International Corporation Air Sciences

Final Work Plan

California Regional PM 10 /PM 2.5 Air Quality Study

Ammonia Emissions Improvement Projects in Support of CRPAQS Aerosol Modeling and Data Analyses: Draft Ammonia Inventory Development

Prepared for

Mr. Don McNerny, Branch Chief Modeling and Meteorology Branch

Air Resources Board

P.O. Box 2815 Sacramento, CA 95812

Prepared by

ENVIRON International Corporation 101 Rowland Way, Suite 220 Novato, CA 94945

Pechan-Avanti 5537-C Hempstead Way

Springfield, VA 22151

April 13, 2001

101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700DISCLAIMER

The statements and conclusions in this report are those of the Contractor and not necessarily those of the California Air Resources Board, the San Joaquin Valleywide Air Pollution Study Agency, or its Policy Committee, their employees or their members. The mention of commercial products, their source, or their us e in connection with material reported herein is not to be construed as actual or implied endorsement of such products.TABLE OF CONTENTS Page

1. INTRODUCTION................................................................................................................ 1-1

2. TECHNICAL APPROACH................................................................................................ 2-1

3. PROJECT MANAGEMENT, RE SOURCES AND SCHEDULE................................... 3-1

4. REFERENCES.................................................................................................................. ... 4-1

TABLES

Table 2-1. Prioritized source categories and objectives........................................................ 2-3 Table 2-2. Emission inventory code s for fertilizer application............................................. 2-5 Table 2-3. Suggested source classifi cation for soil/plant canopy systems............................ 2-7 Table 2-4. Suggested EIC/CES codes for agricultural burning............................................. 2-9

Table 2-5. Livestock emission factors................................................................................. 2-12 Table 2-6. Suggested EIC/CES c odes for livestock operations .......................................... 2-13

Table 2-7. Native animal emission factors.......................................................................... 2-16

Table 2-8. Domestic sour ces emission factors. ................................................................... 2-17 Table 2-9. Suggested EIC/CES c odes for industrial sources .............................................. 2-19

Table 2-10. USGS Land Cover/Land Use Codes (Anderson Level 2 Classification Scheme)........................................................ 2-23

Table 2-11. USGS Land Use/Land Cover Cate gory Codes (Modified Level 2). ................. 2-24

Table 2-12. NLCD Land Cover Classification Codes........................................................... 2-26 Table 2-13. Spatial surrogates for area and mobile sources.................................................. 2-42 Table 2-14. Temporal allocation by source category..................................................2-43 Table 3-1. Estimated labor hours by task.............................................................................. 3-4 Table 3-2. Summary of init ial schedule and costs for CRPAQS Draft Ammonia Inventory Development. ........................................... 3-5

FIGURES

Figure 1-1. San Joaquin Valley Study Area ................................................................................. 1-2 Figure 2-1. Generalized landcover of Califor nia from the Gap Analysis Program..........2-28 Figure 2-2. Farmland mapping a nd monitoring survey area........................................2-

30 Figure 2-3. 1998 DWR Landuse survey coverage for Kern county..............................2-

31 Figure 2-4. Simple approach to estimati ng ammonia emissions within the GIS..............2-34 Figure 2-5. Estimating ammonia emissi ons using a model to account for

environmental

effects...........................................................................2-35 Figure 2-6. The main GLOBEIS control

interface.....................................................2-37 Figure 2-7. Selection of input data and model options in GLOBEIS.............................2-

37Figure 2-8. Isoprene emission inve ntory for the Houston area prepared with

GLOBEIS......................................................................2-39 Figure 2-9. Proposed CCOS and CRPAQS modeling domains...................................2-

41 Figure 3-1. Project management

structure.................................................................3-3April 2001

Projects2:CRPAQS/workplan/Sec1.doc 1-1 1. INTRODUCTION

Background

The California Regional PM 10 /PM 2.5 Air Quality Study (CRPAQS) is a multi-year program to study particulate matter (P M) air pollution in Cent ral California and consis ts of meteorological and air quality monitoring, emissions inventory development, data analysis, and air quality modeling. The primary object ives of the study are to:

• Provide an improved understandi ng of emissions and atmospheri c processes that influence particle formation and transport;

• Develop methods useful to plan ning agencies in formulating a nd assessing candidate control strategies for attaining the federal and stat e PM standards in Central California; and

• Provide reliable tools for estimating the impacts of control strategies fo r PM on visibility, air toxics, and acidic aerosols and on attainment strategies for other pollutants, specifically

ozone.

Chemical reactions of ammonia emissions with airborne oxides of sulfur and nitrogen produce

fine ammonium sulfate and amm onium nitrate particulate matter . These reactions, especially those leading to ammonium nitrate, repres ent a significant percentage of wintertime PM 2.5 in the San Joaquin Valley (SJV). Thus, ammonia plays a major role in determining secondary aerosol

concentrations within the CRP AQS domain. Figure 1-1 displa ys the San Joaquin Valley study

area.

As part of the overall study, grid-b ased aerosol models are to be applied to simulate particulate matter concentrations in the SJV and to investigat e the sensitivity of seco ndary particulate matter formation to various parameters. A first step in the successful application of air quality models to investigate proposed control strategies is the demonstration of acceptable model performance through comparison of modeled estimates and fi eld study observation data. A key requirement for the credible application of air quality mode ls are accurate input data specifying three dimensional meteorology and ground- level emission rate estimates. Ideally these inputs are to be developed with adequate spatial and temporal re solution in order to accurately represent the complex chemical and transport processes occurri ng in the atmosphere which contribute to the formation of particulate matter pollution.

Thus, the development of a state-of-the-science ammonia emission inventory for the San Joaquin Valley study area is the s ubject of this project. Accurate em ission inventories based on the best

available ammonia emissions information and processing methodologies will be critical in supporting numerous aspects of the CRPAQS activ ities over the coming y ears including ambient data analysis and photochemical modeling.April 2001

Projects2:CRPAQS/workplan/Sec1.doc 1-2 -124 -123 -122 -121 -120 -119 -118 -117 -116

Degrees Longitude 33 34 35 36 37 38 39 40 41 42Degrees Latitude

Bakersfield

Barstow Chico

Fresno

King City

Los Angeles Marysville

Merced Modesto Redding

Sacramento

Salinas San Joaquin

San Luis Obispo

Santa Barbara Stockton Susanville

Tehachapi Tulare Ukiah Weed

San Francisco

Figure 1-1. San Joaquin Valley Study Area.April 2001

Projects2:CRPAQS/workplan/Sec1.doc 1-3 Project Goals and Objectives

The primary goal of the project is to develop a draft, ground-level, gridded ammonia emission inventory for use in data analysis and grid-b ased aerosol modeling for the CRPAQS. The study will take place within California’s San Joaquin Valle y (SJV) and the draft inventory is to be used to assess the sensitivity of aerosol model estimat es to changes in such variables as spatial and temporal resolution, and emissions magnitude s for various source ca tegories of ammonia emissions. The ammonia inventory is to be deve loped at a spatial reso lution of 1-km by 1-km with a temporal resolution of 1-hour using the best available emission source data and processing methodologies. The geographic extent of the gr idded inventory will cover the SJV study area, although where feasible, data will be collected for the entire stat e of California. In addition, a stated objective of the project is to develop a standard inpu t data library and processing procedures in order to facilitate future in-hous e refinements or revisions to the inventory in a programmatic way.

A secondary goal of the study is the developmen t of an ammonia capab le GIS-based emission modeling system. Since for many ammonia emi ssion source categories, emission estimates are intimately linked to land cover/ land use (LULC) characteristics, an emissions model which

combines the GIS data on LULC distributions with LULC specific emission factors to calculate gridded emissions in a single processing step would be ideal. In addition, the increased efficiency with which the inve ntory are generated within the GIS-based emission model will facilitate future updates and revisions with a minimal of processing effort.

Finally, it must be emphasized that due to th e many on-going field stud ies and modeling input development projects sponsored by CARB in support of both the Central California Ozone Study (CCOS) and the CRPAQS, numerous sources of in formation relevant to the development of the ammonia emission inventory are curr ently either not yet available or in a state of flux. Thus, it will be imperative to maintain on-going coordi nation with these projects and the associated results. However, due to various scheduling issues and the need to develop a state-of-the-science draft ammonia emission inventory for this project, it may not be possible to rely solely on the availability of pertinent data from these stud ies. Therefore, where necessary the technical approach to the development of a draft gridded inventory is based on the most current relevant literature and existing sources of data. As the required data processing will be performed programmatically, the resulting inve ntory may be easily updated an d/or modified in the event that more detailed source-specific data and information from these special studies becomes available during the course of the project. Given the current status of existing ARB and CRPAQS contracts, and based on the results of the data coll ection efforts under Task 2, the technical approach presented here may be subject to revision.

Work Tasks

Below we briefly summarize the work tasks of the CRPAQS Ammonia Inventory Development Project for addressing the particular goals and obj ectives of the study.April 2001

Projects2:CRPAQS/workplan/Sec1.doc 1-4 Task 1: Prepare a Detailed Workplan

Under this task, a detailed workplan defining the issues to be addr essed and detailing the technical approach to be used for devel oping inventory improvements and draft modeling inventories will be prepared. Project manageme nt, including preparation of monthly progress reports and regularly scheduled conference calls , will also be performed under this task.

Task 2: Collect Data and Cr eate Standard Input Library

Data collection and creation of a standardized input da ta library is performed as part of this task. As part of the data collection tasks, ammonia emission sources a nd data sources are reviewed for each emission source category. Specific data elem ents and formats for each source category will be developed and documented. Procedures for ar chiving and processing “raw” data into the standardized data library are also developed and documented.

Task 3: Develop Ammonia Emissions Model

The development of an ammonia capable GIS-based emissions model will be investigated in this

task. Enhancements to the GLOBEIS model developed by ENVIRON will be performed in

coordination with recent GIS-based emission model development currently being performed/sponsored by the ARB. The resul ting GIS-based emission model will be fully documented and used in the development of the draft ammonia modeling inventory to performed

under Task 4.

Task 4: Produce Draft A mmonia Modeling Inventory

Under this task, a draft gridded ammonia emission inventory will be prepared for approval. The grid-based ammonia inventories will be de veloped for two periods corresponding to the CRPAQS 1999-2001 field measurement study periods. The inventory will be generated with 1- hour temporal resolution and 1-km by 1-km sp atial resolution encompassing the entire San Joaquin Valley study area. The inventory wi ll be developed using the ammonia model developed under Task 3. Alternative emissions processing systems will also be investigated for use in development of the modeling inventory as a fallback position.

Task 5: Documentation

Under this task, a cumulative Draft Final Report documenting all aspects of the work performed under each task during the course of the project is to be prepared and submitted for review. A Final Report incorporating comments received from the project Technical Committee will be prepared for submission. A Users’ Guide detailin g all processing procedures, data sources, input data library and processing routin es used in the development of the ammonia emission inventory will be prepared. Regular system backups of all project data is also conducted under this task.April 2001

Projects2:CRPAQS/workplan/Sec1.doc 1-5 Purpose

This document is the Work Plan for the CRP AQS “Draft Ammonia Inventory Development” study. It describes the technical approach for the comple tion of the study, including an identification and prioritization of ammonia em ission sources within the San Joaquin Valley study domain, identification and review of existing ammonia inventories and relevant literature, spatial surrogates and temporal profile data by source category, and the procedures and methodologies to be used for data archival , processing, emission i nventory gridding and documentation and reporting. Where appropria te, the following items are included:

• Identification and review of the sources of a mmonia emissions within the San Joaquin Valley study domain;

• Reviews of existing ammonia emissions lite rature, ammonia invent ories and processing techniques, including ongoing emissions-relate d work sponsored by the Study Agency, the California Air Resources Board (ARB), and the South Coast Air Quality Management District (SCAQMD);

• Identification and recommendation of the mo st promising and practical processing techniques and data inputs for producing accu rate gridded, hourly emissions estimates

statewide and within th e CRPAQS modeling domain;

• Detailed identification of spatial surrogate and temporal profile data to be collected and/or updated, by emission source category;

• Detailed description of input data, including associated source category, data source, units, file formats, spatial and temporal resoluti on and accuracy, and geographic and GIS-specific attributes;

• Documentation of the process for creating gri dded emissions estimates from collected data;

• Detailed description of quali ty assurance (QA), quality c ontrol (QC) and documentation procedures;

• Specification of all hardware, software, and operating systems to be used, as well as, procedures to be used for providing regular system backups;

• Task-specific resource allocation and costs; and

• Project management, organization and schedule of work to be completed.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-1 2. TECHNICAL APPROACH

In this section, the technical approach for the completion of the gridded ammonia emission inventory improvements is presented and discussed. The approach is organized according to the

tasks outlined in the Statemen t of Work (SOW) for the project study. The SOW defines the following five tasks:

• Task 1: Prepare a Detailed Work Plan

• Task 2: Collect Data and Cr eate Standard Input Library

• Task 3: Develop Ammonia Emission Model

• Task 4: Produce Draft A mmonia Modeling Inventory

• Task 5: Documentation and Reporting

Task 1: Prepare a Detailed Work Plan

The purpose of this task is to develop a detailed Work Plan describing the approach to be used for the completion of the draft ammonia inventory development. As discussed above in Section 1, this document details the technical approach for the completion of the study, including an identification and prioritization of ammonia em ission sources within the San Joaquin Valley study domain, identification and review of existing ammonia inventories and relevant literature, spatial surrogates and temporal profile data by source category, and the procedures and methodologies to be used for data archival , processing, emission i nventory gridding and documentation and reporting.

Task 2: Collect Data and Create Standard Input Library

The objective of this task is the collection of data and the creati on of a standardized input data library. As part of the data collection effort s, ammonia emission sources and data sources are reviewed for each emission source category and r ecommendations regarding the most promising processing methodologies are presented and summarized.

