International Corporation Air Sciences

Final Work Plan

California Regional PM10/PM2.5 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 415.899.0700 
DISCLAIMER 

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 use 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, RESOURCES AND SCHEDULE................................... 3-1 

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

TABLES 

Table 2-1. Prioritized source categories and objectives........................................................ 2-3 

Table 2-2. Emission inventory codes for fertilizer application............................................. 2-5 

Table 2-3. Suggested source classification 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 codes for livestock operations .......................................... 2-13 

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

Table 2-8. Domestic sources emission factors. ................................................................... 2-17 

Table 2-9. Suggested EIC/CES codes 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 Category 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 initial 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 California from the Gap Analysis Program..........2-28 

Figure 2-2. Farmland mapping and monitoring survey area........................................2-

30 

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

31 

Figure 2-4. Simple approach to estimating ammonia emissions within the GIS..............2-34 

Figure 2-5. Estimating ammonia emissions 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-

37 
Figure 2-8. Isoprene emission inventory 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.................................................................3-3 
April 2001 

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

1. INTRODUCTION 

Background 

The California Regional PM10/PM2.5 Air Quality Study (CRPAQS) is a multi-year program to 

study particulate matter (PM) air pollution in Central California and consists of meteorological 

and air quality monitoring, emissions inventory development, data analysis, and air quality 

modeling. The primary objectives of the study are to: 

• Provide an improved understanding of emissions and atmospheric processes that influence 

particle formation and transport; 

• Develop methods useful to planning agencies in formulating and assessing candidate control 

strategies for attaining the federal and state PM standards in Central California; and 

• Provide reliable tools for estimating the impacts of control strategies for 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 ammonium nitrate particulate matter. These reactions, especially 

those leading to ammonium nitrate, represent a significant percentage of wintertime PM2.5 in the 

San Joaquin Valley (SJV). Thus, ammonia plays a major role in determining secondary aerosol 

concentrations within the CRPAQS domain. Figure 1-1 displays the San Joaquin Valley study 

area. 

As part of the overall study, grid-based aerosol models are to be applied to simulate particulate 

matter concentrations in the SJV and to investigate the sensitivity of secondary 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 field study observation data. A key requirement 

for the credible application of air quality models 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 resolution in order to accurately represent the 

complex chemical and transport processes occurring 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 subject of this project. Accurate emission inventories based on the best 

available ammonia emissions information and processing methodologies will be critical in 

supporting numerous aspects of the CRPAQS activities over the coming years 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

42

Degrees 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-based aerosol modeling for the CRPAQS. The study 

will take place within California’s San Joaquin Valley (SJV) and the draft inventory is to be used 

to assess the sensitivity of aerosol model estimates to changes in such variables as spatial and 

temporal resolution, and emissions magnitudes for various source categories of ammonia 

emissions. The ammonia inventory is to be developed at a spatial resolution 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 gridded inventory will cover the SJV study area, 

although where feasible, data will be collected for the entire state of California. In addition, a 

stated objective of the project is to develop a standard input data library and processing 

procedures in order to facilitate future in-house refinements or revisions to the inventory in a 

programmatic way. 

A secondary goal of the study is the development of an ammonia capable GIS-based emission 

modeling system. Since for many ammonia emission 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 inventory 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 the many on-going field studies and modeling input 

development projects sponsored by CARB in support of both the Central California Ozone Study 

(CCOS) and the CRPAQS, numerous sources of information relevant to the development of the 

ammonia emission inventory are currently either not yet available or in a state of flux. Thus, it 

will be imperative to maintain on-going coordination 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 studies. 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 inventory may be easily updated and/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 collection 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 objectives 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 addressed and detailing the 

technical approach to be used for developing inventory improvements and draft modeling 

inventories will be prepared. Project management, including preparation of monthly progress 

reports and regularly scheduled conference calls, will also be performed under this task. 

Task 2: Collect Data and Create Standard Input Library

Data collection and creation of a standardized input data library is performed as part of this task. 

As part of the data collection tasks, ammonia emission sources and data sources are reviewed for 

each emission source category. Specific data elements and formats for each source category will 

be developed and documented. Procedures for archiving 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 resulting 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 Ammonia Modeling Inventory

Under this task, a draft gridded ammonia emission inventory will be prepared for approval. The 

grid-based ammonia inventories will be developed 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 spatial resolution encompassing the entire San 

Joaquin Valley study area. The inventory will 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 detailing all processing procedures, data sources, input 

data library and processing routines 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 CRPAQS “Draft Ammonia Inventory Development” 

study. It describes the technical approach for the completion of the study, including an 

identification and prioritization of ammonia emission 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 inventory gridding and 

documentation and reporting. Where appropriate, the following items are included: 

• Identification and review of the sources of ammonia emissions within the San Joaquin Valley 

study domain; 

• Reviews of existing ammonia emissions literature, ammonia inventories and processing 

techniques, including ongoing emissions-related 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 most promising and practical processing 

techniques and data inputs for producing accurate gridded, hourly emissions estimates 

statewide and within the 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 resolution and accuracy, and geographic and GIS-specific 

attributes; 

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

• Detailed description of quality assurance (QA), quality control (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 Statement 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 Create Standard Input Library 

• Task 3: Develop Ammonia Emission Model 

• Task 4: Produce Draft Ammonia 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 emission 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 inventory 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 creation of a standardized input data 

library. As part of the data collection efforts, ammonia emission sources and data sources are 

reviewed for each emission source category and recommendations 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 methodologies and data sources 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 contribution of each source 

category in the complete emission inventory has either not yet been collected or is not yet 

available, a source category prioritization based on emissions magnitude is not possible at this 

time. Table 2-1 summarizes the methodologies and sources of emissions/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 importance 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 believes that this approach will result in the most 

accurate and credible ammonia emission inventory 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 temporal and spatial allocation 

data that have been used in the past for the development 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. However, 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 therefore alternate methods and data sources are described in 

subsequent sections of this document.
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc Table 2-1. Prioritized source categories and objectives. 

