Eastern Research Group, Inc. 

2005 MEXICALI EMISSIONS 

INVENTORY 

Final Report 

Prepared for: 

Western Governors’ Association 

Denver, Colorado 

U.S. Environmental Protection Agency, Region 9 

San Francisco, California 

Secretaría de Protección al Ambiente de Baja California 

Mexicali, Baja California, Mexico 

February 27, 2009 
ERG Project No: 3657.00.004.001 

2005 MEXICALI EMISSIONS INVENTORY 

Final Report 

Prepared for: 

Western Governors’ Association 

Denver, Colorado 

U.S. Environmental Protection Agency, Region 9 

San Francisco, California 

Secretaría de Protección al Ambiente de Baja California 

Mexicali, Baja California, Mexico 

Prepared by: 

Eastern Research Group, Inc. (ERG) 

Sacramento, California 

with 

Ingeniería en Control Ambiental y Riesgo Industrial, S. de R.L. (ICAR) 

Mexicali, Baja California, Mexico 

and 

TransEngineering 

El Paso, Texas 

February 27, 2009 
TABLE OF CONTENTS 
 

Section Page 

ES.0 EXECUTIVE SUMMARY ...........................................................................................ES-1 
 

ES.1 Methodology..................................................................................................... ES-1 
 

ES.2 Summary of Results.......................................................................................... ES-2
 


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

1.1 Inventory Characteristics ..................................................................................... 1-2 
 

1.2 Data Management and Formatting....................................................................... 1-5 
 

1.3 Data Quality Objectives....................................................................................... 1-5 
 

1.4 Report Organization............................................................................................. 1-7
 


2.0 POINT SOURCES........................................................................................................... 2-1
 


2.1 Federal Jurisdiction Point Source Methodology.................................................. 2-1 
 

2.2 State Jurisdiction Point Source Methodology......................................................2-4 
 

2.3 Results.................................................................................................................. 2-7 
 

2.4 Quality Assurance................................................................................................ 2-8
 


3.0 AREA SOURCES............................................................................................................ 3-1
 


3.1 Methodology........................................................................................................ 3-1
 


3.1.1 Fuel Combustion Categories.................................................................... 3-5 
 

3.1.2 Evaporative VOC Categories................................................................... 3-5 
 

3.1.3 Agricultural Categories............................................................................ 3-6 
 

3.1.4 Ammonia Categories ...............................................................................3-8 
 

3.1.5 Paved Road Dust...................................................................................... 3-8 
 

3.1.6 Unpaved Road Dust ............................................................................... 3-12 
 

3.1.7 Miscellaneous Burning Categories ........................................................ 3-13 
 

3.1.8 Border Crossings.................................................................................... 3-13 
 

3.1.9 Brick Kilns............................................................................................. 3-13 
 

3.1.10 Charbroiling, Construction Dust, and Bakeries .................................... 3-14 
 

3.1.11 Landfills ................................................................................................ 3-14 
 

3.1.12 Wastewater Treatment .......................................................................... 3-14
 


3.2 Results3-14 
 

3.3 Quality Assurance.............................................................................................. 3-19
 


4.0 ON-ROAD MOBILE SOURCES.................................................................................... 4-1 
 

4.1 Methodology........................................................................................................ 4-1 
 

4.2 Results4-2 
 

4.3 Quality Assurance................................................................................................ 4-4
 


5.0 NONROAD MOBILE SOURCES .................................................................................. 5-1 
 

5.1 Methodology........................................................................................................ 5-1
 


2005 Mexicali Emissions Inventory i
 Final, 2/27/09
 
TABLE OF CONTENTS (Continued) 

Section Page 

5.1.1 Aircraft..................................................................................................... 5-1 
 

5.1.2 Locomotives............................................................................................. 5-2 
 

5.1.3 Agricultural and Construction Equipment............................................... 5-2
 


5.2 Results..................................................................................................................5-3 
 

5.3 Quality Assurance................................................................................................ 5-4
 


6.0 EMISSION INVENTORY RESULTS............................................................................ 6-1
 


7.0 REFERENCES ................................................................................................................ 7-1 
 

APPENDIX A: AREA SOURCE CATEGORY FORMS 

APPENDIX B: TECHNICAL REPORT (DRAFT), ESTIMATION OF TRAVEL DEMAND 

AND VEHICULAR TRAFFIC CONGESTION OF THE MEXICALI 

REGION 

Tables Page 

ES-1 2005 Mexicali Emissions Inventory Summary (Tons per Year) ...................................ES-3 
 

1-1 2005 Mexicali Emissions Inventory Summary (Tons per Year) ..................................... 1-5 
 

2-1 Listing of State Jurisdiction Point Sources ...................................................................... 2-5 
 

2-2 2005 Mexicali Federal Jurisdiction Point Source Emissions (Tons per Year) ................ 2-9 
 

2-3 2005 Mexicali State Jurisdiction Point Source Emissions (Tons per Year) .................... 2-9 
 

3-1 General Emission Estimation Methodologies for Area Sources in Mexicali .................. 3-2 
 

3-2 2005 Mexicali Area Source Emissions (Tons per Year) ............................................... 3-15 
 

4-1 2005 Mexicali On-Road Mobile Source Emissions (Tons per Year).............................. 4-4 
 

4-2 2005 Imperial County and Mexicali On-Road Mobile Source Emissions 
 

(Tons per Year)................................................................................................................ 4-6 
 


5-1 2005 Mexicali Nonroad Mobile Source Emissions (Tons per Year)............................... 5-4 
 

6-1 2005 Mexicali Emissions Inventory Summary (Tons per Year) ..................................... 6-1 
 

2005 Mexicali Emissions Inventory ii
 Final, 2/27/09
 
Figures Page 

1-1 State of Baja California and Five Municipalities............................................................. 1-3 
 

1-2 Municipality and Mexicali and Most Populous Localities .............................................. 1-4 
 

2-1 Location of Federal and State Jurisdiction Point Sources in the Urban Portion 
 

of Mexicali....................................................................................................................... 2-3 
 

3-1 Location of Paved and Unpaved Road Dust Sampling Sites........................................... 3-9 
 

3-2 Pavement Coverage of Mexicali Roads......................................................................... 3-11 
 

4-1 Mexicali Loaded Roadway Network Under Weekday Conditions.................................. 4-3 
 

6-1 Mexicali NOx Emissions.................................................................................................. 6-2 
 

6-2 Mexicali SO2 Emissions .................................................................................................. 6-2 
 

6-3 Mexicali VOC Emissions ................................................................................................ 6-3 
 

6-4 Mexicali CO Emissions ................................................................................................... 6-3
 

6-5 Mexicali PM10 Emissions ................................................................................................ 6-4 
 

6-6 Mexicali PM2.5 Emissions................................................................................................ 6-4 
 

6-7 Mexicali NH3 Emissions.................................................................................................. 6-5 
 

6-8 Mexicali CH4 Emissions.................................................................................................. 6-5 
 

2005 Mexicali Emissions Inventory iii
 Final, 2/27/09
 
ACRONYMS 
 

AGEB 	 area geoestadístics básicas (basic geographic area) 

ANAFAPYT Asociación Nacional de Fabricantes de Pinturas y Tintas (National Association 

of Paint and Dye Manufacturers) 

ARB 	 Air Resources Board, California 

BC Baja California 

CANALAVA 	 Cámara Nacional de la Industria de Lavanderías (National Chamber of the Dry 

Cleaning Industry) 

CFE Comisión Federal de Electricidad (Federal Electric Commission) 

CH4 	 methane 

CO carbon monoxide 

COA 	 Cédula de Operación Anual (Annual Operating Report) 

DQOs data quality objectives 

EIIP 	 Emission Inventory Improvement Program 

EIP 	 Emission Inventory Plan 

ERG 	 Eastern Research Group, Inc. 

FIFRA 	 Federal Insecticide, Fungicide and Rodenticide Act 

GIS 	 geographical information system 

HDDV 	 heavy-duty diesel vehicle 

HDGV 	 heavy-duty gasoline vehicle 

IC internal combustion 

ICAO International Civil Aviation Organization 

ICAR 	 Ingeniería en Control Ambiental y Riesgo Industrial, S. de R.L. 

ID identification number 

INEGI 	 Instituto Nacional de Estadística, Geografía e Informática (National Institute of 

Statistics, Geography, and Computing) 

kg kilogram 

2005 Mexicali Emissions Inventory iv 

Final, 2/27/09 
km kilometers 

lb pound 

lbs pounds 

LDDT light-duty diesel truck 

LDDV light-duty diesel vehicle 

LDGT light-duty gasoline truck 

LDGV light-duty gasoline vehicle 

LPG liquefied petroleum gas 

LTO landing and take-off 

m2

 square meters 

m3

 cubic meters 

MC motorcycle 

Mg megagram 

mm Hg millimeters of mercury 

mph miles per hour 

NAICS North America Industry Classification System 

NEI National Emissions Inventory 

NH3 	 ammonia 

NIF National Emissions Inventory Input Format 

NOx nitrogen oxides 

NPK nitrogen-phosphorus-potassium 

PEMEX Petróleos Mexicanos 

PM particulate matter 

PM2.5	 	 particulate matter less than 2.5 micrometers in equivalent aerodynamic 

diameter. 

PM10	 	 particulate matter less than 10 micrometers in equivalent aerodynamic diameter 

2005 Mexicali Emissions Inventory v 

Final, 2/27/09 
PROAIRE Programa para Mejorar la Calidad del Aire (Program to Improve Air Quality) 

QA quality assurance 

QAPP quality assurance project plan 

QC quality control 

s second 

SAGARPA Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación 

(Secretariat of Agriculture, Livestock, Rural Development, Fisheries, and Food) 

SCC source classification code 

scf standard cubic feet 

SEMARNAT Secretaría de Medio Ambiente y Recursos Naturales (Secretariat of the 

Environment and Natural Resources) 

SENER Secretaría de Energía (Secretariat of Energy) 

SLRC San Luis Río Colorado, Sonora 

SO2 sulfur dioxide 

SPA Secretaría de Protección al Ambiente de Baja California (Secretary of 

Environmental Protection of Baja California) 

TDM travel demand model 

TIM time-in-mode 

TSP total suspended particulate 

U.S. EPA United States Environmental Protection Agency 

VKT vehicle kilometers traveled 

VMT vehicle miles traveled 

VOC volatile organic compound 

WGA Western Governors’ Association 

WRAP Western Regional Air Partnership 

yr year 

2005 Mexicali Emissions Inventory vi 

Final, 2/27/09 
ES.0 EXECUTIVE SUMMARY 
 

In response to requests from the Secretary of Environmental Protection of Baja California 

(Secretaría de Protección al Ambiente de Baja California – SPA) and the California Air 

Resources Board (ARB), the Western Governors’ Association (WGA), with funding from the 

U.S. Environmental Protection Agency (U.S. EPA), hired a contractor team to develop the 2005 

Mexicali inventory. The contractor team was led by Eastern Research Group, Inc. (ERG) and 

supported by two Mexico-based subcontractors (i.e., Ingeniería en Control Ambiental y Riesgo 

Industrial, S. de R.L. [ICAR] and TransEngineering). This 2005 Mexicali emissions inventory 

updates previous inventories developed for Mexicali, Baja California (BC), including Mexicali’s 

Programa para Mejorar la Calidad del Aire (PROAIRE) for a base year of 1996 (PROAIRE, 

1999) and the 1999 Mexico National Emissions Inventory (NEI) Program (ERG, 2006). 

The 2005 Mexicali emissions inventory was developed for the entire municipality of 

Mexicali. Annual emissions (both tons per year and megagrams per year) were estimated for 

point sources, area sources, on-road mobile sources, and nonroad mobile sources. Emissions 

were estimated for the following pollutants: nitrogen oxides (NOx), sulfur dioxide (SO2), 

volatile organic compounds (VOC), carbon monoxide (CO), particulate matter (PM) less than 10 

micrometers (μm) in aerodynamic diameter (PM10), PM less than 2.5 μm in aerodynamic 

diameter (PM2.5), ammonia (NH3), and methane (CH4). 

ES.1 Methodology 

The point source emissions inventory was developed using existing emissions data 

provided by the Secretariat of Environment and Natural Resources (SEMARNAT) and the 

Secretary of Environmental Protection of Baja California (SPA). The area source and nonroad 

mobile source emissions inventory were developed using activity data collected from Mexicali 

government agencies and other local contacts. The on-road mobile source emission inventory 

was developed using vehicle activity data (i.e., vehicle kilometers traveled [VKT]) obtained from 

traffic and congestion modeling. 

2005 Mexicali Emissions Inventory ES-1 

Final, 2/27/09 
ES.2 Summary of Results 

Table ES-1 provides a summary of the results of the 2005 Mexicali emissions inventory, 

by source type and pollutant. Table ES-1 shows that: 

• 	 Emissions from federal jurisdiction point sources are considerably larger than 

emissions from state jurisdiction point sources, even though the ratio of state-tofederal jurisdiction point sources is greater than 10-to-1. 

• 	 NOx and SO2 emissions are dominated by point sources. 

• 	 VOC emissions are emitted by area (i.e., mainly various evaporative source 

categories) and on-road mobile sources. 

• 	 On-road mobile sources are the predominant source of CO. 

• 	 Area sources are the largest contributors of PM10 and PM2.5 (i.e., due to unpaved and 

paved road dust), NH3 (i.e., from livestock ammonia and fertilizer application), and 

CH4 (i.e., from the landfill). 

The final inventory was formatted to be compatible with U.S. EPA’s National Emissions 

Inventory Format (NIF), and SMOKE/IDA format (i.e., used for photochemical and other 

modeling). In addition, spatial surrogates for gridding area source emissions into a 4 km grid 

system for modeling were developed. Also, geographic information system (GIS)-based shape 

files obtained for Mexicali’s AGEBs (i.e., area geoestadístics básicas or basic geographic areas) 

which were used in the development of some area source category emissions, were provided 

along with the final inventory files for purposes of spatially distributing area source emissions 

over the inventory domain for purposes of air quality modeling. 

2005 Mexicali Emissions Inventory ES-2 

Final, 2/27/09 
Table ES-1. 2005 Mexicali Emissions Inventory Summary (Tons per Year) 

Source Type NOx SO2 VOC CO PM10 PM2.5 NH3 CH4 

Point – 

Federal 13,931.7 3,650.9 659.3 3,636.2 1,537.7 128.4 

Point – State 444.0 973.2 73.0 479.9 89.7 

Area 1,205.5 131.1 15,301.5 18,854.0 50,879.3 6,753.5 9,015.2 6,033.3 

On-Road 

Mobile 8,570.4 168.7 8,977.1 60,603.4 765.0 665.8 252.4 144.4 

Nonroad 

Mobile 4,485.0 60.9 542.9 3,206.9 550.2 533.3 0.0 1.5 

Total 28,636.5 4,984.8 25,553.8 86,780.4 53,821.8 8,081.0 9,267.6 6,179.2 

Blanks indicate that pollutant emissions were not applicable or were not estimated due to a lack 

of data. 

2005 Mexicali Emissions Inventory ES-3 

Final, 2/27/09 
1.0 INTRODUCTION 
 

The first emissions inventory developed for Mexicali, Baja California (BC) was 

published as part of Mexicali’s Programa para Mejorar la Calidad del Aire (PROAIRE) for a 

base year of 1996 (PROAIRE, 1999). The next and most recent emissions inventory for 

Mexicali was developed and published as part of the 1999 Mexico National Emissions Inventory 

(NEI) Program; however, the industrial source emissions in the 1999 Mexico NEI for Mexicali 

were based on the previous 1996 PROAIRE inventory (ERG, 2006). 

The Secretary of Environmental Protection of Baja California (Secretaría de Protección al 

Ambiente de Baja California – SPA) has requested an update to the emissions inventory for the 

municipality of Mexicali for the year 2005. In addition, the California Air Resources Board 

(ARB) has requested an updated inventory for purposes of air quality modeling in the border 

area of Imperial County, California, and Mexicali. 

In response to these needs, the Western Governors’ Association (WGA), with funding 

from the U.S. Environmental Protection Agency (U.S. EPA), sponsored this project to update the 

Mexicali emissions inventory to 2005. The Contractor team, hired under Contract No. 30430-01, 

was led by Eastern Research Group, Inc. (ERG), and included Ingeniería en Control Ambiental y 

Riesgo Industrial, S. de R.L. (ICAR), and TransEngineering. ICAR was responsible for data 

collection related to area and point sources, and TransEngineering was responsible for estimating 

on-road motor vehicle activity data which were used to estimate mobile source emissions and to 

determine the distribution of vehicle activity over paved versus unpaved roads. 

The specific objectives of the 2005 Mexicali emissions inventory were to provide a 

current and accurate technical basis for air quality planning in the municipality of Mexicali. 

Also, the results of the 2005 Mexicali emissions inventory (for the non-methane pollutants) are 

formatted for use in air quality modeling to better understand the contribution of Mexicali 

emissions to concentrations of criteria pollutant levels in that region. 

2005 Mexicali Emissions Inventory 1-1 

Final, 2/27/09 
1.1 Inventory Characteristics 

The characteristics of the 2005 Mexicali emissions inventory are as follows: 

• 	 Base year: 2005. 

• 	 Temporal resolution: Annual emissions, in terms of megagrams per year (Mg/year) 

and tons per year (tons/year). Some intermediate activity data are presented in 

Imperial units. The inventory results are presented in Imperial units (i.e., tons per 

year) in the English version of the final report and in metric units (i.e., megagrams 

[Mg] per year) in the Spanish version of the final report. 

• 	 Geographic domain: Entire municipality of Mexicali. Figure 1-1 shows the entire 

state of Baja California and its five municipalities (i.e., Mexicali, Tijuana, Tecate, 

Playas de Rosarito, and Ensenada). Figure 1-2 shows the municipality of Mexicali 

and location of the most populous “localities” (e.g., cities, towns, communities, etc.) 

with population greater than 3,000. 

