to estimate the PM10 emissions from agricultural land preparations in Fresno county. The following steps are performed: Step 1: Crop Acreage. The acres harvested for a few of the crops in Fresno county are shown in

the ‘Acres’ column of the table. These data are available from the county agricultural commissioner annual reports or the CDFA. The 2000 acreage data are summarized in the agricultural tilling background document.

Step 2: Insert emission Factor for Crop. Using the crop profile in Table 2.a to get the appropriate

crop emission factor. Step 3: Compute Crop Emissions. Multiply the annual harvested acreage for each crop by the

emission factor and divide by 2000 lbs/ton to get the annual PM10 emissions. Emissions = (Acres x Emission Factor) / 2000

Step 4: Compute County Total Emissions. Sum the crop PM10 emissions for each county to

compute the total county agricultural soil preparation particulate matter emissions. Step 5: Compute TSP. Divide the PM10 emissions by a factor of 0.4543.

Table C. Estimating Agricultural Land Preparation PM10 Emissions in Fresno County

Crop Crop Profile Acres

Emission Factor

(lbs PM10/acre)

PM10 Emissions

(tons/yr)

TSP Emissions

(tons/yr) Wheat all Wheat 69500 3.7 128.6 235.8 Rice Milling Rice 6160 20 61.6 113.0 Cotton lint pima Cotton 33400 8.9 148.6 272.6 Apples All Citrus 3205 0.07 0.1 0.2 Etc… … … … … Total … … … …

REFERENCES 1. Flocchini, R.G., James, T.A., et al. Sources and Sinks of PM10 in the San Joaquin

Valley (Interim Report), a study for United States Department of Agriculture Special Research Grants Program. Contract Nos. 94-33825-0383 and 98-38825-6063. August 10, 2001.

2. California Agricultural Statistics Service. 2000 acreage extracted from agricultural

commissioner’s reports. Sacramento, CA. 3. Gaffney, P.H., Yu, H. Agricultural Harvest: Geologic Particulate Matter Emission

Estimates, Background Document. California Air Resources Board. December 2002. 4. Terry Cassel, Informal write-up for SJV Ag Tech Committee, Evaluation of ARB

application of UCD emission factors, July 12, 2002.

DRAFT

7.4-6

UPDATED BY Hong Yu, Patrick Gaffney January 2003

DRAFT

7.4-7

TABLE 1 2000 Agricultural Land Preparation PM10 and TSP Emissions

EIC: 620-614-5400-0000; CES: 47332; Activity: Acre-Passes

Air Basin

ID# County Acreage AcrePass PM10 Emissions (tons/year)

TSP Emissions (tons/year)

GBV 2 ALPINE 0 0 0 014 INYO 4,660 7,230 10.1 22.326 MONO 10,935 20,972 33.2 73.1

LC 17 LAKE 18,457 13,503 21.8 48.0LT 9 EL DORADO 396 221 0.2 0.5

31 PLACER 2,233 9,702 16.9 37.2MC 3 AMADOR 6,196 8,514 8.7 19.1

5 CALAVERAS 1,880 1,485 2.3 5.29 EL DORADO 2,908 1,620 1.7 3.8

22 MARIPOSA 1,239 3,785 3.1 6.829 NEVADA 303 308 0.2 0.531 PLACER 13,624 59,183 103.0 226.832 PLUMAS 12,950 18,778 25.9 57.046 SIERRA 4,500 6,525 9.0 19.855 TUOLUMNE 770 1,117 1.5 3.4

MD 15 KERN 280,668 775,461 771.9 1,699.119 LOS ANGELES 13,790 44,740 41.1 90.533 RIVERSIDE 68,678 173,202 184.3 405.736 SAN BERNARDINO 11,171 21,093 22.7 49.9

NC 8 DEL NORTE 2,440 3,538 4.9 10.712 HUMBOLDT 1,600 2,170 3.1 6.823 MENDOCINO 16,301 15,063 11.2 24.749 SONOMA 36,726 39,854 37.1 81.753 TRINITY 175 254 0.4 0.8

NCC 27 MONTEREY 319,356 1,361,021 1,517.3 3,339.835 SAN BENITO 56,726 164,716 193.8 426.744 SANTA CRUZ 19,652 59,829 63.1 138.8

NEP 18 LASSEN 76,895 121,815 171.2 376.825 MODOC 363,716 545,563 773.0 1,701.547 SISKIYOU 103,879 169,712 259.9 572.0

SC 19 LOS ANGELES 9,194 29,827 27.4 60.330 ORANGE 11,328 43,467 36.2 79.733 RIVERSIDE 84,186 212,313 226.0 497.436 SAN BERNARDINO 27,349 51,641 55.6 122.3

SCC 40 SAN LUIS OBISPO 109,106 271,633 288.7 635.542 SANTA BARBARA 115,638 428,580 406.2 894.156 VENTURA 99,157 224,330 241.6 531.9

SD 37 SAN DIEGO 58,006 65,430 61.8 135.9SF 1 ALAMEDA 8,789 17,848 17.5 38.5

7 CONTRA COSTA 31,480 97,355 85.3 187.721 MARIN 6,336 9,167 12.6 27.728 NAPA 33,436 34,263 26.4 58.138 SAN FRANCISCO 0.041 SAN MATEO 3,897 13,085 11.6 25.543 SANTA CLARA 21,268 73,223 69.4 152.748 SOLANO 32,379 82,817 82.8 182.249 SONOMA 23,481 25,481 23.7 52.2

SJV 10 FRESNO 1,189,319 4,315,089 3,624.9 7,979.015 KERN 596,420 1,647,854 1,640.2 3,610.516 KINGS 573,639 2,312,623 1,933.0 4,254.920 MADERA 302,610 514,663 530.4 1,167.424 MERCED 503,793 1,385,331 1,463.4 3,221.239 SAN JOAQUIN 548,940 1,368,065 1,469.9 3,235.550 STANISLAUS 386,435 845,789 933.5 2,054.854 TULARE 761,224 1,505,110 1,450.5 3,192.8

SS 13 IMPERIAL 589,996 1,412,555 1,917.8 4,221.433 RIVERSIDE 68,678 173,202 184.3 405.7

SV 4 BUTTE 198,477 672,571 1,174.5 2,585.46 COLUSA 292,410 1,320,937 1,950.7 4,293.9

11 GLENN 224,816 777,154 1,220.0 2,685.531 PLACER 6,477 28,136 49.0 107.834 SACRAMENTO 132,613 377,036 409.2 900.845 SHASTA 26,401 52,022 78.3 172.448 SOLANO 129,514 331,268 331.1 728.751 SUTTER 262,736 974,087 1,487.0 3,273.152 TEHAMA 52,769 52,858 82.5 181.557 YOLO 325,147 1,137,427 1,202.5 2,646.958 YUBA 74,329 231,120 401.9 884.6

STATE TOTAL 9,374,598 26,766,332 29,499.9 64,934.9 Fraction of PM10 = 0.45 (PM10 Emissions = TSP x 0.4543)

DRAFT

7.4-8

TABLE 2.a Summary of Crop Profile, Acre-Pass, and Emission Factor

Crop profile Land Preparation Operations Category Acre-Pass Emission Factor

Operation (lbs/Acre-pass)

Crop (lbs/Acre/year)