Source Category Emissions/Activity Temporal Information Spatial In

Fertilizer Application (see Table 2-2 for source classification codes) 

Incorporate emissions inventory directly from Krauter and Potter 

Temporal extent of category – annual with primary activity in spring and summer; Temporal resolution of inventory – daily (via incorporation of data from Krauter and Potter). 

Spatial extenstatewide agSpatial extenspecific crop provided by entire state. 

Livestock (see Table 2-6 for source classification codes) 

Emission factors from James et al;

Gharib and Cass; and Battye et al. Activity data from Calif. Cattlemens Assoc., Western Dairymans Assoc., USDA; RWCQBs; American Horse Council Foundation 

Diurnal based on Wilkinson et al. and James et al. 

To existing l(e.g., NLCD

Natural Soils/Plant Canopies (see Table 2-3 for source classification codes) 

Incorporate CAL-CASA derived inventory directly from Krauter and Potter. 

Temporal extent of category – annual; Temporal resolution of inventory - Seasonally from CAL-CASA model. 

Spatial extenstatewide; Spatial exteninventory – Krauter and 

Agricultural Burning (see Table 2-4 for source classification codes) 

Use emission ratios of NH3 to CO from studies by Yokelson and Goode. Activity data from Les Fife/UC Berkeley or surveys of local agricultural extension offices. 

Temporal extent of the category – fall and winter seasons; Temporal resolution of the inventory - Monthly – using the activity calendar developed by Fife and/or the Team. 

Spatial extenagricultural Spatial exteninventory - sshape files frdeveloped fothe San Joaq

Prescribed Burning/Wildfires 930-932-0200-000 (47316) 

Use emission ratios of NH3 to CO from studies by Yokelson and Goode. Activity data from CDFFP. 

Temporal extent of the category – annual with primary activity in summer and fall seasons; Temporal resolution of the inventory - Seasonally. 

Spatial extenstatewide; Spatial extenstatewide wifrom CDFFP 

Residential Wood Burning 610-600-0230-000 (82115) 610-602-0230-000 (82123) 

Emission and Activity estimates based on EPA’s EIIP alternative methods using data from Energy Information Administration’s State Energy Data Report and Residential Energy Consumption Survey. NH3 emissions based on ratios of NH3 to CO from studies by Yokelson and Goode. 

Temporal extent of the category – fall, winter and spring seasons; Temporal resolution of the inventory - Monthly. 

Spatial extenSpatial extenstatewide allusing data fron wood-burWeighting ourban areas t

On-Road Mobile Sources 700-000-0000-000 

Fleet-wide emission factor from Fraser and Cass or Lord et al. Gridded VMT from CARB. 

Temporal extent of the category – annual; Temporal resolution of the inventory -

Spatial extenSpatial extenstatewide gr
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc Source Category Emissions/Activity Temporal Information Spatial In

Allocate based on daily and weekly profiles obtained from CARB. 

CARB. 

Industrial sources (see Table 2-9 for source classification codes) 

Emission factor data developed for SCAQMD by ATC; Activity data from major ammonia suppliers in the state. Geothermal source from the ARB CEIDARS database 

Temporal extent of the category – annual; Temporal resolution of the inventory – annual (assuming no temporal profile) 

Spatial extenSpatial extenstatewide areallocated by Geothermal allocated as 

Sewage Treatment Plants 130-190-0300-0000 (66713) 

Emission and activity data from SWRCB database; local surveys of POTWs 

Temporal extent of the category – annual; Temporal resolution of the inventory – annual (assuming no temporal profile) 

Spatial extenSpatial extenstatewide; faand allocated 

Composting 140-190-0300-0000 (66714) 

Emissions data from Solid Waste Information System database, if available; emission factors developed by ATC for SCAQMD 

Temporal extent of the category – annual; Temporal resolution of the inventory – annual (assuming no temporal profile) 

Spatial extenSpatial extenstatewide; Sgeo-coded asources. 

Landfills 120-122-0240-0000 (57281) 

Emission estimation method – same as existing SJV inventory. Data 

from SWIS database; California Integrated Waste Management Board; ARB emission inventory database (CEIDARS) 

Temporal extent of the category – annual; Temporal resolution of the inventory – annual (assuming no temporal profile) 

Spatial extenSpatial extenstatewide, gepoint source

Domestic Sources (see Table 2-8 for source classification codes) 

Emission factor data from various literature sources (see Table 2-8); Activity data from U.S. Bureau of the Census; California State Board of Equalization 

Temporal extent of the category – annual; Temporal resolution of the inventory – annual (assuming no temporal profile) 

Spatial extenSpatial extenstatewide; spurban land uNLCD and U

Native Animals 950-912-0300-0001 (83288) Deer 950-912-0300-0002 (83289) Bear 950-912-0300-0003 (83290) Pigs 

Emission factor data from Warn, et al.; activity data from CDFG. 

Temporal extent of the category – annual; Temporal resolution of the inventory – annual (assuming no temporal profile) 

Spatial extenSpatial extenstatewide; spshrubland/fodata from NLdatabases. Sfrom CDFG
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-5 

Review of Literature, Data Sources 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 contacts with technical 

committee members, these data should be available 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. According to Potter et al (2000), the initial ammonia 

emission estimates were derived by assuming a constant 5.5% loss of fertilizer nitrogen. For the 

final emission inventory, different methods of application may be assessed (e.g., methods for 

incorporation of fertilizer into the soil) which lead to lower emission factors (one the order of 0.5 

to 1% nitrogen loss). 

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

EIC Description1 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 application 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 temporally 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 Potter will cover the entire state. 

Another possible source of activity and spatial allocation data is the pesticide usage database 

from the California Department of Pesticide Regulation (CDPR). The Team has ordered a CDROM containing preliminary 1999 data (year 2000 data will not be available until 2002), as well 

as information for the previous 5 years. This database will cover the entire state. According to 

discussions with CDPR and county 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 data mentioned above, the application calendar will cover crops grown 

within the CRPAQS domain, although it may be representative 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 resolution (e.g., down to specified weeks of the year), then these factors 

will be developed. 