Summary of Source Category Prioritization

Through an extensive review of existing ammonia emission inventories, relevant literature and data sources, the most promising processing me thodologies and data sour ces are identified for use in the development of a gridded ammonia emission inventory for the San Joaquin Valley study area. While all the relevant data needed to quantify the relative cont ribution of each source category in the complete emission inventory has either not yet been co llected or is not yet available, a source category prio ritization based on emissions magn itude is not possible at this time. Table 2-1 summarizes the methodologies and sources of emissi ons/activity data, and temporal and spatial allocation proposed for each source category. The table entries are ordered as to reflect the project team’s understanding of the relative importa nce of each source category as well as to reflect the importance of each emission source with respect to potential improvements that may be realized. The team belie ves that this approach will result in the most

accurate and credible ammonia emission inventor y given the available project resources.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-2 Finally, it should be noted that are several common sources of te mporal and spatial allocation data that have been used in the past for the de velopment of emission inventories. These include the default temporal allocation profiles used in common emission models such as EPS2.0, EMS95 and SMOKE, as well as the standard USGS LULC databases derived from the 1:100,000 scale (~200 meter resolution) LULC data. Ho wever, for the prioritized emission source categories presented in Table 2-1 below, the Team does not believe that any of these do a very good job of allocation, and theref ore alternate methods and data sources are described in subsequent sections of this document.April 2001Projects2:CRPAQS/workplan/Sec2.doc2-3Table 2-1.Prioritized source categories and objectives.Source Category Emissions/Activity Temporal Information Spatial InformationFertilizer Application(see Table 2-2 for sourceclassification codes)Incorporate emissions inventorydirectly from Krauter and PotterTemporal extent of category – annualwith primary activity in spring andsummer;Temporal resolution of inventory – daily(via incorporation of data from Krauterand Potter).Spatial extent of the category –statewide agricultural areas;Spatial extent/resolution of inventory -specific crop fields using shape filesprovided by Krauter/Potter for theentire state.Livestock(see Table 2-6 for sourceclassification codes)Emission factors from James et al;Gharib and Cass; and Battye et al.Activity data from Calif. CattlemensAssoc., Western Dairymans Assoc.,USDA; RWCQBs; American HorseCouncil FoundationDiurnal based on Wilkinson et al. andJames et al.To existing land use/land cover data(e.g., NLCD, USGS); PointNatural Soils/Plant Canopies(see Table 2-3 for sourceclassification codes)Incorporate CAL-CASA derivedinventory directly from Krauter andPotter.Temporal extent of category – annual;Temporal resolution of inventory -Seasonally from CAL-CASA model.Spatial extent of the category –statewide;Spatial extent/resolution of theinventory – statewide allocation usingKrauter and Potter shape files.Agricultural Burning(see Table 2-4 for sourceclassification codes)Use emission ratios of NH3 to COfrom studies by Yokelson andGoode. Activity data from LesFife/UC Berkeley or surveys oflocal agricultural extension offices.Temporal extent of the category – falland winter seasons;Temporal resolution of the inventory -Monthly – using the activity calendardeveloped by Fife and/or the Team.Spatial extent of category – statewideagricultural areas;Spatial extent/resolution of theinventory - specific crop fields usingshape files from Krauter/Potterdeveloped for fertilizer application forthe San Joaquin Valley only.Prescribed Burning/Wildfires930-932-0200-000 (47316)Use emission ratios of NH3 to COfrom studies by Yokelson andGoode. Activity data from CDFFP.Temporal extent of the category –annual with primaryactivity in summerand fall seasons;Temporal resolution of the inventory -Seasonally.Spatial extent of the category –statewide;Spatial extent/resolution of inventory –statewide with coverages developedfrom CDFFP and NIFC dataResidential Wood Burning610-600-0230-000 (82115)610-602-0230-000 (82123)Emission and Activity estimatesbased on EPA’s EIIP alternativemethods using data from EnergyInformation Administration’s StateEnergy Data Report and ResidentialEnergy Consumption Survey. NH3emissions based on ratios of NH3 toCO from studies by Yokelson andGoode.Temporal extent of the category – fall,winter and spring seasons;Temporal resolution of the inventory -Monthly.Spatial extent of category – statewide;Spatial extent/resolution of inventory –statewide allocated to the county-levelusing data from U.S. Bureau of Censuson wood-burning households.Weighting of activity between rural andurban areas to be investigated.On-Road Mobile Sources700-000-0000-000Fleet-wide emission factor fromFraser and Cass or Lord et al.Gridded VMT from CARB.Temporal extent of the category –annual;Temporal resolution ofthe inventory-Spatial extent of category – statewide.Spatial extent/resolution of inventory –statewidegridded VMTprovided byApril 2001Projects2:CRPAQS/workplan/Sec2.doc2-4Source Category Emissions/Activity Temporal Information Spatial InformationAllocate based ondaily and weeklyprofiles obtained from CARB.CARB.Industrial sources(see Table 2-9 for sourceclassification codes)Emission factor data developed forSCAQMD by ATC; Activity datafrom major ammonia suppliers inthe state. Geothermal source fromthe ARB CEIDARS databaseTemporal extent of the category –annual;Temporal resolution of the inventory –annual (assuming no temporal profile)Spatial extent of category – statewide.Spatial extent/resolution of inventory –statewide area source spatiallyallocated by ZIP code coverages.Geothermal sources geo-coded andallocated as point sourcesSewage Treatment Plants130-190-0300-0000 (66713)Emission and activity data fromSWRCB database; local surveys ofPOTWsTemporal extent of the category –annual;Temporal resolution of the inventory –annual (assuming no temporal profile)Spatial extent of category – statewide.Spatial extent/resolution of inventory –statewide; facility addresses geo-codedand allocated as point sources.Composting140-190-0300-0000 (66714)Emissions data from Solid WasteInformation System database, ifavailable; emission factorsdeveloped by ATC for SCAQMDTemporal extent of the category –annual;Temporal resolution of the inventory –annual (assuming no temporal profile)Spatial extent of category – statewide.Spatial extent/resolution of inventory –statewide; SWIS-supplied addressesgeo-coded and allocated as pointsources.Landfills120-122-0240-0000 (57281)Emission estimation method – sameas existing SJV inventory.Datafrom SWIS database; CaliforniaIntegrated Waste ManagementBoard; ARB emission inventorydatabase (CEIDARS)Temporal extent of the category –annual;Temporal resolution of the inventory –annual (assuming no temporal profile)Spatial extent of category – statewide.Spatial extent/resolution of inventory –statewide, geo-coded and allocated aspoint sources.Domestic Sources(see Table 2-8 for sourceclassification codes)Emission factor data from variousliterature sources (see Table 2-8);Activity data from U.S. Bureau ofthe Census; California State Boardof EqualizationTemporal extent of the category –annual;Temporal resolution of the inventory –annual (assuming no temporal profile)Spatial extent of category – statewide.Spatial extent/resolution of inventory –statewide; spatial allocation based onurban land use/land cover data fromNLCD and USGS LULC databases.Native Animals950-912-0300-0001 (83288) Deer950-912-0300-0002 (83289) Bear950-912-0300-0003 (83290) PigsEmission factor data from Warn, etal.; activity data from CDFG.Temporal extent of the category –annual;Temporal resolution of the inventory –annual (assuming no temporal profile)Spatial extent of category – statewide.Spatial extent/resolution of inventory –statewide; spatial allocation based onshrubland/forest land use/land coverdata from NLCD and USGS LULCdatabases. Spatial distribution of deerfrom CDFG.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-5 Review of Literature, Data S ources and Processing Methodologies

Fertilizer Application

The primary objectives for this source category are to better spatially and temporally allocate the emissions. The primary source of information for activity data and spatial allocation will be the GIS shape files produced in the Potter et al ( 2000) study. According to co ntacts with technical committee members, these data should be av ailable in May of 2001. After reviewing the information from Potter et al, the Team will develop a set of EIC codes specific to primary crop types. A depiction of how this would look is shown in Table 2-2. The emission factors used by Potter et al will be used in this project. A ccording to Potter et al (2000), the initial ammonia emission estimates were derived by assuming a consta nt 5.5% loss of fertilizer nitrogen. For the final emission inventory, differe nt methods of application may be assessed (e.g., methods for incorporation of fertilizer into th e soil) which lead to lower emi ssion factors (one the order of 0.5 to 1% nitrogen loss).

Table 2-2 . Emission inventory codes for fertilizer application.

EIC Description 1 EIC CES Existing Code Misc. Agricultural Losses 420-418-6000-0000 47050 Proposed Codes Fertilization Losses – Grain and Hay 420-418-6001-0001 47051 Fertilization Losses – Truck Crops 420-418-6001-0002 47052

Fertilization Losses – Citrus 420-418-6001-0003 47053

Fertilization Losses – Rice 420-418-6001-0004 47054

Fertilization Losses – Pasture 420-418-6001-0005 47055 Fertilization Losses – Vineyards 420-418-6001-0006 47056

Fertilization Losses – Deciduous Fruit and Nuts 420-418-6001-0007 47057 Fertilization Losses – Field Crops 420-418-6001-0008 47058

1 Crop names and number of categories taken from Potter et al (2000).

The advantages of having the fertilizer applica tion loss emissions disaggregated as shown in

Table 2-2 include: 1. The ability to incorporate new emissions data (i.e., emission factors) which will be crop-specific; 2. The ability to allocate emissions tempora lly based on fertilizer application schedules (as described below); 3. The availability of crop-specific spatial coverages (i.e., from Krauter and Potter).

The information from Krauter and Po tter will cover the entire state.

Another possible source of activity and spatial al location data is the pesticide usage database from the California Department of Pesticide Re gulation (CDPR). The Team has ordered a CDROM containing preliminary 1999 data (year 2000 da ta will not be availa ble until 2002), as well as information for the previous 5 years. This data base will cover the entire state. According to

discussions with CDPR and count y agricultural offices, these data should allow the team to develop crop coverages.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-6 This source of data will be considered for use, if the data from Krauter and Potter can not be obtained in time for this study.

Another source of information relative to both activity (mass of fertilizer applied per acre) and temporal allocation is the study being conducted for the California Department of Food and Agriculture (CDFA) by King. According to CDFA, a draft report should have been available by

December 2000 and forwarded to one or more members of the technical committee (Cady, 2000). The final report was due by February 28, 2001. The Team requests that a copy of the draft report be forwarded to the Team’s Project Manager as soon as it is made available. As with the spatial coverage da ta mentioned above, the applicati on calendar will cover crops grown within the CRPAQS domain, although it may be re presentative of the rest of the state.

The Team will evaluate application schedule information from King and Krauter in order to develop temporal allocation factors for each crop type. If sufficient data are found to justify a higher level of temporal resoluti on (e.g., down to specified weeks of the year), then these factors will be developed.

CARB has developed some temporal allocation f actors for agricultural practices and has made

them available on the ARB web site. These factors relate to operations generating PM emissions, however, and are not likely to be of much value in this study.

Another source of information for both temporal and spatial allocation will be surveys conducted by Team members to fill in any data gaps f ound in the information sources described above. These surveys would be conducted with both co unty agricultural offices and university researchers. Spatial coverage would cover only the CRPAQS domain.

The final source of activity data is the fertilizer sales statistics from the CDFA. These are the same data used in the existing SJV method with the emission factors from Battye et al (1994). Reports of fertilizer sales (over 100 tons) by county are issued twi ce per year. The entire state is

covered. It is not clear how we ll fertilizer sales data repres ent actual application, however. Given the availability of the other data sources mentioned above, this information will likely be used to perform a quality assurance check (e.g., by comparing annual sales in the domain to the estimated mass of fertilizer applied that was calc ulated from information from Krauter, Potter, King, and other sources).

Agricultural and Natural Soil/Plant Canopy Systems

As pointed out in the Team’s proposal, it is high ly uncertain whether natural soils (referred to here as soil/plant canopy systems) are net emi ssion sources over annual time frames. Studies conducted in Europe have shown that the system s studied can be net sources or net sinks of ammonia. This issue is complicated by the inte ractions of ammonia with in the soil/plant canopy system, and should not be simplified by just looki ng at emissions from the soil. For example, depending on the system, the plant canopy can take up gaseous ammonia directly.

The Team will incorporate the inventory by Potte r et al (2000). This inventory should be available by May 2001. One issue with this inventor y that should be noted is that it represents the soil component of the system only. Hence, any uptake by the plant canopy is not accounted

for. Table 2-3 presents a suggested source categori zation scheme for soil/plant canopy systems.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-7 By treating each system separately, future refine ments to the inventory will be easier to make (e.g., plugging in new emission factors, ad justing temporal allocation factors).

Table 2-3 . Suggested source classification for soil/plant canopy systems.

EIC Description 1 EIC CES Existing Codes Agricultural Biogenics – Day Time 910-910-0250-0001 82255 Agricultural Biogenics – Night Time 910-910-0250-0002 82263 Non-Agricultural Biogenics – Vegetative 910-912-0250-0000 83287 Proposed CodesSoil/Plant Canopy Systems – Cropland and SemiAgricultural Land 910-910-0250-0010 82270

Soil/Plant Canopy Systems – Evergreen

Needleleaf Forest 910-912-0250-0010 83290

Soil/Plant Canopy Systems – Mixed Forest 910-912-0250-0011 83291 Soil/Plant Canopy Systems – Open Shrubland

and Desert 910-912-0250-0012 83292

Soil/Plant Canopy Systems – Grassland 910-912-0250-0013 83293 Soil/Plant Canopy Systems – Bare Soil 910-912-0250-0014 83294 Soil/Plant Canopy Systems – Deciduous

Broadleaf Forest 910-912-0250-0015 83295

Soil/Plant Canopy Systems –

Woodlands/Wooded Grasslands 910-912-0250-0016 83296

1 System names and number of categorie s taken from Potter et al (2000).

Coverage will include the entire state and will be based on the GIS shape files provided by Potter et al (2000).

Biomass Burning (Prescribed Burning/Wildfires , Agricultural Burning, and Residential Wood Burning)

Each of these categories could be significant du ring certain episodes (e.g., wildfires in summer, agricultural burning in the fall). Staff at U.C. Berkeley were contacted regarding their ongoing work for CARB in developing spatial coverages a nd emission inventories of wildfires/prescribed burning and agricultural burning (S carborough, 2000). For wildfires/ prescribed burns, the work completed to date is primarily a method for developing 1 kilometer by 1 kilometer spatial coverages and not the actual c overages (or inventories) themse lves. For wildfires, some 1998 activity data were collected. Hence, these data ar e not likely to be of high value for this project (which needs year 2000/2001 data).

Berkeley staff are also looking at methods for developing spatial covera ges for agricultural burning from permit data collected by Mr. Les Fife (Scarborough, 2000). According to discussions with Mr. Fife, work completed to date has been a database of calendar year 2000 agricultural burns in th e Sacramento Valley. By Spring 2001 , Mr. Fife intends to deliver a database covering both the Sacramento and San Jo aquin Valleys (i.e., Kern to Shasta counties; Fife, 2000). Temporal resolution is to be down to the day on which th ese agricultural burns occur. Spatially, the burns will be allocat ed to specific crop types (e.g., rice, wheat).April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-8 For residential wood burning (i.e., wood burned in fireplaces or woodstove s), ideally the amount of wood consumed by households for a given time period would be determined by survey (EPA, 1997). Once wood-burning households are identified, respondent s would be asked about the frequency and the amount of wood burned ove r a certain time period (e.g., per week). Information on the device type (e.g., fireplaces w ith and without inserts, woodstoves) may also be requested. Performing a survey in the re quired time frame for the SJV study area is not feasible. U.C. Berkeley staff are currently c onducting some residentia l wood surveys in three

areas. The results are not likel y to be available in time for this study (projected August 2001 completion). Other alternative approaches are recommended and are discussed below.

Recommendations for Prescribed Burns/Wildfires

Due to the uncertainty in the availability of emissions and spatial coverages from Berkeley staff during the period of this st udy, recommendations are as follows for developing emission estimates. Emissions data are lacking for amm onia from this category, which explains their absence from the current inventory (STI, 1998). Some remote sensing work done by researchers in the early 1990’s reported NH3 to NOx ratios of 5:1 using remote sensing methods (Griffith et al, 1991). More recent information includes st udies by Yokelson et al (1996, 1999) and Goode et al (1999, 2000). Emissions will be estimated by first determining the total CO emissions using

either AP-42 emission factors or emission rates estimated in the studies reviewed above. The CO emissions will be multiplied by the appropriate NH3 to CO emissions ratio to obtain NH3

emissions.