• 	 Geographic Resolution: In addition to total municipality-level emissions by pollutant 

and source type, emissions were estimated at various other levels of detail, including: 

− 	 Exact location (i.e., latitude/longitude) for point sources. 

− 	 Spatial surrogates at the level of basic geographic areas (area geoestadística 

básica – AGEB) are also provided. An AGEB is analogous to a census tract in 

the U.S. These surrogates provide the spatial allocation of emissions to 4 

kilometer (km) × 4 km grid cells within the ARB modeling domain (i.e., 

approximately 36 km to 50 km south of the California—BC border). 

• 	 Pollutants: 

− Nitrogen oxides (NOx) 
 

− Sulfur dioxide (SO2) 
 

− Volatile organic compounds (VOC) 
 

− Carbon monoxide (CO) 
 

− Particulate matter (PM) less than 10 micrometers (μm) in aerodynamic diameter 
 

(PM10) 
 

− PM less than 2.5 μm in aerodynamic diameter (PM2.5) 
 

− Ammonia (NH3) 
 

− Methane (CH4). 
 

• 	 Source Types and Categories: Point sources, area sources, on-road mobile sources, 

and nonroad mobile sources. Each of these source types will be discussed in their 

individual sections. 

2005 Mexicali Emissions Inventory 1-2 

Final, 2/27/09 
Figure 1-1. State of Baja California and Five Municipalities 

2005 Mexicali Emissions Inventory 1-3
 Final, 2/27/09
 
Figure 1-2. Municipality of Mexicali and its Most Populous Localities 

2005 Mexicali Emissions Inventory 1-4
 Final, 2/27/09
 
1.2 Data Management and Formatting 

Development of the 2005 Mexicali emissions inventory required the compilation of 

considerable amounts of emissions and other related inventory data. Electronic spreadsheets 

were used to compile, calculate, and summarize emissions data. 

The final inventory was formatted to be compatible with U.S. EPA’s National Emissions 

Inventory Format (NIF), and SMOKE/IDA format (i.e., used for photochemical and other 

modeling). In addition, spatial surrogates for gridding the area source emissions into a 4 km grid 

system for modeling will be developed. Also, geographic information system (GIS)-based shape 

files of the obtained for Mexicali’s AGEBs, which were used in the development of some area 

source category emissions, will be provided along with the final inventory files for purposes of 

spatially distributing area source emissions over the inventory domain for purposes of air quality 

modeling. 

1.3 Data Quality Objectives 

The data quality objectives (DQOs) were established as part of the Emission Inventory 

Plan (EIP)/Quality Assurance Project Plan (QAPP) developed for this project. The specific 

DQOs, and criteria used to evaluate whether the DQOs are achieved for the 2005 Mexicali 

emissions inventory are as follows: 

1. 	 A comprehensive emissions inventory for all source types (i.e., point, area, on-road 

motor vehicle, and nonroad mobile sources) and all major categories within these 

source types. 

Acceptance criteria: The source types and major categories accounted for in the 

EIIP guidance documents are present in the Mexicali emissions inventory, and/or any 

differences are documented. 

2. 	 Identification of 80% of all federal jurisdiction and state jurisdiction point sources 

located within the municipality of Mexicali. 

Acceptance criteria: A list of point sources under the federal (SEMARNAT 

Delegation), and state (SPA) jurisdictions compares to the emissions data, by source, 

provided by those agencies. 

3. 	 Point sources: Emissions estimates with an accuracy of ± 25% for at least 10% of the 

facilities. 

2005 Mexicali Emissions Inventory 1-5 

Final, 2/27/09 
Acceptance criteria: Estimated emissions for 10% of the total facilities compare to 

measured emissions of all pollutants with ± 25% accuracy. The emissions data 

measurements will be obtained from subcontractor ICAR, and will be based on data 

collected over the past five years in Mexicali. 

4. 	 Area, On-Road Motor Vehicle, and Nonroad Mobile Sources: Emissions estimates 

with an accuracy of ±50%. 

Acceptance criteria: Estimated emissions using emission factors/models compare to 

emissions estimated using a fuel-balance approach for all applicable pollutants. The 

fuel-balance approach will use national statistics, while the preferred inventory 

methodology will use PEMEX and local fuel distribution data. 

5. 	 Documentation: All EI results are transparent and reproducible. 

Acceptance criteria: Written and electronic inventory documentation provides 

detailed explanations of data collected, data measured/analyzed, values 

estimated/calculated, assumptions made, and emission factors used so that the results 

can be duplicated. 

Most of these acceptance criteria have been met, and in some cases exceeded, in the 

development of the 2005 Mexicali emissions inventory. Instances where the acceptance criteria 

could not be met are documented (i.e., see Area Sources, Section 3.3). 

In general, the following quality assurance checks were performed to ensure the DQOs 

were achieved: 

• 	 Accuracy checks – Spreadsheets were developed to calculate emissions, and 100% of 

the formulas were checked for accuracy. 100% of the electronic calculations were 

checked (50% by hand), and any errors were corrected. 

• 	 Completeness checks – The list of pollutants and source categories for the 1999 

Mexico NEI was compared against all of the inventory files to ensure that emissions 

are estimated for the correct pollutants and source categories. Any errors were 

corrected. 

• 	 Representativeness – The emission factors and activity data were reviewed to ensure 

they are appropriate and current for the source categories for 100% of the source 

categories inventories. All adjustment factors (e.g., used to allocate national activity 

to a state- or municipality-level rate) were reviewed to ensure they are representative 

of actual conditions affecting emissions generated during the base year of 2005. 

Specific quality assurance procedures followed during the estimation of emissions for 

each source type will be described in report Sections 2.0, 3.0, 4.0, and 5.0 for point sources, area 

2005 Mexicali Emissions Inventory 1-6 

Final, 2/27/09 
sources, on-road mobile sources, and nonroad mobile sources, respectively, as part of the final 

report. 

1.4 Report Organization 

The remainder of this report provides a complete and detailed description of all methods, 

assumptions, data, and calculations used to develop the emissions inventory, along with the 

results by source category. The report is organized as follows: 

• 	 Section 2.0 describes the point source estimation methodology and results. 

• 	 Section 3.0 describes the area source estimation methodology and results. 

• 	 Section 4.0 describes the on-road mobile source estimation methodology and results. 

• 	 Section 5.0 describes the nonroad mobile source methodology and results. 

• 	 Section 6.0 provides the overall 2005 Mexicali emissions inventory results. 

• 	 Section 7.0 lists all references used in the development of this emissions inventory. 

• 	 Appendix A contains the forms showing how each area source category’s emissions 

were calculated (i.e., method/emission factor/formula, reference, activity data, sample 

calculation). 

• 	 Appendix B contains a detailed explanation of the analysis conducted to estimate onroad mobile source activity data. 

2005 Mexicali Emissions Inventory 1-7 

Final, 2/27/09 
2.0 POINT SOURCES 
 

Point sources are stationary industrial sources that release emissions from fixed points 

(e.g., a stack or vent). In Mexico, point source emissions data are transmitted to the federal 

and/or state environmental agencies (depending upon sector-based jurisdiction) by means of the 

Annual Operating Certificate (Cédula de Operación Annual – COA). As a result, the 

development of point source emissions data for the 2005 Mexicali emissions inventory relied 

heavily on the cooperation of the government agencies that maintain emission registries – the 

Secretariat of Environment and Natural Resources (SEMARNAT) for federal jurisdiction point 

sources and the SPA for state jurisdiction point sources. The procedures used to compile 

emissions from the federal and state jurisdiction point sources are described below. 

2.1 Federal Jurisdiction Point Source Methodology 

There are 16 federal jurisdiction point sources located in the municipality of Mexicali, 

which was confirmed by SEMARNAT. These point sources are listed below with their 

sector/subsector, as applicable: 

• 	 CFE Campo y Centrales Geotermoeléctrico de Cerro Prieto (Electricity Generation) 

• 	 CFE Central Turbo Gas (Electricity Generation) 

• 	 Dafmex, S. de R.L. de C.V. (Metallurgical/Ferrous Metal Foundry) 

• 	 Energía Azteca X, S. de R.L. de C.V. (Electricity Generation) 

• 	 Energía de Baja California, S. de R.L. de C.V., Central Termoeléctrica (Electricity 

Generation) 

• 	 Fábrica de Papel San Francisco, S.A. de C.V. (Pulp and Paper/Paper Production) 

• 	 Fevisa Industrial, S.A. de C.V. (Glass/Container Glass Manufacture) 

• 	 Fundiciones San Martin o Felix Jesus del Valle Zamora (Metallurgical/Iron and Steel 

Foundry) 

• 	 Infra, S.A. de C.V., Sucursal Mexicali (Chemical/Industrial Gases) 

• 	 Kenworth Mexicana, S.A. de C.V. (Automotive/Truck Manufacture) 

• 	 Kyowa Mexicali, S.A. de C.V. (Plastic Parts Manufacturing) 

2005 Mexicali Emissions Inventory 2-1 Final, 2/27/09 
• 	 Maquiladora San Diego, S.A. de C.V. (Metallurgical – Iron and Steel Foundry) 

• 	 Petróleos Mexicanos (PEMEX) Refinación Planta Mexicali (Petroleum/Petroleum 

Products Storage and Distribution 

• 	 Quimical, S.A. de C.V. (Chemical/Pesticides and Agricultural Chemicals) 

• 	 Termoeléctrica de Mexicali, S. de R.L. de C.V. (Electricity Generation) 

• 	 Vitro AFG, S.A. de C.V. (Glass/Plane Glass Manufacture) 

The physical location of some of the federal jurisdiction (and state jurisdiction) point 

sources in the urban portion of Mexicali is illustrated in Figure 2-1. The federal jurisdiction 

point sources are shown as red dots (and the state jurisdiction point sources are shown as green 

dots). Some addition federal and state jurisdiction point sources located in the rural areas of 

Mexicali are not shown on Figure 2-1. 

ICAR made the initial data request to SEMARNAT for existing information regarding 

emissions for the year 2005 from Mexicali federal jurisdiction point sources. SEMARNAT 

provided two electronic files in Excel format: “Empresas Comp Federal (EMIS 230108).xls” 

and “DATGEN_MEXICALI_05.xls”. The “Empresas Comp Federal (EMIS 230108).xls” file 

provided a listing of all federal jurisdiction sources located in the state of Baja California, as well 

as those federal jurisdiction sources that reported 2005 emissions to SEMARNAT. The 

“DATGEN_MEXICALI_05.xls” file consisted of several sheets of which the following were 

most relevant for the compilation of emissions: 

• 	 Hoja1 – Complete facility description; 

• 	 EqComb – Emissions from combustion equipment; 

• 	 ActProc – Emissions from non-combustion equipment and processes; 

• 	 Chim – Stack parameters; and 

• 	 EqCont – Description of control equipment. 

In general, the information contained in the “DATGEN_MEXICALI_05.xls” file was 

fairly complete. However, much of the data contained in this file was represented in codes, 

2005 Mexicali Emissions Inventory 2-2 Final, 2/27/09 
Figure 2-1. Location of Federal and State Jurisdiction Point Sources in the Urban 
 

Portion of Mexicali 
 

2005 Mexicali Emissions Inventory 2-3 Final, 2/27/09 
abbreviations, and acronyms. As a result, ICAR made a subsequent request to SEMARNAT to 

provide the relevant data dictionaries needed to decipher the file. Subsequently, SEMARNAT 

provided two data dictionary files associated with the “DATGEN_MEXICALI_05.xls” file: 

“Diccionario_Datos_Datgen.xls” and “Codigos_catalogo.xls”. The 

“Diccionario_Datos_Datgen.xls” file provided the data dictionary and formats used by the 

“DATGEN_MEXICALI_05.xls” file, while the “Codigos_catalogo.xls” file provided a key for 

all of the codes, abbreviations and acronyms. 

Using the “Empresas Comp Federal (EMIS 230108).xls” and 

“DATGEN_MEXICALI_05.xls”, along with the data dictionary files, ICAR compiled emissions 

from the federal jurisdiction point sources. 

2.2 State Jurisdiction Point Source Methodology 

There are 173 state jurisdiction point sources located in the municipality of Mexicali. As 

seen in Figure 2-1, many of these state jurisdiction point sources are clustered in the city’s 

industrial parks, which was confirmed by SEMARNAT. The names for these facilities are listed 

in Table 2-1. 

ICAR made an initial request to SPA for existing information regarding emissions for the 

year 2005 from Mexicali state jurisdiction point sources. SPA provided two electronic files: “2­

REGISTROS (2005).xls” and “SIARE.mdb”. The “2-REGISTROS (2005).xls” file provided a 

listing of all companies that submitted 2005 emission estimates to SPA and identified the paper 

file location within SPA offices of these emission submittals. The “SIARE.mdb” file is an 

Access database which contains such information as: 

• Facility name 

• File number 

• Inventory year 

• Type and number of process that generate emissions 

• Emission controls (if any) 

• Pollutant type (in units of kg/hr and kg/year) 

2005 Mexicali Emissions Inventory 2-4 Final, 2/27/09 
Table 2-1. Listing of State Jurisdiction Point Sources 

Facility Name 

Accuride International, S.A. de C.V. (Nelson 6) 

Facility Name 

Cervecería de Baja California, S.A. de C.V. 

Expopartes, S.A. 

Accuride International, S.A. de C.V. (Pta-Oeste, Nelson-12) Chromalloy, S.A. de C.V. (Planta 1) Fabricaciones y M

Agribrands Purina México, S.A. de C.V. Chromalloy, S.A. de C.V. (Planta Ii) Fanosa, S.A. de CAlimentos y Frituras de Baja California, S.A. de C.V. Cias. Periodísticas del Sol de Pacífico, S.A. de C.V. Femsa, S. de R.LAmerican Meter de Mexico, S.A. de C.V. Compre Block Sucursal Criba Fetasa Culiacán, SAmp Industrial Mexicana, S.A. de C.V. Compre Block, S.A. de C.V. Fetasa Industrial, Arrendadora del Colorado del Norte, S.A. de C.V. Con Mex de Mexicali, S.A. Fetasa Mexicali, S

Ascotech, S.A. de C.V. Concretos Apasco, S.A. de C.V. Flexcel-Mexicali, Asociacion Rural de Interes Colectivo de Baja California  ́80 de R.I. Concretos Bajanor de Mexicali, S.A. de C.V. Frituras de MexicBenq Mexicana, S.A. de C.V. Constructora Gallego, S.A. de C.V. (Planta 1) Fruvemex Mexica

Bi Technologies, S.A. de C.V. Constructora Gallego, S.A. de C.V. (Planta 2) Furukawa Mexico

Bimbo, S.A. de C.V. Constructora Gusa, S.A. de C.V. Grayline de Méxi

Black & Decker Hhi México, S. de R.L. de C.V. (Pta-2) Controles de Mexicali, S. de R.L. Grupo Industrial P

Black & Decker Hhi México, S. de R.L. de C.V. K1 Dafmex, S. de R.L. de C.V. Guaycan QuimicoBlack & Decker Hhi México, S. de R.L. de C.V. K2 Dataproducts Imaging Solution, S.A. de C.V. Harinera de Maíz 

Black & Decker Hhi México, S. de R.L. de C.V. P1 Delivamex, S. de R.L. de C.V. Helados y Mezcla

Block Centinela, S.A. de C.V. Display Orion Mexicana, S.A. de C.V. Honeywell Aeros

Bloquera Moderna, S.A. de C.V. Eco Constructora, S.A. de C.V. 

Honeywell Produ(Radiadores) 

Blue Star, S.A. de C.V. Electronica Vanguard, S.A. de C.V. 

Honeywell Produ(Turbos) 

Breg México, S. de R.L. de C.V. Embotelladora de Mexicali, S.A. de C.V. Hykam Electronic

Cajas Agricolas, S.A. de C.V. Emermex, S.A de C.V. Ideas Y/O Enriqu

Calidad Internacional de Pulido, S.A. de C.V. Emermex, S.A. de C.V. (Edif-3) 

Iluminación CoopC.V. 

Carley de México, S.A. de C.V. Empaques de Cartón Titán Impresora y EditoCd Electrónica de Mexico, S.A de C.V. Empresas L.M., S. de R.L. de C.V. Industria de Refre

Cementos Químicos Industriales, S.A. de C.V. Energéticos y Grasas del Norte, S.A. de C.V. 

Industrial Vallera Technologies) 

Cemex Concretos, S.A. de C.V. Ensambladores Electrónicos de México, S.A. 

Industrial Vallera 

Vishay) 

Cemex México, S.A. de C.V. Ensambles de Mexicali, S.A. de C.V. 

Industrial Vallera (Loveday) 

Centro Camionero de Mexicali, S.A. de C.V. Ensambles de Silleria Mexicana, S.A. de C.V. 

Industrial Vallera Motors) 

2005 Mexicali Emissions Inventory 2-5 

Final, 2/27/09 
Table 2-1. Continued 

Facility Name Facility Name 

Industrial Vallera de Mexicali, S.A. de C.V. (Martech-Mdi West) Mextearinas, S.A. de C.V. Sabritas, S. de R.L

Industrial Vallera de Mexicali, S.A. de C.V. (Másimo) Molinerea del Valle, S.A. de C.V. 