CARB has developed some temporal allocation factors 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 found in the information sources described above. 

These surveys would be conducted with both county 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 twice per year. The entire state is 

covered. It is not clear how well fertilizer sales data represent 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 calculated 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 highly uncertain whether natural soils (referred to 

here as soil/plant canopy systems) are net emission sources over annual time frames. Studies 

conducted in Europe have shown that the systems studied can be net sources or net sinks of 

ammonia. This issue is complicated by the interactions of ammonia within the soil/plant canopy 

system, and should not be simplified by just looking 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 Potter et al (2000). This inventory should be 

available by May 2001. One issue with this inventory 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 categorization scheme for soil/plant canopy systems. 
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-7 

By treating each system separately, future refinements to the inventory will be easier to make 

(e.g., plugging in new emission factors, adjusting temporal allocation factors). 

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

EIC Description1 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 Codes 

Soil/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 categories 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 during 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 and emission inventories of wildfires/prescribed 

burning and agricultural burning (Scarborough, 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 coverages (or inventories) themselves. For wildfires, some 1998 

activity data were collected. Hence, these data are 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 coverages 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 the Sacramento Valley. By Spring 2001, Mr. Fife intends to deliver a 

database covering both the Sacramento and San Joaquin Valleys (i.e., Kern to Shasta counties; 

Fife, 2000). Temporal resolution is to be down to the day on which these agricultural burns 

occur. Spatially, the burns will be allocated 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 woodstoves), 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, respondents would be asked about the 

frequency and the amount of wood burned over a certain time period (e.g., per week). 

Information on the device type (e.g., fireplaces with and without inserts, woodstoves) may also 

be requested. Performing a survey in the required time frame for the SJV study area is not 

feasible. U.C. Berkeley staff are currently conducting some residential wood surveys in three 

areas. The results are not likely 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 study, recommendations are as follows for developing emission 

estimates. Emissions data are lacking for ammonia 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 studies 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 data (acres burned) will be gathered for the 2000 calendar year 

from the California Department of Forestry and Fire Protection (CDFFP). However, the CDFFP 

data do not account for fires occurring on U.S. Forest Service, State Parks, National Parks, and 

Indian lands. As such, these agencies will need to be contacted separately to determine the 

availability of statistics on prescribed burning. The team will allocate the activity based on 

either: allocating the state totals to forested land; or preferably, if data are received by California 

Region, allocation of the regional totals to forested land in each region. At the same time, the 

team will gather information from the above agencies on acreage burned by season or month, as 

available. Steve Shaw of SJV APCD also indicated that prescribed acres 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 recent 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 wildfires occurring on all lands, and not CDFFP lands 

exclusively. A 2000 summary report may not be available in time for this project, but the team 

will keep apprised of its availability. 

If the 2000 CDFFP report is not available, the team would need to supplement CDFFP data with 

data from other agencies, similar to prescribed burning. Activity data will be obtained from the 

U.S. Forest Service, and other Federal agencies 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 estimates for this category, the U.S. Forest Service 
April 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 State and private lands, up to the year 1999. The effort to update 

inventory year 2000 criteria pollutant emissions for 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 prescribed 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 (data for January 2000 through 

November 2000 are currently posted). Weekly incident 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 whether 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 example 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 mentioned under the fertilizer application section above, 

alternate sources of activity and spatial allocation data include the CDPR pesticide usage 

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

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

EIC Description1 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 source category (emissions are not in 

the current inventory; STI, 1998). EPA currently does not have information for estimating 

ammonia emissions. We will adopt surrogate emission factors from AP-42 (e.g., for CO) and the 

information sources noted under prescribed burns/wildfires above. 

Temporal allocation will be based on information gathered from Les Fife. Mr. Fife reports that 

his database will be complete for the Sacramento and San Joaquin Valleys by Spring of 2001 

(Fife, 2000). Since his data will not cover the 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 contacts to county agricultural extension offices 

outside of the Sacramento and San Joaquin Valleys. 

Recommendations for Residential Wood Combustion 
April 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 information 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 expected to be available for inclusion in this 

study. 

For the NEI, residential wood combustion activity was estimated for the year 1990 by running 

the County Wood Consumption Estimation Model. For each year since 1990 up to 1999, 

county-level activity estimates (i.e., cords of wood) 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 meteorological 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. Heating 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 houses with active fireplaces is 

derived by subtracting the estimated number of wood heating houses (available from the 1990 

Census of Population and Housing-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 model equation was developed that estimates energy 

consumption using inputs such as number of households burning wood, housing design 

characteristics, heating degree days, and fuel heating values. 

In discussions with ARB, year 2000 estimates for residential wood combustion are likely to be 

grown from 1999 estimates, using the annual change in population as a growth factor. ARB last 

used the method described above for the inventory year 1997, and has grown the estimates using 

county-level population for each year since then. 

Because the same NH3 emission factors would likely be applied to all activity data, regardless of 

whether the activity corresponds to fireplaces or wood stoves, a method that distinguishes 

between the activity from these two types of devices may not add value. If, in the process of 

researching this issue, it is found that emission factors or seasonal 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). This 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 estimate 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 occurs during the cold season (typically in the fall, winter and 

early spring). Temporal allocation profiles have been developed by ARB to apply to annual 

estimates of wood burned by households. Both Statewide profiles and profiles specific for the 

SJV APCD have been developed (ARB, 1997). Heating degree-day data for 2000 can also be 

obtained from National Climatic Data 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 specific 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 contacts) to improve the activity data or temporal 

allocation factors. 

As with prescribed burning and wildfires, contacts 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 emissions may be estimated based on ratios of 

NH3 to CO emission rates from other biomass combustion sources (e.g., wildfires). Care must 

be taken in the use of these ratios, however since 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 category, it is clearly important to be able to 

distinguish between urban and rural residential 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 allocation of livestock emissions. 