Alfalfa Unspecified Discing 1.25 1.2 Land Maintenance Land Planing 0.2 12.5 4Almonds Float Land Planing 0.25 12.5 3.13Citrus Unspecified Discing 0.06 1.2 0.07Corn List & Fertilize Weeding 1 0.8 Mulch Beds Discing 1 1.2 Finish Disc Discing 1 1.2 Land Maintenance Land Planing 0.2 12.5 Stubble Disc Discing 1 1.2 6.9Cotton Land Preparation Discing 4 1.2 Land Maintenance Land Planing 0.2 12.5 Seed Bed Preparation Weeding 2 0.8 8.9DryBeans Land Maintenance Land Planing 0.2 12.5 Chisel Discing 1 1.2 Shaping Weeding 1 0.8 Disc Discing 2 1.2 Listing Weeding 1 0.8 7.7Garbanzo Chisel Discing 1 1.2 Listing Weeding 1 0.8 Shaping Weeding 1 0.8 Disc Discing 2 1.2 Land Maintenance Land Planing 0.2 12.5 7.7Garlic Land Maintenance Land Planing 0.2 12.5 Disc & Roll Discing 1 1.2 Chisel Discing 1 1.2 List Weeding 1 0.8 Shape Beds Weeding 1 0.8 6.5Grapes-Raisin Terrace Weeding 1 0.8 Spring Tooth Weeding 0.2 0.8 Subsoil Ripping 0.05 4.6 Disc & Furrow-out Discing 1 1.2 Level (new vineyard) Land Planing 0.02 12.5 2.6Grapes-Table Subsoil Ripping 0.05 4.6 Disc & Furrow-out Discing 0.5 1.2 0.83Grapes-Wine Level (new vineyard) Land Planing 0.02 12.5 Spring Tooth Weeding 0.2 0.8 Subsoil Ripping 0.05 4.6 Disc & Furrow-out Discing 0.75 1.2 1.5Lettuce* Land Maintenance Land Planing 0.2 12.5 Disc & Roll Discing 2/2 1.2 Chisel Discing 2/2 1.2 List Weeding 2/2 0.8 Plane Land Planing ½ 12.5 Shape Beds & Roll Weeding 2/2 0.8 12.75Melon Plow Discing 1 1.2 Shape Beds Weeding 1 0.8 Land Maintenance Land Planing 0.2 12.5 Disc Discing 1 1.2 5.7No Land Prep. Unspecified Discing 0 1.2 0Onions List Weeding 1 0.8 Shape Beds Weeding 1 0.8 Land Maintenance Land Planing 0.2 12.5 Chisel Discing 1 1.2 Disc & Roll Discing 1 1.2 6.5Rice Chisel Discing 1 1.2 Land Maintenance Land Planing 0.2 12.5 Post Burn/Harvest Disc Discing 0.5 1.2 Roll Weeding 1 0.8 3 Wheel Plane Land Planing 1 12.5 Harrow Disc Discing 1 1.2 Stubble Disc Discing 1 1.2 20Safflower List Weeding 1 0.8 Land Maintenance Land Planing 0.2 12.5 Stubble Disc Discing 1 1.2 4.5

DRAFT

7.4-9

Crop profile Land Preparation Operations Category Acre-Pass Emission Factor Operation

(lbs/Acre-pass) Crop

(lbs/Acre/year) Sugar Beets Disc Discing 1 1.2 Land Plane Land Planing 1 12.5 Subsoil-deep chisel Ripping 1 4.6 Stubble Disc Discing 1 1.2 List Weeding 1 0.8 Land Maintenance Land Planing 0.2 12.5 22.8Tomatoes Bed Preparatin Weeding 2 0.8 Land Preparation Discing 5 1.2 Land Maintenance Land Planing 0.2 12.5 10.1Vegetables Land Maintenance Land Planing 0.2 12.5 Unspecified Discing 5 1.2 8.5Wheat Stubble Disc Discing 1 1.2 Land Maintenance Land Planing 0.2 12.5 3.7

* Lettuce profile acre-passes are divided by 2 except for land maintenance operation to remove double cropping count because double cropping is accounted for in the 'Harvested Acres' in the emission calculations. (e.g., if the same land is harvested twice in the same year, the same acreage is counted twice in the county Ag. commissioner crop reports)

TABLE 2.b Summary of Land Preparation Operations and Assigned Operation Categories

Operation Category Emission Factor

(lbs/Acre-pass)Chisel Discing 1.2 Disc Discing 1.2 Disc & Furrow-out Discing 1.2 Disc & Roll Discing 1.2 Finish Disc Discing 1.2 Harrow Disc Discing 1.2 Land Preparation Discing 1.2 Mulch Beds Discing 1.2 Plow Discing 1.2 Post Burn/Harvest Disc Discing 1.2 Stubble Disc Discing 1.2 Unspecified Discing 1.2 3 Wheel Plane Land Planing 12.5 Float Land Planing 12.5 Land Plane Land Planing 12.5 Laser Level Land Planing 12.5 Level Land Planing 12.5 Level (new vineyard) Land Planing 12.5 Plane Land Planing 12.5 Land Maintenance Land Planing 12.5 Subsoil Ripping 4.6 Subsoil-deep chisel Ripping 4.6 Bed Preparatin Weeding 0.8 List Weeding 0.8 List & Fertilize Weeding 0.8 Listing Weeding 0.8 Roll Weeding 0.8 Seed Bed Preparation Weeding 0.8 Shape Beds Weeding 0.8 Shape Beds & Roll Weeding 0.8 Shaping Weeding 0.8 Spring Tooth Weeding 0.8 Terrace Weeding 0.8 Sulfur Dusting None 0

DRAFT

7.4-10

TABLE 3 Summary of CDFA Commodity and Assigned Crop Profile

CDFA Commodity

Code CDFA Crop Name Crop Profile Used Emission Factor

(lbs/Acre/year) 101999 WHEAT ALL Wheat 3.70 104999 RYE GRAIN Wheat 3.70 106199 RICE MILLING Rice 20.00 106269 FIELD CROP BY-PRODUCTS Cotton 8.90 108999 FOOD GRAINS MISC. Corn 6.90 111559 CORN WHITE Corn 6.90 111991 CORN GRAIN Corn 6.90 111992 CORN SILAGE Corn 6.90 112999 OATS GRAIN Wheat 3.70 113994 BARLEY MALTING Wheat 3.70 113995 BARLEY FEED Wheat 3.70 113999 BARLEY UNSPECIFIED Wheat 3.70 114991 SORGHUM GRAIN Wheat 3.70 121219 COTTON LINT UPLAND Cotton 8.90 121229 COTTON LINT PIMA Cotton 8.90 121299 COTTON LINT UNSPECIFIED Cotton 8.90 132999 SUGAR BEETS Sugar Beets 22.80 151999 COTTONSEED Cotton 8.90 153999 PEANUTS ALL Safflower 4.50 158269 SAFFLOWER Safflower 4.50 158316 SUNFLOWER SEED PLANTING Corn 6.90 158319 SUNFLOWER SEED Corn 6.90 158499 JOJOBA Melon 5.70 161131 BEANS LIMA LG. DRY DryBeans 7.70 161132 BEANS LIMA BABY DRY DryBeans 7.70 161199 BEANS LIMA UNSPECIFIED DryBeans 7.70 161717 BEANS KIDNEY RED DryBeans 7.70 161721 BEANS PINK DryBeans 7.70 161741 BEANS BLACKEYE (PEAS) DryBeans 7.70 161742 BEANS GARBANZO Garbanzo 7.70 162399 BEANS FAVA DryBeans 7.70 163999 PEAS DRY EDIBLE DryBeans 7.70 169999 BEANS DRY EDIBLE UNSPEC. DryBeans 7.70 171019 WHEAT SEED Wheat 3.70 171049 RYE SEED Wheat 3.70 171069 RICE SEED Rice 20.00 171129 OATS SEED Wheat 3.70 171139 BARLEY SEED Wheat 3.70 171519 COTTON SEED PLANTING Cotton 8.90 171582 SAFFLOWER SEED PLANTING Safflower 4.50 171619 BEANS SEED DryBeans 7.70 171639 PEAS SEED DryBeans 7.70 171949 FIELD CROPS SEED MISC. Corn 6.90 171959 SEED VEG & VINECROP Vegetables 8.50 172119 SEED ALFALFA Alfalfa 4.00 172289 SEED CLOVER UNSPECIFIED Alfalfa 4.00 173079 SEED BERMUDA GRASS Alfalfa 4.00 173669 SEED SUDAN GRASS Alfalfa 4.00 173999 SEED GRASS UNSPECIFIED Alfalfa 4.00 178999 SEED OTHER (NO FLOWERS) Alfalfa 4.00 181999 HAY ALFALFA Alfalfa 4.00 188499 HAY GRAIN Alfalfa 4.00 188799 HAY WILD Alfalfa 4.00 188899 HAY SUDAN Alfalfa 4.00 188999 HAY OTHER UNSPECIFIED Alfalfa 4.00 194599 PASTURE IRRIGATED No Land Prep. 0.00 194699 PASTURE RANGE No Land Prep. 0.00 194799 PASTURE FORAGE MISC. No Land Prep. 0.00 195199 SILAGE Wheat 3.70 195299 HAY GREEN CHOP Alfalfa 4.00 195399 STRAW Alfalfa 4.00 198199 RICE WILD Rice 20.00 198999 FIELD CROPS UNSPECIFIED Corn 6.90 201119 ORANGES NAVEL Citrus 0.07 201519 ORANGES VALENCIA Citrus 0.07 201999 ORANGES UNSPECIFIED Citrus 0.07 202999 GRAPEFRUIT ALL Citrus 0.07