For prescribed burning, activity da ta (acres burned) will be gathered for the 2000 calendar year from the California Department of Forestry a nd Fire Protection (CD FFP). However, the CDFFP

data do not account for fires occurr ing on U.S. Forest Service, St ate Parks, National Parks, and Indian lands. As such, these agencies will need to be contacted separately to determine the availability of statistics on pr escribed burning. The team will allocate the act ivity based on either: allocating the state totals to forested land; or pr eferably, if data are received by California Region, allocation of the regional totals to forest ed land in each region. At the same time, the team will gather information from the above ag encies on acreage burned by season or month, as

available. Steve Shaw of SJV APCD also indi cated that prescribed acr es burned for the year 2000 might be compiled by the end of January 2001 for counties within SJV APCD jurisdiction. This data set, which will be made available to us, will provide the number of acres burned per county, as well as the day on which the prescribed burns occurred for temporally allocating the activity.

Historically, wildfire emissions have been estimated by ARB, as opposed to the individual air districts. As mentioned above, ARB’s most recen t data base of wildfire acreage is for 1998. Therefore, wildfire acreage data for California will need to be obtained from the appropriate agencies. CDFFP does track wildfires, and in discussions with CDFFP, they annually publish statistics on wildfire acres, which includes wild fires occurring on all lands, and not CDFFP lands exclusively. A 2000 summary report may not be avai lable in time for this project, but the team will keep apprised of its availability. If the 2000 CDFFP report is not available, the te am would need to supplement CDFFP data with data from other agencies, similar to prescribed bu rning. Activity data wi ll be obtained from the U.S. Forest Service, and other Federal agenci es under the Department of the Interior (e.g., Bureau of Indian Affairs, Department of Fish and Wildlife, Bureau of Land Management). For updating national criteria pollutant emission estima tes for this category, the U.S. Forest ServiceApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-9 has provided Pechan with State-level estimates of acres burned due to wildfires on U.S. Forest Service lands, as well as Stat e and private lands, up to the year 1999. The effort to update inventory year 2000 criteria pollutant emissions fo r the National Emissions Inventory (NEI) will likely occur in mid summer 2001, but any wildfire activity data collected in a timely manner for the NEI could potentially be used for this effort.

Another source for obtaining wildfire and prescr ibed burning data is the National Interagency Fire Center (NIFC). The NIFC provides year-to-date statistics on the number of acres burned in the southern and northern regions of California via their web-site (dat a for January 2000 through November 2000 are currently posted). Weekly inci dent management reports may be useful for temporally allocating the annual activity. The NIFC agency will be contacted to find out if there are any limitations to using these incident report data, and whethe r any gaps exist in their data (e.g., does it cover all categories of lands including Federal, State and private).

Recommendations for Agricultural Burning

As with fertilizer application, the team will disaggregate this category into a number of subcategories by assigning new EIC and CES codes specific to crop type (since activity will vary spatially and temporally by crop type). An exampl e is shown in Table 2-4 below. Spatially, the same coverages developed above for fertilizer application will be used (from shape files developed by Krauter and Potter). As mentione d under the fertilizer application section above, alternate sources of activity and spatial allo cation data include the CDPR pesticide usage

database and land use/land cove r data (e.g., CDWR and USGS).

Table 2-4. Suggested EIC/CES codes for agricultural burning.

EIC Description 1 EIC CES Existing Code Agricultural Burning - Field Crops 670-662-0262-0000 47258 Proposed Codes Agricultural Burning – Crop A 670-662-0262-0001 47259 Agricultural Burning – Crop B 670-662-0262-0002 47260 Agricultural Burning – Crop C 670-662-0262-0003 47261 Agricultural Burning – Crop D 670-662-0262-0004 47262

1 Crop names and number of categories to be determined

.

There are no other known sources of activity data for this sour ce category (emissions are not in the current inventory; STI, 1998). EPA current ly does not have information for estimating ammonia emissions. We will adopt surrogate em ission factors from AP-42 (e.g., for CO) and the information sources noted under pres cribed burns/wildfires above.

Temporal allocation will be based on information ga thered from Les Fife. Mr. Fife reports that his database will be complete for the Sacram ento and San Joaquin Valleys by Spring of 2001 (Fife, 2000). Since his data will not cover th e entire CRPAQS domain, the Team will ascertain whether there is a need to adjust the temporal profiles for any crop type to be more representative of the domain. This will be done with telephone c ontacts to county agricultural extension offices outside of the Sacramento and San Joaquin Valleys.

Recommendations for Resi dential Wood CombustionApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-10 The Team recommends generating year 2000 estimates based on the EPA’s Emission Inventory Improvement Program (EIIP) alternative method for this category (EPA, 1997). This method specifies the use of State-level estimates of the amount of wood consumed for California according to the Energy Information Administration’ s (EIA’s) State Energy Data Report. This

State estimate is then allocated to the county level using the number of wood- burning households in each county, as reported by the 1990 United States Census. Since these data represent 1990,

the Team will determine whether newer informati on will be available for this study from the 2000 census. As mentioned above, U.C. Berkeley is conducting research in estimating emissions from this category, however the results are not ex pected to be available for inclusion in this study.

For the NEI, residential wood combustion activ ity was estimated for the year 1990 by running the County Wood Consumption Estimation Mode l. For each year since 1990 up to 1999, county-level activity estimates (i.e., cords of w ood) were updated using the national total for residential wood consumption and heating degree days. For most States, monthly heating degree days are obtained from one representative meteorol ogical station in the State. For California, 2 stations, one in Northern California and one in Southern California, are used for Northern and Southern California counties, respectively. H eating degree data for 2000 from the National Climatic Data Center (NCDC) are likely to be available by June 2001.

ARB estimates emissions for all air districts within the State. For fireplaces, ARB multiplies an average wood consumption per fireplace estimate (0.28 cords per year) by the estimated number of houses with active fireplaces. The estimated number of house s with active fireplaces is derived by subtracting the estimated number of wood heating houses (available from the 1990 Census of Population and Hous ing-Summary Tape File 3A) from the total number of wood burning houses. The total number of wood burning houses is estimated by multiplying the fraction of houses burning wood by the total number of houses. The 1988 report , The California Residential Wood Consumption Survey, Draft , lists the percentage of wood burning households by Air Basin. ARB estimates emissions for wood stoves differently than emissions for fireplaces, since wood stove activity is believed to correlate more closely with temperature (i.e., heating degree days). For wood stoves, a mode l equation was developed that estimates energy consumption using inputs such as number of households burning wood, housing design characteristics, heating degree da ys, and fuel heating values.

In discussions with ARB, year 2000 estimates fo r residential wood combustion are likely to be grown from 1999 estimates, using th e annual change in population as a growth factor. ARB last

used the method described above for the invent ory year 1997, and has grow n the estimates using county-level population for each year since then.

Because the same NH3 emission factor s would likely be applied to a ll activity data, regardless of whether the activity corresponds to fireplaces or wood stoves, a method that distinguishes between the activity from these tw o types of devices may not add va lue. If, in the process of researching this issue, it is found that emission factors or s easonal profiles are available by device type, the Team will make an effort to distinguish the activity among the various devices separate EIC/CES codes will be used for each). Th is distinction could be made using data from

EIA’s Housing Characteristics 19xx , which is part of its Residential Energy Consumption Survey. This publication has information to es timate the percent of households in the Western region that use fireplaces versus wood stoves.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-11 Most residential wood burning occu rs during the cold season (typical ly in the fall, winter and early spring). Temporal allocation profiles ha ve been developed by ARB to apply to annual estimates of wood burned by households. Both St atewide profiles and prof iles specific for the SJV APCD have been developed (ARB, 1997). Heating degree-day data for 2000 can also be

obtained from National Climatic Da ta Center and used to update these profiles specifically for the inventory year in question, if warranted. Other factors, such as the price of electricity, may also affect the wood-burning activity for a specifi c time period during the inventory year being modeled. The Team will examine potential factors and will determine if information is available (e.g., from the literature or from State agency co ntacts) to improve the ac tivity data or temporal

allocation factors.

As with prescribed burning and wildfires, contac ts with EPA staff and searches of the literature to date have not yielded NH3 emission factors specific for residential wood combustion. Consistent with ARB’s current approach, NH3 em issions may be estimated based on ratios of

NH3 to CO emission rates from other biomass co mbustion sources (e.g., wild fires). Care must be taken in the use of these ratios, however sinc e NH3 emissions from sources such as wildfires can come from the underlying soil as well as the burned biomass.

Based on other ongoing work for this source categor y, it is clearly important to be able to distinguish between urban and rural reside ntial wood burning activity. Per unit household, activity in urban areas is lower than in rural areas (where wood can be an important primary or supplemental fuel source; Houck, 1997). The team will develop weighting factors to account for this issue from the U.S. Department of Energy household energy consumption surveys (e.g., DOE, 1993) and the U.S. Bureau of Census American Housing Survey.

Livestock

The ENVIRON team will attempt to improve on the current limitations in the Livestock ammonia emissions inventory. We believe that significant improvements can be made in the spatial and especially the temporal al location of livestock emissions.

The ENVIRON team will use the emission factor s from the study by James et al (1997) to

estimate emissions for confined operations (dairies and beef f eedlots. For range cattle, the grazing emission factor presented in Battye et al (1994) will be used (based on the work of Asman in Europe). The team will use methods similar to those used by CARB in 1999 to spatially allocate populations to Ca lifornia air basins. Emission factors for goats will be from Bouwman et al. (1997); emission factors for rabbits will be from Gharib and Cass (1984). All other emission factors will be from Battye at al . (1994). Livestock emis sion factors are shown on Table 2-5. Proposed emission inve ntory codes are given in Table 2-6.

Table 2-5 . Livestock emission factors.

Livestock Emission Factor, lbs/animal/yr Emission Factor Source

Beef Cows (confined operations) 74 James et al. (1997) Milk Cows (confined operations) 74 James et al (1997) c Manure Spreading 5.6 Battye et al. (1994) Range Cattle 1.54 Battye et al. (1994) Hogs and Pigs 20.3 Battye et al. (1994) b
Sheep and Lambs 7.43 Battye et al. (1994) bApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-12 Layers and pullets 1.00 Battye et al. (1994) b
Broilers and other meat type chickens 0.37 Battye et al. (1994) b
Horses 26.90 Battye et al. (1994) b
Mules, Burros and Donkeys 26.9 Battye et al. (1994) b
Goats 1.28 Bouwman, et al. (1997) d
Rabbits 0.37 Gharib and Cass (1984) e

We will investigate in detail the basis for propos ed diurnal variations in dairy related ammonia emissions (Muck and Steenhuis, 1982; Wilkinson et al., 1998; James et al., 1997; and Schmidt and Winegar, 1996). We will specifically conc entrate on algorithms which describe the emissions in terms of commonly available e nvironmental parameters (i.e., wind speed, temperature and solar radiation) because they wi ll provide a sound basis for future refinement of the inventory. The current understanding of th e biological and physical processes accounting for

the creation of emission of ammonia from an imal excreta is insufficient to develop a theoretically based temporal model. However, an understanding of these processes will be used to validate an empirical mode l like the one presented in Wilk inson et al. (1998). That model assumes wind speed, temperature, solar radiat ion, and relative humidity are the independent variables. Because models like that presente d in Wilkinson et al. (1998) require significant amounts of local environmental data, processi ng constraints may require a more general approach. We believe that several regional profiles can be produced which would represent

diurnal and seasonal variation of ammonia emi ssions. These profiles would be compiled from

the results of a model lik e Wilkinson et al. (1998). The author s of Wilkinson et al. (1998) and James et al. (1997) will be contacted to obtain any ne wly developed information.

Other temporal variations may occur because of management practices regarding manure removal, feed cycles and seasonal population fluctuations. The ENVIRON team will consult with the California Cattleman’s Association and the Western Dairy Association to determine how these practices may affect seasonal ammoni a emissions and recommend a seasonal temporal profile if it can be supported.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-13 Table 2-6. Suggested EIC/CES Codes for Livestock Operations
EIC Description EIC CES Existing Code

Livestock Wastes 630-618-0262-0000 66605 Proposed Codes Cattle - Confined Dairy Operations 630-618-0262-0001 66606 Cattle - Confined Beef Operations 630-618-0262-0002 66607 Cattle - Grazing 630-618-0262-0003 66608 Cattle - Manure Spreading 630-618-0262-0004 66609 Poultry - Broiler Chickens 630-618-0264-0001 66612 Poultry - Layer and Pullet Chickens 630-618-0264-0002 66613 Poultry - Turkeys 630-618-0264-0003 66614 Hogs and Pigs 630-618-0266-0001 66618

Goats 630-618-0268-0001 66620

Rabbits 630-618-0270-0001 66622 Sheep and Lambs 630-618-0272-0001 66624 Mules, Burros and Donkeys 630-618-0274-0001 66626

Horses 630-618-0276-0001 66628

The temporal profiles discussed above were all de veloped for dairies and may not be applicable for other livestock. However, because test data to determine temporal profiles for other livestock are lacking, the profiles develope d for dairies could be used. This method is assumed to be appropriate because excreta from other animals are assumed to react similarly to dairy cow

excreta.

For spatial allocation, the ENVIRON team w ill pursue data from California Cattleman’s Association, Western Dairy Association, local Regional Water Quality Control Boards (RWQCBs), county vector control offices and/or county health departments, and the Department

of Water Resources land use database. We will identify as many dairy facilities, with populations, as possible, filling in data from multiple data sources if required. These facilities will be distributed as point sources. If spatial loca tion data is not available from the relevant data source it will be determined by geo-coding the facility address. The ENVIRON team will focus on identifying the largest faciliti es, especially large poultry facil ities, in order to pinpoint the majority of these emissions.

The same approach will be used for poultry oper ations. Using primarily data from the county vector control offices and/or county health departments, the ENVIRON team will geo-code facility addresses to allocate these emissions as point sources.

Beef cattle will be allocated to the grasslands/herbaceous classi fication, code 71, in the National Land Cover Data (NLCD) database. Half of the horses, mules, burros and Donkey emissions will be allocated to the pasture/hay classi fication, code 81. These emissions represent commercial operations. Half of the emissions will be allocated to low intensity residential classification, code 21, representi ng home stabled animals. Half of the hogs, rabbits, sheep and goats will be allocated to pa sture/hay classification, code 81, and half will be allocated to grasslands/herbaceous classification, code 71. Fo llowing a review of the animal population data, the team will consult with local agricultural exte nsion offices to determine the best allocation of

these emissions.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-14 Activity data for dairies will be pursued thr ough California Cattleman’s Association, Western Dairyman’s Association, the RWQCBs, Bran ch, the Department of Agriculture’s 1997 Agricultural Census (USDA 1999a). Activity data for horses will be pursued from the Department of Agriculture’s Equine Study (USDA 1999b), Department of Agriculture’s 1997 Agricultural Census (USDA 1999a), and Americ an Horse Council Foundation. On farm horse populations will be taken from the Agricult ural Census (USDA 1999a), non-farm horse populations will be developed base d on the ratio of non-farm to fa rm horses reported at the state level in the American Horse Council Survey.