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

estimate emissions for confined operations (dairies and beef feedlots. 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 California 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 emission factors are shown 

on Table 2-5. Proposed emission inventory 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)b 
April 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 proposed 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 concentrate on algorithms which describe the 

emissions in terms of commonly available environmental parameters (i.e., wind speed, 

temperature and solar radiation) because they will provide a sound basis for future refinement of 

the inventory. The current understanding of the biological and physical processes accounting for 

the creation of emission of ammonia from animal excreta is insufficient to develop a 

theoretically based temporal model. However, an understanding of these processes will be used 

to validate an empirical model like the one presented in Wilkinson et al. (1998). That model 

assumes wind speed, temperature, solar radiation, and relative humidity are the independent 

variables. Because models like that presented in Wilkinson et al. (1998) require significant 

amounts of local environmental data, processing 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 emissions. These profiles would be compiled from 

the results of a model like Wilkinson et al. (1998). The authors of Wilkinson et al. (1998) and 

James et al. (1997) will be contacted to obtain any newly 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 ammonia 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 developed 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 developed 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 will 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 location 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 facilities, especially large poultry facilities, in order to pinpoint the 

majority of these emissions. 

The same approach will be used for poultry operations. 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 classification, 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 classification, code 81. These emissions represent 

commercial operations. Half of the emissions will be allocated to low intensity residential 

classification, code 21, representing home stabled animals. Half of the hogs, rabbits, sheep and 

goats will be allocated to pasture/hay classification, code 81, and half will be allocated to 

grasslands/herbaceous classification, code 71. Following a review of the animal population data, 

the team will consult with local agricultural extension 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 through California Cattleman’s Association, Western 

Dairyman’s Association, the RWQCBs, Branch, 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 American Horse Council Foundation. On farm horse 

populations will be taken from the Agricultural Census (USDA 1999a), non-farm horse 

populations will be developed based on the ratio of non-farm to farm 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 list 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 ENVIRON team will survey POTWs to determine the 

extent to which sludge drying occurs at the facilities or off-site within 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 Management 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 the relevant activity data, amount 

of waste composted, to develop ammonia emissions estimates for significant portion of these 

facilities. The ENVIRON team will use activity data from the database, if available, and will 

contact facilities within 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 waste types will be used to estimate emissions 

Composting emissions will be allocated as point 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 Management Board to identify landfills in the Study Area. Activity 

data, the amount of methane produced, will be developed from data reported in California Air 

Resources Board emission inventory database (CEIDARS). This data is reported as total organic 

gases (TOG) and reactive organic gases (ROG). Total methane is calculated as the difference of 

TOG minus ROG. No new emissions data for ammonia 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 landfills 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 found 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 sources based on the latitude/longitude information 

from the SWIS database. Because of the scarcity 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 (STI 1998). Native animals accounted for only 

0.01% of the recent South Coast Air Basin inventory (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 emission factors developed for the South Coast Air 

Basin inventory (ATC 2000). These emission factors, 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 Weighta kg 

Adjusted Warn et al. 

Emission Factorb, 

lb/head-yr 

Deer 13,500 0.14 79.4 11.1 

Bear 0.25/ sq. milec 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, Washington, D.C. by Alliance Technologies Corporation, 

Chapel Hill, N.C., EPA Contract No. 68-02-4374, Work Assignment No. 43 , June. 

b Weight of white-tail deer taken from http://www.thewildones.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 collected 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 data to be too sparse to develop good population 

estimates (ATC 2000, STI 1998.) In addition, a relatively large population 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 factors developed by Warn et al. 

Emissions from native animal waste will be allocated 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 the relatively minor contribution from this source, 

no temporal profiles will be developed. 

Domestic Sources 

Domestic sources include ammonia emissions associated with pets, cigarette smoke, untreated 

human waste, perspiration, respiration and household 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 Alliance 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): Assessment 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 the urban and suburban settings. A determination of 

the setting, urban or suburban, will be made for 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 distribution and per capita consumption from the California State 

Board of Equalization in conjunction with 2000 census data from the U.S. Bureau of the Census. 

Activity data for untreated human waste includes the population of homeless people and the 

population of infants of diaper wearing age. Most counties have a service department 

responsible for the homeless population. The ENVIRON 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 traditionally been inventoried based on reported emissions such as the 

SARA 313 Toxic Release Inventory (TRI) or the Air Resources Boards Air Toxics Database 

(ATEDS). This method allows emissions allocations as point sources, and, in the case of 

ATEDS, the assignment of appropriate release parameters. Reporting to ATEDS is based on 

facility size, as determined by criteria pollutant 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 were identified in the 1991 South 

Coast Air Basin inventory (Radian 1991) requires an unwarranted strain on resources. 

The ENVIRON team will obtain data from major ammonia suppliers in the State, including 

UNOCAL, LaRoche Industries Incorporated and Hill Brothers Chemical Corporation for 

ammonia usage. This data will be aggregated to zip codes to protect the proprietary nature of the 

supplier data. Supplier data will be segregated by use for selective catalytic reduction, 

refrigeration, metal treating, blueprinting and wastewater treatment. Emission factors for each 

category are reported in the recent South Coast 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. 

• NOx Control – Ammonia is injected into the exhaust of boilers, gas turbines and other 

process equipment to reduce NOx emissions. The injected ammonia reacts on a 1:1 molar 

ratio of NH3 to NOx. Excess ammonia is added to attempt to react all NOx. Good 

engineering practice results in approximately 10 ppm of unreacted NH3 released from the 

stack (BACT requirement) by reducing NOx from 100 ppm to 10 ppm. The 10 ppm of 

excess ammonia that does not react to reduce NOx, 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, upsets 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 blueprint processing. By mass balance, each pound of 

ammonia supplied is also emitted. The emission factor is 100 percent of usage. 
April 2001 

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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 comprised approximately 1% of the statewide 

ammonia emission inventory. Emission form geothermal sources will be obtained form the ARB 

and included in the inventory as point sources. 

Table 2-9. Suggested EIC/CES Codes for Industrial Sources. 