DRAFT

7.4-11

TABLE 3 (continued) Summary of CDFA Commodity Code and Assigned Crop Profile

CDFA Commodity


(lbs/Acre/year) 203999 TANGERINES & MANDARINS Citrus 0.07 204999 LEMONS ALL Citrus 0.07 205999 LIMES ALL Citrus 0.07 206999 TANGELOS Citrus 0.07 207999 KUMQUATS Citrus 0.07 208059 CITRUS BY-PRODUCTS MISC. Citrus 0.07 209999 CITRUS UNSPECIFIED Citrus 0.07 211999 APPLES ALL Citrus 0.07 212199 PEACHES FREESTONE Citrus 0.07 212399 PEACHES CLINGSTONE Citrus 0.07 212999 PEACHES UNSPECIFIED Citrus 0.07 213199 CHERRIES SWEET Citrus 0.07 214199 PEARS BARTLETT Citrus 0.07 214899 PEARS ASIAN Citrus 0.07 214999 PEARS UNSPECIFIED Citrus 0.07 215199 PLUMS Citrus 0.07 215399 PLUMCOTS Citrus 0.07 215999 PLUMS DRIED Citrus 0.07 216199 GRAPES TABLE Grapes-Table 0.83 216299 GRAPES WINE Grapes-Wine 1.50 216399 GRAPES RAISIN Grapes-Raisin 2.60 216999 GRAPES UNSPECIFIED Grapes-Wine 1.50 217999 APRICOTS ALL Citrus 0.07 218199 NECTARINES Citrus 0.07 218299 PERSIMMONS Citrus 0.07 218399 POMEGRANATES Citrus 0.07 218499 QUINCE Citrus 0.07 218839 CHERIMOYAS Citrus 0.07 218889 BIOMASS ORCHARD Almonds 3.13 218899 FRUITS & NUTS UNSPECIFIED Citrus 0.07 221999 AVOCADOS ALL Citrus 0.07 224999 DATES Citrus 0.07 225999 FIGS DRIED Citrus 0.07 226999 OLIVES Citrus 0.07 228019 GUAVAS Citrus 0.07 229999 KIWIFRUIT Citrus 0.07 230639 BERRIES BLACKBERRIES Grapes-Table 0.83 230869 BERRIES BOYSENBERRIES Grapes-Table 0.83 234799 BERRIES LOGANBERRIES Grapes-Table 0.83 236199 BERRIES RASPBERRIES Grapes-Table 0.83 237199 BERRIES STRAWBERRIES F MKT Melon 5.70 237299 BERRIES STRAWBERRIES PROC. Melon 5.70 237999 BERRIES STRAWBERRIES UNSPEC Melon 5.70 239999 BERRIES BUSHBERRIES UNSPEC. Grapes-Table 0.83 261999 ALMONDS ALL Almonds 3.13 263999 WALNUTS ENGLISH Almonds 3.13 264999 PECANS Almonds 3.13 265999 WALNUTS BLACK Almonds 3.13 266999 CHESTNUTS Almonds 3.13 267999 MACADAMIA NUTS Almonds 3.13 268079 PISTACHIOS Almonds 3.13 268099 ALMOND HULLS Almonds 3.13 301999 ARTICHOKES Melon 5.70 302199 ASPARAGUS FRESH MARKET Melon 5.70 302299 ASPARAGUS PROCESSING Melon 5.70 302999 ASPARAGUS UNSPECIFIED Melon 5.70 303999 BEANS LIMA GREEN DryBeans 7.70 304199 BEANS SNAP FRESH MARKET DryBeans 7.70 304299 BEANS SNAP PROCESSING DryBeans 7.70 304399 BEANS FRESH UNSPECIFIED DryBeans 7.70 304999 BEANS SNAP UNSPECIFIED DryBeans 7.70 305999 BEETS GARDEN Sugar Beets 22.80 306999 RAPPINI Sugar Beets 22.80 307189 BROCCOLI FOOD SERVICE Vegetables 8.50 307199 BROCCOLI FRESH MARKET Vegetables 8.50 307299 BROCCOLI PROCESSING Vegetables 8.50 307919 BROCCOLI UNSPECIFIED Vegetables 8.50 308999 BRUSSELS SPROUTS Melon 5.70

DRAFT

7.4-12


CDFA Commodity


(lbs/Acre/year) 309999 CABBAGE CH. & SPECIALTY Lettuce 11.50 310999 CABBAGE HEAD Lettuce 11.50 313189 CARROTS FOOD SERVICE Sugar Beets 22.80 313199 CARROTS FRESH MARKET Sugar Beets 22.80 313299 CARROTS PROCESSING Sugar Beets 22.80 313999 CARROTS UNSPECIFIED Sugar Beets 22.80 314189 CAULIFLOWER FOOD SERVICE Vegetables 8.50 314199 CAULIFLOWER FRESH MARKET Vegetables 8.50 314299 CAULIFLOWER PROCESSING Vegetables 8.50 314999 CAULIFLOWER UNSPECIFIED Vegetables 8.50 316189 CELERY FOOD SERVICE Lettuce 11.50 316199 CELERY FRESH MARKET Lettuce 11.50 316299 CELERY PROCESSING Lettuce 11.50 316999 CELERY UNSPECIFIED Lettuce 11.50 318999 RADICCHIO Lettuce 11.50 320999 CHIVES Lettuce 11.50 322999 COLLARD GREENS Lettuce 11.50 323999 CORN SWEET ALL Corn 6.90 325999 CUCUMBERS Vegetables 8.50 330999 EGGPLANT ALL Vegetables 8.50 331999 ENDIVE ALL Lettuce 11.50 332999 ESCAROLE ALL Lettuce 11.50 333999 ANISE (FENNEL) Lettuce 11.50 335999 GARLIC ALL Garlic 6.50 337999 KALE Lettuce 11.50 338999 KOHLRABI Lettuce 11.50 339196 LETTUCE BULK SALAD PRODS. Lettuce 11.50 339999 LETTUCE UNSPECIFIED Lettuce 11.50 340999 LETTUCE HEAD Lettuce 11.50 341999 LETTUCE ROMAINE Lettuce 11.50 342999 LETTUCE LEAF Lettuce 11.50 343999 MELONS CANTALOUPE Melon 5.70 348999 MELONS HONEYDEW Melon 5.70 354299 MELONS UNSPECIFIED Melon 5.70 354999 MELONS WATERMELON Melon 5.70 355999 MUSHROOMS No Land Prep. 0.00 356999 MUSTARD Lettuce 11.50 357999 OKRA Lettuce 11.50 358999 ONIONS Onions 6.50 359999 PARSLEY Lettuce 11.50 361299 PEAS GREEN PROCESSING DryBeans 7.70 361999 PEAS GREEN UNSPECIFIED DryBeans 7.70 363999 PEPPERS BELL Tomatoes 10.10 364999 PEPPERS CHILI HOT Tomatoes 10.10 366999 PUMPKINS Melon 5.70 367999 RADISHES Sugar Beets 22.80 368999 RHUBARB Lettuce 11.50 370999 RUTABAGAS Sugar Beets 22.80 372999 ONIONS GREEN & SHALLOT Onions 6.50 374189 SPINACH FOOD SERVICE Lettuce 11.50 374199 SPINACH FRESH MARKET Lettuce 11.50 374299 SPINACH PROCESSING Lettuce 11.50 374999 SPINACH UNSPECIFIED Lettuce 11.50 375999 SQUASH Melon 5.70 376999 SWISS CHARD Lettuce 11.50 378199 TOMATOES FRESH MARKET Tomatoes 10.10 378299 TOMATOES PROCESSING Tomatoes 10.10 378999 TOMATOES UNSPECIFIED Tomatoes 10.10 380999 TURNIPS ALL Sugar Beets 22.80 381999 GREENS TURNIP & MUSTARD Lettuce 11.50 387999 LEEKS Onions 6.50 391999 POTATOES IRISH ALL Sugar Beets 22.80 392999 POTATOES SWEET Sugar Beets 22.80 393999 HORSERADISH Onions 6.50 394199 SALAD GREENS NEC. Lettuce 11.50 394999 PEAS EDIBLE POD (SNOW) DryBeans 7.70 395999 VEGETABLES ORIENTAL ALL Vegetables 8.50 396999 SPROUTS ALFALFA & BEAN Lettuce 11.50