Sánchez QuiñóneIndustriales) 

Industrial Vallera de Mexicali, S.A. de C.V. (Merchants Metals) Muebles y Diseños El Bajio, S.A. de C.V. Sds de Mexico, S

Industrial Vallera de Mexicali, S.A. de C.V. (Taymar Plastics) Navarrete Rayos Noel Alejandro Service Container 

Industrias SL, S.A. de C.V. North Safety de Mexicali, S. de R.L. de C.V. Sigma Lapos, S.A

Industrias Zahori, S.A. de C.V. Novamex México, S.A. de C.V. Sigma Lapos, S.AInovación en Concretos, S.A. de C.V. NSF Mexicali, S. de R.L. de C.V. Skyworks SolutioInternational Assembly Specialists, S.A. de C.V. Nutricion Maxima de Baja California, S.A. de C.V. 

Solís Meraza MarMaderas Satélite) 

Isoclima de México, S.A. de C.V. Nutrimentos Mexicanos, S.A. de C.V. SPG de México, SIsoclima de México, S.A. de C.V. Omega Industries, S.A. de C.V. Stone Container d

J. Cox de México, S.A. de C.V. Operaciones de Clase Mundial, S.A. de C.V. Strategic MateriaJean Charles Mexicali, S.A. de C.V. Orthodental, S.A. de C.V. Sukarne Producci

Jumex Mexicali, S.A. de C.V. Panasonic Electric Works Mexicana, S.A. de C.V. Suntek Manufactu

Kang Seo Mexicana, S.A. de C.V. Partes Remanufacturadas de México, S.A. de C.V. Super Muros, S.A

Kb Foam de Mexicali, S.A. de C.V. Pegamentos Industriales Galileo, S.A. de C.V. Technologies Sol

Kerns Beverages de México, S. de R.L. de C.V. Pims, S.A. de C.V. 

Tecnologías InterC.V. 

Kwang Sung Electronics México, S.A. de C.V. (Pta-1) Placas Termodinámicas, S.A. de C.V. Tecnomex IndustKyowa Mexicali, S.A. de C.V. Ponce Elizalde Armida (Transformadora de Maderas Tramex) Tecnomex IndustL.G. Innotek Mexicali, S.A. de C.V. Procesadora y Empacadora de Carnes del Norte, S.A. de C.V. Temcomex, S.A. Lam-Mex, S.A. Prodofi, S. de R.L. de C.V. 

Teradyne ConnecC.V. 

Lancer Orthodontics de México, S.A. de C.V. Productos de Acero de Baja, S.A. de C.V. Thomson Lechera Mexicana, S.A. de C.V. Productos de Barro, S.A. de C.V. Tri-State de MexiLG Electronics Mexicali, S.A. de C.V. Productos Hola del Noroeste, S.A. de C.V. Tubos de Acero I

L-N Safety Glass, S.A. de C.V. Productos Urólogos de Mexico, S.A de C.V. Valutech Out Sou

Lopez Cazares Rafael Promotora Industrial Per, S.A. de C.V. Veladoras del Pac

Lopez Cazares Rafael (Rancho) Pulidora de Baja California, S.A. de C.V. Vimifos, S.A. de 

Maquiladora San Diego, S.A. de C.V. Pulidora Los Ángeles, S.A. de C.V. Wabash Technolo

Marko de México, S. de R.L. de C.V. Puligamex, S.A. de C.V. Yiro Fabrica, S. dMasterwork Electronics de México, S.A. de C.V. Rheem Mexicali, S. de R.L. de C.V. Zahori, S.A. de C

Megaplast, S.A. de C.V. Robert Bosch Tool de México, S.A. de C.V. 

2005 Mexicali Emissions Inventory 2-6 

Final, 2/27/09 
• Type of fuel combusted 

• Number of stacks 

It should be noted that emissions were reported at the facility-level, but not at the process- or 

equipment-level. Also, stack parameters (i.e., stack coordinates, stack height, stack diameter, 

exit gas velocity, and exit gas temperature) were not included in the “SIARE.mdb” file. 

Based upon information contained in these two files, ICAR developed an Excel 

spreadsheet (“state_point_source_R0.xls”) that compiled the following data for each facility: 

• Facility name 

• Facility identification number (ID) 

• Main activity 

• Facility address 

• Facility point of contact 

• SCIAN code 

• Emission quantification (kg/year) 

• Type of control 

• Facility latitude 

• Facility longitude 

• Emission points (i.e., the quantity and type of processes that generate emissions) 

• Material type (i.e., type of fuel used) 

• Basis of emissions estimate. 

All of these data were based upon information obtained from the “SIARE.mdb” file. 

2.3 Results 

The federal jurisdiction point source emissions are shown in Table 2-2. Some 

conclusions that can be drawn from Table 2-2 are that the power plants are the largest sources of 

NOx, CO, and PM10. The PEMEX facility is the largest source of VOC emissions, while the 

2005 Mexicali Emissions Inventory 2-7 

Final, 2/27/09 
glass plants (i.e., Fevisa and Vitro AFG) and the paper plant (i.e., Fábrica de Papel San 

Francisco) are the three largest sources of SO2. It should be noted that there are no emissions 

reported from the Federal Electric Commission (Comisión Federal de Electricidad-CFE) 

geothermal power plant in Cerro Prieto. 

Emissions for the 173 state jurisdiction point sources are shown in Table 2-3, by industry 

group. From Table 2-3, it is clear that the Petroleum and Coal Products Manufacturing sector is 

the largest emitter of SO2 and a significant contributor of NOx. The Other Industrial 

Manufacturing sector has the largest NOx emissions. Computer, Communication, and Electronic 

Manufacturing is the largest emitter of VOC, while Food Manufacturing and Primary Metals are 

the primary contributors of CO emissions. Emissions for PM2.5, NH3, and CH4 are not included 

in Table 2-2 because emissions of these pollutants were not estimated by SPA. 

2.4 Quality Assurance 

After compiling emissions for federal jurisdiction and state jurisdiction point sources, 

ICAR conducted quality assurance (QA) of these emissions. ICAR’s QA of the federal 

jurisdiction point sources used a QA checklist that focused on the following aspects: 

• 	 Facility information (i.e., North American Industry Classification System [NAICS] 

codes, address, latitude and longitude) 

• 	 Process/equipment information (i.e., process/equipment description, source 

classification code [SCC], stack assignment) 

• 	 Emissions (i.e., pollutant completeness, emissions contribution, particulate matter, 

hydrocarbons, emission units, emission reasonableness) 

• 	 Stack parameters (i.e., stack height, stack diameter, exit gas temperature, exit gas 

velocity, stack latitude and longitude) 

• 	 Operating schedule (i.e., days per week, hours per day) 

• 	 Controls (i.e., control device, control efficiency) 

Overall, ICAR’s QA evaluation indicated fairly complete data for the federal jurisdiction 

point sources. However, a few specific data elements needed to satisfy the required 

characteristics of the Mexicali inventory were missing from some or all of the federal jurisdiction 

2005 Mexicali Emissions Inventory 2-8 

Final, 2/27/09 
Table 2-2. 2005 Mexicali Federal Jurisdiction Point Source Emissions (Tons per 
 

Year) 
 

Facility Name NOx SO2 VOC CO PM10 PM2.5 

CFE Central Turbo Gas Mexicali 27.4 11.8 1.9 2.4 

CFE Campo y Centrales Geothermoeléctrico de Cerro 

Prieto 

Dafmex, S. de R.L. de C.V. 0.4 0.1 0.0 0.3 0.0 0.0 

Energía Azteca X, S. de R.L. de C.V. 5,757.9 7.5 12.4 1,433.2 548.4 

Energía de Baja California, S. de R.L. de C.V. Central 

Termoeléctrica 2,385.5 3.1 5.1 593.8 227.2 

Fevisa Industrial, S.A. de C.V. 277.4 2,500.2 0.5 29.0 140.2 91.6 

Fábrica de Papel San Francisco, S.A. de C.V. 206.8 788.6 0.5 80.6 44.7 29.3 

Infra, S.A. de C.V. Sucursal Mexicali 0.2 

Kyowa Mexicali, S.A. de C.V. 0.1 0.0 76.3 0.1 0.0 0.0 

Kenworth Mexicana, S.A. de C.V. 9.4 1.8 75.6 1.3 0.1 0.1 

Maquiladora San Diego, S.A. de C.V. 0.9 0.4 0.2 30.0 2.4 2.2 

PEMEX Refinación Planta Mexicali 458.9 

Quimical, S.A. de C.V. 0.0 0.0 0.0 0.0 0.0 0.0 

Termoeléctrica de Mexicali, S. de R.L. de C.V. 4,664.2 6.1 10.1 1,161.0 444.2 

Vitro AFG, S.A. de C.V. 601.4 330.1 19.4 233.9 128.0 5.1 

Fundiciones San Martin o Felix Jesus del Valle Zamora 0.2 1.1 0.1 71.1 0.0 0.0 

Total 13,931.7 3,650.9 659.3 3,636.2 1,537.7 128.4 

Table 2-3. 2005 Mexicali State Jurisdiction Point Source Emissions (Tons per Year) 

3-Digit 

NAICS NAICS Description NOx SO2 VOC CO PM10 

212 Mining (metallic and nonmetallic) 1.47 0.00 0.00 0.09 12.92 

311 Food industry 70.11 60.84 0.00 243.61 22.53 

312 Beverages/tobacco industry 1.80 0.00 0.00 0.23 0.00 

321 Wood products industry 0.00 0.00 0.00 0.00 0.72 

322 Paper industry 5.58 0.00 3.35 2.67 2.80 

323 Publishing industry 0.00 0.00 0.14 0.00 0.00 

324 Petroleum and coal products manufacturing 133.76 732.91 0.00 10.07 2.71 

325 Chemical industry 0.31 0.00 0.00 0.03 0.00 

326 Plastics and rubber industry 27.22 116.90 2.08 9.03 7.97 

327 Nonmetallic materials manufacturing 11.04 0.12 4.48 7.06 3.27 

331 Primary metals industry 11.31 19.95 0.00 121.58 0.24 

332 Metal products manufacturing 4.81 0.00 7.64 16.05 6.89 

333 Equipment and machinery manufacturing 5.94 0.00 2.36 7.48 0.48 

334 

Computer, communication, electronics 

manufacturing 2.13 0.19 33.98 16.09 9.45 

335 Electricity generation equipment manufacturing 0.00 0.00 0.03 0.00 0.02 

336 Transportation equipment manufacturing 0.71 0.00 1.42 12.90 12.28 

337 Furniture manufacturing 1.99 0.00 14.53 6.06 1.28 

339 Other industrial manufacturing 157.76 0.00 3.22 26.85 5.99 

493 Storage service 8.02 42.27 0.00 0.08 0.00 

Total 443.97 973.19 73.23 479.88 89.55 

2005 Mexicali Emissions Inventory 2-9 

Final, 2/27/09 
point sources. The first data element was process/equipment SCC, which was missing from all 

sources; this was gap filled using engineering judgment based upon the specific process 

equipment description and fuel type. The stack latitude and longitude coordinates were also 

missing from a number of the sources. In most cases, these coordinates were for sources with a 

large number of processes that typically did not have well-defined stacks (e.g., vents, etc.). For 

these sources, the facility latitude and longitude coordinates were assigned to the stack(s). 

The most significant data problem identified by ICAR’s QA evaluation of the state 

jurisdiction point sources was that none of the facilities had process- or stack-level emissions. 

After identifying this issue, ERG and ICAR focused on the seven most significant state 

jurisdiction point source emitters (i.e., Bimbo, Chromalloy, Sabritas, Sukarne, Temcomex, 

Thomson, and Zahori). ICAR searched the SPA paper files to obtain process- and stack-level 

emissions for these seven sources. Data obtained from the SPA paper files included the 

following: 

• Number of sources 

• Process/equipment description and codes 

• Stack assignment 

• Emission units 

• Stack coordinates 

• Stack height 

• Stack diameter 

• Exit gas velocity 

• Exit gas temperature 

• Operating schedule (days/year, hours/day) 

• Controls (device and efficiency) 

In addition to the detailed data collected for the seven most significant state jurisdiction 

point source emitters listed above, similar process- and stack-level data from the SPA files were 

2005 Mexicali Emissions Inventory 2-10 

Final, 2/27/09 
subsequently researched for another 85 state jurisdiction point sources. However, project 

constraints prevented similar data collection for the remaining 81 facilities. 

Two of the five data quality objectives (DQOs) for the Mexicali inventory project are 

applicable to point source emissions. These are as follows: 

1. 	 Identification of 80% of all federal jurisdiction and state jurisdiction point sources 

located within the municipality of Mexicali. 

Acceptance criteria: A list of point sources under the federal (SEMARNAT 

Delegation), and state (SPA) jurisdictions compares to the emissions data, by source, 

provided by those agencies. 

2. 	 Point sources: Emissions estimates with an accuracy of ± 25% for at least 10% of the 

facilities. 

Acceptance criteria: Estimated emissions for 10% of the total facilities compare to 

measured emissions of all pollutants with ± 25% accuracy. The emissions data 

measurements will be obtained from subcontractor ICAR, and will be based on data 

collected over the past five years in Mexicali. 

Based on the federal jurisdiction and state jurisdiction point source methodologies and 

quality assurance used, both DQOs were met. With regard to DQO #1, all federal jurisdiction 

and state jurisdiction point sources were included. The lists of federal jurisdiction and state 

jurisdiction point sources were checked multiple times with SEMARNAT and SPA to ensure 

completeness. For DQO #2, ICAR checked process- and stack-level data obtained from 

SEMARNAT electronic files (for federal jurisdiction sources) and from SPA paper files (for 

state jurisdiction sources) against information they had previously obtained from their industrial 

clients (2 federal jurisdiction sources and 38 state jurisdiction sources). Overall, emissions data 

were within ± 25% accuracy. 

2005 Mexicali Emissions Inventory 2-11 

Final, 2/27/09 
3.0 AREA SOURCES 
 

Area sources are sources that are too numerous and dispersed to be effectively included 

in the point source inventory. Area sources include some commercial and/or industrial sources 

such as autobody shops and dry cleaners, fugitive sources such as paved and unpaved road dust, 

and fuel combustion in small boilers. The methodology used to estimate the 2005 Mexicali area 

source emissions is described below. 

3.1 Methodology 

Wherever possible, local activity data were collected in Mexicali and used to estimate 

area source emissions. Local activity data collection was facilitated by ICAR, who are 

conveniently located in Mexicali. ICAR contacted some local sources of activity data directly 

(e.g., PEMEX, fire departments, etc.); in other situations, ICAR worked through SPA to reach 

these contacts (e.g., natural gas and LPG distributors, Department of Health, etc.). 

If local activity data could not be collected from local contacts, then data from publicly 

available government agencies were used. In addition, some methodologies and data from the 

development of the 1999 Mexico NEI and recent emissions inventory work conducted in 2007 in 

San Luis Río Colorado, Sonora and the northeastern (agricultural) portion of Mexicali were also 

used (ERG, 2006; ENVIRON and ERG, 2008). 

General estimation methodologies for related groups of area source categories are 

described below and summarized in Table 3-1. Detailed documentation for each area source 

category is provided through the Area Source Category Forms, which are located in Appendix A. 

The Area Source Category Forms succinctly summarize the following for each source category: 

category description, pollutants, method, activity data, emission factors, notes and assumptions, 

emission calculations, and spatial allocation surrogates. 

2005 Mexicali Emissions Inventory 3-1 Final, 2/27/09 
Table 3-1. General Emission Estimation Methodologies for Area Sources in Mex

Source Category Pollutants Methodology Activity Data Spatial Allocation 

Industrial Fuel Combustion – Distillate & Residual Fuel Oil, Natural Gas, LPG 

NOx, SO2, VOC, CO, 

PM10, PM2.5, CH4 AP-42 emission factors Local use statistics 

Employee data by sector 

Commercial/Institutional Fuel Combustion – Natural Gas & LPG 

NOx, SO2, VOC, CO, 

PM10, PM2.5, CH4 AP-42 emission factors Local use statistics 

Employee data by sector 

Residential Fuel Combustion – Natural Gas & LPG 

NOx, SO2, VOC, CO, 

PM10, PM2.5, CH4 AP-42 emission factrs Local use statistics Household data 

Architectural Coating VOC 

Mexico-specific per capita emission factors Population Population 

Industrial Surface Coating VOC 

Mexico-specific per employee emission factors 

Employment data by sector 

Employee data by sector 

Autobody Refinishing VOC 

Mexico-specific per employee emission factors Employment data Employee data 

Graphic Arts VOC 

Mexico-specific per capita emission factors Population Population 

Dry Cleaning VOC 

Mexico-specific per employee emission factors Employment data Employee data 

Degreasing VOC 

EIIP per employee emission factors 

Employment data by sector Employee data 

Consumer Solvents VOC 

EIIP per capita emission factors (slightly adjusted) Population Population 

Traffic Markings VOC 1999 Mexico NEI results N/A 

Paved road network 

Asphalt Application VOC 1999 Mexico NEI results N/A 

Paved road network 

2005 Mexicali Emissions Inventory 3-2 

Final, 2/27/09 
Table 3-1. Continued 

Source Category Pollutants Methodology Activity Data Spatial Allocation 

Petroleum Transport and Distribution VOC EIIP emission factors 

Local fuel throughput Population 

Agricultural Burning 

NOx, SO2, VOC, CO, 

PM10, PM2.5 ARB emission factors Burned acreage 

Agricultural land use data 

Agricultural Tilling PM10, PM2.5 ARB emission factors Planted acreage 

Agricultural land use data 

Agricultural Harvesting PM10, PM2.5 ARB emission factors Harvested acreage 

Agricultural land use data 

Agricultural Pesticides VOC EIIP emission factors 

Harvested acreage, type and quantity of pesticides applied 

Agricultural land use data 

Beef Cattle Feedlots PM10 ARB emission factors 

Number of slaughtered cattle 

Agricultural land use data 

Livestock VOC VOC ARB emission factors 

Number of livestock head 

Agricultural land use data 

Livestock Ammonia NH3 

WRAP NH3 model emission 

factors Number of head 

Agricultural land use data 

Fertilizer Application NH3 

WRAP NH3 model emission 

factors 

Planted acreage, type and quantity of fertilizers applied 

Agricultural land use data 

Paved Road Dust PM10, PM2.5 

AP-42 emission factor equation 

Local VKT, silt loading, vehicle speed, precipitation 

Paved road network 

Unpaved Road Dust PM10, PM2.5 

AP-42 emission factor equation 

Local VKT, silt content, moisture content, vehicle weight and speed, precipitation 

Unpaved road network 

NOx, VOC, CO, PM10, Number of houses 

Structural Fires PM2.5, CH4 EIIP emission factors burned Population 

2005 Mexicali Emissions Inventory 3-3 

Final, 2/27/09 
Table 3-1. Continued 

Source Category Pollutants Methodology Activity Data Spatial Allocation 

Border Crossing (idling vehicles from Mexicali to California) 

NOx, SO2, VOC, CO, 

PM10, PM2.5, NH3 

MOBILE6-Mexico emission factors 

Local traffic counts, idling times Border crossing 

Brick Kilns 

NOx, SO2, VOC, CO, 

PM10, PM2.5 

Utilization of previous inventory Not applicable Brick kiln locations 

Charbroiling/Street Vendors 

NOx, SO2, VOC, CO, 

PM10, PM2.5 1999 Mexico NEI results N/A Population 

Construction Dust PM10, PM2.5 1999 Mexico NEI results N/A Population Landfills VOC, CH4 AP-42 emission factors 

Annual quantity of waste landfilled, opening year Landfill location 

Wastewater Treatment Plant VOC U.S. EPA emission factor 

Wastewater flow rates Plant location 

2005 Mexicali Emissions Inventory 3-4 

Final, 2/27/09 
3.1.1 Fuel Combustion Categories 

Fuel combustion categories include industrial fuel combustion, commercial/institutional 

fuel combustion, and residential fuel combustion. Wherever possible, local collected data were 

used to estimate emissions. If local data were unavailable, publicly available government fuel 

statistics at the national- and regional-level were disaggregated down to the Mexicali 

municipality. 