EIC Description EIC CES 

Refrigeration 499-995-0020-0001 47085 

NOx 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 processed essentially as area sources. Processing mobile source 

emissions as individual links from a transportation 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 configured to handle ammonia emissions directly. This limitation can be 

circumvented by processing the ammonia emissions through DTIM as if they are PM emissions. 

The decision whether to process on-road mobile ammonia emissions on links or at the county 

level is a compromise between the extra precision and additional cost of link-based processing. 

The previous SJV ammonia inventory estimated mobile source emissions at the county level and 

allocated the emissions using spatial 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 indicated 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 Inventory (STI, 1998). The emission factor used by 

Pechan for those estimates is close that obtained 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 developing statewide gridded VMT in support 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 gridded on-road mobile inventory as the product 

of the VMT and a fleet-wide emission factor. Depending on the spatial resolution of the gridded 

VMT, the resulting gridded mobile emission inventory 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 efficient means of archiving data as well as to 

facilitate future update and revisions to the inventory in a systematic way. The data will be 

stored in a GIS format with appropriate attributes to specify activity data for each source 

category, data units, spatial and temporal extent of the data, allocation factors and citations of 

data sources. Processing procedures will be developed 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 “raw” emission data into the GIS. Since most 

data used in the development of the emission inventory 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 spreadsheet export file) into the appropriate GIS 

coverage type (i.e., polygon, point, 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 modified using a variety of 

software tools and text editors, the data will be platform, or operating system, independent and 

thus portable and easily modified. 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 fully defined, nor are the 

specific formats of the “raw” ASCII input data files 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 compiled 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 used for input into other emissions models (EMS-95 

or SMOKE). In consultation with the Technical Committee, additional fields 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 would be cattle (both beef 

and dairy). As part of this inventory effort, the Team will attempt to identify dairies and feedlots 

that can be modeled as point sources (the remaining population would be modeled separately as 

area sources). The data in the spreadsheets include source classification codes, emission factors, 

activity data, release parameters, and temporal allocation data (including data, such as 

meteorological data used to estimate or allocate 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 data exported into 

the AFS point source file. All point source data will be imported into the GIS-based data library 

as ARC/Info point coverages. 

Area Sources 

For on-road sources, the emission estimation method 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 contains the identification of each 

grid cell, the grid-based VMT, the emission factors, weekly and daily cycles (consistent with the 
April 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), spreadsheets 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 factors, citations of reference sources 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 data (e.g., fertilizer 

application, wildfires, soil/plant canopy systems), the spreadsheets will not contain the activity 

data. All of the other data elements will be included along with a citation of the source of the 

spatial surrogate and the land cover type assigned 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 county-level ammonia emissions is accomplished with the aid of 

gridded spatial surrogate data for each emission source category or groups of source categories. 

Spatial surrogates are typically based on the proportion of a known region-wide characteristic 

variable which exists within the modeling domain grid cells (e.g. land use 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 ability to distinguish between various land cover 

characteristics is necessary to properly allocate the county-level emission estimates for certain 

source categories, particularly for fertilizer application and soil emissions. Other desirable 

features of the underlying surrogate databases are the currentness of the data (i.e., for what year 

are the data based on and how often are they updated) and the availability and quality of the data. 

There are numerous Land Use/Land Cover (LULC) databases available from a variety of 

sources. These datasets provide for various spatial resolutions and geographic extent as well as a 

variety of LULC classification schemes. In addition, a limited number of special study datasets 

are in development for generating spatial surrogates 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 resolution) Land Use/Land Cover (LULC) 

• USGS Land Cover Characteristics (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., Potter, 2000; California Department of 

Pesticide Regulation databases, ARC/Info ZIP Code coverages) 

Each of these datasets were evaluated for appropriateness in developing surrogate 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 characterization in relation to ammonia emission source 

categories, currentness, and processing 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 Program 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 the land surface. The data is available in several formats 

including the Geographic Information Retrieval and Analysis System (GIRAS), Composite 

Theme Grid (CTG) and ARC/Info export interchange (E00) formats, all of which are easily 

imported into a GIS system. The data are developed from aerial photographs acquired from 

NASA high-altitude missions as well as other ancillary data sources. Manual interpretation of the 

aerial photographs with additional input from field surveys and other land cover data was used to 

digitize and classify the maps and the land cover data using an Anderson Level 2 classification 

scheme. Table 2-10 presents the 36 LULC categories 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 data is presented in a Universal Transverse 

Mercator (UTM) projection coordinate system at a spatial resolution of approximately 200 

meters. The data are freely available from several 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 have recently completed an updated gridded 

ammonia inventory for the South Coast Air Basin (SCAB) using these data and have developed 

efficient processing techniques within 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, and in some cases more than, 10 years old, it was 

determined that the data would not be a strong 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 (Anderson 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 Commercial 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 Glaciers 
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-24 

USGS Land Cover Characteristics (LCC) 

The USGS North America Land Cover Characteristics (LCC) database is part of an on-going 

global vegetation database development, These data are on a 1-km by 1-km grid for the entire 

North American continent and are presented in four different classification systems and two 

coordinate system projections. The most detailed classification scheme 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-day composites for a slightly larger time 

frame. Each classification system is available in both Lambert Equal Area and Interrupted Goode 

Homolosine coordinate projections. These data were derived from Advanced Very High 

Resolution Radiometer (AVHRR) data with a Normalized Difference Vegetation Index (NDVI). 

AVHRR data are high resolution data produced from orbiting satellites. The NDVI composites 

depict the relative density or vigor of different vegetation types. The NDVI is calculated by 

taking the difference of near-infrared and visible reflectance values divided by total reflectance. 

These values are then scaled from 0 to 200 where values less than 100 are usually clouds, snow 

or water, The NDVI is then composited with the AVHRR data which involves creating a multiband image with five AVHRR channels, the NDVI value, 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 from 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 scheme, the data are available in a number of 

simpler composite classification schemes which may 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 relevant categories are not represented. For 

example, although various types of cropland and pasture are represented, the desired detail in 

terms of crop types (row crops, grains and hay, orchards and vineyards) relevant to certain 

source categories (fertilizer application, soil systems) are not distinguishable. 