DRAFT

7.4-13


CDFA Commodity


(lbs/Acre/year) 398199 CUCUMBERS GREENHOUSE No Land Prep. 0.00 398299 TOMATOES GREENHOUSE No Land Prep. 0.00 398399 TOMATOES CHERRY Tomatoes 10.10 398499 TOMATILLO Tomatoes 10.10 398559 CILANTRO Lettuce 11.50 398599 SPICES AND HERBS Lettuce 11.50 398899 VEGETABLES BABY Vegetables 8.50 398999 VEGETABLES UNSPECIFIED Vegetables 8.50 832919 POTATOES SEED Sugar Beets 22.80 824999 NURSERY FRT/VINE/NUT N-BEAR None 825379 NURSERY PLANTS STRAWBERRY None 834999 NURSERY PLANTS VEG. BEDDING None 851999 CHRISTMAS TREES & CUT GREENS None 861999 NURSERY FLOWER SEEDS None 862480 NURSERY BULBS LILY None 862999 NURSERY FL BLBS./CRMS./RHZ. None 863999 NURSERY FL. PROPG. MTRLS None 864663 NURSERY PLANTS ROSE None 864999 NURSERY PLANTS BEDDING None 866209 FLOWERS MUMS POTTED None 866559 NURSERY PLANTS ORCHID None 866605 FLOWERS POINSETTIA POTTED None 866999 NURSERY PLANTS POT'D UNSP None 867055 FLOWERS ASTERS CUT None 867170 FLOWERS CARNATION CUT STD. None 867171 FLOWERS CARNATION CUT MIN. None 867179 FLOWERS CARNATION UNSPEC. None 867205 FLOWERS CHRYSNTH. CUT STD. None 867206 FLOWERS CHRYSNTH. CUT POM. None 867209 FLOWERS CHRYSNTH. UNSPEC. None 867360 FLOWERS GARDENIAS CUT None 867435 FLOWERS IRISES CUT None 867559 FLOWERS ORCHIDS CUT ALL None 867663 FLOWERS ROSES CUT STANDARD None 867664 FLOWERS ROSES CUT MIN. None 867669 FLOWERS ROSES UNSPECIFIED None 867899 FLOWERS DECORATIVE DRIED None 867999 FLOWERS CUT UNSPECIFIED None 868999 FLOWERS FOLIAGE CUT ALL None 876130 FLOWERS CACTI SUCCULENTS None 879999 FLOWERS FOLIAGE PLANTS None 892999 NURSERY TURF No Land Prep. 0.00 894999 NURSERY HERBAC. PRRNLS None 895999 NURSERY WOODY ORNAMNTALS None 898999 NURSERY HORT. SPECMN. MISC. None 899999 NURSERY PRODUCTS MISC. None 933179 FOREST PRODUCTS FIREWOOD None