Industrial fuel combustion includes distillate fuel oil, residual fuel oil, liquefied 

petroleum gas (LPG), and natural gas while the commercial/institutional and residual fuel 

combustion source categories are limited to LPG and natural gas. Attempts were made by SPA 

and ICAR to contact all of the Mexicali fuel distributors. Natural gas statistics were obtained 

from Ecogas Mexico, S. de R.L. de C.V. (Vivo, 2008). Four LPG distributors were contacted; 

three of these distributors provided 2005 sales information (Álvarez Sánchez, 2008; Escalante, 

2008; Félix Robles, 2008). Sales information from the three LPG distributors that provided 

information were averaged together and then applied to the LPG distributor that did not provide 

information. 

The PEMEX terminal in Mexicali provided sales information regarding gasoline and 

distillate fuel (including motor vehicle, agricultural, and industrial uses), but did not respond to 

requests for information regarding residual fuel oils (Parra Armenta, 2008). Residual fuel oil 

information was obtained from the PEMEX terminal in Rosarito, which markets residual fuel oil 

in Mexicali (Miranda Palazuelos, 2008). 

 Biomass combustion appears to be virtually nonexistent; most heating during the mild 

winters is electric heating (Martinez, 2007). 

3.1.2 Evaporative VOC Categories 

Evaporative VOC categories include the following area source categories: architectural 

surface coating, industrial surface coating, degreasing, graphic arts, traffic markings, dry 

cleaning, consumer products, asphalt application, and gasoline distribution. 

The development of Mexico-specific emission factors as part of the 1999 Mexico NEI for 

architectural surface coating, industrial surface coating, graphic arts, and dry cleaning was a 

2005 Mexicali Emissions Inventory 3-5 Final, 2/27/09 
significant advance. These emission factors were based upon data from the National Association 

of Paint and Dye Manufacturers (Asociación Nacional de Fabricantes de Pinturas y Tintas – 

ANAFAPYT) and the National Chamber of the Dry Cleaning Industry (Cámara Nacional de la 

Industria de Lavanderías – CANALAVA) (ANAFAPYT, 2003; ANAFAPYT, 2004; 

CANALAVA, 2002). These Mexico-specific emission factors were combined with Mexicali 

population and sector-specific employee counts. The industrial surface coating emissions were 

also reconciled with emissions from point source industrial surface coating operations 

Because Mexico-specific emission factors were not developed for degreasing and 

consumer products, per capita and per employee emission factors were obtained from the 

Emission Inventory Improvement Program (EIIP) guidance documents (EIIP, 1996; EIIP, 1997). 

However, recent health survey data collected in San Luis Río Colorado, Sonora indicate lower 

usage of household products corresponding to increased usage of bleach and a virtual absence of 

windshield fluid (Fierro and O’Rourke, 2007; Fierro, 2007; Gonzalez-Ayala, 2007); the EIIP 

emission factors were adjusted to account for these findings. The degreasing emissions were 

also reconciled with emissions from point source degreasing operations. 

Traffic markings and asphalt application quantities were requested from the Mexicali 

Public Works Department; however, these quantities were not available (Reynoso Ambriz, 

2008). As a result, the emission estimates for traffic markings and asphalt application from the 

1999 Mexico NEI were used for the municipality of Mexicali. 

Gasoline sales statistics were obtained from PEMEX (Parra Armenta, 2008), while 

gasoline distribution emission factors were obtained from EIIP guidance (EIIP, 2001a). 

3.1.3 Agricultural Categories 

The northeast portion of Mexicali has significant agricultural activity. Activity data for 

four specific agricultural categories (i.e., pesticides, burning, tilling, and harvesting) were 

requested from both the state and federal agricultural agencies. These four agricultural 

categories were previously estimated for the 2005 San Luis Río Colorado and northeastern 

Mexicali inventory (ENVIRON and ERG, 2008). Originally, it was anticipated that improved 

information could be collected from the state and federal agricultural agencies for Mexicali; 

however, the only useful agricultural information that could be obtained was estimates of 

2005 Mexicali Emissions Inventory 3-6 Final, 2/27/09 
burning of wheat residues, sorghum residues, and weeds in irrigation canals and drainages 

(Rodríguez Hernández, 2008; López Bojorques, 2008). Agricultural burning methodologies 

from the California Air Resources Board (ARB) were used to estimate emissions (ARB, 2005). 

Crop acreage information was provided for the agricultural tilling and harvesting source 

categories; however, this information had previously been obtained from a government database 

(SAGARPA, 2007a). For the previous inventory work in San Luis Río Colorado and 

northeastern Mexicali, municipality-level emissions for agricultural tilling and harvesting were 

spatially allocated in order to estimate the portion of emissions that were contained within that 

project’s modeling domain (ENVIRON and ERG, 2008). For the 2005 Mexicali inventory, the 

previously estimated municipality-level emissions were simply used without the modeling 

domain spatial allocation. The previous agricultural tilling and harvesting estimates were based 

upon ARB methodologies (ARB, 2003a; ARB, 2003b). 

Emissions from agricultural pesticides were estimated by applying pesticide-specific 

emission factors to the quantities of pesticides applied. Typical crop-specific pesticide 

application rates were obtained from state-specific cost production estimates (SAGARPA, 

2007b). However, the application rates were only available for a limited number of crops (i.e., 

garlic, green alfalfa, cotton, broccoli, asparagus, tomatoes [green and red], and wheat). These 

pesticide application rates were applied to the crop-specific planted acres. specific pesticide 

application rates. Pesticide emission factors were obtained from EIIP guidance (EIIP, 2001b). 

Active ingredients were identified based upon Mexico’s Registry of Authorized Pesticides 

(COFEPRIS, 2007). Formulation types were also assumed based upon the predominant 

formulation types listed in the Registry. Active and inactive ingredient fractions, as well as inert 

ingredient VOC fractions, were obtained from the EIIP guidance. Pesticide information (i.e., 

density, % active ingredient, and vapor pressure) was obtained from various databases 

maintained by California’s Department of Pesticide Regulation (DPR, 2007). 

Beef cattle feedlots are a source of fugitive dust caused by cattle movement over dusty 

ground. Slaughter statistics were used to estimate the number of cattle in feedlots. There are 

approximately 11 feedlots in Mexicali, all located outside of the urban area (Rodríguez 

Hernández, 2008). It has been estimated that cattle stay in the feedlots for approximately 90 

2005 Mexicali Emissions Inventory 3-7 Final, 2/27/09 
days prior to being slaughtered (Sukarne, 2008). The beef cattle feedlot emission factor was 

obtained from ARB (ARB, 2004). 

VOC emissions from livestock (i.e., beef cattle, dairy cattle, swine, sheep, and goats were 

also estimated. State- and district-level livestock headcounts were obtained from a SAGARPA 

database and then allocated to the municipality-level based on slaughter statistics (SAGARPA, 

2007c). The livestock VOC emission factors were obtained from ARB (ARB, 2004). 

3.1.4 Ammonia Categories 

Two important ammonia area source categories in a regional inventory are typically 

livestock ammonia and agricultural fertilizers. State- and district-level livestock headcounts 

were obtained from a SAGARPA database and then allocated to the municipality-level based on 

slaughter statistics (SAGARPA, 2007c). The livestock ammonia emission factors were based on 

information gathered from literature reviews that is currently being implemented in the latest 

Western Regional Air Partnership (WRAP) NH3 modeling (Chinkin et al., 2003). 

Emissions from agricultural fertilizers were estimated by applying fertilizer-specific 

emission factors to the quantities of fertilizer applied. Similar to agricultural pesticides, typical 

crop-specific fertilizer application rates were obtained from state-specific cost production 

estimates (SAGARPA, 2007b). As with the pesticides, application rates were only available for 

a limited number of crops (i.e., garlic, green alfalfa, cotton, broccoli, asparagus, tomatoes [green 

and red], and wheat). Ammonia emission estimates from fertilizer application were developed 

using emission factors from the European Environment Agency (EEA, 2002) as recommended in 

the development and application of the WRAP NH3 model. 

3.1.5 Paved Road Dust 

Paved road dust emissions were estimated using the most recent emission factor equation 

presented in the U.S. EPA Compilation of Air Pollutant Emission Factors (AP-42), Section 

13.2.1 (revised November 2006) (U.S. EPA, 1995). Silt samples were collected at 10 locations 

in Mexicali by ICAR in July 2008. The 10 locations are shown in Figure 3-1 (numbered 1 

through 10) and are as follows: 

• 	 1 – Calzada Lázaro Cárdenas between Calzada Benito Juárez and Calzada Venustiano 

Carranza 

2005 Mexicali Emissions Inventory 3-8 Final, 2/27/09 
Figure 3-1. Location of Paved and Unpaved Road Dust Sampling Sites 

2005 Mexicali Emissions Inventory 3-9 

Final, 2/27/09 
• 	 2 – Calzada Independencia between Boulevard Adolfo López Mateos and Calle Río 

Culiacán 

• 	 3 – Calzada Industrial Palaco between Calzada M. Gómez Morin and Calzada Lázaro 

Cárdenas 

• 	 4 – Francisco I. Madero between Calle Justo Sierra and Calle “F” 

• 	 5 – Calzada Anahuac between Avenida Torino and Avenida Gobernador 

• 	 6 – Avenida Oaxaca between Calle Uxmal and Heroico Colegio Militar 

• 	 7 – Calle Río Mocorito between Calzada Cuauhtémoc and Avenida F. Montes de Oca 

• 	 8 – Calzada Laguna de Xochimilco between Calzada Anahuac and Granada 

• 	 9 – Avenida Río Verde between Calle Cuarta and Calle Novena 

• 	 10 – Avenida Centauro del Norte between Calzada Lázaro Cárdenas and Calzada 

Héctor Terán Terán 

All samples were collected according to the sampling procedures presented in Appendix 

C.1 of AP-42 (U.S. EPA, 1995). The samples were analyzed by a laboratory in Tijuana, 

Laboratorio de Muestreo y Análisis, S.A. de C.V., according to the analytical procedures 

presented in Appendix C.2 of AP-42 (LMA, 2008a). The paved road dust emission factors 

(PM10 and PM2.5) were calculated using the AP-42 methodology; emission factors were 

calculated for each sampling site and then averaged together. A precipitation adjustment factor 

of 0.98562 corresponding to 21 “wet” days was also used (NWS, 2005). 

The vehicle kilometers traveled (VKT) estimate used to estimate paved road dust 

emissions was developed in conjunction with the on-road mobile source VKT development (see 

Section 4.0 of this report for details). Figure 3-2 demonstrates a portion of the analysis to 

determine the split of paved and unpaved analysis; further details are located in Appendix B, 

Section VI, of this report. The estimated paved road dust emission factors were then combined 

with the link-level paved road VKT to obtain link-level paved road dust emissions (GonzalezAyala, 2008a). 

2005 Mexicali Emissions Inventory 3-10 Final, 2/27/09 
Figure 3-2. Pavement Coverage of Mexicali Roads 

Pavement coverage detail for 

urban Mexicali 

2005 Mexicali Emissions Inventory 3-11 Final, 2/27/09 
3.1.6 Unpaved Road Dust 

Unpaved road dust emissions were estimated using the most recent emission factor 

equation presented in AP-42, Section 13.2.2 (revised November 2006) (U.S. EPA, 1995). Silt 

samples were collected at 10 locations in Mexicali by ICAR in July 2008. The 10 locations are 

shown in Figure 3-1 (marked with letters A through J) and are as follows: 

• 	 A – Calle Caoba between Calle Mauto and Calle Olivo 

• 	 B – Calle Mérida between Avenida Nayarit and Avenida Puebla 

• 	 C – Avenida Río Atoyac between Calle Onceava and Calle Terceava 

• 	 D – Avenida Camino Real between Avenida Campestre and Avenida Valle Verde 

• 	 E – Calle Texcoco between Calzada Laguna de Xochimilco and Avenida Acolman 

• 	 F – General Jesus M. Garza at Calle Federación 

• 	 G – Avenida Ricardo Flores Magón between Calle Bernardo Reyes and Calle 

Filomeno Mata 

• 	 H – Calle de las Delicias between Avenida de los Insurgentes and Avenida Juan 

Álvarez 

• 	 I – Calle Venustiano Carranza between Avenida Nuevo León and Avenida Querétaro 

• 	 J – Carretera Relleno Sanitario (on the road to Hipolito Landfill; location in Figure 3­

1 is not to scale) 

All samples were collected according to the sampling procedures presented in Appendix 

C.1 of AP-42. The samples were analyzed by Laboratorio de Muestreo y Análisis, S.A. de C.V. 

in Tijuana according to the analytical procedures presented in Appendix C.2 of AP-42 (LMA, 

2008b). The unpaved road dust emission factors were calculated using the AP-42 methodology. 

Three different emission factors were calculated at each sampling site using different vehicle 

speeds: urban (11 miles per hour [mph]), suburban (18 mph), and rural (25 mph). Three average 

emission factors (i.e., urban, suburban, and rural) were then calculated by averaging together the 

emission factors over all of the sites. A precipitation adjustment factor of 0.94247 corresponding 

to 21 “wet” days was also used (NWS, 2005). 

2005 Mexicali Emissions Inventory 3-12 Final, 2/27/09 
Three different sets of unpaved emission factors (PM10 and PM2.5) were estimated for 

urban, suburban, and rural unpaved roads. The estimated unpaved road dust emission factors 

were then combined with the link-level unpaved road VKT to obtain link-level unpaved road 

dust emissions (Gonzalez-Ayala, 2008b). 

The VKT estimate used for estimating paved road dust emissions was developed in 

conjunction with the on-road mobile source VKT development (see Section 4.0 of this report). 

Figure 3-2 demonstrates a portion of the analysis to determine the split of paved and unpaved 

analysis; further details are located in Appendix B, Section VI, of this report. 

3.1.7 Miscellaneous Burning Categories 

Area source inventories can potentially have a number of miscellaneous burning 

categories. These include wildfires, prescribed fires, on-site incineration, open burning, and 

structural fires. Based on site visits and interviews, the only miscellaneous burning activity that 

occurs in Mexicali is structural fires. The number of structural fires was obtained from the 

Mexicali fire department (Ruiz Burgueño, 2008), while the appropriate emission factors were 

obtained from EIIP guidance (EIIP, 2001a). 

3.1.8 Border Crossings 

Emissions from idling vehicles queued to enter the United States were estimated as an 

area source, and were not included in the on-road mobile sources estimates. There are three 

border crossings in Mexicali: Calexico and Calexico East (both across from urban Mexicali) and 

Andrade (across from Algodones). Vehicle count and queue waiting times were obtained from 

the Bureau of Transportation Statistics (BTS, 2008). Emission factors were estimated using the 

MOBILE6-Mexico emission factor model (ERG, 2003). 

3.1.9 Brick Kilns 

The municipality of Mexicali has two distinct brick kiln areas. The first area is in 

Colonia Campestre and the second area is in Algodones (i.e., northeast Mexicali). Emissions 

from the Algodones brick kiln area were previously estimated as part of the 2007 inventory work 

in San Luis Río Colorado and northeastern Mexicali (ENVIRON and ERG, 2008); these 

emissions were re-used for the Mexicali inventory. Accurate activity data could not be collected 

at Colonia Campestre, therefore emission estimates from the smaller San Luis Río Colorado 

2005 Mexicali Emissions Inventory 3-13 Final, 2/27/09 
brick kiln area (i.e., Colonia La Grullita) were assumed to be representative of the Colonia 

Campestre brick kiln emissions. 

3.1.10 Charbroiling, Construction Dust, and Bakeries 

Detailed Mexicali-specific activity data for charbroiling (restaurants and street vendors), 

construction dust, and bakeries could not be obtained. Emissions for these area source categories 

were extrapolated from the 1999 Mexico NEI (ERG, 2006). Construction dust emissions were 

disaggregated into two categories: residential construction and industrial/commercial 

construction. 