In some cases data from multiple sources will be reconciled to produce the best estimate of animal population.

Wastewater Treatment

The ENVIRON team will identify a preliminary lis t of wastewater treatment plants (POTWs)

from the California State Water Resources Control Board (SWRCB) Database of Waste Dischargers. Because past researchers have noted that not all POTWs are listed on the SWRCB database, the ENVIRON team will contact local sanitation district’s within the study area to identify any additional facilities. The ENVI RON team will survey POTWs to determine the extent to which sludge drying occurs at the faci lities or off-site w ithin the study domain or outside of the study domain and to determine flow rates if not available from the SWRCB database. We will assign emissions specific to sludge drying to those facilities where sludge drying occurs. Emissions for sludge drying will be estimated from field data collected at the Visalia Plant by STI (1998). Emissions from water treatment will be estimated using the emission factor developed for STI (1998) from a regression analysis of self reported ammonia

emissions and flow rates. This emission factor is consistent with the emission factor developed by ATC (2000) from source test results at two POTWs. The activity data, flow rates, will be from the SWRCB database or telephone survey for unlisted facilities. Facility addresses will be geo-coded and the emissions allocated as point sources.

Composting

The ENVIRON team has reviewed the Solid Waste Information System (SWIS) Database of the California Integrated Waste Mana gement Board to determine the extent of current composting operations in the San Joaquin Valley and throughout the State. The database includes almost 200 composting facilities statewide. The database does not contain th e relevant activity data, amount of waste composted, to develop ammonia emissi ons estimates for significant portion of these

facilities. The ENVIRON team w ill use activity data from the da tabase, if available, and will

contact facilities w ithin the study area to obtain activity data for those facilities for which data is lacking in the database. The emission factors developed by ATC (2000) from source test results from facilities composting several different wa ste types will be used to estimate emissions

Composting emissions will be allocated as po int sources based on the latitude/longitude information from the SWIS database. Ammonia emissions information from composting operations is insufficient to develop a temporal profile for this source.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-15 Landfills

The ENVIRON team has reviewed the Solid Waste Information System (SWIS) Database of the California Integrated Waste Mana gement Board to identify landfills in the Study Area. Activity data, the amount of methane produc ed, will be developed from data reported in California Air

Resources Board emission inventor y database (CEIDARS). This data is reported as total organic gases (TOG) and reactive organic ga ses (ROG). Total methane is ca lculated as the difference of

TOG minus ROG. No new emissions data for a mmonia was found during the literature search. The team will adopt the same weight ratio of 0.7 NH3 to methane used in the previous inventory (STI, 1998) in order to estimate emissions.

The ENVIRON team will fill in data for large landf ills identified in the SWIS database which are not reported in CEIDARS. Data for similar sized landfills will be used to fill in this data.

Previous researchers have f ound this source category to be minor (ATC 2000, Radian 1991, STI 1998), therefore, the ENVIRON team will not expend significant resources in contacting facilities to produce the most comprehensive database possible or filling in data lacking from the

CEIDARS database

Landfill emissions will be allocated as point so urces based on the latitude/longitude information

from the SWIS database. Because of the scarci ty of emissions data, and the relatively minor contribution from this source, no temporal profiles will be developed.

Native Animals

This source category is considered a minor source. Native animals were not included in the most recent inventory for the San Joaquin Valley (S TI 1998). Native animals accounted for only

0.01% of the recent South Coast Air Basin i nventory (ATC 2000). The ENVIRON team will develop emissions estimates for this category without expending an unwarranted amount of

effort for this minor source.

Emissions from small and ubiquitous animals (e. g., rodents and birds) are assumed accounted for

in the soil emissions estimates.

Emission factors for several wild animals have been presented, however these factors vary considerably. The ENVIRON team will use emi ssion factors developed for the South Coast Air Basin inventory (ATC 2000). These emission f actors, developed from a generic herbivore

emission factor from Warn et. al ., (1990). Are shown on Table 2-7.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-16 Table 2-7 . Native animal emission factors.

Game Total Warn et. al. (1990) Emission Factor,

lb/kg-head-yr Weight a kg Adjusted Warn et al.

Emission Factor b

, lb/head-yr

Deer 13,500 0.14 79.4 11.1 Bear 0.25/ sq. mile c 0.14 300 42.0

a Warn T.E., Zelmanowitz S., and Saeger M. (1990): Development and Selection of Ammonia Emission Factors for 1985 NAPAP Emissions Inventory. EPA-600/7-90-014. Final Report Prepared for Office of Research and Development U.S. Environmental Protection Agency, Wash ington, D.C. by Alliance Technologies Corporation, Chapel Hill, N.C., EPA Contract No. 68-0 2-4374, Work Assignment No. 43 , June.

b Weight of white-tail deer taken from http://www.thewild ones.org/CHA.Animals97/deer.html. Weight of bears taken from http://www.bearden.org/blkbear.html.

c. Bear Density from Department of Fish and Game, Black Bear Management Plan, July 1998.

Activity data for bears and deer will be collect ed from the California Department of Fish and Game (CDFG). Activity data for bears will be based on the 0.25 bear per square mile density

estimate from the CDFG black bear management plan (CDFG 1998). Activity data for game animals will be pursued through the most recent game take hunter survey report by the CDFG. Past researchers, however, have found this da ta to be too sparse to develop good population estimates (ATC 2000, STI 1998.) In addition, a re latively large populatio n of wild pigs are present within the state and contribute about as much ammonia emissions as do bears. Therefore, activity data for wild pigs will be obtained from the CDFG and used to estimate

emissions based on emission factor s developed by Warn et al.

Emissions from native animal waste will be a llocated to shrub land, code 51 and forest land, codes 41, 42, and 43. In the case of deer, the CDFG has spatial distribution information within the state by district, which will be used to better spatially allocate emissions from this source. Because of the scarcity of emissions data, and th e relatively minor contribution from this source, no temporal profiles will be developed.

Domestic Sources

Domestic sources include ammonia emissions asso ciated with pets, cigarette smoke, untreated human waste, perspiration, respiration and hous ehold ammonia use. The ENVIRON team will use emission factors developed for the South Coast Air Basin inventory (ATC 2000.) This recent study provides the most comprehensive analysis of literature for these sources. Emission

factors for domestic sources are shown in Table 2-8.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-17 Table 2-8 . Domestic sources emission factors.

Source Proposed EIC Codes Emission Factor, lbs/person/year Emission Factor

Reference Respiration 690-995-0000-0001 0.004 e Perspiration 690-995-0000-0002 0.550 e

Household Use 690-995-0000-0003 0.050 a

Untreated Human Waste 690-995-0000-0004 0.050 e Cigarettes 690-995-0000-0005 0.000 c,d Infant - Cloth Diapers 690-995-0000-0006 6.900 b Infant - Disposable Diapers 690-995-0000-0007 0.360 b

Cats 690-995-0000-0008 0.029 e Dogs 690-995-0000-0009 0.265 e

Homeless 690-995-0000-0010 11.000 b

a Census of Population and Housing, U.S. Bureau of the Census, Current Population Reports, Series P23-194, Population Profile of the United States: 1997. U.S. Government Printing Office, Washington, D.C., 1998.

b Radian, 1987 Ammonia Emission Inventory (1991), September.

c California State Board of Equalization, "Table 30B - Cigarette Distributions and Per Capita Consumption, 1959- 60 to 1997-98." 1997 Annual Report, A-41

d Warn T.E., Zelmanowitz S., and Saeger M. (1990): Development and Selection of Ammonia Emission Factors for 1985 NAPAP Emissions Inventory. Final Report Prepared for Office of Research and Development U.S. Environmental Protection Agency, Washington, D.C. by A lliance Technologies Corporation, Chapel Hill, N.C., PB90-235094, EPA-600/7-90-014, June.

e Sutton, M.A., C.J. Place, M. Eager, D. Fowler, and R.I. Smith (1995): Asse ssment of the Magnitude of Ammonia Emissions in the United Kingdom, Atmospheric Environment, (29):1393-1411.

Activity data for pets will be developed based on the ratio of pets per capita developed for the 1991 South Coast Air Basin inventory (Radian 1991) and 2000 census data from the U.S. Bureau of the Census. These ratios are specific to th e urban and suburban settings. A determination of the setting, urban or suburban, will be made fo r each county, based on the predominant setting within each county.

Activity data will be based on per capita cigarette consumption data from the most current annual report on cigarette distri bution and per capita consump tion from the California State Board of Equalization in conjuncti on with 2000 census data from the U.S. Bureau of the Census.

Activity data for untreated human waste incl udes the population of ho meless people and the population of infants of diaper wearing age. Most counties have a service department responsible for the homeless population. The EN VIRON team will survey the States most populous counties to determine an appropriate ratio of homeless to total population for each county. Infant population will be calculated as the sum of the under one year old population , the one to two year old population, the two to three year old population and half of the three to four year old population from the 2000 census data from the U.S. Bureau of the Census.

Activity data for respiration and perspiration will be the total population from the 2000 census

data from the U.S. Bureau of the Census.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-18 All domestic sources will be allocated based on total population from the 2000 census data from the U.S. Bureau of the Census except for infants, which will be distributed based on the diaper wearing population described above.

Industrial sources

Industrial sources have traditiona lly been inventoried based on reported emissions such as the

SARA 313 Toxic Release Inventor y (TRI) or the Air Resources Boards Air Toxics Database (ATEDS). This method allows emissions alloca tions as point sources, and, in the case of ATEDS, the assignment of appr opriate release parameters. Reporting to ATEDS is based on facility size, as determined by criteria pollu tant emissions. Reporting under SARA 313 has a very large threshold and excludes facilities with certain SIC codes. Therefore, many facilities that may have significant ammonia emissions are not included in these databases. Additionally,

the number of individual facilities (Several thousand facilities we re identified in the 1991 South Coast Air Basin inventory (Radian 1991) require s an unwarranted strain on resources.

The ENVIRON team will obtain data from majo r ammonia suppliers in the State, including UNOCAL, LaRoche Industries Incorporated an d Hill Brothers Chemical Corporation for ammonia usage. This data will be aggregated to zip codes to protect the pr oprietary nature of the supplier data. Supplier data wi ll be segregated by use for selective cataly tic reduction, refrigeration, metal treating, blueprinting and wa stewater treatment. Emission factors for each category are reported in the recent South Coas t Air Basin inventory (ATC 2000). Suggested EIC/CES codes are presented in Table 2-9. Emission factors are:

• Refrigeration – Fugitive emissions from ammonia refrigeration occur as a result of system leaks. Therefore, by mass balance, each pound of ammonia supplied is also emitted. The emission factor is 100 percent of usage.

• NO

x Control – Ammonia is injected into the exhaust of boilers, ga s turbines and other process equipment to reduce NO

x emissions. The injected ammonia reacts on a 1:1 molar

ratio of NH 3 to NOx. Excess ammonia is added to attempt to react all NOx. Good engineering practice results in a pproximately 10 ppm of unreacted NH 3 released from the stack (BACT requirement) by reducing NOx fr om 100 ppm to 10 ppm. The 10 ppm of

excess ammonia that does not react to reduce NO x, but is emitted directly to the atmosphere is called ammonia slip. The 10 ppm ammonia slip can be correlated to 10% of the ammonia injected. The emission factor is 10 percent of usage.

• Metal Heat Treating – Ammonia is used in the nitriding process to heat treat steel. Based on minimal escape during the nitriding process, approximately 10% of ammonia supplied is emitted. The emission factor is 10 percent of usage.

• Waste Water Treatment – Ammonia is used in wastewater treatment for neutralization. Based on minimal losses during transfer, upset s and improper operation, approximately 15% of ammonia supplied is emitted because ammonia is highly soluble in water. The emission factor is 15 percent of usage.

• Blueprinting – Ammonia is used in blueprin t processing. By mass balance, each pound of ammonia supplied is also emitted. The emission factor is 100 percent of usage.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-19 Industrial source emissions will be allocated as area sources based on the ZIP code coverage.

In addition to those industrial source listed above, the CEIDARS database includes ammonia emission from geothermal sources which comp rised approximately 1% of the statewide ammonia emission inventory. Emission form geothe rmal sources will be obtained form the ARB

and included in the inve ntory as point sources.

Table 2-9. Suggested EIC/CES Codes for Industrial Sources.
EIC Description EIC CES Refrigeration 499-995-0020-0001 47085

NO

x Control 499-995-0020-0002 47086 Metal Heat Treating 499-995-0020-0003 47087

Waste Water Treatment 499-995-0020-0004 47089 Blueprinting 499-995-0020-0005 47089

Mobile Sources

Two strategies are often used for mobile sources , namely link based or county level emissions. County level emissions are proce ssed essentially as area sources . Processing mobile source emissions as individual links from a transpor tation model preserves the maximum possible information on spatial distribution, temporal allocation and vehicle fleet characteristics from the transportation model. EPS2, EMS-95 and SMOKE can all handle link based emissions, but frequently a specialized program such as DTIM is used instead. DTIM is widely used in California, but it is not configur ed to handle ammonia emissions di rectly. This limitation can be circumvented by processing the ammonia emission s through DTIM as if they are PM emissions

. The decision whether to process on-road mobile ammonia emissions on li nks or at the county level is a compromise between th e extra precision and additional cost of link-based processing. The previous SJV ammonia inventory estimated m obile source emissions at the county level and allocated the emissions using spatia l surrogates and temporal profiles (i.e., like an area source).

The Team will assess the latest emissions data (e .g., Fraser and Cass, 1998; Lord et al, 2000) to determine the most appropriate data for use in a statewide mobile inventory. The Lord et al study provide results that indicate d that emissions from on-road vehicles may be underestimated (this study was conducted in the South Coast Air Basin). The current SJV estimates are taken

from Pechan’s 1990 National Particulates Invent ory (STI, 1998). The emission factor used by

Pechan for those estimates is close that obtaine d by Fraser and Cass [72 milligrams per kilometer (mg/km)]. The value obtained in the Lord et al study was 138 mg/km. The Team will contact these researchers to gather additional underlying data for this emission factor.

The ARB is currently developi ng statewide gridded VMT in s upport of the Central California Ozone Study (CCOS). The Team will obtain these gridded VMT data and, with the appropriate emission factors for ammonia, will develop the gr idded on-road mobile inventory as the product

of the VMT and a fleet-wide emission factor. De pending on the spatial re solution of the gridded VMT, the resulting gridded mobile emission inve ntory may require re-allocation to the 1 km resolution of the final inventory.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-20 Development of Standard Input Library

As part of this task, a standard input data library will be developed and documented. The purpose of the data library is to provide an e fficient means of archiving data as well as to facilitate future update and revisions to the i nventory in a systematic way. The data will be stored in a GIS format with appropriate attr ibutes to specify activ ity data for each source category, data units, spatial and te mporal extent of the data, allo cation factors and citations of data sources. Processing procedures will be deve loped to convert and/or reformat the “raw” data into an appropriate form for input to the GIS- based data library. The ARC/Info Macro Language (AML) will be used for processing and importing “r aw” emission data into the GIS. Since most data used in the development of the emission invent ory will likely be available, or can be easily re-formatted into, a spreadsheet format the AML programs will be developed to ingest a general ASCII format (e.g., a comma-delimited spreadsh eet export file) into the appropriate GIS coverage type (i.e., polygon, poi nt, or line coverages). This approach provides several advantages with respect to future revisions or updates of the data , as well as portability of the data library. As the general ASCII format can be developed and modifi ed using a variety of software tools and text editors, the data will be platform, or operating system, independent and thus portable and easily modifie d. In addition, the modification of data would not require any specific software tools, computer programming languages, scripts or operating systems. As the details of the AML processors and GIS-based data library are not yet fu lly defined, nor are the specific formats of the “raw” ASCII input data file s finalized, we refer to these simple formats in general terms as spreadsheets. All data and processing routines will be fully documented in detail and provided to the Technical Committee for future in-house processing.