DRAFT

7.4-14

TABLE 4 2000 Agricultural Land Preparation PM10 emissions and Seasonal Profile

AB CO County JANT FEBT MART APRT MAYT JUNT JULT AUGT SEPT OCTT NOVT DECT GBV 2 ALPINE 14 INYO 0.090 0.007 0.007 0.007 0.007 0.007 0.009 0.013 0.013 0.281 0.281 0.277 26 MONO 0.083 0.030 0.030 0.030 0.030 0.030 0.037 0.044 0.044 0.217 0.217 0.210LC 17 LAKE 0.048 0.000 0.000 0.005 0.021 0.021 0.016 0.016 0.016 0.194 0.331 0.331LT 9 EL DORADO 0.033 0.000 0.000 0.016 0.071 0.071 0.055 0.055 0.055 0.240 0.203 0.203 31 PLACER 0.005 0.000 0.076 0.415 0.415 0.000 0.000 0.000 0.000 0.026 0.031 0.031MC 3 AMADOR 0.077 0.000 0.069 0.011 0.048 0.048 0.037 0.037 0.037 0.198 0.220 0.220 5 CALAVERAS 0.047 0.000 0.043 0.005 0.024 0.024 0.019 0.019 0.019 0.113 0.344 0.344 9 EL DORADO 0.033 0.000 0.000 0.016 0.071 0.071 0.055 0.055 0.055 0.240 0.203 0.203 22 MARIPOSA 0.170 0.000 0.284 0.001 0.003 0.003 0.002 0.002 0.002 0.006 0.264 0.264 29 NEVADA 0.000 0.000 0.000 0.035 0.159 0.159 0.123 0.123 0.123 0.276 0.000 0.000 31 PLACER 0.005 0.000 0.076 0.415 0.415 0.000 0.000 0.000 0.000 0.026 0.031 0.031 32 PLUMAS 0.094 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.302 0.302 0.302 46 SIERRA 0.094 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.302 0.302 0.302 55 TUOLUMNE 0.094 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.302 0.302 0.302MD 15 KERN 0.031 0.054 0.057 0.016 0.016 0.016 0.020 0.021 0.021 0.081 0.337 0.332 19 LOS ANGELES 0.063 0.033 0.047 0.032 0.032 0.032 0.056 0.054 0.054 0.204 0.198 0.194 33 RIVERSIDE 0.055 0.047 0.069 0.020 0.017 0.017 0.073 0.021 0.021 0.162 0.254 0.242 36 SAN BERNARDINO 0.094 0.015 0.060 0.014 0.015 0.015 0.023 0.015 0.015 0.221 0.257 0.256NC 8 DEL NORTE 0.094 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.302 0.302 0.302 12 HUMBOLDT 0.060 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.194 0.373 0.373 23 MENDOCINO 0.008 0.008 0.008 0.038 0.144 0.144 0.114 0.114 0.114 0.261 0.024 0.024 49 SONOMA 0.030 0.003 0.009 0.021 0.086 0.086 0.067 0.067 0.067 0.226 0.169 0.169 53 TRINITY 0.094 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.302 0.302 0.302NCC 27 MONTEREY 0.022 0.029 0.030 0.020 0.023 0.023 0.293 0.025 0.025 0.330 0.110 0.070 35 SAN BENITO 0.036 0.011 0.015 0.004 0.005 0.005 0.248 0.009 0.009 0.345 0.173 0.139 44 SANTA CRUZ 0.013 0.048 0.048 0.017 0.013 0.013 0.250 0.013 0.013 0.278 0.166 0.128NEP 18 LASSEN 0.078 0.000 0.008 0.042 0.042 0.000 0.017 0.000 0.000 0.270 0.271 0.269 25 MODOC 0.088 0.006 0.006 0.006 0.006 0.006 0.008 0.009 0.009 0.273 0.291 0.291 47 SISKIYOU 0.066 0.018 0.018 0.018 0.018 0.018 0.030 0.026 0.026 0.187 0.288 0.287SC 19 LOS ANGELES 0.063 0.033 0.047 0.032 0.032 0.032 0.056 0.054 0.054 0.204 0.198 0.194 30 ORANGE 0.035 0.139 0.147 0.007 0.007 0.007 0.080 0.007 0.007 0.188 0.214 0.160 33 RIVERSIDE 0.055 0.047 0.069 0.020 0.017 0.017 0.073 0.021 0.021 0.162 0.254 0.242 36 SAN BERNARDINO 0.094 0.015 0.060 0.014 0.015 0.015 0.023 0.015 0.015 0.221 0.257 0.256SCC 40 SAN LUIS OBISPO 0.033 0.036 0.041 0.018 0.024 0.024 0.155 0.022 0.024 0.244 0.205 0.176 42 SANTA BARBARA 0.036 0.058 0.065 0.033 0.028 0.028 0.183 0.028 0.028 0.255 0.147 0.109 56 VENTURA 0.022 0.048 0.052 0.012 0.012 0.012 0.273 0.015 0.015 0.317 0.134 0.089SD 37 SAN DIEGO 0.063 0.064 0.043 0.024 0.025 0.025 0.054 0.028 0.028 0.209 0.222 0.215SF 1 ALAMEDA 0.120 0.000 0.132 0.004 0.018 0.018 0.014 0.014 0.014 0.162 0.253 0.253 7 CONTRA COSTA 0.118 0.021 0.160 0.005 0.006 0.006 0.006 0.006 0.006 0.096 0.286 0.284 21 MARIN 0.056 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.179 0.367 0.367 28 NAPA 0.006 0.000 0.000 0.033 0.146 0.146 0.114 0.114 0.114 0.275 0.026 0.026 38 SAN FRANCISCO 41 SAN MATEO 0.027 0.133 0.133 0.014 0.014 0.014 0.014 0.023 0.023 0.126 0.257 0.219 43 SANTA CLARA 0.058 0.036 0.043 0.008 0.010 0.010 0.140 0.017 0.017 0.283 0.202 0.177 48 SOLANO 0.075 0.039 0.089 0.003 0.004 0.004 0.004 0.004 0.004 0.128 0.328 0.318 49 SONOMA 0.030 0.003 0.009 0.021 0.086 0.086 0.067 0.067 0.067 0.226 0.169 0.169SJV 10 FRESNO 0.033 0.062 0.062 0.016 0.021 0.014 0.034 0.035 0.020 0.119 0.297 0.287 15 KERN 0.031 0.054 0.057 0.016 0.016 0.016 0.020 0.021 0.021 0.081 0.337 0.332 16 KINGS 0.047 0.059 0.117 0.003 0.003 0.003 0.003 0.004 0.003 0.036 0.363 0.359 20 MADERA 0.038 0.024 0.059 0.009 0.025 0.025 0.020 0.047 0.020 0.091 0.322 0.321 24 MERCED 0.060 0.044 0.089 0.022 0.023 0.010 0.012 0.012 0.012 0.087 0.316 0.313 39 SAN JOAQUIN 0.073 0.051 0.109 0.032 0.037 0.019 0.020 0.022 0.022 0.116 0.255 0.244 50 STANISLAUS 0.064 0.056 0.113 0.015 0.016 0.005 0.014 0.005 0.005 0.113 0.307 0.286 54 TULARE 0.065 0.034 0.115 0.008 0.008 0.008 0.007 0.012 0.007 0.062 0.341 0.334SS 13 IMPERIAL 0.068 0.035 0.045 0.026 0.026 0.026 0.069 0.035 0.035 0.191 0.225 0.219 33 RIVERSIDE 0.055 0.047 0.069 0.020 0.017 0.017 0.073 0.021 0.021 0.162 0.254 0.242SV 4 BUTTE 0.005 0.004 0.081 0.387 0.387 0.001 0.001 0.001 0.001 0.015 0.060 0.058 6 COLUSA 0.009 0.016 0.079 0.355 0.355 0.002 0.002 0.002 0.002 0.035 0.074 0.070 11 GLENN 0.018 0.013 0.094 0.331 0.331 0.003 0.004 0.004 0.004 0.028 0.087 0.082 31 PLACER 0.005 0.000 0.076 0.415 0.415 0.000 0.000 0.000 0.000 0.026 0.031 0.031 34 SACRAMENTO 0.078 0.014 0.123 0.117 0.123 0.016 0.016 0.016 0.016 0.071 0.205 0.205 45 SHASTA 0.051 0.000 0.028 0.152 0.152 0.000 0.039 0.000 0.000 0.208 0.188 0.182 48 SOLANO 0.075 0.039 0.089 0.003 0.004 0.004 0.004 0.004 0.004 0.128 0.328 0.318 51 SUTTER 0.011 0.012 0.086 0.362 0.362 0.001 0.001 0.001 0.001 0.028 0.071 0.067 52 TEHAMA 0.051 0.024 0.083 0.054 0.054 0.000 0.000 0.000 0.000 0.083 0.331 0.320 57 YOLO 0.062 0.021 0.088 0.136 0.137 0.003 0.003 0.003 0.003 0.095 0.223 0.223 58 YUBA 0.006 0.000 0.082 0.405 0.405 0.000 0.000 0.000 0.000 0.015 0.043 0.043

DRAFT

7.4-15

California Air Resource Board 4/3/2003

Code Commodity VMT/acre/year ReportedGR Grapes (All) 0.38 15 VMT/40 acres/yearCI Citrus 1.23 98 VMT/80 acres/yearTR Tree Fruit 1.24 62 VMT/50 acres/yearTF Tree & Citrus Fruit 1.23 Average of Citrus & Tree FruitNC Nut Crops 0.49 37 VMT/75 acres/yearCL Cotton (large) 0.40 64 VMT/160 acres/yearCS Cotton (small) 2.40 156 VMT/65 acres/yearEX Existing ARB 4.38 175 VMT/40 acres/yearZE Zero 0 0

Notes:

VMT data were provided by growers and compiled by Sierra research for the highlighted rows.

Assignment of VMT to CropsAssign Cotton (large) to crops assumed to typically be large acreagesAssign Cotton (small) to crops assumed to typically be small acreages and vegetablesAssign Citrus & Tree Fruit to all citrus and non-nut fruit trees (the VMT is so similar for these categories that there is no reason to have separate codes)Assign Grapes to wine, table, and raisin grapesAssign Nut Crops to all nut crops

3/6/2003Teleconference with Dave Mitchell, Patia Siong, Jason Baldwin, Manuel Cunha, Karla Fullerton, Roger Isom to get comments on initial assignments and reassign as necessary.

Prepared by:Patrick GaffneyCalifornia Air Resources [email protected] 916-322-7303February 7, 2003