3.1.11 Landfills 

The municipality of Mexicali has one landfill (Hipolito landfill) which is located south of 

the urban area. Emissions were estimated using U.S. EPA’s theoretic first order kinetic model of 

methane production (U.S. EPA, 1995). Many of the site-specific data needed to make an 

accurate estimation of emissions (e.g., methane generation potential, methane generation rate 

constant, temperature, etc.) were not available. The only information that was obtained 

regarding the landfill was the year it opened and the daily quantity of waste landfilled (Villegas 

Ibarra, 2008). Default values were used for all other necessary parameter variables. 

3.1.12 Wastewater Treatment 

The municipality of Mexicali has eight wastewater treatment facilities: four in Mexicali 

and single facilities in Ciudad Morelos (Cuervos), Guadalupe Victoria (Km. 43), Vicente 

Guerrero (Algodones), and San Felipe. These facilities have a combined installed capacity of 

2.964 cubic meters per second (m3

/s) with an actual treatment rate of 1.8494 m3/s (CNA, 2007). 

This treatment rate translates to an annual flow of 58,322,678,400 liters. The VOC emission 

factor for wastewater treatment was then applied to this annual flow (U.S. EPA, 1991). 

3.2 Results 

The 2005 Mexicali area source emissions inventory is presented in Table 3-2. 

Examination of Table 3-2 indicates the following regarding area source emissions in Mexicali in 

2005: 

• 	 Agricultural field burning is the largest source of NOx followed by industrial natural 

gas combustion and residential LPG combustion. 

2005 Mexicali Emissions Inventory 3-14 Final, 2/27/09 
Table 3-2. 2005 Mexicali Area Source Emissions (Tons per Year) 

Source Category NOx SO2 VOC CO PM10 PM2.5 

Industrial Distillate Combustion 66.47 2.29 0.67 0.00 3.27 0.82 

Industrial Residual Combustion 0.00 0.00 1.58 0.00 11.06 10.45 

Industrial Natural Gas Combustion 279.79 0.00 6.30 0.00 8.40 8.73 

Commercial Natural Gas Combustion 2.58 0.02 0.14 2.17 0.20 0.20 

Residential Natural Gas Combustion 7.33 0.05 0.43 3.12 0.59 0.59 Industrial LPG Combustion 56.33 0.16 3.43 32.09 3.02 3.02 

Commercial LPG Combustion 23.63 0.07 1.44 13.46 1.27 1.27 

Residential LPG Combustion 102.97 0.29 6.27 58.65 5.52 5.52 

Paved Road Dust 4,609.79 422.18 

Unpaved Road Dust 42,331.35 4,209.66 Charbroiling/Street Vendors 2.77 9.69 150.18 75.44 60.21 

Bakeries 117.94 Construction Activities – Residential 16.77 3.49 Construction Activities – Industrial/Commercial 118.59 24.65 

Architectural Surface Coating (Solvent-based) 311.48 

Architectural Surface Coating (Water-based) 167.27 Autobody Refinishing 78.74 

Traffic Markings 57.06 Industrial Surface Coating - Wood Furniture 64.78 Industrial Surface Coating - Other Metallic Products 1,007.07 

2005 Mexicali Emissions Inventory 3-15 

Final, 2/27/09 
Table 3-2. Continued 

Source Category NOx SO2 VOC CO PM10 PM2.5 

Industrial Surface Coating – Electronics 32.47 Industrial Surface Coating – Automotive 1,281.22 Industrial Surface Coating - Other Transportation 327.24 Industrial Surface Coating - Other Manufacturing 1,262.62 Industrial Surface Coating - Industrial Maintenance 433.48 Degreasing (Vapor and In-line cleaning - Other) 323.47 Degreasing (Vapor and In-line cleaning – Electronics 284.68 Degreasing (Cold Cleaning – Other) 744.18 Degreasing (Cold Cleaning - Automotive Repair) 565.11 Dry Cleaning 145.74 Graphic Arts 346.90 

Consumer Solvents - Personal Care 993.18 Consumer Solvents – Household 84.62 Consumer Solvents - Automotive Aftermarket 67.61 Consumer Solvents – Coatings 242.98 Consumer Solvents - Adhesives and Sealants 761.13 

Consumer Solvents - Federal Insecticide, Fungicide and 409.59 

2005 Mexicali Emissions Inventory 3-16 

Final, 2/27/09 
Table 3-2. Continued 

Source Category NOx SO2 VOC CO PM10 PM2.5 

Rodenticide Act (FIFRA) Products Consumer Solvents - Miscellaneous 29.58 Asphalt Application 145.97 

Agricultural Pesticides 216.60 

Gasoline Distribution – Stage I 12.72 Gasoline Distribution – Stage II 419.88 Gasoline Distribution - Spillage 25.45 Underground Tank Breathing 38.17 Tank Truck Transit 4.45 Landfills 235.46 

Wastewater Treatment 835.76 Agricultural Tilling 929.76 206.13 

Agricultural Harvesting 767.74 170.21 Agricultural Field Burning 621.30 126.57 1,078.07 17,384.11 1,596.17 1,521.23 

Fertilizer application – Anhydrous ammonia 

Fertilizer application – Urea Fertilizer application – N-P-K Fertilizer application – Calcium nitrate 

Fertilizer application – Diammonium phosphate Beef cattle feedlots 290.72 Livestock – Dairy Cattle (NH3 and VOC) 322.77 

Livestock –Beef Cattle (NH3 and VOC) 1,025.42 Livestock – Swine (NH3 and VOC) 10.92 

2005 Mexicali Emissions Inventory 3-17 

Final, 2/27/09 
Table 3-2. Continued 

Source Category NOx SO2 VOC CO PM10 PM2.5 

Livestock – Sheep (NH3 and VOC) 4.43 

Livestock – Goats (NH3 

and VOC) 2.77 

Structure Fires 0.11 0.85 4.64 0.82 0.76 

Border Crossing 20.65 0.41 46.86 428.04 2.28 1.91 

Brick Kilns 21.55 1.23 704.91 777.55 106.51 102.50 

Total 1,205.48 131.09 15,301.55 18,854.02 50,879.89 6,753.65 

Blanks indicate pollutant(s) are not applicable; 0.00 indicates pollutant emissions are insignificant. 

2005 Mexicali Emissions Inventory 3-18 

Final, 2/27/09 
• 	 Agricultural field burning is also the largest source of SO2. Typically, there would be 

high SO2 emissions from industrial residual combustion, but reconciliation with the 

point source inventory reduced the area source industrial residual combustion 

emissions to zero. In fact, the estimated SO2 emissions from industrial residual 

combustion were 3,275 tons. 

• 	 There are a number of VOC sources that emit approximately 1,000 tons per year: 

agricultural field burning, several types of industrial surface coating, several types of 

consumer solvents, wastewater treatment, and dairy cattle. However, if some of these 

subcategories are aggregated together, then overall, industrial surface coating and 

consumer solvents are the most significant area source emitters of VOC. 

• 	 Paved and unpaved road dust are the most important PM10 and PM2.5 sources, while 

agricultural field burning is also an important source of PM2.5. 

• 	 Fertilizer application and livestock ammonia are the predominant NH3 sources, while 

the landfill is the largest CH4 source. 

3.3 Quality Assurance 

Several general quality assurance checks were conducted for the entire Mexicali 

inventory. These checks (including hand calculations, completeness checks against the 1999 

Mexico NEI, emission factor checks, etc.) were conducted for all area source categories. In 

addition, two of the overall five DQOs for the Mexicali inventory project are applicable to area 

source emissions: 

1. 	 A comprehensive emissions inventory for all source types (i.e., point, area, on-road 

motor vehicle, and nonroad mobile sources) and all major categories within these 

source types. 

Acceptance criteria: The source types and major categories accounted for in the 

EIIP guidance documents are present in the Mexicali emissions inventory, and/or any 

differences are documented. 

2. 	 Area, On-Road Motor Vehicle, and Nonroad Mobile Sources: Emissions estimates 

with an accuracy of ±50%. 

Acceptance criteria: Estimated emissions using emission factors/models compare to 

emissions estimated using a fuel-balance approach for all applicable pollutants. The 

fuel-balance approach will use national statistics, while the preferred inventory 

methodology will use PEMEX and local fuel distribution data. 

The Mexicali area source inventory meets DQO #1. The area source inventory categories 

were based upon the area source categories previously inventoried in the 1999 Mexico NEI. The 

2005 Mexicali Emissions Inventory 3-19 

Final, 2/27/09 
1999 Mexico NEI area source categories were selected by reviewing all source categories 

included in Emission Inventory Improvement Program (EIIP) and other U.S. EPA guidance 

documents; Mexican federal environmental staff then identified those categories that either did 

not exist in Mexico or could be considered to be insignificant. Based upon comments to the draft 

inventory report, one area source category was added (i.e., VOC from beef cattle feedlots) and 

one area source category was disaggregated into two (i.e., construction activities into residential 

construction activities and industrial/commercial construction activities). 

With regard to DQO #2, most area source fuel combustion emissions could not be 

assessed using a fuel-balance approach due to the lack of specific fuel usage data. However, the 

accuracy of the border crossing emission estimates was evaluated. Border crossing emissions 

(i.e., idling emissions) are typically not estimated in the U.S. since the wait times resulting in 

increased emissions generally occur on the Mexican side of the border. The preferred estimation 

methodology is to obtain queue waiting times and then calculate the distance based upon the 

lowest allowable vehicle speed in the MOBILE6 emission factor model (i.e., 2.5 miles per hour 

or 4 kilometers per hour). This calculated distance is then multiplied by the emission factor 

corresponding to the lowest allowable vehicle speed (Brzezinski, 2009). This methodology was 

used to estimate border crossing emissions in Mexicali. All inputs to the model and all 

subsequent post-model emission calculations were rechecked and no errors were found. 

Besides the methodology used to estimate border crossing emissions, accurate idling 

emissions data are relatively unavailable (Brzezinski, 2009). Only one somewhat relevant source 

of data (i.e., a guidance document related to “long duration idling” for heavy-duty diesel 

vehicles) was identified (U.S. EPA, 2004). The typical hourly idling emission factors for heavyduty diesel vehicles identified from this guidance document were 135 g NOx/hour, and 3.68 g 

PM10/hour and g PM2.5/hour. The heavy-duty diesel vehicle emission factors used in the 

Mexicali emission estimates were 15.96 g NOx/mile, 1.27 g PM10/mile, and 1.14 g PM2.5/mile. 

At 2.5 miles per hour, these emission factors translate to 39.9 g NOx/hour, 3.18 g PM10/hour, and 

2.85 g PM2.5/hour. Although the PM10 and PM2.5 emission factors are comparable with those in 

the guidance document, the NOx emission factor differs by a factor of over 3. 

2005 Mexicali Emissions Inventory 3-20 

Final, 2/27/09 
However, discussions with U.S. EPA staff familiar with idling emissions indicated that 

long duration idling does not correspond with border crossing idling. Long duration idling 

involves engine idling of long-haul heavy-duty diesel trucks during mandated rest periods (i.e., 

typically overnight or 10 hours rest for every 14 hours of driving) to operate air conditioning, 

heat, or on-board appliances (e.g., televisions, computers, microwaves, etc.). This type of idling 

occurs with higher engine loads and is fundamentally different than idling that occurs at border 

crossings (Brzezinski, 2009). In addition, queue waiting times for the three Mexicali border 

crossings were considerably shorter than the overnight (or long duration) idling (BTS, 2008). 

Thus, it is not unexpected that the NOx emissions during long duration idling would be 

substantially greater than emissions from idling at border crossings. Given these findings, the 

border crossing emissions were not adjusted. 

2005 Mexicali Emissions Inventory 3-21 

Final, 2/27/09 
4.0 ON-ROAD MOBILE SOURCES 
 

On-road mobile sources include all motor vehicles that are allowed to operate on public 

roadways (e.g., cars, trucks, buses, motorcycles). On-road mobile source emissions include 

exhaust emissions, as well as various types of evaporative emission processes. Several other 

types of emissions are caused by on-road mobile sources, including paved and unpaved road 

dust, Stage II vehicle refueling, and emissions from vehicles idling at the border crossings; these 

source categories are estimated as area sources and are addressed in Section 3.0 of this report. 

Based upon previous experience from developing the 1999 Mexico NEI, availability of 

the VKT data needed to estimate emissions from on-road motor vehicles at the municipalitylevel is very limited in Mexico (with the exception of Mexico City, Guadalajara, and other large 

metropolitan areas). Therefore, traffic and congestion modeling were used to estimate VKT by 

road segment (i.e., link) for the municipality of Mexicali. The link-level VKT estimates were 

combined with emission factors generated by the MOBILE6-Mexico model (i.e., a Mexicospecific version of the MOBILE6 emission factor model) to estimate on-road motor vehicle 

emissions (ERG, 2003). The methodology is described below, and a more detailed explanation 

of the methodology is presented in Appendix B. 

4.1 Methodology 

The traffic volume and congestion modeling used a simplified road network, which 

included freeways, main arterials, collector roads, and “connectors” (i.e., artificial links that 

modeled local traffic flows). Although the road network was simplified, the road network 

consisted of 3,603 individual links. In general, the road network is primarily concentrated in the 

Mexicali urban area, but Federal Highway 2 (connecting Mexicali to Tijuana and San Luis Río 

Colorado), Federal Highway 5 (connecting Mexicali to Ensenada and San Felipe), as well as 

rural roads in the agricultural areas in the northeast and rural areas in the south, are also modeled. 

The traffic modeling relied upon known Mexican trip generation rates combined with 

demographic and socio-economic information (i.e., population, household size, income, and 

employment) for each AGEB (i.e., Mexican equivalent of a census tract). The traffic modeling 

resulted in daily VKT estimates for daily traffic volumes for each link in the roadway network. 

2005 Mexicali Emissions Inventory 4-1 

Final, 2/27/09 
Link-level VKT was then estimated by multiplying each link’s daily traffic volume by its length 

in kilometers. Figure 4-1 shows the Mexicali roadway network and traffic volume intensity 

during a typical week day. 

The traffic volumes for individual roadway network links were assigned to corresponding 

link-specific congested speed emissions factors from MOBILE6-Mexico in order to estimate 

daily link-level emissions. Several sets of emission factors were developed to account for 

different congested speeds (i.e., from 3 to 65 miles per hour [mph]), variations in ambient 

temperature, and seasonality. Overall emissions were estimated by summing the link-level 

emissions over the entire Mexicali road network. 

Emissions were estimated for the following seven vehicle classifications: 

• 	 Light-Duty Gasoline Vehicles (LDGV) 

• 	 Light-Duty Gasoline Trucks (LDGT) 

• 	 Heavy-Duty Gasoline Vehicles (HDGV) 

• 	 Light-Duty Diesel Vehicles (LDDV) 

• 	 Light-Duty Diesel Trucks (LDDT) 

• 	 Heavy-Duty Diesel Vehicles (HDDV) 

• 	 Motorcycles (MC) 

4.2 Results 

The annual on-road mobile source emissions are presented in Table 4-1. Examination of 

Table 4-1 indicates the following regarding on-road mobile source emissions in Mexicali in 

2005: 

• 	 Light-duty gas vehicle and trucks are the primary sources of VOC, CO, NH3, and 

CH4. 

• 	 Heavy-duty diesel vehicles are the major sources of NOx, SO2, PM10, and PM2.5. 

• 	 Light-duty diesel vehicles and trucks, as well as motorcycles are relatively 

insignificant contributors of all pollutants - primarily due to their comparatively small 

fraction of the overall VKT. 

2005 Mexicali Emissions Inventory 4-2 

Final, 2/27/09 
Figure 4-1. Mexicali Loaded Roadway Network Under Weekday Conditions 

Daily vehicle flow 

Detail of roadway loaded network 

for urban Mexicali 

2005 Mexicali Emissions Inventory 4-3
 Final, 2/27/09
 
Table 4-1. 2005 Mexicali On-Road Mobile Source Emissions (Tons per Year) 

NOx SO2 VOC CO PM10 PM2.5 NH3 CH4 

LDGV 1,151.9 25.4 4,550.3 29,441.3 143.3 117.9 124.6 58.4 

LDGT 1,523.4 33.1 3,594.6 26,650.3 184.1 155.4 113.5 61.7 

HDGV 374.8 4.4 393.5 2,475.8 8.8 6.6 3.3 6.6 

LDDV 12.1 0.0 9.9 18.7 2.2 2.2 0.0 0.0 

LDDT 7.7 0.0 5.5 11.0 1.1 1.1 0.0 0.0 

HDDV 5,463.0 105.8 340.6 1,574.1 424.4 382.5 11.0 13.2 

MC 37.5 0.0 82.7 432.1 1.1 0.0 0.0 4.4 

Total 8,570.4 168.7 8,977.1 60,603.4 765.0 665.8 252.4 144.4 

4.3 Quality Assurance 

As discussed in Section 1.3, a number of general quality assurance checks were 

conducted for the entire Mexicali inventory. These checks (including hand calculations, 

completeness checks against the 1999 Mexico NEI, emission factor checks, etc.) were conducted 

for all on-road mobile source categories. In addition, two of the five data quality objectives 

(DQOs) for the Mexicali inventory project are applicable to on-road mobile source emissions: 

1. 	 A comprehensive emissions inventory for all source types (i.e., point, area, on-road 

motor vehicle, and nonroad mobile sources) and all major categories within these 

source types. 

Acceptance criteria: The source types and major categories accounted for in the 

EIIP guidance documents are present in the Mexicali emissions inventory, and/or any 

differences are documented. 

2. 	 Area, On-Road Motor Vehicle, and Nonroad Mobile Sources: Emissions estimates 

with an accuracy of ±50%. 