Table 2-11. USGS Land Use/Land Cover Category 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/Irrigated 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 Wetland 
April 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 developed as part of a cooperative project between 

the U.S. Geological Survey and the U.S. Environmental Protection Agency to produce a 

consistent land cover data layer for the entire conterminous U.S. based on 30-meter Landsat 

thematic mapper (TM) data. The NLCD was developed from TM data acquired from the MultiResolution Land Characterization (MRLC) Consortium, a partnership of federal agencies that 

produce or use land cover data. The partners include USGS (National Mapping, Biological 

Resources, and Water Resources Divisions), the USEPA, the U.S. Forest Service and the 

National Oceanic and Atmospheric Administration. 

Two seasonally distinct TM mosaics are produced, a leaves-on (summer) and a leaves-off 

(spring/fall) version. The TM bands 3,4,5 and 7 are 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 histogram 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 National Aerial Photography program (NAPP) 

aerial photographs as a reference. Invariably, some 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 the use of ancillary data sets. These ancillary 

data sets include the non-base mosaic TM bands and 100-class cluster image; derived TM 

Normalized Difference Vegetation Index (NDVI); various TM band ratios; TM date bands; 3-

arc second DTED, population and 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 Resources Division (BRD). 

Quality assurance of the data is on-going on a Federal Region basis following a revision cycle 

which incorporates feedback from MRLC Consortium partners and affiliated users and is being 

conducted by private sector vendors under contract 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 Cover (LULC), and National Wetland Inventory 

data. The general development procedures for the NLCD is to: (1) mosaic subregional scenes 

and classify them using an unsupervised clustering algorithm, (2) interpret and label the 

cluster/classes using aerial photographs as reference data, (3) resolve the 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 files which are easily imported into 

a GIS (e.g., ARC/Info) and are provided in an Albers Conic Equal Area projection 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 northern and southern portions of the state. The land cover 

characteristics are defined in terms of the 21 separate categories presented 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 vertebrate species and natural land cover types and to facilitate the 

application of this information to land management activities. The National GAP URL is 

http://www.gap.uidaho.edu/gap/. The GAP is conducted as state-level projects and is 
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-27 

coordinated by the USGS Biological Resources Division. Currently the program is developing 

land cover mappings for all U.S. States. Although each state is being developed separately, 

detailed vegetation species covers are being developed 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 land 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 resolution of approximately 50 meters. The land cover 

classifications are based on the National Vegetation 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 accuracy assessment of 

the data pending. The classification system provides for several hundred species designations 

and are group into broad categories and stratified according to primary, secondary and tertiary 

coverages based on percent of land cover in each of several broad regions. Although the spatial 

resolution is approximately 50 meters, due to the structure of the data is not always possible to 

spatially distinguish different species at the required resolution of 1 km. Figure 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 surrogates 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 Farmland Mapping and Monitoring Program 

The Farmland Mapping and Monitoring Program (FMMP) of the California Department of 

Conservation develops land use maps and statistical data for 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 the primary land use categories of important 

farmland and interim farmland map categories. Important farmland map classification include 

several mapping categories including prime farmland, farmland of 

statewide and local importance, grazing land, urban and built-up land, water, and other land. The 
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-29 

interim farmland classification include two subcategories, irrigated and non-irrigated farmland. 

The FMMP databases are updated biennially with the latest update available from 1998. As the 

data is compiled from soil surveys, the extent of the farmland coverage corresponds to the 

availability of what the National Resources Conservation Service (NRCS) 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 included in the database some have only been partially mapped since 

the NRCS has limited its efforts to areas which are considered agriculturally 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 determined that the FMMP database would not be 

appropriate for development of gridding surrogates for ammonia inventories. 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 Resources (DWR) Land Use Survey databases are 

developed by the department’s Division of Planning and Local Assistance in order to aid in 

DWR’s efforts to continually monitor land use for the purpose of determining the amount of and 

changes in the use of water. The data are available 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 approximately every seven years. 

Thus the data are based on varying years, depending on the county, ranging from the late 1980’s 

through the late 1990’s. Databases for each county are available in AUTOCAD DWG format, as 
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-31 

well as ARC/Info shape file format employing a Transverse Mercator coordinate 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 accurate due to the field 

survey verification of the aerial photograph imagery. 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 the 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 spatial allocation of county-level ammonia 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 entire state of California, is based on fairly recent 

year data and is freely available and periodically updated. A preliminary 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 data 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, the underlying data used for surrogate development can be easily 

updated with minimal processing effort. The final gridded surrogate datasets derived from the 

NLCD will be supplemented with the more current DWR database where deemed necessary or 

appropriate. Likewise, for those land cover categories which are not explicitly represented by 

the NLCD (e.g., confined feed lots, detailed crop types), the gridding surrogate data will be 

augmented and verified with the LULC database which best represents these categories. 

Finally, we recognize that given the 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 spatial allocation of ammonia emission 

sources. However, due to project scheduling issues, we cannot rely exclusively on the use of 

these special study data. In addition, as the specific 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 attempt to incorporate additional land cover data as 

allowed by the project schedule and resource constraints. 

The spatial allocation of domestic ammonia emission 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 use 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 will rely on the underlying gridded 

VMT data used for developing the ammonia emission estimates. The final gridded mobile 

source emissions will be gridded to the required 1-km by 1-km resolution of the modeling 

domain. 

As discussed above, the spatial allocation of industrial sources will be based on ZIP code in 

order to protect the confidentiality of the underlying activity data. A similar treatment was used 

in the recent updated gridded ammonia inventory developed for the SCAQMD (ATC, 2000). 