Unpaved Ag1999 Mar_26_2003.xls 1

California Air Resources Board 4/3/2003

Assignment of Unpaved Road VMT Classifications to Commodities

Commodity Code Crop Description Crop Profile

Harvest Category

VMT Category

VMT Category Description

VMT/acre /year

101999 WHEAT ALL Wheat FM_Wheat/1 CL Cotton (large) 0.40104999 RYE FOR GRAIN Wheat FM_Wheat/1 CL Cotton (large) 0.40106199 RICE, FOR MILLING Rice FM_Cotton/2 CL Cotton (large) 0.40106269 FIELD CROP BY PRODUCTS Cotton FM_Cotton/20 CL Cotton (large) 0.40108999 FOOD GRAINS, MISC Corn FM_Cotton/2 CL Cotton (large) 0.40111559 CORN, WHITE Corn VH_Cotton/40 CL Cotton (large) 0.40111991 CORN FOR GRAIN Corn FM_Cotton/2 CL Cotton (large) 0.40111992 CORN FOR SILAGE Corn FM_Cotton/20 CS Cotton (small) 2.40112999 OATS FOR GRAIN Wheat FM_Wheat/1 CL Cotton (large) 0.40113994 BARLEY, MALTING Wheat FM_Wheat/1 CL Cotton (large) 0.40113995 BARLEY, FEED Wheat FM_Wheat/1 CL Cotton (large) 0.40113999 BARLEY, UNSPECIFIED Wheat FM_Wheat/1 CL Cotton (large) 0.40114991 SORGHUM, GRAIN Wheat FM_Wheat/1 CL Cotton (large) 0.40121219 COTTON LINT, UPLAND Cotton FM_Cotton/1 CL Cotton (large) 0.40121229 COTTON LINT, PIMA Cotton FM_Cotton/1 CL Cotton (large) 0.40121299 COTTON LINT, UNSPEC Cotton FM_Cotton/1 CL Cotton (large) 0.40132999 SUGAR BEETS Sugar Beets FM_Cotton/2 CS Cotton (small) 2.40151999 COTTONSEED Cotton FM_Cotton/1 CL Cotton (large) 0.40153999 PEANUTS, ALL Safflower FM_Cotton/2 CS Cotton (small) 2.40158269 SAFFLOWER Safflower FM_Wheat/1 CL Cotton (large) 0.40158316 SUNFLOWER SEED, PLANTING Corn FM_Wheat/1 CS Cotton (small) 2.40158319 SUNFLOWER SEED Corn FM_Wheat/1 CS Cotton (small) 2.40158499 JOJOBA Melon VH_Cotton/40 CS Cotton (small) 2.40161131 BEANS, LIMAS, LG. DRY DryBeans FM_Cotton/2 CS Cotton (small) 2.40161132 BEANS, LIMAS, BABY DRY DryBeans FM_Cotton/2 CS Cotton (small) 2.40161199 LIMA BEANS, UNSPECIFIED DryBeans FM_Cotton/2 CS Cotton (small) 2.40161717 BEANS, RED KIDNEY DryBeans FM_Cotton/2 CS Cotton (small) 2.40161721 BEANS, PINK DryBeans FM_Cotton/2 CS Cotton (small) 2.40161741 BEANS, BLACKEYE (PEAS) DryBeans FM_Cotton/2 CS Cotton (small) 2.40161742 BEANS, GARBANZO Garbanzo FM_Cotton/2 CS Cotton (small) 2.40162399 BEANS, FAVA DryBeans FM_Cotton/2 CS Cotton (small) 2.40163999 PEAS, DRY EDIBLE DryBeans VI_Cotton/20 CS Cotton (small) 2.40169999 BEANS,UNSPEC. DRY EDIBLE DryBeans FM_Cotton/2 CS Cotton (small) 2.40

Unpaved Ag1999 Mar_26_2003.xls Page 1




Harvest Category

VMT Category


VMT/acre /year

171019 SEED WHEAT Wheat FM_Wheat/1 CL Cotton (large) 0.40171049 SEED RYE Wheat FM_Wheat/1 CL Cotton (large) 0.40171069 SEED RICE Rice FM_Cotton/2 CL Cotton (large) 0.40171129 SEED OATS Wheat FM_Wheat/1 CL Cotton (large) 0.40171139 SEED BARLEY Wheat FM_Wheat/1 CL Cotton (large) 0.40171519 SEED, COTTON FOR PLANTING Cotton FM_Cotton/1 CL Cotton (large) 0.40171582 SEED, SAFFLOWER, PLANTING Safflower FM_Wheat/1 CL Cotton (large) 0.40171619 SEED BEANS DryBeans FM_Cotton/2 CS Cotton (small) 2.40171639 SEED PEAS DryBeans VI_Cotton/20 CS Cotton (small) 2.40171949 SEED, MISC FIELD CROP Corn FM_Cotton/20 CL Cotton (large) 0.40171959 SEED, VEG & VINECROP Vegetables FM_Cotton/20 CS Cotton (small) 2.40172119 SEED, ALFALFA Alfalfa FM_Zero/1 CL Cotton (large) 0.40172289 CLOVER, UNSPECIFIED SEED Alfalfa FM_Zero/1 CL Cotton (large) 0.40173079 SEED, BERMUDA GRASS Alfalfa FM_Zero/1 CL Cotton (large) 0.40173669 SEED, SUDAN GRASS Alfalfa FM_Zero/1 CL Cotton (large) 0.40173999 SEED, GRASS, UNSPECIFIED Alfalfa FM_Zero/1 CL Cotton (large) 0.40178999 SEED, OTHER (NO FLOWERS) Alfalfa FM_Cotton/20 CL Cotton (large) 0.40181999 HAY, ALFALFA Alfalfa FM_Zero/1 CL Cotton (large) 0.40188499 HAY, GRAIN Alfalfa FM_Cotton/2 CL Cotton (large) 0.40188799 HAY, WILD Alfalfa FM_Cotton/2 CL Cotton (large) 0.40188899 HAY, SUDAN Alfalfa FM_Zero/1 CL Cotton (large) 0.40188999 HAY, OTHER UNSPECIFIED Alfalfa FM_Cotton/2 CL Cotton (large) 0.40194599 PASTURE, IRRIGATED No Land Prep. ZO_Zero/1 ZE Zero 0.00194699 PASTURE, RANGE No Land Prep. ZO_Zero/1 ZE Zero 0.00194799 PASTURE, MISC. FORAGE No Land Prep. ZO_Zero/1 ZE Zero 0.00195199 SILAGE Wheat FM_Cotton/20 CL Cotton (large) 0.40195299 HAY, GREEN CHOP Alfalfa FM_Zero/1 CL Cotton (large) 0.40195399 STRAW Alfalfa FM_Wheat/1 CL Cotton (large) 0.40198199 RICE, WILD Rice FM_Cotton/2 CL Cotton (large) 0.40198999 FIELD CROPS, UNSPEC. Corn FM_Cotton/20 CL Cotton (large) 0.40201119 ORANGES, NAVEL Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23201519 ORANGES, VALENCIAS Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23201999 ORANGES, UNSPECIFIED Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23





Harvest Category

VMT Category


VMT/acre /year

202999 GRAPEFRUIT, ALL Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23203999 TANGERINES & MANDARINS Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23204999 LEMONS, ALL Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23205999 LIMES, ALL Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23206999 TANGELOS Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23207999 KUMQUATS Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23208059 CITRUS, MISC BY-PROD Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23209999 CITRUS, UNSPECIFIED Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23211999 APPLES, ALL Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23212199 PEACHES, FREESTONE Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23212399 PEACHES, CLINGSTONE Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23212999 PEACHES, UNSPECIFIED Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23213199 CHERRIES, SWEET Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23214199 PEARS, BARLETT Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23214899 PEARS, ASIAN Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23214999 PEARS, UNSPECIFIED Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23215199 PLUMS Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23215399 PLUMCOTS Citrus FF_Cotton/40 TF Tree & Citrus Fruit 1.23215999 PRUNES, DRIED Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23216199 GRAPES, TABLE Grapes-Table VI_Cotton/20 CS Cotton (small) 2.40216299 GRAPES, WINE Grapes-Wine VI_Cotton/20 GR Grapes (All) 0.38216399 GRAPES, RAISIN Grapes-Raisin VI_Cotton/20 GR Grapes (All) 0.38216999 GRAPES, UNSPECIFIED Grapes-Wine VI_Cotton/20 GR Grapes (All) 0.38217999 APRICOTS, ALL Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23218199 NECTARINES Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23218299 PERSIMMONS Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23218399 POMEGRANATES Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23218499 QUINCE Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23218839 CHERIMOYAS Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23218889 ORCHARD BIOMASS Almonds TF_Cotton/40 NC Nut Crops 0.49218899 FRUITS & NUTS, UNSPEC. Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23221999 AVOCADOS, ALL Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23224999 DATES Citrus TF_Almonds/20 TF Tree & Citrus Fruit 1.23