Acceptance criteria: Estimated emissions using emission factors/models compare to 

emissions estimated using a fuel-balance approach for all applicable pollutants. The 

fuel-balance approach will use national statistics, while the preferred inventory 

methodology will use PEMEX and local fuel distribution data. 

The Mexicali on-road mobile source inventory meets DQO #1. Emissions were 

estimated for seven on-road mobile source categories: light-duty gasoline vehicles (LDGV), 

light-duty gasoline trucks (LDGT), heavy-duty gasoline vehicles (HDGV), light-duty diesel 

vehicles (LDDV), light-duty diesel trucks (LDDT), heavy-duty diesel vehicles (HDDV), and 

motorcycles (MC). The MOBILE6-Mexico emission factor does provide emission factors for 28 

2005 Mexicali Emissions Inventory 4-4 

Final, 2/27/09 
disaggregated categories; however, the use of 7 categories is consistent with the 1999 Mexico 

NEI and provides easier reporting. 

Also, the Mexicali on-road mobile source inventory also meets DQO #2. The on-road 

mobile source quality assurance took two forms: comparison of inventory VMT estimates with 

fuel-based VMT estimates and comparison of inventory motor vehicle activity data with Imperial 

County motor vehicle activity data. 

The first quality assurance step was to compare the Mexicali VMT estimates derived 

using traffic and congestion modeling with fuel-based VMT estimates; VMT estimates were 

used instead of VKT estimates for ease of comparison. As part of the Mexicali traffic and 

congestion modeling, weekday VMT was estimated to be 7,876,541 miles (Appendix B, Table 

11) and weekend day VMT was estimated to be 5,496,411 miles (Appendix B, Table 12). 

Assuming 52 weeks per year consisting of 5 weekdays and 2 weekend days, then annual VMT 

was estimated to be 2.63 × 109 miles. As described in the area source fuel combustion and 

evaporative VOC methodology sections of this report (Sections 3.1.1 and 3.1.2), 2005 fuel sales 

statistics were provided by the PEMEX Mexicali bulk terminal. Additional research conducted 

as part of this QA procedure indicated that a significant portion of the fuels sold by the PEMEX 

Mexicali bulk terminal was sold and transported for consumption in Sonora (e.g., San Luis Río 

Colorado, Puerto Peñasco, Sonoyta, etc.) and other parts of Baja California (i.e., Tecate and 

Ensenada). Therefore, PEMEX staff provided revised fuel sales statistics of the fuel actually 

sold in the municipality of Mexicali (i.e., 224,800,401 liters of Magna gasoline, 63,773,310 liters 

of premium gasoline, and 84,678,057 liters of diesel) (Parra Armenta, 2009). Based on a 

PEMEX assumption that 60 percent of the diesel is used in motor vehicles, there is a total of 

89,664,609 gallons of motor vehicle fuel used in Mexicali. The new vehicle fuel economy for 

Mexico light-duty vehicles and trucks has been estimated to be 20.5 miles per gallon (mpg) (An 

and Blumberg, 2005); this value is likely overestimated for the overall in-use vehicle fleet, but 

fuel economy statistics for the overall Mexico in-use vehicle fleet could not be identified. 

Multiplying the motor vehicle fuel statistics by the fuel economy resulted in an annual VMT 

estimate of 1.84 × 109

 miles. The fuel-based VMT estimate is approximately 70 percent of the 

VMT estimate developed using traffic and congestion modeling. Given that the Mexicali VMT 

estimate developed using traffic and congestion modeling has an approximate uncertainty of ±20 

2005 Mexicali Emissions Inventory 4-5 

Final, 2/27/09 
percent (Gonzalez-Ayala, 2008c), then the VMT estimate based on traffic and congestion 

modeling could potentially be overestimated slightly. This is within the 50 percent uncertainty 

specified by DQO #4. 

The second quality assurance step was to compare Mexicali motor vehicle activity data 

with Imperial County motor vehicle activity data for reasonableness based upon comments 

regarding the draft report. Overall Imperial County and Mexicali on-road mobile source 

emissions data are compared in Table 4-2. 

Table 4-2. 2005 Imperial County and Mexicali On-Road Mobile Source Emissions 

(Tons per Year) 

Imperial County 

(tons/day) 

Imperial County 

(tons/year) 

Mexicali 

(tons/year) 

Ratio 

(Mexicali/Imperial) 

NOx 19.59 7,150.4 8,570.4 1.199 

SO2 0.14 51.1 168.7 3.301 

VOC 6.41 2,339.7 8,977.1 3.837 

CO 56.52 20,629.8 60,603.4 2.938 

PM10 0.86 313.9 765.0 2.437 

PM2.5 0.71 259.2 665.8 2.569 

Imperial County emissions data were obtained from ARB’s Almanac Emission Projection 

Data (ARB, 2007). As can be seen in the ratios in Table 4-2, the Mexicali on-road mobile source 

emissions exceed the Imperial County emissions by a factor of 1.2 to 3.8. 

As described in the area source methodology (Section 3.0 and Appendix A), the 2005 

population of the municipality of Mexicali was 855,962 people. The 2005 population of 

Imperial County was estimated to be 153,857 people (U.S. Census, 2008). The ratio of Mexicali 

population to Imperial County population is 5.563. If on-road mobile source emissions were 

proportional to population, then the Mexicali on-road mobile source emissions would exceed the 

Imperial County emissions by a similar factor of 5.563. 

One potential reason why the Mexicali/Imperial County emission ratios are lower (i.e., 

1.2 to 3.8) than the population ratio (i.e., 5.563) is that there is a significantly higher level of 

vehicle ownership in Imperial County compared to the municipality of Mexicali. Chapter 4 of 

the Imperial County Air Quality Management Plan (AQMP) provides a count of 121,707 

vehicles for the year 2002 and an estimate of 161,231 vehicles for the year 2009 (Imperial, 

2008); linear interpolation between these two data points resulted in an estimate of 138,646 

2005 Mexicali Emissions Inventory 4-6 

Final, 2/27/09 
vehicles for the year 2005. This results in an estimated vehicle ownership rate of 0.901 vehicles 

per person. Vehicle registration statistics for the municipality of Mexicali obtained from INEGI 

indicate a total of 422,357 vehicles registered in 2005 (INEGI, 2008), which corresponds to an 

estimated vehicle ownership rate of 0.493 vehicles per person. Therefore, Imperial County 

vehicle ownership rates are significantly higher than in Mexicali. 

Chapter 4 of the Imperial County AQMP also provide estimated daily VMT for 2002 and 

2009 (4.780 × 106 miles and 6.277 × 106 miles, respectively) (Imperial, 2008); linear 

interpolation between these two data points resulted in an estimate of 5,421,571 daily VMT in 

2005, which corresponds to an annual VMT estimate of 1.98 × 109 miles. Overall Mexicali 

VMT is higher than overall Imperial County VMT by a factor of 1.33. Perceptions of even 

higher levels of vehicle activity in Mexicali compared to Imperial County are potentially due to 

Mexicali vehicle activity being concentrated in the urban portion of Mexicali, compared to 

Imperial County vehicle activity being spread among a number communities located throughout 

the county (e.g., El Centro, Brawley, Holtville, Calipatria, Westmorland, etc.). 

Based upon these QA analyses, it was felt that the Mexicali on-road mobile source 

emission estimates were reasonably accurate and that additional adjustments were not needed. 

2005 Mexicali Emissions Inventory 4-7 

Final, 2/27/09 
5.0 NONROAD MOBILE SOURCES 
 

Nonroad mobile sources include all equipment that move under their own power, or that 

are portable, and are not allowed to operate on public roadways. For the 2005 Mexicali 

emissions inventory, nonroad mobile sources include aircraft and airport ground support 

equipment and auxiliary power units, locomotives, and agricultural and construction equipment. 

Another nonroad source type is commercial marine vessels; however, no major ports with 

significant commercial marine vessel activity exist in Mexicali. Initially, it was thought that 

there might be some fishing vessels in some of the communities located on the Gulf of 

California. However, fishing vessel activity data were not available. Some fishing occurs out of 

San Felipe, but this appears to consist of tourist fishing, rather than commercial fishing. Due to 

the lack of data and apparent insignificance of these emissions, emissions for commercial marine 

vessels were not estimated. 

5.1 Methodology 

The methodologies used to estimate emissions for the various nonroad mobile source 

types operating in Mexicali are described below. 

5.1.1 Aircraft 

There are two airports located in the municipality of Mexicali: General Rodolfo Sánchez 

Taboada International Airport (east of the Mexicali urban area) and San Felipe International 

Airport (located in the southern portion of the municipality). Operator-specific landing and 

takeoff (LTO) statistics and aircraft fleet composition data were obtained from local airport 

officials (Sanchez Resendiz, 2008). These data included information for both the Mexicali and 

San Felipe airports; however, the data were not associated with a particular airport. Therefore, it 

was assumed that all flight data were associated with the General Rodolfo Sánchez Taboada 

International Airport airport because it is much larger than San Felipe International Airport. 

Because of the large amount of agricultural activity in Mexicali, crop dusting (or aerial spraying) 

is a significant activity in the area. Crop dusting activity data (including LTO data, flight 

lengths, and fuel usage) were provided by staff from the Mexicali crop dusting industry 

association (Tamayo Moreno, 2008). There were also numerous other general aviation landing 

2005 Mexicali Emissions Inventory 5-1 

Final, 2/27/09 
strips located throughout Baja California, but these were not included in the inventory because 

LTO data for these landing strips were not available. 

The aircraft operations information was input into the latest version of the Emissions and 

Dispersion Modeling System (EDMS 5.0.2, release June 29, 2007) model. The EDMS model 

was developed by the Federal Aviation Administration and is recommended for use by U.S. 

EPA. The model contains the latest emission factors from the International Civil Aviation 

Organization’s (ICAO) Engine Exhaust Emissions Data Bank. The default time-in modes for 

taxi out, take off, climb out, approach, and taxi in were used to run the model. The model output 

included emissions by aircraft type and total emissions, including emissions for aircraft, ground 

support equipment, and auxiliary power units. Emissions from other airport-related activities 

(e.g., parking lots, roadways, stationary sources within airport boundaries, etc.) were not 

included in the estimates because these sources addressed within other emission inventory 

categories. 

5.1.2 Locomotives 

A railway line extends from Mexicali to Puerto Peñasco, Sonora. In order to estimate 

emissions for locomotives operating within the municipality of Mexicali (i.e., in the locomotive 

yard and from line haul activities), local locomotive fuel consumption data are needed; however, 

these data were not available. Visual observations ERG staff indicated that there is very limited 

locomotive activity in the municipality. Due to the lack of data needed to estimate these 

emissions for Mexicali, the emissions calculated for the 1999 Mexico NEI were used without 

adjustment. These are felt to be appropriate because of the observed limited locomotive activity. 

5.1.3 Agricultural and Construction Equipment 

As part of the 1999 Mexico NEI project, a Mexico-specific version of the U.S. EPA’s 

NONROAD emissions model was developed (i.e., NONROAD-Mexico), which provided a 

streamlined way to estimate emissions from agricultural and construction equipment (ERG, 

2005). To develop a Mexico-specific version of this model, various parameters were adjusted to 

better reflect conditions in Mexico, including: average annual temperature, altitude, and diesel 

sulfur content. Also, Mexican state-level agricultural and construction equipment populations 

were incorporated into the model. Although other nonroad equipment types (lawn and garden, 

2005 Mexicali Emissions Inventory 5-2 

Final, 2/27/09 
logging, etc.) exist in Mexico, agricultural and construction equipment were given highest 

priority during the development of the NONROAD-Mexico model because it was believed that 

they emitted the most emissions as compared to the other equipment types. 

Because the NONROAD-Mexico model contained data which would generate emissions 

for 1999, only, it was desirable to update the equipment population and operation files to 2005 in 

order to estimate 2005 emissions for the municipality of Mexicali. However, sufficient recent 

data were not available. 

Due to the lack of data needed to update the NONROAD-Mexico model, the 

municipality-level nonroad equipment emissions from the 1999 Mexico NEI generated using the 

NONROAD-Mexico model were extrapolated using 2005 population for Mexicali. This 

extrapolation method was previously used to estimate nonroad source emissions in the San Luis 

Río Colorado emission inventory, which included the northeast portion of the municipality of 

Mexicali (ENVIRON and ERG, 2008). First, for agricultural equipment, it was assumed that 

agricultural land area would be fairly stable over time. As a result, the agricultural equipment 

population would likely not grow significantly between 1999 and 2005. For construction 

equipment, it was assumed that equipment population growth was proportional to population 

growth. The population of the municipality of Mexicali grew from 764,602 to 855,962 over the 

same time period; this corresponds to a growth rate of 1.1195 (INEGI, 2000; INEGI, 2006). This 

growth factor was applied to the 1999 municipality-level emissions estimated using 

NONROAD-Mexico to obtain 2005 emissions for agricultural and construction equipment. 

5.2 Results 

The annual nonroad mobile source emissions are presented in Table 5-1; NH3 and CH4 

emissions were not estimated for nonroad mobile sources. As seen in the table, the largest 

emissions are from construction and agricultural equipment. The construction and agricultural 

equipment are dominated by diesel-powered equipment which will cause relatively high NOx, 

PM10, and PM2.5 emissions. Locomotives have infrequent activity and the portion of aircraft 

flight durations that occur in the inventory domain are comparatively short relative to the length 

of an entire flight, with the exception of cropdusters. 

2005 Mexicali Emissions Inventory 5-3 

Final, 2/27/09 
Table 5-1. 2005 Mexicali Nonroad Mobile Source Emissions (Tons per Year) 

Source Category NOx SO2 VOC CO PM10 PM2.5 

Aircraft – Commercial 15.2 1.9 6.8 31.7 0.3 0.3 

Airport Auxiliary Power Units 3.7 0.3 1.1 32.4 0.1 0.1 

Airport Ground Support 

Equipment 0.6 0.1 0.0 0.4 0.0 0.0 

Locomotives 206.2 1.8 7.8 20.4 5.1 4.6 

Construction Equipment 3,100.2 41.6 320.4 1,552.3 348.9 338.5 

Agricultural Equipment 1,158.5 14.6 195.7 785.3 195.7 189.8 

Aircraft – Cropdusters 

(General) 0.5 0.6 11.1 784.4 0.0 0.0 

Total 4,485.0 60.9 542.9 3,206.9 550.2 533.3 

5.3 Quality Assurance 

As discussed in Section 1.3, a number of general quality assurance checks were 

conducted for the entire Mexicali inventory. These checks (including hand calculations, 

completeness checks against the 1999 Mexico NEI, emission factor checks, etc.) were conducted 

for all area source categories. In addition, two of the five DQOs for the Mexicali inventory 

project are applicable to nonroad mobile source emissions: 

1. 	 A comprehensive emissions inventory for all source types (i.e., point, area, on-road 

motor vehicle, and nonroad mobile sources) and all major categories within these 

source types. 

Acceptance criteria: The source types and major categories accounted for in the 

EIIP guidance documents are present in the Mexicali emissions inventory, and/or any 

differences are documented. 

2. 	 Area, On-Road Motor Vehicle, and Nonroad Mobile Sources: Emissions estimates 

with an accuracy of ±50%. 

Acceptance criteria: Estimated emissions using emission factors/models compare to 

emissions estimated using a fuel-balance approach for all applicable pollutants. The 

fuel-balance approach will use national statistics, while the preferred inventory 

methodology will use PEMEX and local fuel distribution data. 

The Mexicali nonroad mobile source inventory meets DQO #1. The nonroad mobile 

source inventory categories were based upon the nonroad mobile source categories previously 

inventoried in the 1999 Mexico NEI. The 1999 Mexico NEI nonroad mobile source categories 

were selected by reviewing all source categories included in Emission Inventory Improvement 

Program (EIIP) and other U.S. EPA guidance documents; Mexican federal environmental staff 

2005 Mexicali Emissions Inventory 5-4 

Final, 2/27/09 
then identified those categories that either did not exist in Mexico or could be considered to be 

insignificant. Some U.S.-specific nonroad mobile source categories typically inventoried were 

excluded in Mexico (e.g., lawn and garden equipment, logging equipment, recreational vehicles, 

etc.). It should also be noted that the aircraft and locomotive categories were included as area 

sources in the 1999 Mexico NEI, but were classified as nonroad mobile sources for the Mexicali 

inventory. Also, two nonroad mobile source categories not previously inventoried in the 1999 

Mexico NEI were included in the Mexico inventory (i.e., airport auxiliary power units and 

airport ground support equipment). Finally, based upon comments to the draft inventory report, 

one additional nonroad mobile source category was added (i.e., cropdusting aircraft). 

With regard to DQO #2, there were insufficient fuel usage data to assess the nonroad 

mobile source emissions using a fuel balance approach. 

2005 Mexicali Emissions Inventory 5-5 

Final, 2/27/09 
6.0 EMISSION INVENTORY RESULTS 
 

The overall 2005 Mexicali emissions inventory results are shown in Table 6-1. The 

results are presented by source type and pollutant. Figures 6-1 through 6-8 show the distribution 

of emissions by source type for each pollutant. The table and figures show that: 

• 	 Emissions from federal jurisdiction point sources are considerably larger than 

emissions from state jurisdiction point sources, even though the ratio of state-tofederal jurisdiction point sources is greater than 10-to-1. 

• 	 NOx and SO2 emissions are primarily emitted by point sources. 

• 	 VOC emissions are emitted by area (i.e., mainly various evaporative source 

categories) and on-road mobile sources. 

• 	 On-road mobile sources are the predominant source of CO. 

• 	 Area sources are the largest sources of PM10 and PM2.5 (i.e., due to unpaved and 

paved road dust), as well as NH3 (i.e., from livestock ammonia and fertilizer 

application) and CH4 (i.e., from the landfill). 