Point Sources

For point sources, inventory data will be compile d into an EPS work file format (AFS). Note that the AFS format is essentially a spreadsheet format and as such will be readily imported to the GIS through use of the AML processors. The point source file in this format can be used for direct input into EPS, and the data could be us ed for input into other emissions models (EMS-95 or SMOKE). In consultation with the Technical Committee, additional fiel ds will be added to the database, such as ARB EIC codes.

For several source categories, part of the standard input library will include underlying spreadsheets that contain emissions data. An example source category woul d be cattle (both beef and dairy). As part of this inventory effort, th e Team will attempt to identify dairies and feedlots that can be modeled as point s ources (the remaining population w ould be modeled separately as area sources). The data in the spreadsheets incl ude source classification c odes, emission factors, activity data, release paramete rs, and temporal allocation da ta (including data, such as meteorological data used to estimate or allocat e emissions). The spreadsheets will also include citations for the origin of each data field. For future updates to the inventory, these spreadsheets can be easily revised (e. g., with new activity data or emission factors) and the da ta exported into the AFS point source file. All point source data w ill be imported into the GIS-based data library as ARC/Info point coverages.

Area Sources

For on-road sources, the emission estimation met hod will be to apply the appropriate emission factor to the gridded VMT, provided by the ARB, to obtain the final ammonia emission estimates. The standard input library will be a spreadsheet that cont ains the identification of each grid cell, the grid-based VMT, the emission factor s, weekly and daily cycl es (consistent with theApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-21 gridded inventory). As mentioned above for the point source files, the spreadsheets used for area

sources will contain citations of the source of each data element. On-road mobile source data will be imported into ARC/Info using appropriate AML’s as polygon coverages.

For area sources where the spatial coverage is not used as the activity data (e.g., livestock, residential wood, POTWs), spreadsh eets will be developed much like those for the point sources. Data will include the source classification codes, emission factor, activity data (e.g., county-level population), temporal allocation fact ors, citations of reference s ources and spatial surrogates and imported into the GIS-based data library as polygon coverages.

For area sources where the spatial surrogate is used as the activity da ta (e.g., fertilizer application, wildfires, soil/plant canopy systems) , the spreadsheets will not contain the activity

data. All of the other data elem ents will be included along with a citation of the source of the spatial surrogate and the land cover type a ssigned for estimating emissions for each source category. These data will be imported into the GIS-based data library as polygon coverages.

Spatial Surrogates

The spatial allocation of count y-level ammonia emissions is accomplished with the aid of gridded spatial surrogate data fo r each emission source category or groups of source categories. Spatial surrogates are typically based on the pr oportion of a known regi on-wide characteristic variable which exists within the modeling domai n grid cells (e.g. land us e categories, population, socioeconomic data, etc.). The development of an accurate gridded ammonia emission inventory at a spatial resolution of 1-km by 1-km requires surrogate data at a comparable resolution. In addition, in the case of land use data, the abil ity to distinguish betw een various land cover characteristics is necessary to properly allocate the county-level emissi on estimates for certain source categories, particularly for fertilizer a pplication and soil emi ssions. Other desirable features of the underlying surroga te databases are the currentness of the data (i.e., for what year

are the data based on and how ofte n are they updated) and the availa bility and quality of the data.

There are numerous Land Use/Land Cover (LUL C) databases availabl e from a variety of sources. These datasets provide for various spatial resolutions a nd geographic extent as well as a variety of LULC classification schemes. In ad dition, a limited number of special study datasets are in development for generating spatial surrogat es for other CRPAQS studies as well as for the CCOS. The following LULC databases have been identified for possible use in development of gridded spatial surrogates:

• USGS 1:100,000 scale (~200 m resolutio n) Land Use/Land Cover (LULC)

• USGS Land Cover Characteristic s (LCC) (~1 km resolution)

• USGS National Land Cover Data (NLCD) (~30 m resolution)

• California Gap Analysis Land Cover/ Vegetation Data (~50 m resolution)

• California Department of Conservation Farmland Mapping and Monitoring Program Agricultural and Urban Land Use data

• Department of Water Resources Land Use Survey Data

• Various Special Study Databases (e.g., Po tter, 2000; California Department of Pesticide Regulation databases, ARC/Info ZIP Code coverages)

Each of these datasets were evaluated for appr opriateness in developing su rrogate indicators for

ammonia emission sources. Evaluations were made with respect to availability, completeness,April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-22 accuracy, spatial resolution, land cover characteri zation in relation to ammonia emission source categories, currentness, and proces sing resource requirements.

USGS 1:100,000 Scale Land Use/Land Cover (LULC)

The USGS 1:100,000 scale Land Use/Land Cover (LULC) database were developed as part of the U.S. Geological Survey National Mapping Progr am and provide land cover characteristics for most of the contiguous U.S. and Hawaii. The data describe the vegetation, water, natural

surface and cultural features on th e land surface. The data is available in several formats including the Geographic Information Retrieva l and Analysis System (GIRAS), Composite Theme Grid (CTG) and ARC/Info export interchange (E00) format s, all of which are easily imported into a GIS system. The data are de veloped from aerial photographs acquired from NASA high-altitude missions as well as other ancillary data sources . Manual interp retation of the aerial photographs with additional in put from field surveys and othe r land cover data was used to digitize and classify the maps and the land cove r data using an Anderson Level 2 classification scheme. Table 2-10 presents the 36 LULC categorie s defined for this database. The data files are organized by quadrangle, each of which may be based on different representative dates. The dates of data acquisition range from the mid 1970’s through the 1980’s with the final datasets corresponding to a nominal 1990 base year. The da ta is presented in a Universal Transverse Mercator (UTM) projection coor dinate system at a spatial resolution of approximately 200 meters. The data are freely available from se veral sources, particularly the EPA’s anonymous FTP site, ftp.epa.gov/pub/spdata / .

The USGS LULC database has been used extensively in the past for the development of gridded surrogate indicators. ENVIRON and ATC ha ve recently completed an updated gridded ammonia inventory for the South Coast Air Basin (SCAB) using these data and have developed efficient processing techniques w ithin ARC/Info for applications involving the generation of regional scale datasets from multiple LULC quadrangles. However, during the development of the SCAB surrogate database, a number of LULC data errors were discovered which required manual corrections. In addition, as the data is based on aerial photographs and other land cover surveys which are at least, a nd in some cases more than, 10 years old, it was

determined that the data would not be a str ong candidate for use as a base dataset for the development of statewide gridding surrogates.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-23 Table 2-10. USGS Land Cover/Land Use Codes (A nderson Level 2 Classification Scheme).

Code Description 1 Urban or Built-up Land

11 Residential

12 Commercial and Services

13 Industrial 14 Transportation/Communication/Utilities

15 Industrial and Comme rcial Complexes 16 Mixed Urban or Built-up Land 17 Other Urban or Built-up Land 2 Agricultural Land 21 Cropland and Pasture 22 Orchards/Groves/Vineyards/Nurseries/Ornamental Horticultural 23 Confined Feeding Operation/Other Agricultural Lands 24 Other Agricultural Land 3 Rangeland 31 Herbaceous Rangeland 32 Shrub and Brush Rangeland 33 Mixed Rangeland

4 Forest Land

41 Deciduous Forest Land 42 Evergreen Forest Land

43 Mixed Forest Land

5 Water Bodies

51 Streams and Canals

52 Lakes

53 Reservoirs 54 Bays and Estuaries

6 Wetlands

61 Forested Wetlands

62 Nonforested Wetlands

7 Barren Land 71 Dry Salt Flats

72 Beaches 73 Sandy Areas not Beaches 74 Bare Exposed Rock 75 Strip Mines/Quarries/Gravel Pits

76 Transitional Areas

8 Tundra

81 Shrub and Brush Tundra

82 Herbaceous Tundra

83 Bare Ground Tundra

84 Wet Tundra

85 Mixed Tundra

9 Perennial Snow or Ice

91 Perennial Snowfields

92 GlaciersApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-24 USGS Land Cover Characteristics (LCC)

The USGS North America Land Cover Characterist ics (LCC) database is part of an on-going global vegetation database development, These da ta are on a 1-km by 1-km grid for the entire North American continent and are presented in four different classifi cation systems and two coordinate system projections. The most deta iled classification sche me includes 205 categories

for the entire continent of North America. The data are available in monthly composites spanning from April 1992 to March 1993 and 10-da y composites for a slightly larger time frame. Each classification system is available in both Lambert Equal Area and Interrupted Goode Homolosine coordinate projec tions. These data were deri ved from Advanced Very High Resolution Radiometer (AVHRR) da ta with a Normalized Difference Vegetation Index (NDVI). AVHRR data are high resolution data produced fr om orbiting satellites. The NDVI composites depict the relative density or vigor of different vegetation type s. The NDVI is calculated by taking the difference of near-infra red and visible reflectance values divided by total reflectance.

These values are then scaled from 0 to 200 wher e values less than 100 are usually clouds, snowor water, The NDVI is then composited with the AVHRR data which involves creating a multiband image with five AVHRR channels, the NDVI valu e, four bands of computed satellite/solar geometry, and a band which identifies the specific daily overpass from which each pixel was

extracted. The data can be obtained fr om the ftp site at edcftp.cr.usgs.gov . Quality assurance of the database is in varying stages and lacks accompanying ground-truthing.

In addition to the 205 detailed classification sc heme, the data are available in a number of simpler composite classification schemes which ma y be more appropriate for use in developing ammonia emission source category surrogate indicators. Table 2-11 presents the USGS Land

Use/Land Cover Modified Level 2 classification scheme consisting of 24 land cover categories.

While the USGS LULC classification scheme presented distinguishes the basic land cover categories for surrogate development, certain re levant categories are not represented. For example, although various types of cropland and pa sture are represented, th e desired detail in terms of crop types (row crops, grains and hay, orchards and vineyards) relevant to certain source categories (fertilizer application, soil systems) ar e not distinguishable.

Table 2-11. USGS Land Use/Land Cover Catego ry Codes (Modified Level 2).

Value Code Description 1 100 Urban and Built-up Land 2 211 Dryland Cropland and Pasture 3 212 Irrigated Cropland and Pasture 4 213 Mixed Dryland/Irriga ted Cropland and Pasture 5 280 Cropland/Grassland Mosaic 6 290 Cropland/Woodland Mosaic

7 311 Grassland

8 321 Shrubland

9 330 Mixed Shrubland/Grassland

10 332 Savanna

11 411 Deciduous Broadleaf Forest

12 412 Deciduous Needleleaf Forest 13 421 Evergreen Broadleaf Forest 14 422 Evergreen Needleleaf Forest

15 430 Mixed Forest

16 500 Water Bodies

17 620 Herbaceous WetlandApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-25

Value Code Description

18 610 Wooded Wetland 19 770 Barren or Sparsely Vegetated

20 820 Herbaceous Tundra

21 810 Wooded Tundra

22 850 Mixed Tundra

23 830 Bare Ground Tundra

24 900 Snow or Ice

USGS National Land Cover Data (NLCD)

The National Land Cover Data (NLCD) was devel oped as part of a cooperative project between the U.S. Geological Survey and the U.S. E nvironmental Protection Agency to produce a consistent land cover data layer for the enti re conterminous U.S. based on 30-meter Landsat thematic mapper (TM) data. The NLCD was deve loped from TM data acquired from the MultiResolution Land Characterization (MRLC) Consortiu m, a partnership of federal agencies that produce or use land cover data. The partners include USGS (National Mapping, Biological Resources, and Water Resources Divisions), th e USEPA, the U.S. Forest Service and the

National Oceanic and Atmo spheric Administration.

Two seasonally distinct TM mosaics are pr oduced, a leaves-on (sum mer) and a leaves-off (spring/fall) version. The TM bands 3,4,5 and 7 ar e mosaiced for both the leaves-on and leavesoff versions, and a base scene is selected for each mosaic. Other scenes are adjusted to mimic the spectral properties of the base scene using hi stogram matching in regions of spatial overlap. Following the selection of a base scene, the 4 TM bands of the base mosaic are clustered to produce a single 100-class image, with each of the spectrally distinct clusters/classes subsequently assigned to one or more Anderson level 1 or 2 land cover classes using National High Altitude Photography program (NHAP) and Na tional Aerial Photography program (NAPP) aerial photographs as a reference. Invariably, so me individual clusters/classes will be confused

between two or more land cover classes. The separation of confused clusters/classes into appropriate NLCD classes is accomplished with th e use of ancillary data sets. These ancillary

data sets include the non-base mosaic TM ba nds and 100-class cluster image; derived TM Normalized Difference Vegetati on Index (NDVI); various TM ba nd ratios; TM date bands; 3- arc second DTED, population a nd housing density data; USGS Land Use/Land Cover; and National Wetland Inventory data. Additional source of ancillary data include soils data, unique state and/or regional land cover datasets, and data from other federal programs including the National Gap Analysis Program (GAP) of the USGS Biological Resour ces Division (BRD). Quality assurance of the data is on-going on a Federal Region basis following a revision cycle which incorporates feedback fr om MRLC Consortium partners a nd affiliated users and is being conducted by private sector vendors under contra ct to the USEPA according to a defined QA protocol established by the USGS and the USEPA.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-26 The base data set for the project was leaves -off Landsat TM data, nominal-1992 acquisitions. Ancillary data layers include leaves-on TM, USGS 3-arc second Digital Terrain Elevation Data (DTED) and derived slope, aspect and shaded relief, Bureau of the Census population and housing density data, USGS Land Use/Land Co ver (LULC), and National Wetland Inventory data. The general development procedures for th e NLCD is to: (1) mosaic subregional scenes and classify them using an unsupervised clus tering algorithm, (2) interpret and label the cluster/classes using aerial photographs as refere nce data, (3) resolve th e labeling of confused cluster/classes using appropriate ancillary data sources, and (4) incorporate land cover information from other data sets and perform manual edits to augment and refine the basic

classification.

The NLCD datasets are available as flat generic raster image f iles which are easily imported into a GIS (e.g., ARC/Info) and are provided in an Al bers Conic Equal Area pr ojection at a spatial resolution of 30-meters. The data can be obtained from the following URL http://edcwww.cr.usgs.gov/pub/edcuser/vogel/states/ , and for California is available as two separate data files for the nor thern and southern portions of the state. The land cover

characteristics are defined in terms of the 21 se parate categories presen ted in Table 2-12.

Table 2-12. NLCD Land Cover Classification Codes.