Harvest Category

VMT Category


VMT/acre /year

225999 FIGS, DRIED Citrus TF_Almonds/20 TF Tree & Citrus Fruit 1.23226999 OLIVES Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23228019 GUAVAS Citrus TF_Cotton/40 TF Tree & Citrus Fruit 1.23229999 KIWIFRUIT Citrus TF_Cotton/40 GR Grapes (All) 0.38230639 BERRIES, BLACKBERRIES Grapes-Table FH_Cotton/40 GR Grapes (All) 0.38230869 BERRIES, BOYSENBERRIES Grapes-Table FH_Cotton/40 GR Grapes (All) 0.38234799 BERRIES, LOGANBERRIES Grapes-Table FH_Cotton/40 GR Grapes (All) 0.38236199 BERRIES, RASPBERRIES Grapes-Table FH_Cotton/40 GR Grapes (All) 0.38237199 STRAWBERRIES, FRESH MKT Melon FH_Cotton/40 CS Cotton (small) 2.40237299 STRAWBERRIES, PROC Melon FH_Cotton/40 CS Cotton (small) 2.40237999 STRAWBERRIES, UNSPECIFIED Melon FH_Cotton/40 CS Cotton (small) 2.40239999 BERRIES, BUSH, UNSPECIFIED Grapes-Table FH_Cotton/40 CS Cotton (small) 2.40261999 ALMONDS, ALL Almonds TN_Almonds/1 NC Nut Crops 0.49263999 WALNUTS, ENGLISH Almonds TN_Almonds/1 NC Nut Crops 0.49264999 PECANS Almonds TN_Almonds/10 NC Nut Crops 0.49265999 WALNUTS, BLACK Almonds TN_Almonds/1 NC Nut Crops 0.49266999 CHESTNUTS Almonds TN_Almonds/10 NC Nut Crops 0.49267999 MACADAMIA NUT Almonds TN_Almonds/10 NC Nut Crops 0.49268079 PISTACHIOS Almonds TN_Almonds/10 NC Nut Crops 0.49268099 ALMOND HULLS Almonds TN_Almonds/1 NC Nut Crops 0.49301999 ARTICHOKES Melon VH_Cotton/40 CS Cotton (small) 2.40302199 ASPARAGUS, FRESH MKT Melon FM_Cotton/2 CS Cotton (small) 2.40302299 ASPARAGUS, PROC Melon FM_Cotton/2 CS Cotton (small) 2.40302999 ASPARAGUS, UNSPECIFIED Melon FM_Cotton/2 CS Cotton (small) 2.40303999 BEANS, GREEN LIMAS DryBeans FM_Cotton/2 CS Cotton (small) 2.40304199 BEANS, SNAP FR MKT DryBeans VI_Cotton/20 CS Cotton (small) 2.40304299 BEANS, SNAP PROC DryBeans VI_Cotton/20 CS Cotton (small) 2.40304399 BEANS FRESH UNSPECIFIED DryBeans VI_Cotton/20 CS Cotton (small) 2.40304999 BEANS, UNSPECIFIED SNAP DryBeans VI_Cotton/20 CS Cotton (small) 2.40305999 BEETS, GARDEN Sugar Beets FM_Cotton/2 CS Cotton (small) 2.40306999 RAPINI Sugar Beets VH_Cotton/40 CS Cotton (small) 2.40307189 BROCCOLI,FOOD SERV Vegetables VH_Cotton/40 CS Cotton (small) 2.40307199 BROCCOLI, FR MKT Vegetables VH_Cotton/40 CS Cotton (small) 2.40





Harvest Category

VMT Category


VMT/acre /year

307299 BROCCOLI, PROC Vegetables VM_Cotton/40 CS Cotton (small) 2.40307919 BROCCOLI, UNSPECIFIED Vegetables VH_Cotton/40 CS Cotton (small) 2.40308999 BRUSSELS SPROUTS Melon VH_Cotton/40 CS Cotton (small) 2.40309999 CABBAGE, CH. & SPECIALTY Lettuce VH_Cotton/40 CS Cotton (small) 2.40310999 CABBAGE, HEAD Lettuce VH_Cotton/40 CS Cotton (small) 2.40313189 CARROTS, FOOD SERV Sugar Beets VM_Cotton/20 CS Cotton (small) 2.40313199 CARROTS, FR MKT Sugar Beets VM_Cotton/20 CS Cotton (small) 2.40313299 CARROTS, PROC Sugar Beets VM_Cotton/20 CS Cotton (small) 2.40313999 CARROTS, UNSPECIFIED Sugar Beets VM_Cotton/20 CS Cotton (small) 2.40314189 CAULIFLOWER, FOOD SERV Vegetables VH_Cotton/40 CS Cotton (small) 2.40314199 CAULIFLOWER, FR MKT Vegetables VH_Cotton/40 CS Cotton (small) 2.40314299 CAULIFLOWER, PROC Vegetables VH_Cotton/40 CS Cotton (small) 2.40314999 CAULIFLOWER, UNSPECIFIED Vegetables VH_Cotton/40 CS Cotton (small) 2.40316189 CELERY, FOOD SERV Lettuce VH_Cotton/40 CS Cotton (small) 2.40316199 CELERY, FR MKT Lettuce VH_Cotton/40 CS Cotton (small) 2.40316299 CELERY, PROC Lettuce VH_Cotton/40 CS Cotton (small) 2.40316999 CELERY, UNSPECIFIED Lettuce VH_Cotton/40 CS Cotton (small) 2.40318999 RADICCHIO Lettuce VH_Cotton/40 CS Cotton (small) 2.40320999 CHIVES Lettuce VH_Cotton/40 CS Cotton (small) 2.40322999 COLLARD GREENS Lettuce VH_Cotton/40 CS Cotton (small) 2.40323999 CORN, SWEET ALL Corn VH_Cotton/40 CS Cotton (small) 2.40325999 CUCUMBERS Vegetables VH_Cotton/40 CS Cotton (small) 2.40330999 EGGPLANT, ALL Vegetables VH_Cotton/40 CS Cotton (small) 2.40331999 ENDIVE, ALL Lettuce VH_Cotton/40 CS Cotton (small) 2.40332999 ESCAROLE, ALL Lettuce VH_Cotton/40 CS Cotton (small) 2.40333999 ANISE (FENNEL) Lettuce FM_Cotton/2 CS Cotton (small) 2.40335999 GARLIC, ALL Garlic FM_Cotton/2 CS Cotton (small) 2.40337999 KALE Lettuce VH_Cotton/40 CS Cotton (small) 2.40338999 KOHLRABI Lettuce VH_Cotton/40 CS Cotton (small) 2.40339196 LETTUCE, BULK SALAD PRODS. Lettuce VH_Cotton/40 CS Cotton (small) 2.40339999 LETTUCE, UNSPECIFIED Lettuce VH_Cotton/40 CS Cotton (small) 2.40340999 LETTUCE, HEAD Lettuce VH_Cotton/40 CS Cotton (small) 2.40341999 LETTUCE, ROMAINE Lettuce VH_Cotton/40 CS Cotton (small) 2.40





Harvest Category

VMT Category


VMT/acre /year

342999 LETTUCE, LEAF Lettuce VH_Cotton/40 CS Cotton (small) 2.40343999 MELON, CANTALOUPE Melon FH_Cotton/40 CS Cotton (small) 2.40348999 MELON, HONEYDEW Melon FH_Cotton/40 CS Cotton (small) 2.40354299 MELON, UNSPECIFIED Melon FH_Cotton/40 CS Cotton (small) 2.40354999 MELON, WATER MELONS Melon FH_Cotton/40 CS Cotton (small) 2.40355999 MUSHROOMS No Land Prep. ZO_Zero/1 ZE Zero 0.00356999 MUSTARD Lettuce VH_Cotton/40 CS Cotton (small) 2.40357999 OKRA Lettuce VH_Cotton/40 CS Cotton (small) 2.40358999 ONIONS Onions FM_Cotton/2 CS Cotton (small) 2.40359999 PARSLEY Lettuce VH_Cotton/40 CS Cotton (small) 2.40361299 PEAS, GREEN, PROCESSING DryBeans VI_Cotton/20 CS Cotton (small) 2.40361999 PEAS, GREEN, UNSPECIFIED DryBeans VI_Cotton/20 CS Cotton (small) 2.40363999 PEPPERS, BELL Tomatoes VH_Cotton/40 CS Cotton (small) 2.40364999 PEPPERS, CHILI, HOT Tomatoes VH_Cotton/40 CS Cotton (small) 2.40366999 PUMPKINS Melon FM_Cotton/20 CS Cotton (small) 2.40367999 RADISHES Sugar Beets VH_Cotton/40 CS Cotton (small) 2.40368999 RHUBARB Lettuce VH_Cotton/40 CS Cotton (small) 2.40370999 RUTABAGAS Sugar Beets FM_Cotton/2 CS Cotton (small) 2.40372999 ONIONS, GREEN & SHALLOTS Onions VH_Cotton/40 CS Cotton (small) 2.40374189 SPINACH, FOOD SERV Lettuce VH_Cotton/40 CS Cotton (small) 2.40374199 SPINACH, FR MKT Lettuce VH_Cotton/40 CS Cotton (small) 2.40374299 SPINACH, PROC Lettuce VH_Cotton/40 CS Cotton (small) 2.40374999 SPINACH UNSPECIFIED Lettuce VH_Cotton/40 CS Cotton (small) 2.40375999 SQUASH Melon VH_Cotton/20 CS Cotton (small) 2.40376999 SWISSCHARD Lettuce VH_Cotton/40 CS Cotton (small) 2.40378199 TOMATOES, FRESH MARKET Tomatoes HD_Cotton/40 CS Cotton (small) 2.40378299 TOMATOES, PROCESSING Tomatoes FM_Cotton/20 CL Cotton (large) 0.40378999 TOMATOES, UNSPECIFIED Tomatoes FM_Cotton/20 CS Cotton (small) 2.40380999 TURNIPS, ALL Sugar Beets FM_Cotton/2 CS Cotton (small) 2.40381999 GREENS, TURNIP & MUSTARD Lettuce VH_Cotton/40 CS Cotton (small) 2.40387999 LEEKS Onions VH_Cotton/40 CS Cotton (small) 2.40391999 POTATOES, IRISH ALL Sugar Beets FM_Cotton/2 CL Cotton (large) 0.40392999 SWEET POTATOES Sugar Beets FM_Cotton/2 CL Cotton (large) 0.40