Table 6-1. 2005 Mexicali Emissions Inventory Summary (Tons per Year) 

Source Type NOx SO2 VOC CO PM10 PM2.5 NH3 CH4 

Point – 

Federal 13,931.7 3,650.9 659.3 3,636.2 1,537.7 128.4 

Point – State 444.0 973.2 73.0 479.9 89.7 

Area 1,205.5 131.1 15,301.5 18,854.0 50,879.3 6,753.5 9,015.2 6,033.3 

On-Road 

Mobile 8,570.4 168.7 8,977.1 60,603.4 765.0 665.8 252.4 144.4 

Nonroad 

Mobile 4,485.0 60.9 542.9 3,206.9 550.2 533.3 0.0 1.5 

Total 28,636.5 4,984.8 25,553.8 86,780.4 53,821.8 8,081.0 9,267.6 6,179.2 

Blanks indicate that pollutant emissions were not applicable or were not estimated due to a lack 

of data. 

2005 Mexicali Emissions Inventory 6-1 

Final, 2/27/09 
- Figure 6-1. Mexicali NOx Emissions 

Non-Road Mobile 

Point - Federal 

48.7% 

Point - State 

1.6% 

On-Road Mobile 

29.9% 

15.7% 

Area 

4.2% 

Figure 6-2. Mexicali SO2 Emissions -

Non-Road Mobile 

1.2% 

On-Road Mobile
 


3.4%
 


Area 

2.6% 

Point - State 

19.5% 

Point - Federal 

73.2% 

2005 Mexicali Emissions Inventory 6-2 

Final, 2/27/09 
Figure 6-3. Mexicali VOC Emissions -

Point - Federal 

2.6% 

Point - State 

2.1% 

Non-Road Mobile 

0.3% 

On-Road Mobile 

35.1% 

Area 

59.9% 

Figure 6-4. Mexicali CO Emissions -

Point - Federal 

4.2% Point - State 

3.7% 

Non-Road Mobile 

0.6% 

Area 

21.7% 

On-Road Mobile 

69.8% 

2005 Mexicali Emissions Inventory 6-3 

Final, 2/27/09 
Figure 6-5. Mexicali PM - 10 Emissions 

Point - Federal 

Non-Road Mobile 2.9% 

1.0% 

On-Road Mobile Point - State 

1.4% 0.2% 

Area 

94.5% 

Figure 6-6. Mexicali PM - 2.5 Emissions 

Point - Federal 

1.6% 

Point - State Non-Road Mobile 

0.0% 6.6% 

On-Road Mobile 

8.2% 

Area 

83.6% 

2005 Mexicali Emissions Inventory 6-4 

Final, 2/27/09 
Figure 6-7. Mexicali NH - 3 Emissions 

Non-Road Mobile Point - Federal 

0.0% 0.0% 

Point - State On-Road Mobile 

0.0% 2.7% 

Area 

97.3% 

- Figure 6-8. Mexicali CH4 Emissions 

Point - Federal Non-Road Mobile 

0.0% 

0.0% Point - State 

On-Road Mobile 

0.0% 

2.3% 

Area 

97.6% 

2005 Mexicali Emissions Inventory 6-5 

Final, 2/27/09 
7.0 REFERENCES 
 

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An and Blumberg, 2005. “The Case for Mexico to Improve Vehicle Fuel Efficiency”, 

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ARB, 2003a. Area Sources Methods – Section 7.4 (Agricultural Land Preparation). California 

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CANALAVA, 2002. National dry cleaning solvent statistics provided by the National Chamber 

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Chinkin, L.R., P.A. Ryan, and D.L. Coe. 2003. “Recommended Improvements to the CMU 

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EIIP, 1996. Consumer and Commercial Solvent Use. Volume III, Chapter 5. Emission 

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EIIP, 2001c. Structure Fires. Volume III, Chapter 18. Emission Inventory Improvement 

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EIIP, 2001d. Asphalt Paving. Volume III, Chapter 17. Emission Inventory Improvement 

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Final, 2/27/09 
ERG, 2006. Mexico National Emissions Inventory, 1999 – Final. Prepared for the Secretariat of 

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Escalante, 2008. LPG statistics provided by E. Escalante, Kino Gas del Noroeste, S.A. de C.V., 

Mexicali, Baja California. August 7. 

Félix Robles, 2008. LPG statistics provided by C.R. Félix Robles, Hidrocarburos del Noroeste, 

S.A. de C.V., Mexicali, Baja California. August 13. 

Fierro, 2007. Personal communication with Marian Fierro (Arizona College of Public Health, 

University of Arizona, Tucson, Arizona) regarding bleach use in SLRC. May 4. 

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S. Gonzalez-Ayala, TransEngineering, Ciudad Juárez, Chihuahua. December. 

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2005 Mexicali Emissions Inventory 7-3 

Final, 2/27/09 
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Laboratorio de Muestro y Analisis, S.A. de C.V., Tijuana, Baja California. July 24. 

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Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (SAGARPA), Mexicali, Baja 

California. August 13. 

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PEMEX – Rosarito Terminal, Rosarito, Baja California. October 2. 

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Secretaría de Medio Ambiente, Recursos Naturales y Pesca, Secretaría de Salud, and Delegación 

Federal SEMARNAP, Baja California. December. 

Reynoso Ambriz, 2008. Personal communication with F. Reynoso Ambriz, Obras Públicas del 

Ayuntamiento de Mexicali, Mexicali, Baja California. August 13. 

Rodríguez Hernández, 2008. Agricultural burning information provided by A. Rodríguez 

Hernández, Secretario de Fomento Agropecuario, Mexicali, Baja California. May 19. 

Ruiz Burgueño, 2008. Fire statistics provided by M.A. Ruiz Burgueño, Dirección del Heroico 

Cuerpo de Bomberos, Mexicali, Baja California. May 6. 

Ruiz Méndez, 2003. PEMEX municipality-level gasoline station information provided by A. 

Ruiz Méndez, PEMEX, Mexico City, Mexico. July 30. 

SAGARPA, 2007a. Anuario Estadístico de la Produccíon Agrícola. Prepared by the Secretariat 

of Agriculture, Livestock, Rural Development, Fisheries, and Food (Secretaría de Agricultura, 

Ganadería, Desarrollo Rural, Pesca y Alimentación). 

SAGARPA, 2007b. Vínculo Informático para la Obtención de Costos de Producción (VIOCS), 

Estructuras de Costos de Producción (ECP), Versión 2.0. Prepared by the Secretariat of 

2005 Mexicali Emissions Inventory 7-4 

Final, 2/27/09 
Agriculture, Livestock, Rural Development, Fisheries, and Food (Secretaría de Agricultura, 

Ganadería, Desarrollo Rural, Pesca y Alimentación). 

SAGARPA, 2007c. Servicio de Información Agroalimentaria y Pesquera (SIAP). Prepared by 

the Secretariat of Agriculture, Livestock, Rural Development, Fisheries, and Food (Secretaría de 

Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación). 

Sanchez Resendiz, 2008. Mexicali airport landing-and-takeoff (LTO) statistics provided by C. 

Sanchez Resendiz, Dirección de Aeropuerto. May 15. 

Sukarne, 2008. Beef cattle residence time estimates provided by Sukarne Produccion, S.A. de 

C.V., Mexicali, Baja California. September. 

Tamayo Moreno, 2008. Crop dusting activity data provided by A. Tamayo Moreno, Asociación 

de Pilotos Privados Aplicadores de Plaguicida. October 31. 

U.S. Census Bureau, 2007. Annual Estimates of the Population for the United States, Population 

Division, U.S. Census Bureau, Washington, D.C. 

U.S. Census Bureau, 2008. Annual Estimates of Population Change for Counties of California 

and County Rankings: April 1, 2000 to July 1, 2007. Population Division, U.S. Census Bureau, 

Washington, D.C. 

U.S. EPA, 1991. Procedures for the Preparation of Emission Inventories for Carbon Monoxide 

and Precursors of Ozone. Volume I: General Guidance for Stationary Sources. EPA-450/4/91­

016. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, 

Research Triangle Park, North Carolina. 

U.S. EPA, 1995. Compilation of Air Pollution Emission Factors (AP-42) – Volume I: 

Stationary Point and Area Sources, Fifth Edition. Various sections. U.S. Environmental 

Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North 

Carolina. January. 

U.S. EPA, 2004. Guidance for Quantifying and Using Long Duration Truck Idling Emission 

Reductions in State Implementation Plans and Transportation Conformity. EPA420-B-04-001. 

U.S. Environmental Protection Agency, Office of Transportation and Air Quality. January. 

Valdes Cantu, 2003. PEMEX fuel sulfur contents (diesel, combustoleo, marine diesel, and 

Intermedio 15) provided by N. Valdes Cantu, PEMEX, Mexico City, Mexico. December 11. 

Villegas Ibarra, 2008. Landfill statistics provided by E. Villegas Ibarra, Dirección de Ecología 

de Mexicali. May 14. 

Vivo, 2008. Natural gas statistics provided by A.R. Vivo, Ecogas Mexico S. de R.L. de C.V., 

Mexicali, Baja California. August 26. 

2005 Mexicali Emissions Inventory 7-5 

Final, 2/27/09 
APPENDIX A: 
 

AREA SOURCE CATEGORY FORMS
 

SOURCE TYPE: Area SOURCE CATEGORY: Industrial Fuel Combustion – Distillate 

DESCRIPTION: 

Industrial consumption of distillate fuel. Emission sources include boilers, furnaces, heaters, internal combustion (IC) 

engines, etc. 

POLLUTANTS: 

NOx, SOx, VOC, CO, PM10, PM2.5, and CH4 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• 	 Regional-level total distillate fuel usage (Parra Armenta, 2008; Parra Armenta, 2009) 

EMISSION FACTORS: 

• 	 NOx – 24 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 SOx – 5.966 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 VOC – 0.2 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 CO – 5.0 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 PM10 – 1.0 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 PM2.5 – 0.25 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 NH3 – 0.052 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

NOTES AND ASSUMPTIONS: 

• 	 It was assumed that 30% of the total distillate was industrial distillate. 

• 	 Bulk terminal-weighted average sulfur content of distillate fuel was calculated to be 0.038% (Valdes Cantu, 2003). 

• 	 Industrial area source distillate emissions were reconciled with the industrial point source inventory by subtracting point 

source inventory distillate emissions from the area source distillate emissions. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from distillate fuel oil combustion in the industrial sector in Mexicali. 

Total distillate use (Mexicali terminal) = 84,678,057 liters/yr 

Industrial fraction = 0.3 (assumed) 

Total industrial distillate use = 84,678,057 liters/yr x 0.3 = 25,403,417 liters/yr = 6,711,603 gallons/yr 

Annual municipality-level emissions (reconciled with area sources): 

NOx emissions = 24 lbs/1,000 gallons × (6,711,603 gallons) = 80.5 tons – 14.1 tons = 66.5 tons 

SOx emissions = 5.966 lbs/1,000 gallons × (6,711,603 gallons) = 20.0 tons – 17.7 tons = 2.3 tons 

VOC emissions = 0.2 lbs/1,000 gallons × (6,711,603 gallons) = 0.7 tons – 0.0 tons = 0.7 tons 

CO emissions = 5.0 lbs/1,000 gallons × (6,711,603 gallons) = 16.8 tons – 75.0 tons = -58.2 tons (set to zero) 

PM10 emissions = 1.0 lbs/1,000 gallons × (6,711,603 gallons) = 3.4 tons – 0.1 tons = 3.3 tons 

PM2.5 emissions = 0.25 lbs/1,000 gallons × (6,711,603 gallons) = 0.8 tons – 0.0 tons = 0.8 tons 

CH4 emissions = 0.052 lbs/1,000 gallons × (6,711,603 gallons) = 0.2 tons – 0.0 tons = 0.2 tons 

A-1
 

SOURCE TYPE: Area SOURCE CATEGORY: Industrial Fuel Combustion – Residual 

DESCRIPTION: 

Industrial consumption of residual fuel. Emission sources include boilers, furnaces, heaters, internal combustion (IC) 

engines, etc. 

POLLUTANTS: 

NOx, SOx, VOC, CO, PM10, PM2.5, and CH4 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• 	 Regional-level residual fuel usage (Miranda Palazuelos, 2008) 

EMISSION FACTORS: 

• 	 NOx – 47 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 SOx – 580.74 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 VOC – 0.28 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 CO – 5.0 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 PM10 – 32.36 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 PM2.5 – 21.08 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

• 	 CH4 – 1.0 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) 

NOTES AND ASSUMPTIONS: 

• 	 Bulk terminal-weighted average sulfur content of residual fuel was estimated to be 3.699% (combustóleo, Intermedio 

15, and Industrial Combustible) (Valdes Cantu, 2003). 

• 	 Industrial area source residual emissions were reconciled with the industrial point source inventory by subtracting point 

source inventory residual emissions from the area source residual emissions. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from residual fuel oil combustion in the industrial sector in Mexicali. 

Regional residual use (Rosarito terminal) = 121,974,708 liters/yr = 32,225,815 gallons/yr 

Mexicali fraction = 0.35 (assumed) 

Total industrial distillate use = 32,225,815 gallons/yr x 0.35 = 11,279,035 gallons/yr 

Annual municipality-level emissions (reconciled with area sources): 

NOx emissions = 47 lbs/1,000 gallons × (11,279,035 gallons) = 265.1 tons – 435.6 tons = -170.5 tons (set to zero) 

SOx emissions = 580.74 lbs/1,000 gallons × (11,279,035 gallons) = 3,275.1 tons – 3,776.2 tons = -501.1 tons (set to 

zero) 

VOC emissions = 0.28 lbs/1,000 gallons × (11,279,035 gallons) = 1.6 tons – 0.0 tons = 1.6 tons 

CO emissions = 5.0 lbs/1,000 gallons × (11,279,035 gallons) = 28.2 tons – 31.9 tons = -3.7 tons (set to zero) 

PM10 emissions = 32.36 lbs/1,000 gallons × (11,279,035 gallons) = 182.5 tons – 171.4 tons = 11.1 tons 

PM2.5 emissions = 21.08 lbs/1,000 gallons × (11,279,035 gallons) = 118.9 tons – 108.4 tons = 10.5 tons 

CH4 emissions = 1.0 lbs/1,000 gallons × (11,279,035 gallons) = 5.6 tons – 0.0 tons = 5.6 tons 

A-2
 

SOURCE TYPE: Area SOURCE CATEGORY: Industrial Fuel Combustion – Natural Gas 

DESCRIPTION: 

Industrial combustion of natural gas. Emission sources include boilers, furnaces, heaters, IC engines, etc. 

POLLUTANTS: 

NOx, SOx, VOC, CO, PM10, PM2.5, CH4 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• 	 Annual natural gas quantity used in the industrial sector (Vivo, 2008) 

EMISSION FACTORS: 

• 	 NOx – 280 lb/106 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• 	 SOx – 0.6 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• 	 VOC – 5.5 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• 	 CO – 84 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• 	 PM10 – 7.6 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• 	 PM2.5 – 7.6 lb/106 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• 	 CH4 – 2.3 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

NOTES AND ASSUMPTIONS: 

• 	 Industrial area source natural gas emissions were reconciled with the industrial point source inventory by subtracting 

point source inventory natural gas emissions from the area source natural gas emissions. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from industrial natural gas usage in Mexicali. 

Annual industrial natural gas usage = 109.6 x 106 m3 = (3,871.7 x 106 scf) 

Annual municipality-level emissions – Mexicali: 

NOx – (3,871.7 x 106 scf) x 280 lb/106 scf = 542.0 tons – 262.2 tons = 279.8 tons 

SOx – (3,871.7 x 106

 scf) x 0.6 lb/106 scf = 1.2 tons – 1.9 tons = -0.7 tons (set to zero) 

VOC – (3,871.7 x 106

 scf) x 5.5 lb/106 scf = 10.6 tons – 4.3 tons = 6.3 tons 

CO – (3,871.7 x 106

 scf) x 84 lb/106 scf = 162.6 tons – 216.8 tons = -54.2 tons (set to zero) 

PM10 – (3,871.7 x 106

 scf) x 7.6 lb/106 scf = 14.7 tons – 6.3 tons = 8.4 tons 

PM2.5 – (3,871.7 x 106

 scf) x 7.6 lb/106 scf = 14.7 tons – 6.0 tons = 8.7 tons 

CH4 – (3,871.7 x 106

 scf) x 2.3 lb/106 scf = 4.5 tons – 0.0 tons = 4.5 tons 

A-3
 

SOURCE TYPE: Area SOURCE CATEGORY: 

Commercial/Institutional Fuel 

Combustion – Natural Gas 

DESCRIPTION: 

Commercial/institutional combustion of natural gas. Emission sources include boilers, furnaces, heaters, internal 

combustion (IC) engines, etc. 

POLLUTANTS: 

NOx, SOx, VOC, CO, PM10, PM2.5, CH4 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• Annual natural gas quantity used in the commercial sector (Vivo, 2008) 

EMISSION FACTORS: 

• NOx – 100 lb/106 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• SOx – 0.6 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• VOC – 5.5 lb/106 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• CO – 84 lb/106 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• PM10 – 7.6 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• PM2.5 – 7.6 lb/106 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• CH4 – 2.3 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

NOTES AND ASSUMPTIONS: 

• None. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from commercial natural gas usage in Mexicali. 