Code Description 11 Open Water

12 Perennial Ice/Snow 21 Low Intensity Residential 22 High Intensity Residential 23 Commercial/Industrial/Transportation 31 Bare Rock/Sand/Clay 32 Quarries/Strip Mines/Gravel Pits

33 Transitional

41 Deciduous Forest 42 Evergreen Forest

43 Mixed Forest

51 Shrubland 61 Orchards/Vineyards/Other

71 Grasslands/Herbaceous 81 Pasture/Hay 82 Row Crops

83 Small Grains

84 Fallow

85 Urban/Recreational Grasses 91 Woody Wetlands 92 Emergent Herbaceous Wetlands

California Gap Analysis Program Land Cover/Vegetation Data

The purpose of the Gap Analysis Program (GAP) is to provide regional assessments of the

conservation status of native vert ebrate species and natural land cover types and to facilitate the application of this information to land mana gement activities. The National GAP URL is http://www.gap.uidaho.edu/gap/ . The GAP is conducted as state-level projects and isApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-27 coordinated by the USGS Biologi cal Resources Division. Current ly the program is developing land cover mappings for all U.S. States. Alt hough each state is being developed separately, detailed vegetation species covers are being de veloped based on predetermined classifications. The status of the land cover mappings is at varying levels of completion depending on the individual state. For California, the GAP la nd cover mappings were completed in 2000. The California GAP URL is http:// www.biogeog.ucsb.edu/projects/gap/ and provides more detailed information specific to the state of California as well access to the GAP datasets in the form of ARC/Info coverages. The data is available in an Albers Conical Equal Area projection coordinate system at a nominal spatial resoluti on of approximately 50 meters. The land cover classifications are based on the National Vege tation Classification system and are derived primarily from Thematic Mapper imagery from 1990 with ancillary input data from aerial photography and other maps mostly from the 1980’s through 1995, with accu racy assessment of the data pending. The classification system provides for several hundred species designations and are group into broad categor ies and stratified according to pr imary, secondary and tertiary coverages based on percent of land cover in each of several broad regions. Although the spatial resolution is approximately 50 mete rs, due to the structure of the data is not always possible to spatially distinguish different sp ecies at the required re solution of 1 km. Fi gure 2-1 displays the primary land cover classifications for the state of California. The lack of distinguishable land

cover features at a fine scale resolution and due to the early dates of source data, it was

determined that the GAP database would not be the most appropriate source of data for development of gridding surr ogates for ammonia inventories.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-28 Figure 2-1. Generalized landcover of California from the Gap Analysis Program.

California Department of Conservation Fa rmland Mapping and Monitoring Program

The Farmland Mapping and Monitoring Program (FMMP) of the California Department of Conservation develops land use maps and statistical data fo r use in analyzing impacts on California’s agricultural resources. The program was established in 1982 in response to a need for assessing the location, quality, and quantity of agricultural lands and the conversion of these lands over time. The program provides a consistent and impartial analysis of agricultural land use and land use changes throughout California. The database is compiled on a county level and is based on soil survey information, aerial photographs at various resolution and field

reconnaissance with a minimum mapping unit of 10 acres. The land use data is classified according to several subcategories based on th e primary land use categories of important farmland and interim farmland map categories. Important farmland map classification include several mapping categories includi ng prime farmland, farmland of

statewide and local importance, grazing land, urban and built-up la nd, water, and other land. TheApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-29 interim farmland classification in clude two subcategories, irriga ted and non-irrigated farmland.

The FMMP databases are updated biennially with th e latest update available from 1998. As the data is compiled from soil surveys, the extent of the farmland covera ge corresponds to the availability of what the National Resources Conservation Service (NRC S) considers “modern soil surveys”, and therefore the current coverage is limited to 48 of the state’s 58 counties. In addition, of the 48 counties include d in the database some have only been partially mapped since

the NRCS has limited its efforts to areas which are considered agricultu rally important. Figure 2-2 displays the extent of the data coverage in terms of the important and interim farmland map categories as well as unmapped regions. The data can be obtained in vector format from the

World Wide Web at http://www.consrv.ca.gov/dlrp/FMMP/ and is available in an Alberts Equal Area projection coordinate system. Because the data coverage is limited and, in several cases, incomplete for the study area, it was determin ed that the FMMP database would not be appropriate for development of gridding surrogates for ammonia i nventories. In addition, the lack of the high resolution, detailed land use classifications necessary for ammonia emissions surrogate indicators preclude their use for this study.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-30 Figure 2-2. Farmland mapping and monitoring survey area.

Department of Water Resources Land Use Survey

The California Department of Water Resour ces (DWR) Land Use Survey databases are developed by the department’s Division of Pla nning and Local Assistance in order to aid in DWR’s efforts to continually monitor land use fo r the purpose of determining the amount of and changes in the use of water. The data are ava ilable at the county level and include detailed agricultural land use classifications, as well as lesser detailed urban and native vegetation land uses. The data was compiled from aerial photography with subsequent extensive field verification and are updated on a rotating basis by county approxi mately every seven years. Thus the data are based on varying years, depe nding on the county, rangi ng from the late 1980’s through the late 1990’s. Databases for each coun ty are available in AUTOCAD DWG format, asApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-31 well as ARC/Info shape file format employing a Transverse Mercator co ordinate projection and can be obtained from the DWR’s developing web site at http://dplasp.water.ca.gov/landwateruse/ . The classification scheme used allows for up to three land use attribute per polygon (land parcel) and is considered very accura te due to the field survey verification of the aerial photograph imager y. Figure 2-3 displays a typical land use map for Kern county based on 1998 data.

While this database was not determined to be appropriate for the development of statewide gridding surrogates, due to th e varying dates of data acquisition and less resolved nonagricultural land use classifications, it will be used for verification purposes and for use in augmentation of statewide datasets with the detailed agricultural land use categories.

Figure 2-3. 1998 DWR Landuse survey coverage for Kern county.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-32 Given the resource restraints of the project, the following approach will be taken to develop gridded surrogates for the spatia l allocation of county-level a mmonia emission estimates. The NLCD will be used as the primary data source, as this database provides the relevant land cover

characteristics at the best resolution for the enti re state of California, is based on fairly recent year data and is freely availabl e and periodically updated. A prel iminary evaluation of the data

reveals that the various land use categories are well represented at a resolution of approximately 1-km. While currently under-going final quality assurance, the da ta is periodically updated to reflect the status of the QA process, and since the data covers the entire state of California available from a single source, th e underlying data used for surrogate development can be easily updated with minimal processing effort. The fina l gridded surrogate datasets derived from the NLCD will be supplemented with the more curre nt DWR database where deemed necessary or appropriate. Likewise, for those land cover ca tegories which are not explicitly represented by the NLCD (e.g., confined feed lots, detailed cr op types), the gridding surrogate data will be augmented and verified with the LULC databa se which best represen ts these categories.

Finally, we recognize that given th e current in-progress status of various other data collection and development projects sponsored by the ARB in support of the CRPAQS, there may be more appropriate and/or more current data available for use in spatia l allocation of ammonia emission sources. However, due to project scheduling i ssues, we cannot rely exclusively on the use of these special study data. In addition, as the spec ific episodes (annual, seasonal, episodic) for the final inventory has not yet been determined, it is unknown which of these special study databases would be most appropriate. Therefore it will be more appropriate to use a single consistent data source for the entire study area with possible augmentation from regional and/or seasonally specific data sources applicable to a particular episode, resulting in a more generally applicable gridding surrogate database. In addition, the ENVIRON team anticipates continued collaboration with these projects and will attemp t to incorporate additional land cover data as allowed by the project schedul e and resource constraints.

The spatial allocation of domestic ammonia emi ssion sources will be based on gridded surrogate indicators developed from population data. The 1990 US Census data in FoxPro format will be processed into the appropriate file formats for us e in the ammonia emission model. If available, data from the 2000 US Census, as well as local data sources, will be incorporated into the database for development of gridding surrogates for domestic sources.

The spatial allocation of the on -road mobile source emissions w ill rely on the underlying gridded VMT data used for developing the ammonia em ission estimates. The final gridded mobile source emissions will be gridded to the requi red 1-km by 1-km resolution of the modeling

domain.

As discussed above, the spatial allocation of indu strial sources will be based on ZIP code in order to protect the confidentiali ty of the underlying activity data. A similar treatment was used in the recent updated gridded ammonia invent ory developed for the SCAQMD (ATC, 2000). ZIP code coverages, in ARC/In fo format, were obtained from Geographic Data Technology, Inc. and processed in the ARC/Info GIS system in a manner similar to LULC data. The ZIP code coverages are based on recent year 2000 information.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-33 Quality Assurance/Quality Control

Quality assurance (QA) will be conducted on all data collected during the development of the ammonia emission inventory. Emission calculation will be checked to ensure that the methodology calculations were performed correctly. Emission estimates will also be examined to ensure that the developed inventory is reas onable. Various archiving procedures will be developed and documented to ensure that the in tegrity of all emissions data is maintained,

included emission factor data and sources, spatial and temporal al location data, and final gridded inventories. Appropriate file naming conventions will be established to facilitate efficient revision control of all input data and future updates and revision to the ammonia inventories.

For point sources, the Team will use in-house pr ograms for QA/QC of point source emissions files. These programs have been developed to identify out of range release parameters (e.g., stack heights, temperatures) and source locations (UTM coordinates). In addition, the Team will use emission density plots to evaluate whet her emissions are bei ng properly located and temporally allocated.

For area sources, the Team will rely on the review of emission density plots to verify proper spatial and temporal allocation. Daily emission density plots for each season will be made for each of the source categories described above. Te am members will verify th at the plots show the proper spatial allocation and that the emissions s hown can be scaled up to match the annual totals for each category.

Task 3: Develop Ammonia Emission Model

The objective of this task will be to develop an emissions model that will accurately and efficiently prepare gridded emissions inventor ies for the SJV based on the data assembled through this study. The model w ill address all sources of ammonia for which emissions are

related to land area – area sources. Point sour ces will be treated separately by geo-coding each source location and emissions characteristics. Mobile sources will also be treated separately to take advantage of the link-based emissions modeling systems (e.g., DTIM) developed specifically for mobile sources. As noted in the RFP for the projec t, this completion and level of

effort for this task is subordinate to Ta sk 4, producing a draft ammonia inventory.

Review of Existing and On-going Devel opment of GIS-based Emissions Models

The ARB has several ongoing projects wher e the contractors ar e using bottom-up

GIS models to estimate ammonia emissions from specific source categories. UC-Berkeley is estimating emissions of pollutants (including amm onia) from wildfires. The UC-Berkeley project coordinator is James Scarborough and some in formation on the project is available at http://www.gisc.berkele y.edu/~jscar/emissions/ . There is no information on ammonia emissions

estimates at this source. Ammonia emissions from wildfires are not consider ed a high priority at

this time.

The ARB also has a contract on fertilizer am monia emissions with CSU-Fresno that includes development of a GIS-based soil emissions m odel called CAL-CASA. Th e CAL-CASA model is being developed by Dr. Chris Potter at NASA-Ames Research Center. This contract is almost completed but the final report is not yet availabl e. Some information is available from a NASA web page at http://geo.arc.nasa.gov/sge/casa/ . Ammonia emissions from fertilizer emissions areApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-34 considered important, but at this time it is uncl ear what results will be available from the CSUFresno study and how they can be incorporated into this work.

Overview of Approach

The need for a separate area source emissions model depends upon the amount of detail to be included in the emission relationships. In the simple case where ammonia emissions are estimated from land area multiplied by an emission factor, there is no need for a separate model because (1) the calculation is simple to accomp lish within the GIS (e.g., ARC/Info) and (2) since there are no day specific effects on emissions the calculation need only be performed once. A schematic of the calculation is shown in Figure 2-4.

Figure 2-4 . Simple approach to estimating ammonia emissions within the GIS.

The simple approach shown in Figure 2-4 negl ects environmental impacts (e.g., temperature dependence, seasonal dependence) on ammonia em issions. Incorporating environmental effects is a superior approach provided that informati on on environmental effects is available. One result is likely to be a need to prepare day sp ecific inventories (simila r to biogenic emissions modeling) which means that the emissions calc ulation must be repeated several times and efficiency becomes important. This suggests a different calculation appr oach, similar to that used in biogenic emissions preparation, as s hown schematically in Figure 2-5. Under this approach, the landcover data are pre-processed using GIS into a gridde d landcover database. This operation need only be performed once. An emissions model then combines the gridded

landcover data with the emission factors and ma kes adjustments to account for environmental

effects. Landcover

Data

Emission

Factor Table GIS (Arc/Info)

Gridded

EmissionsApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-35 Figure 2-5 . Estimating ammonia emissions using a m odel to account for environmental effects.

There are several advantag es to this approach:

• Improved efficiency because the land cover data are gridded only once.

• Improved efficiency because emissions calculations are performed at the resolution of the modeling grid which is generally lower than the resolution of the raw landcover data.

• Improved usability because the emissions m odeling step is separated from the GIS processing, therefore expensive GIS software is not required to prepare new emission

inventories.

A prototype emissions modeling system of the t ype shown in Figure 2-5 already exists in the

GLOBEIS model. GLOBEIS wa s developed for modeling biogenic VOC and NOx emissions,

but also includes ammonia. The ammonia emi ssion algorithms in the current version of GLOBEIS are very simple and so improvement s will be required for this study. Depending on the availability of information concerning spec ific algorithms used in the CAL-CASA model, these will also be incorporated as part of the enhancements.

Current GLOBEIS Capabilities

GLOBEIS, short for the Global Biosphere Emissi ons and Interactions System, was developed by

Dr. Alex Guenther at the National Center for At mospheric Research (NCAR) and Stella Shepard, Gary Wilson and Dr. Greg Yarwood at ENVIRON. The idea behind GLOBEIS is to provide a biogenic emissions modeling system that embodi es up-to-date scientif ic information in a Landcover

Data GIS (Arc/Info)

Gridded

Landcover

Emissions

Model (GLOBEIS)

Gridded Day Specific

Emissions Gridded Environmental

Data - Temperature - Fertilizer Application

- other

Other Environmental

Data

- Seasonal factors

- Diurnal factors

- othe r Emission

Factor TableApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-36 package that is easy to use, eas y to update, and compatible with a wide range of input data (hence “Global”). The development of GLOB EIS is described in Yarwood et al. (1999).

GLOBEIS model runs under Microsoft Access on Windows based personal computers. The following computer resources are needed to run GLOBEIS effectively:

• Recent CPU such as Pentium II or AMD K6 family

• Minimum 64 Mb RAM

• 300 Mb hard disk space

• Microsoft Access 97 or Access 2000

A regional scale, day specific biogenic emissi on inventory for photochemical modeling can be prepared in minutes using GLOBEIS.

GLOBEIS is designed to provide interactive control of the emissions modeling process through menu screens to select which modules to run (Fig ure 2-6) and to specify input data and options (Figure 2-7).April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-37 Figure 2-6 . The main GLOBEI S control interface.