ZIP code coverages, in ARC/Info 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 reasonable. Various archiving procedures will be 

developed and documented to ensure that the integrity of all emissions data is maintained, 

included emission factor data and sources, spatial and temporal allocation 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 programs 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 whether emissions are being 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. Team members will verify that the plots show the 

proper spatial allocation and that the emissions shown 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 inventories for the SJV based on the data assembled 

through this study. The model will address all sources of ammonia for which emissions are 

related to land area – area sources. Point sources 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 project, this completion and level of 

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

Review of Existing and On-going Development of GIS-based Emissions Models

The ARB has several ongoing projects where the contractors are using bottom-up 

GIS models to estimate ammonia emissions from specific source categories. UC-Berkeley is 

estimating emissions of pollutants (including ammonia) from wildfires. The UC-Berkeley project 

coordinator is James Scarborough and some information on the project is available at 

http://www.gisc.berkeley.edu/~jscar/emissions/. There is no information on ammonia emissions 

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

this time. 

The ARB also has a contract on fertilizer ammonia emissions with CSU-Fresno that includes 

development of a GIS-based soil emissions model called CAL-CASA. The 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 available. Some information is available from a NASA 

web page at http://geo.arc.nasa.gov/sge/casa/. Ammonia emissions from fertilizer emissions are 
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-34 

considered important, but at this time it is unclear 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 accomplish 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 neglects environmental impacts (e.g., temperature 

dependence, seasonal dependence) on ammonia emissions. Incorporating environmental effects 

is a superior approach provided that information on environmental effects is available. One 

result is likely to be a need to prepare day specific inventories (similar to biogenic emissions 

modeling) which means that the emissions calculation must be repeated several times and 

efficiency becomes important. This suggests a different calculation approach, similar to that 

used in biogenic emissions preparation, as shown schematically in Figure 2-5. Under this 

approach, the landcover data are pre-processed using GIS into a gridded landcover database. 

This operation need only be performed once. An emissions model then combines the gridded 

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

effects. 

Landcover 

Data

Emission 

Factor Table

GIS 

(Arc/Info)

Gridded 

Emissions
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-35 

Figure 2-5. Estimating ammonia emissions using a model to account for environmental effects. 

There are several advantages to this approach: 

• Improved efficiency because the landcover 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 modeling 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 type shown in Figure 2-5 already exists in the 

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

but also includes ammonia. The ammonia emission algorithms in the current version of 

GLOBEIS are very simple and so improvements will be required for this study. Depending on 

the availability of information concerning specific 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 Emissions and Interactions System, was developed by 

Dr. Alex Guenther at the National Center for Atmospheric 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 embodies up-to-date scientific 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

- other

Emission 

Factor Table
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-36 

package that is easy to use, easy to update, and compatible with a wide range of input data 

(hence “Global”). The development of GLOBEIS 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 emission 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 (Figure 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 GLOBEIS control interface. 

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

Projects2:CRPAQS/workplan/Sec2.doc 2-38 

The main input data for GLOBEIS are: 

• GIS data on gridded LULC distributions 

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

• Emission factors by plant species (internal data table) 

• Gridded environmental data (temperature, solar radiation or cloud cover) 

• Seasonal biomass adjustments (satellite data based) 

Data processing tasks for GLOBEIS have been divided to make best use 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 preparation step is a straightforward 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 assumptions. 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 regenerated in a single step. 

The output from GLOBEIS includes a gridded, temporally allocated, 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 demonstrates 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 inventory for the Houston area prepared with GLOBEIS. 

Enhancements to GLOBEIS for this Study 

As discussed above, GLOBEIS was designed as a biogenic emissions processing system but 

support for ammonia was built into the model. Several simple improvements to GLOBEIS are 

proposed to allow maximum use of available ammonia emissions information and improve the 

efficiency of the system for processing ammonia emissions. 

• Switch to turn off the biogenic emissions calculations 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 and temporal profiles by source category. 

• Add environmental relationships between ammonia emission factor and environmental 

factors (e.g., dependence on temperature) to provide day specific emissions. This would be 

analogous to the current built-in emission relationships between isoprene emission factors 

and sunlight/temperature. GLOBEIS already reads 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 and 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 modeling 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 Ammonia Modeling Inventory 

The purpose of this task is develop the draft 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 measurement 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 PM2.5 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, respectively. Ideally, the draft gridded 

ammonia emission inventories will be developed for time periods corresponding to these field 

measurement studies. However, at this time, the 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 collection 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 constraints. 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 resolution 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 area and consists of 190 x 190 grid cells. 

The draft gridded ammonia emission inventories this project will be developed at a horizontal 

resolution of 1-km by 1-km and 1-hour temporal 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 periods will be determined 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

585.1

LCP 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 emission source category is summarized with 

respect to spatial and temporal allocation 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 sources within certain categories previously 

modeled as area sources (e.g., livestock) 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 sources 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 based on existing gridded VMT. See 

discussion on on-road mobile source above. 

Fertilizer Application Crop coverages developed by Potter et al (2000) 

Soils and Plant Canopy Systems Coverages 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 residential 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 treated as stationary point sources: dairy cattle, 

poultry operations, POTWs, composting and landfills. 

Temporal Allocation

Table 2-14 provides a list of the suggested temporal allocation by source category. Details on 

the sources of temporal allocation methods and data 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 Potter 
April 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 available data, monthly 

allocation may be possible. 

Agriculture Burning Monthly – based 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 (minimum) – 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 Diurnal (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 shown 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 Ammonia Emission Model

Area source emissions will be processed using the enhanced GIS-based GLOBEIS model 

developed for this study. In addition, if feasible, point source emission data will also be treated 

within the GIS model. As the development of the 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 discussion 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 file formats, processing procedures, and output 

data formats. 

EPS2.0 Processing
April 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 source categories within the GIS-based model. In 

addition, given the limited resources available for the development of the GIS model, it may not 

be possible to fully implement all of the desired processing elements within the budget and 

scheduling constraints of the project. Therefore, the EPA’s Emission 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 gridding of stationary point sources. Those 

emission categories to be treated as point sources 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 based software program that is included with 

EPA UAM modeling system. The EPS2.0 is a complete integrated package that has been 

applied to many urban and regional areas and operated by many different groups. Like many 

emission models, it includes separate modules for the treatment of surrogate distributions, area 

sources, mobile sources and stationary point sources 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 and output emission mass totals by county and source category. 