Harvest Category

VMT Category


VMT/acre /year

393999 HORSERADISH Onions VH_Cotton/40 CS Cotton (small) 2.40394199 SALAD GREENS NEC Lettuce VH_Cotton/40 CS Cotton (small) 2.40394999 PEAS, EDIBLE POD (SNOW) DryBeans VI_Cotton/20 CS Cotton (small) 2.40395999 VEGETABLES, ORIENTAL, ALL Vegetables VH_Cotton/40 CS Cotton (small) 2.40396999 SPROUTS, ALFALFA & BEAN Lettuce VH_Cotton/40 CS Cotton (small) 2.40398199 CUCUMBERS, GREENHOUSE No Land Prep. ZO_Zero/1 ZE Zero 0.00398299 TOMATOES, GREENHOUSE No Land Prep. ZO_Zero/1 ZE Zero 0.00398399 TOMATOES, CHERRY Tomatoes VH_Cotton/40 CS Cotton (small) 2.40398499 TOMATILLO Tomatoes VH_Cotton/40 CS Cotton (small) 2.40398559 CILANTRO Lettuce VH_Cotton/40 CS Cotton (small) 2.40398599 SPICES AND HERBS Lettuce VH_Cotton/40 CS Cotton (small) 2.40398899 VEGETABLES, BABY Vegetables VH_Cotton/40 CS Cotton (small) 2.40398999 VEGETABLES, UNSPECIFIED Vegetables VM_Cotton/20 CS Cotton (small) 2.40


Winston H. Hickox Agency Secretary

California Environmental Protection Agency

Printed on Recycled Paper

Air Resources Board Alan C. Lloyd, Ph.D.

Chairman 1001 I Street • P.O. Box 2815 • Sacramento, California 95812 • www.arb.ca.gov

Gray DavisGovernor

MEMORANDUM

TO: SJV PM10 SIP Emission Inventory Group FROM: Patrick Gaffney DATE: July 31, 2002 SUBJECT: Update and Review of 1999 Paved Road Dust Emissions The draft update calculations for the paved road dust emissions estimates are complete. Now, your help is needed to double check the results and make sure everything makes sense. In addition, some unresolved questions still need to be answered. The major results are provided below, as well as a summary of the major assumptions and some pending questions. For those who are deeply interested in all of this, there is a companion spreadsheet: Paved Road Dust SJV 1999.xls. The spreadsheet provides the updated emissions, and compares the current and updated values for each county and road class. The spreadsheet also includes the summarized raw VMT data, the rainfall correction data, and the emission factors.

Emissions Estimation

Based on initial estimates, the overall emissions for PM10 paved road dust in the SJV decrease by 28% using the updated VMT data and the rainfall data (23,178 to 16,721 tpy PM10). About 23% of the decrease is due the VMT apportioning

changes; the remainder is from the rainfall correction. The overall VMT for the valley shows a 1% decrease from what

is currently in the inventory for 1999. This means that the bulk of the emissions decreases are due to apportioning more of the VMT to freeways than was done previously (which produces fewer emissions per VMT). Kings county is showing a 119% emissions increase due to a

large increase in rural road VMT (726 to 1590 tpy). For the entire valley, the emission changes by road type are:

+63% Freeway, -26% Arterial, -26% Collector, -56% Local, -33% Rural, -28% Overall

2

VMT Updates

Provided by Barbara Joy from EarthMatters via email 7/10/2002. Compiled from reports generated by SJV TPAs. Some TPAs provided data as VMT, some as 1000’s VMT;

there are some possible mislabeling problems with the units. The overall VMT only decreased by 1% from values currently in

the emission inventory (29,788 to 29,617 million VMT/year). However, substantially more of the VMT is assigned to the freeways, producing a significant reduction in the emissions estimates. By road type for the entire valley, the VMT changes are:

+74% Freeway, -21% Arterial, -21% Collector, -30% Local, -4% Rural, -1% overall. Based on labeling, Merced provided year 2000 data instead of

1999. Used data as 1999. Is the Kern county data for the full county or just the

SJVUAPCD portion? For San Joaquin County VMT, the road types Rural Local (zero

VMT) and Centroid (5.9% of VMT) were provided. All VMT were assigned to Rural road category. Is this right?

Rainfall Correction

Provided by Shawn Ferreria, SJVUAPCD. Compiled from the Western Regional Climate Center.

Information from multiple sites in each county were averaged for each month. For most sites the data used is based on about 50 years of

averaged data. Monitors beyond the valley floor, at higher elevations, were

excluded. The rainfall adjustment reduced overall paved road dust

emissions by about 5%

Emission Factor

Emission factors are based on the AP-42 EPA methodology using California specific silt loadings. Assumed average vehicle fleet weight of 2.4 tons. Applied rainfall correction factor to reduce emissions based on

the number of days with measurable rain each month

AP-42 Analysis – Paved & Unpaved Road Dust 7/17/02 Paved Road Dust Methodology In September 2001, EPA published a draft updated methodology for estimating emissions from paved road dust. A key change in the methodology is the inclusion of a factor to provide an emission reduction based on precipitation. The precipitation factor is used to alter the base emission factor, and can be applied annually, seasonally, or monthly. The adjustment is of the form: Rainfall Adjustment = Emission Factor * (1 - P/2N) where P = number of days with at least 0.254 mm (0.01 in) of precipitation during the averaging

period and N = number of days in the average period (e.g., 365 for annual, 91 for seasonal, 30 for

monthly) Full EF = k(sL/2)0.65 (W/3)1.5 (1 – P/2N) (K= constant, sL = silt loading, W = average vehicle weight, 2.4 tons assumed) So, 6 wet days would reduce the estimated emissions for the month by 10%. Some issues: If used, recommend a monthly correction factor

- Is there information reasonably available showing the typical number of dry/wet days each month (possibly 10 year average)

- Ideally, would need data for each county The proposed precipitation value of 0.01 seems trivial. Should a higher

number such as 0.1 be used? If so, is the correction factor still valid? Rainfall information can be used to develop seasonal emission profiles

Unpaved Road Dust Methodology EF = [ k (s/12)a (W/3)b / Mdry/0.2)c ] x [(365 – p)/365] S = surface silt content, W = mean vehicle weight, Mdry = worst case surface moisture, p = number og days with at least 0.01 in precipitation per year ARB currently uses an average emission factor derived from UC Davis and DRI studies. Emission factor = 2.0 lbs PM10/mile traveled. Currently,. Some issues: ARB EF does not account for local emission variations, but it is from

California roads Consider application of rainfall correction factor to reduce emissions for

wet days Are averaged county rainfall values reasonably available? Currently annual emissions are apportioned monthly by rainfall profiles,

but emission factors are not adjusted Unpaved road VMT is on an annual basis and emissions are apportioned

by month based on rainfall

Detailed Documentation for Fugitive Dust and Ammonia ... · 2000 Ag LP and Hvst PM10.xls Output...

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