Figure 2-7 . Selection of input data a nd model options in GLOBEIS.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-38 The main input data for GLOBEIS are:

• GIS data on gridded LU LC distributions

• Plant species distributions for each LU LC category (internal data table)

• Emission factors by plant spec ies (internal data table)

• Gridded environmental data (tempera ture, solar radiat ion or cloud cover)

• Seasonal biomass adjustments (satellite data based)

Data processing tasks for GLOBEIS have been di vided to make best us e of the capabilities of

GIS and database software tools. The LULC data for GLOBEIS are prepared using GIS tools such as ARC/Info or ARCView. This data prep aration step is a strai ghtforward application for

the GIS software. The emission calculations are performed in GLOBEIS, which is based on the Microsoft ACCESS database, because this step is essentially a data synthesis and crossreferencing application and is well suited to a modern database program like ACCESS. The reporting capabilities available within ACCESS also allow strong QA/QC capabilities to be built into GLOBEIS. The system is easy to maintain and upgrade because it is based on widely used

software tools.

A significant advantage of GLOBEIS is the ease with which an emission inventory can be regenerated with alternate assump tions. For example, all of the emission factor data are readily accessible in data tables within the model, so an alternate emission factor can be used and the inventory regenerate d in a single step.

The output from GLOBEIS include s a gridded, temporally allocat ed, speciated emissions file ready for use in a photochemical modeling. An example emission inventory for the Houston area is shown in Figure 2-8. This figure demonstr ates the ability of a GIS based system (like GLOBEIS) to accurately resolve areas of high and low emissions density without introducing

artifacts related to geopolitical boundaries.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-39 Figure 2-8 . Isoprene emission invent ory for the Houston area prepared with GLOBEIS.

Enhancements to GLOBEIS for this Study

As discussed above, GLOBEIS wa s designed as a biogenic emi ssions processing system but support for ammonia was built into the model. Several simple improvements to GLOBEIS are proposed to allow maximum use of available am monia emissions information and improve the efficiency of the system for processing ammonia emissions.

• Switch to turn off the biogenic emissions calcula tions if they are not being used (or possibly adapt GLOBEIS into a new system for ammonia only). This would reduce unnecessary computational and data requirements.

• Add the ability to specify seasonal a nd temporal profiles by source category.

• Add environmental relationships between a mmonia emission factor and environmental factors (e.g., dependence on temperature) to provi de day specific emissions. This would be analogous to the current built-i n emission relationships betw een isoprene emission factors and sunlight/temperature. GLOBEIS already re ads gridded hourly temperature and radiation data, and other environmental data inputs can be added if needed. Specific additional environmental data which will be included, primarily for the treatment of natural soils emissions, would be relative humidity, soil pH a nd soil moisture. These data are available from, and will be provided by, the ARB.

• Add the ability to treat point source emission data.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-40 • Update look-up tables for source specific and landuse specific emissions factors.

The objective is to build an emissions mode ling system that makes the maximum use of currently available information and also can accommodate more detailed information as it

becomes available from future studies.

Task 4: Produce Draft A mmonia Modeling Inventory

The purpose of this task is develop the draf t gridded ammonia emissions modeling inventory

based on the best available data and processing methodologies.

Modeling Episodes and Domain

The CRPAQS is currently conducting a field m easurement program consisting of fourteen months (annual program) of monitoring throughout the San Joaquin Valley and surrounding areas. The field program includes a number of episodic field programs within the annual monitoring period. The winter episodic field study will focus on collection of PM 2.5 data and is being conducted from mid-November through January, 2001. The summer and fall episodic studies were to have taken place during July 1, 2000 through August 31, 2000 and from September 15, 2000 through November 15, 2000, resp ectively. Ideally, the draft gridded

ammonia emission inventories wi ll be developed for time peri ods corresponding to these field measurement studies. However, at this time, th e precise episodes for particulate modeling have not yet been finalized.

As specified in the Statement of Work for the project, the modeling domain will cover the entire San Joaquin Valley study area. However, as the exact specification of the modeling domains and grid structure has not been finalized, data colle ction efforts will focus on the SJV study area, but

will also include the entire state of California for those cases were the data collection efforts would not jeopardize the project in terms of resource constraint s. Figure 2-9 displays the proposed modeling domains for the CCOS and CRPAQS studies in a Lambert Conformal Projection (LCP) coordinate system at a grid re solution of 4-km. For the 4-km Statewide grid, the domain consists of 273 x 273 grid cells, while the CCOS and CRPAQS 4-km domain is centered over the San Joaquin Valley study ar ea and consists of 190 x 190 grid cells.

The draft gridded ammonia emission inventories th is project will be developed at a horizontal resolution of 1-km by 1-km and 1-hour tem poral resolution. The modeling domain will correspond to the CRPAQS grid shown in Figure 2- 9 and will consist of 760 x 760 grid cells.

The draft inventories will be developed for two time periods coinciding with the 1999-2001 CRPAQS field measurement study. These two pe riods will be determin ed by the Technical

Committee.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-41 -497.1 -417.1 -337.1 -257.1 -177.1 -97.1 -17.1 62.9 142.9 222.9 302.9 382.9 462.9 542.9

LCP Easting (km) -494.9 -454.9 -414.9 -374.9 -334.9 -294.9 -254.9 -214.9 -174.9 -134.9

-94.9 -54.9 -14.9 25.1 65.1 105.1 145.1 185.1 225.1 265.1 305.1 345.1 385.1 425.1 465.1 505.1 545.1 585.1LCP Northing (km)

CRPAQS Statewide 4km Domain

Figure 2-9. Proposed CCOS and CRPAQS Modeling Domains.April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-42 Treatment of Emission Source Categories

The recommended treatment for each ammonia em ission source category is summarized with respect to spatial and temporal al location in the following subsections.

Spatial Allocation

Point sources will be spatially allocated using the appropriate coordinate projection system for the CRPAQS modeling domain. This includes s ources within certain categories previously modeled as area sources (e.g., live stock) where the Team has identified information (e.g., dairy locations) to model the sources as point sources . For area and mobile sources, the Team will spatially allocate the emissions with the s ources of data shown in Table 2-13 below.

Table 2-13. Spatial surrogates for area and mobile sources.

Source Category Spatial Surrogate

On-Road Mobile Sources Allocated ba sed on existing gridded VMT. See

discussion on on-road mobile source above. Fertilizer Application Crop coverage s developed by Potter et al (2000) Soils and Plant Canopy Systems Coverage s developed by Potter et al (2000) Agriculture Burning Crop coverages developed by Potter et al (2000) Prescribed Burns/Wildfires Coverages developed from CDFFP and NIFC data. Residential Wood Burning NLCD resi dential LULC and census data Livestock: Beef Cattle NLCD LULC coverages Livestock: Hogs, Rabbits, Sheep,

Goats NLCD LULC coverages

Livestock: Horses, Mules, Donkeys, Burros NLCD LULC coverages

Native Animals NLCD LULC coverages Industrial ZIP code coverages

Domestic Sources Human NLCD LULC coverages and census data

The following emission source categories will treate d as stationary point sources: dairy cattle, poultry operations, POTWs, composting and landfills.

Temporal Allocation

Table 2-14 provides a list of th e suggested temporal allocation by source category. Details on the sources of temporal allocation methods and da ta sources are given under the descriptions for each source category under Task 2 above.

Table 2-14 . Temporal allocation by source category.

Source Category Primary Temporal Allocation On-Road Mobile Sources Existing CCOS temporal allocation Fertilizer Application Monthly (minimum) – based on data from PotterApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-43 et al. Depending on available data, weekly allocation may be possible. Soils and Plant Canopy Systems Seasonal (minimum) - based on data from Potter et al. Depending on av ailable data, monthly allocation may be possible. Agriculture Burning Monthly – base d on data from Fife, discussions with county agricultural offices. Prescribed Burns/Wildfires Seasonal (minimum) – based on activity data gathered from government agencies. Monthly allocation may be possible. Residential Wood Burning Monthly (m inimum) – based on calculations of degree-heating days. Weekly may be possible. Livestock: Dairy Cattle Diurnal (to be determined) based on Wilkinson

et al. and James et al. Livestock: Beef Cattle Diurnal (to be determined) based on Wilkinson

et al. and James et al. Livestock: Poultry Di urnal (to be determined) based on Wilkinson

et al. and James et al. Livestock: Horses, Mules, Donkeys, Burros Diurnal (to be determined) based on Wilkinson

et al. and James et al. Livestock: Hogs, Rabbits, Sheep,

Goats Diurnal (to be determined) based on Wilkinson

et al. and James et al.

Native Animals None

Industrial Point None Composting None

Landfills None Sewage Treatment Plants None

Domestic Sources None

In addition to the primary allocation schemes s hown in Table 2-14 above, the Team will also be investigating methods and data sources for diurnal allocation of emissions (daily cycles) for certain source categories (e.g., livestock, fertilizer application, soil/plant canopy systems). For all source categories, the Team will provide recommendations for daily and weekly cycles.

Application of Amm onia Emission Model

Area source emissions will be processed usi ng the enhanced GIS-based GLOBEIS model developed for this study. In addi tion, if feasible, point source em ission data will also be treated within the GIS model. As the development of th e model is currently in the conceptual planning stages, it is too early to specify precisely the details concerning the application of the model. The general processing techniques are presented above in the disc ussion of the development of the ammonia emission model. As part of the model development, a detailed Users’ Guide will be produced specifying the required input data f ile formats, processing procedures, and output

data formats.

EPS2.0 ProcessingApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-44 As noted in the RFP, the project requirements specify the need for a contingency plan in the unlikely event that the development of the GIS emission model is delayed or if it is determined

that it would not be feasible to treat certain s ource categories within th e GIS-based model. In addition, given the limited resour ces available for the developmen t of the GIS model, it may not be possible to fully implement all of the de sired processing elements within the budget and scheduling constraints of the pr oject. Therefore, the EPA’s Em ission Processing System, EPS2.0 (EPA, 1992) will be configured for use as a fallback to the development of gridded, hourly ammonia emissions, as well as for use in the gr idding of stationary po int sources. Those emission categories to be treated as point sour ces include some portion of livestock emission, dairy and poultry farms, composting and landfill operations, POTW, sewage treatment facilities

and industrial sources. The EPS2.0 has been used recently to develop an updated ammonia emission inventory for the South Coast Air Basin. (ATC, 2000).

The EPS2.0 is a publicly available FORTRAN base d software program that is included with EPA UAM modeling system. The EPS2.0 is a co mplete integrated p ackage that has been applied to many urban and regional areas and operated by many different groups. Like many emission models, it includes sepa rate modules for the treatment of surrogate distributions, area sources, mobile sources and stationary point so urces and provides the necessary tools for the chemical speciation and temporal allocation of county-level emission estimates. In addition, it includes several levels of quality assurance/quality control in the form of message files containing summaries of input a nd output emission mass totals by county and source category. While the standard output format of the gridde d emission inventories pr ocessed with EPS2.0 are in a form compatible with several grid-based air quality models (C AMx, UAM-V, UAM-IV), the model can be easily modified to provide th e required EMS-95 compatible output formats for this project.

The EPS2.0 modules used for developing gridde d emission inventories are summarized as

follows:

• Spatial Distributions : The EPS2.0 includes FORTRAN-based program that can generate surrogate distributions from landuse and census data for gridding countylevel emission data. However, the software can also use surrogate distribution data generated from external program , including GIS. EPS2.0 application for this project will make use of gridding surrogate data developed with a GIS, as discussed above.

• Area Source Module : The EPS2.0 uses FORTRAN based software to spatially allocate the county-level area source emissions to the gridded surr ogate distributions imposing user-provided temporal and chemi cal speciation profile data. For ammonia emission, no chemical speciation is required.

• Mobile Source Module : The EPS2.0 integrates count y-level VMT by roadway class with county- and temperature-specific MO BILE5/6 simulations to estimate countylevel mobile source emission that are spat ially allocated according to surrogate

distributions. Provisi ons for separating the mobile source emission by emissions mode (e.g., exhaust and evaporative) are also accounted for. As the ammonia

emissions from mobile sources will be leve raged off of existing or in-progress mobile inventory development, as discussed previousl y, it will not be necessa ry to utilize this module of the processing system.

• Point Source Module : The EPS2.0 point source modules performs the same functions

of other similar emissions models. Thes e functions include the mapping of point source locations to the grid projection syst em of the modeling domain, application of seasonal and day-of-week adjustments, a nd the chemical speciation and temporalApril 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-45 allocation of emission based on the poin t source SCC code and user-provided speciation and diurnal emissions profiles. The EPS2.0 point source module also estimates plume rise for each point source. For ammonia emission point sources, no chemical speciation is required and the stack parameters (stack height, exit velocity and exit gas temperatures) will be set to default values to ensure that their emission are allocated to the lowest, or surface la yer, of the gridded emission data files.

While the EPS2.0 does not include an explicit Quality Assurance/Quality Control module, it does provide for several levels of QA/QC in th e form of emission summaries by source category and county/region. These reports will used as an additional layer of QA/QC in the overall development of the gridded ammonia emission inventory.

As both the GIS-based ammonia emissions m odel and the EPS2.0 processing system produce gridded output data in a form at compatible with the UAM-IV modeling system, a straight forward re-formatting of the data will be requ ired to produce the requi red EMS95 compatible data formats for the final draft gridded inventory. Processing software will be developed in the most appropriate programming la nguage (FORTRAN, Perl, etc.) to perform this function and will be documented and included as a project de liverable submitted to the Technical Committee.

Quality Assurance/Quality Control

Final quality assurance of the draft gridded ammonia emission inventory will be performed based on the various message file generated by the emission model proc essing. These include numerous range checks on stack parameters (for point sources) and emission mass summaries by source category and county/region. Graphical summa ries in the form of bar and/or pie charts will be used to provide an overall representa tion of the draft inventory and to identify any possible problems. Finally, plots of the spatial distribution of the emission density for each source category will be prepared and used for evaluating the reasonableness of the overall inventory with respect to the spatial and temporal allocation of emission estimates.

Task 5: Documentation

The ENVIRON team will prepare and submit a cumulative Draft Final Report documenting all aspects of the work performed for each task during the course of th e project. After review by the Technical Committee, the ENVIRON team will m odify the Draft Final Report in accordance with TC comments and submit a Final Report. In fulfilling the requirements of the project, the Final Report will include (at minimum):

• Executive Summary;

• Summary of the critical review of existi ng ammonia literature, ammonia inventories and processing techniques (performed under Task 1 and 2);

• Summary of gridded ammonia emissions inve ntory improvement issues addressed in the project;

• Identification and recommendation of most promising processing techniques and input data for development of accurate gr idded ammonia emissions estimates for the study domain;

• Documentation of the development/ada ptation of ammonia emissions model;

• Detailed description of gr idded inventory development (performed under Task 4);April 2001

Projects2:CRPAQS/workplan/Sec2.doc 2-46 • Ammonia emission inventory summa ry (by source category); and

• Recommendations and Conclusions.

Documentation describing the QA/QC procedures us ed, as well as detailed descriptions of the standardized input data libraries developed under Task 2 will be provided as part of this task. All computer codes developed or used to perform work during the project will be internally

documented and submitted in electronic form. A va riety of software applications and operating system platforms may be required for completion of the project. These include spreadsheet and database applications (Excel and Access r unning under Windows) and GIS applications (ARC/Info running under Windows NT and AML).