While the standard output format of the gridded emission inventories processed with EPS2.0 are 

in a form compatible with several grid-based air quality models (CAMx, UAM-V, UAM-IV), 

the model can be easily modified to provide the required EMS-95 compatible output formats for 

this project. 

The EPS2.0 modules used for developing gridded 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 surrogate distributions 

imposing user-provided temporal and chemical speciation profile data. For ammonia 

emission, no chemical speciation is required. 

• Mobile Source Module: The EPS2.0 integrates county-level VMT by roadway class 

with county- and temperature-specific MOBILE5/6 simulations to estimate countylevel mobile source emission that are spatially allocated according to surrogate 

distributions. Provisions 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 leveraged off of existing or in-progress mobile 

inventory development, as discussed previously, it will not be necessary 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. These functions include the mapping of point 

source locations to the grid projection system of the modeling domain, application of 

seasonal and day-of-week adjustments, and the chemical speciation and temporal 
April 2001 

Projects2:CRPAQS/workplan/Sec2.doc 2-45 

allocation of emission based on the point 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 layer, 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 the 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 model and the EPS2.0 processing system produce 

gridded output data in a format compatible with the UAM-IV modeling system, a straight 

forward re-formatting of the data will be required to produce the required EMS95 compatible 

data formats for the final draft gridded inventory. Processing software will be developed in the 

most appropriate programming language (FORTRAN, Perl, etc.) to perform this function and 

will be documented and included as a project deliverable 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 processing. These include 

numerous range checks on stack parameters (for point sources) and emission mass summaries by 

source category and county/region. Graphical summaries in the form of bar and/or pie charts 

will be used to provide an overall representation 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 the project. After review by the 

Technical Committee, the ENVIRON team will modify 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 existing ammonia literature, ammonia inventories 

and processing techniques (performed under Task 1 and 2); 

• Summary of gridded ammonia emissions inventory improvement issues addressed in 

the project; 

• Identification and recommendation of most promising processing techniques and 

input data for development of accurate gridded ammonia emissions estimates for the 

study domain; 

• Documentation of the development/adaptation of ammonia emissions model; 

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

Projects2:CRPAQS/workplan/Sec2.doc 2-46 

• Ammonia emission inventory summary (by source category); and 

• Recommendations and Conclusions. 

Documentation describing the QA/QC procedures used, 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 variety of software applications and operating 

system platforms may be required for completion of the project. These include spreadsheet and 

database applications (Excel and Access running 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 Program Manager in electronic form on appropriate 

media to be determined in consultation with the TC. As the ENVIRON team’s facilities include 

networked Unix workstations with over 150 Gbytes of disk storage capacity with access to 4mm 

DAT, 8mm Exabyte, and DLT tape drives, data archival and system backups can be performed 

efficiently without the need for obtaining additional 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 input 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 written for performance of the project will be 

submitted in electronic form, compatible with Microsoft Word. 
April 2001 

Projects2:CRPAQS/workplan/Sec3.doc 3-1 

3. PROJECT MANAGEMENT, RESOURCES AND SCHEDULE 

In this section we describe the 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 Mansell, a Senior Consultant at ENVIRON, serving as Project 

Manager. Dr. Mansell’s responsibilities as Project Manager will include: 

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

• providing overall technical direction and 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 subcontractors; and 

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

In addition to overall project management, the ENVIRON staff will provide technical support 

for the review and recommendation of improved processing techniques and data inputs 

performed by ENVIRON’s subcontractors under Tasks 1 and 2. ENVIRON staff will also lead 

the development and/or adaptation of an ammonia emissions modeling system as well as 

developing the procedures for the creation of standardized data libraries, quality 

assurance/quality control procedures and gridding of the draft ammonia modeling inventory. 

Additional ENVIRON staff assigned to this project include Dr. Greg 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 experience with the development and application of 

emissions inventory modeling systems. Mr. Cuong Tran, an Associate at ENVIRON, will 

assume responsibilities for GIS-related activities, including acquisition and processing of 

appropriate GIS (ARC/Info) databases and the processing and development of required gridded 

spatial surrogate data. Mr. Tran will be assisted by Ms. Coulter-Burke, also an Associate at 

ENVIRON. Ms. Jimenez, an Associate at ENVIRON, 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-Avanti as a subcontractor. Pechan-Avanti will 

develop the ammonia inventory as well as leading the effort to review existing ammonia 

emissions, ammonia inventories, processing techniques and ammonia emission sources. In 

addition, Pechan-Avanti will provide recommendations regarding the most promising processing 

techniques and data inputs, including emissions factors, activity data, and temporal and spatial 

allocation 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 resource allocation by task. Corresponding to the 

technical approach detailed in Section 2 above, five tasks are presented. Table 3-2 presents a 
April 2001 

Projects2:CRPAQS/workplan/Sec3.doc 3-2 

proposed project schedule by month for the 2-1/2 year study program. Also included are 

anticipated dates of delivery for 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 progresses. These include a Draft and Final 

Workplan, input data for draft inventories, the draft inventory and an ammonia emission model, 

progress reports, and a Draft and Final Report.
April 2001 

Projects2:CRPAQS/workplan/Sec3.doc Figure 3-1. Project management structure.
April 2001 

Projects2:CRPAQS/workplan/Sec3.doc 3-4 

Table 3-1. Estimated labor hours by task. (Note: This table will be revised due to ATC default) 

Staff Task1 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 2001 

Projects2:CRPAQS/workplan/Sec3.doc Table 3-2. Summary of initial schedule and costs for CRPAQS Draft Ammonia Inventory Development.  Month 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 

Tas

k

Costs 

1a 

1b 

$10,263 

2a 

2b 

$35,441 

3 $16,766 4 $22,314 

5a 

5b 

$13,568 

 p p p p p p p p p p p p p p p p p p p p p p p 

 dwp fwp Task 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 report d = Deliver draft final report f = Deliver final report dwp = Deliver Draft Workplan fwp = Deliver Final Workplan 
April 2001 

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

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