Regular periodic (quarterly) system backups of all project data will be performed under this task. System backups will be delivered to the Progr am Manager in electronic form on appropriate

media to be determined in consultation with th e TC. As the ENVIRON team’s facilities include networked Unix workstations with over 150 Gbytes of disk storage capac ity with access to 4mm DAT, 8mm Exabyte, and DLT tape drives, data archival and syst em backups can be performed efficiently without the need for obtaining addi tional hardware resources. The recommended method and media for storage and transfer of data and system backups consists of using the Unix ‘tar’ archiving utility and 8mm Exabyte tape media.

In addition to the above, the ENVIRON team will produce and deliver a Users’ Guide, as stipulated in the RFP. The Users’ Guide will detail all processing procedures, and include a library of all utilized processing routines and inpu t data. The Guide will be written to facilitate the generation of additional ammonia inventories by the program sponsors.

All documentation and reports developed and/or wr itten for performance of the project will be submitted in electronic form, compatible with Microsoft Word.April 2001

Projects2:CRPAQS/workplan/Sec 3.doc 3-1 3. PROJECT MANAGEMENT, RESOURCES AND SCHEDULE

In this section we describe th e project management structure, resource allocation and schedule. Our project management structure (Figure 3-1) is designed to clearly delineate the staffing, responsibilities, and lines of authority for the project so as to maximize the use of available resources and ensure a high quality product.

Mr. Ralph Morris, an experienced Principal at ENVIRON, will serve as the project’s Principal Investigator with Dr. Gerard Ma nsell, a Senior Consultant at ENVIRON, serving as Project Manager. Dr. Mansell’s responsibilit ies as Project Manager will include:

• serving as primary contact with the CRPAQS project study officers;

• providing overall technical direction a nd oversight of all work performed;

• review of all project deliverables;

• preparation of monthly progress reports;

• management of project schedule and budget;

• management and coordination of the ENVIRON Team’s s ubcontractors; and

• attending conference calls and meetings with the CRPAQS project officers.

In addition to overall project management, th e ENVIRON staff will provide technical support for the review and recommendation of improve d processing techniqu es and data inputs performed by ENVIRON’s subcontractors under Ta sks 1 and 2. ENVIRON staff will also lead the development and/or adaptation of an am monia emissions modeling system as well as developing the procedures for the creation of standardized data libraries, quality assurance/quality control procedures and griddi ng of the draft ammoni a modeling inventory. Additional ENVIRON staff assigned to this project include Dr. Gr eg Yarwood, Mr. Gary Wilson, Mr. Cuong Tran, Ms. Michele Jimenez, Ms. Rosa Chi and Shannon Coulter-Burke. Dr. Yarwood, a Senior Consultant, will take the lead role in investigation and development and/or adaptation of an ammonia emissions model. Dr. Yarwood will be assisted by Mr. Gary Wilson, also a Senior Consultant, who has extensive expe rience with the development and application of emissions inventory modeling systems. Mr. Cuong Tran, an Associate at ENVIRON, will assume responsibilities for GIS-related activit ies, including acquisition and processing of appropriate GIS (ARC/Info) data bases and the processing and development of required gridded spatial surrogate data. Mr. Tran will be assisted by Ms. Coulte r-Burke, also an Associate at ENVIRON. Ms. Jimenez, an Associate at ENVI RON, will provide technical assistance required for data collection, processing and gridding the ammonia emissions inventory. Ms. Chi , an Associate with ENVIRON, will provide technical assistance with data collection efforts as well as the processing and gridding of the ammonia emissions inventory.

The ENVIRON Team also includes Pechan-Ava nti as a subcontractor. Pechan-Avanti will develop the ammonia inventory as well as lead ing the effort to review existing ammonia emissions, ammonia inventories, processing t echniques and ammonia emission sources. In addition, Pechan-Avanti will provide recommendati ons regarding the most promising processing techniques and data inputs, including emissions factors, activity data, a nd temporal and spatialallocation data. Mr. Stephen Roe will provide day-to-day management functions for PechanAvanti staff. Mr. Roe will be assisted by Ms. Kirsten Thesing and Ms. Melissa Dagan. Table 3-1 provides an estimated break down of re source allocation by task. Corresponding to the technical approach detailed in Section 2 above, five tasks are presented. Table 3-2 presents aApril 2001

Projects2:CRPAQS/workplan/Sec 3.doc 3-2 proposed project schedule by month for the 2- 1/2 year study program. Also included are anticipated dates of delivery fo r those deliverables required of the project. As certain

deliverables are due one or two months after receipt of TC comments, the dates shown are approximate and will be finalized as work pr ogresses. These include a Draft and Final Workplan, input data for draft i nventories, the draft inventory and an ammonia emission model, progress reports, and a Draft and Final Report.April 2001Projects2:CRPAQS/workplan/Sec3.doc3-3Figure 3-1.Project management structure.April 2001

Projects2:CRPAQS/workplan/Sec 3.doc 3-4 Table 3-1. Estimated labor hours by task. ( Note: This table will be revised due to ATC default)

Staff Task 1 Total

1 2 3 4 5

ENVIRON

R. Morris (Principal) 4 0 0 0 4 8

G. Mansell (Senior Consultant) 32 32 32 20 40 156

G. Yarwood (Senior Consultant) 6 0 8 0 0 14

G. Wilson (Senior Consultant) 0 0 24 40 0 64

M. Jimenez (Senior Associate) 0 18 0 60 8 86

C. Tran (Associate) 0 40 0 100 8 148

R. Chi (Associate) 0 30 0 0 0 30

S. Coulter-Burke (Associate) 0 32 0 0 20 52

Support 4 4 4 4 4 20

Subtotal ENVIRON 46 156 68 224 84 578

Pechan

S. Roe (Project Director) 16 48 80 0 12 156

K. Thesing (Environmental Scientist) 0 100 40 0 8 148

A. Bollman (Environmental Scientist) 0 100 40 0 8 148

K Aguilar (Contracts Mgr.) 20 0 0 0 0 20

M. Dagan (Staff Analyst) 0 200 0 0 0 200

Support 2 16 0 0 8 26

Subtotal Pechan 38 464 160 0 36 698

Total 84 704 228 224 120 1276

1 Task 1: Prepare a Detailed Work Plan; Task 2: Collect Data and Create Standard Input Library; Task 3: Develop Ammonia Emissions Model; Task 4: Produce Draft Ammonia Modeling Inventory; Task 5: Documentation.April 2001Projects2:CRPAQS/workplan/Sec3.doc3-5Table 3-2.Summary of initial schedule and costs forCRPAQS Draft Ammonia Inventory Development.Month1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30TaskCosts1a1b$10,2632a2b$35,4413$16,7664$22,3145a5b$13,568p p p p p p p p p p p p p p p p p p p p p p p p p p p p p pdwp fwp d fTask 1a:Prepare a Detailed Work Plan; Deliver Draft (1/01)Task 1b:Prepare a Detailed Work Plan; Deliver Final (2/01)Task 2a:Collect Data and Create Standard input Library (9/01)Task 2b:Deliver Inputs to Draft Inventories (3/02)Task 3:Develop Ammonia Emissions Model (3/02)ask 4:Produce Draft Ammonia Modeling Inventory (3/02)Task 5a:Documentation: Deliver Draft Final Report (11/01)Task 5b:Documentation: Deliver Final Report (2/03)p = Progress reportd = Deliver draft final reportf = Deliver final reportdwp = Deliver Draft Workplanfwp = Deliver Final WorkplanApril 2001

Projects2:CRPAQS/workplan/ Sec4refs.doc 4-1 4. REFERENCES

ARB, 1997. Emission Inventory Procedural Manual, Volume III B Methods for Assessing Area
Source Emissions, California Environmental Protec tion Agency, Air Resources Board,

October 1997.

ATC, 2000. 1997 Gridded Ammonia Emission Inventory Update for the South Coast Air Basin

, prepared for the South Coast Air Quality Management District by ATC Associates, Arcadia CA. August

Battye, R., W. Battye, C. Overcash, and S. Fudge, 1994. Development and Selection of

Ammonia Emission Factors . EPA/600/R-94/190. Final report prepared for U.S. Environmental Protection Agency, Office of Re search and Development. EPA Contract No. 68-D3-0034, Work Assignment 0-3.

Bouwman, A.F., D.S. Lee, W.A.H. Asman, F. J Dentener, and. K.W. Van Der Hoek, 1997. A Global High Resolution Emissi on Inventory for Ammonia, Global Biogeochemical Cycles , Vol. II, No. 4, pp. 561-587.

DOE, 1993. U.S. Department of Ener gy, Energy Information Administration, Household Energy Consumption and Expenditures, 1990, DOE/EIA-0321(90), 1993.

DOF, 2000. City/County Populations and Housing Estimates, 1991-2000, with 1990 Census

Counts , State of California, Department of Fi nance, Sacramento, California. May 2000.

EPA, 1997. Volume III: Chapter 2, Residential Wood Combustion, prepared by Radian Corporation for the Area Sources Committee, Emission Inventory Improvement Program, U.S. Environmental Protection Agency, September 1997.

EPA, 1992. “Users Guide for the Urban Airshed Model. Volume IV: User’s Manual for the Emissions Preprocessor System 2.0”. USEP A, Office of Air Quality Planning and Standards, Research Triangle Pa rk, North Carolina. June, 1992.

Fife, 2000. Les Fife, private cons ultant, personal communication w ith S. Roe, E.H. Pechan & Associates, Inc., December 2000.

Fraser and Cass, 1998. M.P. Fraser, and Cass, G.R., “Detection of Excess Ammonia Emissions from In-Use Vehicles and the Implic ations for Fine Particle Control”, Environmental Science & Technology , 32, 1053-1057, 1998.

Gharib, S., and G.R. Cass, 1984. Ammonia Emissions in th e South Coast Air Basin . Prepared by Environmental Quality Laboratory, Californi a Institute of Technology, Pasadena, CA. Open file report 84-2, December.

Goode et al, 2000. Goode, J.G., R.J. Yokelson, R.A. Susott, and D.E. Ward, R.E. Babbitt, M.A. Davies, and W.M. Hao, “Measurements of excess O3, CO2, CO, CH4, C2H4, C2H2, HCN, NO, NH3, HCOOH, CH3COOH, HCHO , and CH3OH in 1997 Alaskan biomass burning plumes by airborne Fourier tran sform infrared spectroscopy (AFTR)”, Journal of Geophysical Research , vol. 105, no. D17, pp. 22147-22166, Sep. 16, 2000.April 2001

Projects2:CRPAQS/workplan/ Sec4refs.doc 4-2 Goode et al, 1999. Goode, J.G., R.J. Yokelson, R.A. Susott, and D.E. Ward, “Trace Gas emissions from laboratory biomass fires measured by open-path Fourier transform infrared spectroscopy: Fires in grass and surface fuels”, Journal of Geophysical

Research , vol. 104, no. D17, pp. 21237-21245, Sep. 20, 1999.

Griffith et al, 1991. Griffith, D.W.T, W. Mankin, M. Coffey, D. Ward, and A. Riebau, “FTIR Remote Sensing of Bioma ss Burning Emissions of CO 2 , CO, CH 4 , CH 2 O, NO, NO 2 , NH 3 , and N 2 O”, Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, MIT Press, Levine, J.S. (ed), 1991.

Houck, 1997. Houck, H., Omni Environmental Services, Inc., memorandum to J. Mangino, Eastern Research Group, November 19, 1997.

James, T, N. Fritas, L. Ashbaugh, and D. Meyer, 1997. Field Estimates of Volatilization from

Cattle Production Facilities. Proceedings from the Air And Waste Management Association Conference - Emission Inventory: Planning for the Future, October 28-30, pp 259-267.

Lord et al, 2000. Lord III, H.C., M.M. Baum, E.S. Kiyomiya, S. Kumar, and A.M. Lappas, “Multi-Component Remote Sensing of Ve hicle Exhaust by Dispersive Absorption Spectroscopy”, Environmental Science & Technology , 34(13), 2851-2858, 2000.

Muck, R.E., and T.S. Steenhuis, 1982. Nitrogen Losses from Manure Storages

. Agricultural

Wastes 4, 41-54.

Potter et al, 2000. Potter, C., C. Krauter, and S. Klooster, Statewide Inventory Estimates of Ammonia Emissions from Native Soils and Chem ical Fertilizers in California, Draft

, prepared for the California Air Resources Board, December 31, 2000.

Radian, 1991. Development of the Ammonia Emisson Inventory for the Southern California Air Quality Study Rep ort prepared for the California Ai r Resources Board, Sacramento CA by Radian Corporation, Sacramento CA, Sept.

Scarborough, 2000. James Scarborough, U.C. Berkel ey, personal communication with S. Roe, E.H. Pechan & Associates, Inc., December, 2000.

Schmidt, C.E., and E. Winegar, 1996. Results of the Measurements of PM 10 Precursor Compounds from Dairy Industry Livestock Waste . Technical report prepared for the South Coast Air Quality Management District.

STI., 1998. Technical Support Study 15: Evaluati on and Improvement of Methods for Determining Ammonia Emissions in the San Joaquin Valley . Prepared for the California Air Resources Board by Sono ma Technology, Inc., January.

USDA, 1999a. 1997 Census of Agriculture . USDA website

USDA, 1999b. U.S. Equine Inventory , National Statistics Service ( NASS), Agricultural Statistics Board, U.S. Department of Agriculture, NASS wbsiteApril 2001

Projects2:CRPAQS/workplan/ Sec4refs.doc 4-3 Warn, T.E., Zelmanowitz, S., and Saeger, M., 1990. Development and Selection of Ammonia

Emission Factors for Office of Research and Development U.S. Environmental Protection Agency, Washington, D.C. by Alliance EPA-600/7-90-014, June. EPA Contract No. 68-02-4374, Work Assignment No. 43.

Wilkinson, James G., et al. A Model to Estimate Temporally Re solved Ammonia Emissions at a Dairy, for presentation at the Air & Wa ste Management Association’s 91 st Annual Meeting & Exhibition, June 14-18, 1998 Sa n Diego, California, 98-WA45B.04 (A883).

Winegar E., 1999. Review of Literature Sources for Emi ssions of Ammonia from Dairy Farms

, Applied Measurement Science.

Yarwood, G., G. Wilson, C. Emery, and A. Guen ther. 1999. Development of GLOBEIS – A State of the Science Biogenic Emissions Mode ling System. Final Report. Prepared for

Texas Natural Resource Conser vation Commission. December.

Yokelson, R.J., J.G. Goode, D.E. Ward, R.A. Susott, R.E. Babitt, D.D. Wade, I. Bertschi, D.W.T. Griffith, and W.M. Hao, “Emissions of formaldehyde, acetic acid, methanol, and other trace gases from biomass fires in No rth Carolina measured by airborne Fourier transform infrared spectroscopy”, Journal of Geophysical Research , vol. 104, no. D23, pp. 30109-30125, Dec. 20, 1999.

Yokelson, R.J., D.W.T. Griffith, and D.E. Ward, “Open-path Four ier transform infrared studies of large-scale laboratory biomass fires”, Journal of Geophysical Research , vol. 101, no. D15, pp. 21067-21080, Sep. 20, 1996.