Annual industrial natural gas usage = 1.462 x 106

 m3 = (51.6 x 106 scf) 

Annual municipality-level emissions – Mexicali: 

NOx – (51.64 x 106

 scf) x 100 lb/106 scf = 2.58 tons 

SOx – (51.64 x 106

 scf) x 0.6 lb/106 scf = 0.02 tons 

VOC – (51.64 x 106

 scf) x 5.5 lb/106 scf = 0.14 tons 

CO – (51.64 x 106

 scf) x 84 lb/106 scf = 2.17 tons 

PM10 – (51.64 x 106

 scf) x 7.6 lb/106 scf = 0.20 tons 

PM2.5 – (51.64 x 106

 scf) x 7.6 lb/106 scf = 0.20 tons 

CH4 – (51.64 x 106

 scf) x 2.3 lb/106 scf = 0.06 tons 

A-4
 

SOURCE TYPE: Area SOURCE CATEGORY: 

Residential Fuel Combustion – Natural 

Gas 

DESCRIPTION: 

Residential combustion of natural gas for heating and cooking. 

POLLUTANTS: 

NOx, SOx, VOC, CO, PM10, PM2.5, CH4 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Households 

ACTIVITY DATA: 

• Annual natural gas quantity used in the residential sector (Vivo, 2008) 

EMISSION FACTORS: 

• NOx – 94 lb/106 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• SOx – 0.6 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• VOC – 5.5 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• CO – 40 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• PM10 – 7.6 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• PM2.5 – 7.6 lb/106 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

• CH4 – 2.3 lb/106

 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) 

NOTES AND ASSUMPTIONS: 

• None. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from residential natural gas usage in Mexicali. 

Annual residential natural gas usage = 4.417 x 106 m3 = (156.01 x 106 scf) 

Annual municipality-level emissions – Mexicali: 

NOx – (156.01 x 106

 scf) x 94 lb/106 scf = 7.33 tons 

SOx – (156.01 x 106

 scf) x 0.6 lb/106 scf = 0.05 tons 

VOC – (156.01 x 106

 scf) x 5.5 lb/106 scf = 0.43 tons 

CO – (156.01 x 106

 scf) x 40 lb/106 scf = 3.12 tons 

PM10 – (156.01 x 106

 scf) x 7.6 lb/106 scf = 0.59 tons 

PM2.5 – (156.01 x 106

 scf) x 7.6 lb/106 scf = 0.59 tons 

CH4 – (156.01 x 106

 scf) x 2.3 lb/106 scf = 0.18 tons 

A-5
 

SOURCE TYPE: Area SOURCE CATEGORY: Industrial Fuel Combustion – LPG 

DESCRIPTION: 

Industrial combustion of liquefied petroleum gas (LPG). Emission sources include boilers, furnaces, heaters, internal 

combustion (IC) engines, etc. 

POLLUTANTS: 

NOx, SOx, VOC, CO, PM10, PM2.5 ,and CH4 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• 	 Annual LPG used in the industrial sector (Álvarez Sánchez, 2008; Escalante, 2008; Félix Robles, 2008) 

EMISSION FACTORS: 

• 	 NOx – 13.8 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 SOx – 0.0039 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 VOC – 0.84 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 CO – 7.86 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 PM (PM10 and PM2.5 ) – 0.74 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 CH4 – 0.2 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

NOTES AND ASSUMPTIONS: 

• 	 Three out of four LPG distributors (i.e., Gas Silza, Kino Gas del Noroeste, and Hidrocarburos del Noroeste) responded 

to requests for information. The fourth distributor (i.e., Gas Butano Propano de Baja California) did not respond. It was 

assumed that LPG distributed by Gas Butano Propano was equal to the average of the quantities distributed by Gas 

Silza and Kino Gas. 

• 	 LPG is assumed to contain 60% propane and 40% butane. 

• 	 Sulfur content of propane and butane is assumed to be 0.4114 gr/100 ft3

. 

• 	 Emission factor for total PM is assumed to represent PM10 and PM2.5 emissions. 

• 	 Industrial area source LPG emissions were reconciled with the industrial point source inventory by subtracting point 

source inventory LPG emissions from the area source LPG emissions. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from industrial LPG usage in Mexicali. 

Industrial LPG usage = 8,167,952 gallons/year 

Annual NOx emissions = 13.8 lbs/1,000 gallons × (8,167,952 gallons) = 56.4 tons – 0.1 tons = 56.3 tons 

Annual SOx emissions = 0.039 lbs/1,000 gallons × (8,167,952 gallons) = 0.2 tons – 0.0 tons = 0.2 tons 

Annual VOC emissions = 0.84 lbs/1,000 gallons × (8,167,952 gallons) = 3.4 tons – 0.0 tons = 3.4 tons 

Annual CO emissions = 7.86 lbs/1,000 gallons × (8,167,952 gallons) = 32.1 tons – 0.0 tons = 32.1 tons 

Annual PM10 emissions = 0.74 lbs/1,000 gallons × (8,167,952 gallons) = 3.0 tons – 0.0 tons = 3.0 tons 

Annual PM2.5 emissions = 0.74 lbs/1,000 gallons × (8,167,952 gallons) = 3.0 tons – 0.0 tons = 3.0 tons 

Annual CH4 emissions = 0.2 lbs/1,000 gallons × (8,167,952 gallons) = 0.8 tons – 0.0 tons = 0.8 tons 

A-6
 

SOURCE TYPE: Area SOURCE CATEGORY: Commercial Fuel Combustion – LPG 

DESCRIPTION: 

Commercial combustion of liquefied petroleum gas (LPG). Emission sources include boilers, furnaces, heaters, 

internal combustion (IC) engines, etc. 

POLLUTANTS: 

NOx, SOx, VOC, CO, PM10, PM2.5 ,and CH4 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• 	 Annual LPG used in the commercial sector (Álvarez Sánchez, 2008; Escalante, 2008; Félix Robles, 2008) 

EMISSION FACTORS: 

• 	 NOx – 13.8 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 SOx – 0.0039 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 VOC – 0.84 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 CO – 7.86 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 PM (PM10 and PM2.5 ) – 0.74 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 CH4 – 0.2 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

NOTES AND ASSUMPTIONS: 

• 	 Three out of four LPG distributors (i.e., Gas Silza, Kino Gas del Noroeste, and Hidrocarburos del Noroeste) 

responded to requests for information. The fourth distributor (i.e., Gas Butano Propano de Baja California) did not 

respond. It was assumed that LPG distributed by Gas Butano Propano was equal to the average of the quantities 

distributed by Gas Silza and Kino Gas. 

• 	 LPG is assumed to contain 60% propane and 40% butane. 

• 	 Sulfur content of propane and butane is assumed to be 0.4114 gr/100 ft3

. 

• 	 Emission factor for total PM is assumed to represent PM10 and PM2.5 emissions. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from commercial LPG usage in Mexicali. 

Commercial LPG usage = 3,424,855 gallons/year 

Annual NOx emissions = 13.8 lbs/1,000 gallons × (3,424,855 gallons) = 23.6 tpy 

Annual SOx emissions = 0.039 lbs/1,000 gallons × (3,424,855 gallons) = 0.1 tpy 

Annual VOC emissions = 0.84 lbs/1,000 gallons × (3,424,855 gallons) = 1.4 tpy 

Annual CO emissions = 7.86 lbs/1,000 gallons × (3,424,855 gallons) = 13.5 tpy 

Annual PM10 emissions = 0.74 lbs/1,000 gallons × (3,424,855 gallons) = 1.3 tpy 

Annual PM2.5 emissions = 0.74 lbs/1,000 gallons × (3,424,855 gallons) = 1.3 tpy 

Annual CH4 emissions = 0.2 lbs/1,000 gallons × (3,424,855 gallons) = 0.3 tpy 

A-7
 

SOURCE TYPE: Area SOURCE CATEGORY: Residential Fuel Combustion – LPG 

DESCRIPTION: 

Residential combustion of liquefied petroleum gas (LPG) for heating and cooking. 

POLLUTANTS: 

NOx, SOx, VOC, CO, PM10, PM2.5 ,and CH4 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• 	 Annual LPG used in the residential sector (Álvarez Sánchez, 2008; Escalante, 2008; Félix Robles, 2008) 

EMISSION FACTORS: 

• 	 NOx – 13.8 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 SOx – 0.0039 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 VOC – 0.84 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 CO – 7.86 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 PM (PM10 and PM2.5 ) – 0.74 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

• 	 CH4 – 0.2 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) 

NOTES AND ASSUMPTIONS: 

• 	 Three out of four LPG distributors (i.e., Gas Silza, Kino Gas del Noroeste, and Hidrocarburos del Noroeste) responded 

to requests for information. The fourth distributor (i.e., Gas Butano Propano de Baja California) did not respond. It was 

assumed that LPG distributed by Gas Butano Propano was equal to the average of the quantities distributed by Gas 

Silza and Kino Gas. 

• 	 LPG is assumed to contain 60% propane and 40% butane. 

• 	 Sulfur content of propane and butane is assumed to be 0.4114 gr/100 ft3

. 

• 	 Emission factor for total PM is assumed to represent PM10 and PM2.5 emissions. 

• 	 Residential emission factors are assumed to be identical to commercial emission factors. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from residential LPG usage in Mexicali. 

Residential LPG usage = 14,923,787 gallons/year 

Annual NOx emissions = 13.8 lbs/1,000 gallons × (14,923,787 gallons) = 103.0 tpy 

Annual SOx emissions = 0.039 lbs/1,000 gallons × (14,923,787 gallons) = 0.3 tpy 

Annual VOC emissions = 0.84 lbs/1,000 gallons × (14,923,787 gallons) = 6.3 tpy 

Annual CO emissions = 7.86 lbs/1,000 gallons × (14,923,787 gallons) = 58.7 tpy 

Annual PM10 emissions = 0.74 lbs/1,000 gallons × (14,923,787 gallons) = 5.5 tpy 

Annual PM2.5 emissions = 0.74 lbs/1,000 gallons × (14,923,787 gallons) = 5.5 tpy 

Annual CH4 emissions = 0.2 lbs/1,000 gallons × (14,923,787 gallons) = 1.5 tpy 

A-8
 

SOURCE TYPE: Area SOURCE CATEGORY: Architectural Surface Coatings 

DESCRIPTION: 

Surface coatings such as paints, paint primer, varnish, or lacquer applied to architectural surfaces. 

POLLUTANTS: 

VOC 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• 	 2005 Mexicali population (INEGI, 2006) 

EMISSION FACTORS: 

• 	 VOC (solvent-based) – 0.728 lbs/person-yr (ERG, 2006) 

• 	 VOC (water-based) – 0.391 lbs/person-yr (ERG, 2006) 

NOTES AND ASSUMPTIONS: 

• 	 Mexico-specific emission factors derived as part of the development of the 1999 Mexico National Emissions Inventory 

using data from ANAFAPYT (ANAFAPYT, 2003). 

SAMPLE CALCULATIONS: 

Estimate annual emissions from architectural surface coatings in Mexicali.
 


Activity data: 
 

Mexicali population = 855,962 
 

Annual municipality-level emissions: 
 

VOC emissions (solvent-based) = 0.728 lbs/person-yr × (855,962 people) = 311.5 tons 
 

VOC emissions (water-based) = 0.391 lbs/person-yr × (855,962 people) = 167.3 tons 
 

Total VOC emissions = 311.5 tons + 167.3 tons = 478.8 tons
 

A-9
 

SOURCE TYPE: Area SOURCE CATEGORY: Industrial Surface Coating 

DESCRIPTION: 

Coatings such as paints, varnish, lacquer, and paint primer. Surface coatings are applied to a wide variety of products, 

including furniture, cans, automobiles, airplanes and other transportation equipment, machinery, household appliances, flat 

wood, wire, and other miscellaneous products. In addition, coatings are used in maintenance operations at industrial facilities. 

POLLUTANTS: 

VOC 

METHOD: 

Per employee emission factors derived from Mexico paint sales. 

SPATIAL ALLOCATION: 

Employee data by sector 

ACTIVITY DATA: 

• 	 2005 Mexicali employee counts (INEGI, 2005) 

• 	 Wood and metal furniture (NAICS 33711x, 33712x, 33721x) 

• 	 Other metallic products (NAICS 332xxx, 333xxx) 

• 	 Electronics and electrical equipment (NAICS 334xxx, 335xxx) 

• 	 Automotive industry (NAICS 3361xx, 3362xx, 3363xx) 

• 	 Other transportation industry (NAICS 3364xx, 3365xx, 3366xx, 3369xx) 

• 	 Other manufacturing industries (NAICS 339xxx) 

• 	 Industrial maintenance coatings (NAICS 31xxxx, 32xxxx, 33xxxx) 

EMISSION FACTORS: 

• 	 Per employee emission factors (ERG, 2006) 

• 	 VOC – Wood and metal furniture – 270.24 lbs/employee-yr 

• 	 VOC – Other metallic products – 225.13 lbs/employee-yr 

• 	 VOC – Electronics and electrical equipment – 3.33 lbs/employee-yr 

• 	 VOC – Automotive industry – 376.19 lbs/employee-yr 

• 	 VOC – Other transportation industry – 256.66 lbs/employee-yr 

• 	 VOC – Other manufacturing industries – 342.82 lbs/employee-yr 

• 	 VOC – Industrial maintenance coatings – 13.98 lbs/employee-yr 

NOTES AND ASSUMPTIONS: 

• 	 Mexico-specific emission factors derived as part of the development of the 1999 Mexico National Emissions Inventory 

using data from ANAFAPYT (ANAFAPYT, 2003). 

• 	 Reconciliation with the point source inventory was conducted by subtracting point source emission estimates from any 

point sources with NAICS codes covered by the industrial surface coating area source category. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from industrial surface coating in Mexicali. 
 

Activity data: 
 

Mexicali employee count = 547 (wood and metal furniture) 
 

Mexicali employee count = 8,990 (other metallic products) 
 

Mexicali employee count = 19,677 (electronics and electrical equipment) 
 

Mexicali employee count = 6,812 (automotive industry) 
 

Mexicali employee count = 2,550 (other transportation industry) 
 

Mexicali employee count = 7,367 (other manufacturing industries) 
 

Mexicali employee count = 62,015 (industrial maintenance coatings) 
 

Annual municipality-level emissions: 
 

VOC emissions = 270.24 lbs/employee-yr × (547 employees) = 73.9 tons – 9.1 tons = 64.8 tons 
 

VOC emissions = 225.13 lbs/employee-yr × (8,990 employees) = 1,012.0 tons – 4.9 tons = 1,007.1 tons
 


VOC emissions = 3.33 lbs/employee-yr × (19,677 employees) = 32.8 tons – 0.3 tons = 32.5 tons 
 

VOC emissions = 376.19 lbs/employee-yr × (6,812 employees) = 1,281.3 tons – 0.1 tons = 1,281.2 tons
 


VOC emissions = 256.66 lbs/employee-yr × (2,550 employees) = 327.2 tons – 0.0 tons = 327.2 tons 
 

VOC emissions = 342.82 lbs/employee-yr × (7,367 employees) = 1,262.8 tons – 0.2 tons = 1,262.6 tons
 


VOC emissions = 13.98 lbs/employee-yr × (62,015 employees) = 433.5 tons – 0.0 tons = 433.5 tons 
 

Total VOC emissions = 64.8 tons + 1,007.1 tons + 32.5 tons + 1,281.2 tons + 327.2 tons + 1,262.6 tons + 433.5 tons = 

4,408.9 tons 

A-10
 

SOURCE TYPE: Area SOURCE CATEGORY: Autobody Refinishing 

DESCRIPTION: 

Repair and restoration of automobiles, light trucks, and other vehicle bodies. Most repair jobs include refinishing on a 

portion of the vehicle; new vehicle coating is excluded from this source category. 

POLLUTANTS: 

VOC 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• 	 2005 Mexicali employee count (NAICS 811121) (INEGI, 2005) 

EMISSION FACTORS: 

• 	 VOC – 277.25 lbs/employee-yr (ERG, 2006) 

NOTES AND ASSUMPTIONS: 

• 	 Mexico-specific emission factor derived as part of the development of the Mexico National Emissions Inventory using 

data from ANAFAPYT (ANAFAPYT, 2003). 

SAMPLE CALCULATIONS: 

Estimate annual emissions from autobody refinishing in Mexicali. 

Activity data: 

Mexicali employee count = 568 

Municipality-level emissions: 

Annual VOC emissions = 277.25 lbs/employee-yr × (568 employees) = 78.7 tons 

Total VOC emissions = 78.7 tons 

A-11
 

SOURCE TYPE: Area SOURCE CATEGORY: Graphic Arts 

DESCRIPTION: 

Various graphic arts processes including typography, offset (web and sheet), rotogravure, silk screening, and 

flexography. 

POLLUTANTS: 

VOC 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• 	 2005 Mexicali population (INEGI, 2006) 

EMISSION FACTORS: 

• 	 VOC – 0.811 lbs/person-yr (ERG, 2006) 

NOTES AND ASSUMPTIONS: 

• 	 Mexico-specific emission factor derived as part of the development of the Mexico National Emissions Inventory using 

data from ANAFAPYT (ANAFAPYT, 2004). 

SAMPLE CALCULATIONS: 

Estimate annual emissions from graphic arts in Mexicali. 

Activity data: 

Mexicali population = 855,962 

Municipality-level emissions: 

Annual VOC emissions = 0.811 lbs/person-yr × (855,962 people) = 346.9 tons 

Total VOC emissions = 346.9 tons 

A-12
 

SOURCE TYPE: Area SOURCE CATEGORY: Traffic Markings 

DESCRIPTION: 

Various emission sources in this source category include painting of centerlines, edge stripes, directional markings, 

and paved and unpaved surfaces to improve traffic flow. These include solvent- and water-based paints. 

POLLUTANTS: 

VOC 

METHOD: 

Estimates from Mexico NEI (ERG, 2006) 

SPATIAL ALLOCATION: 

Paved road network 

ACTIVITY DATA: 

• 	 Not applicable 

EMISSION FACTORS: 

• 	 Not applicable 

NOTES AND ASSUMPTIONS: 

• 	 Data request for quantities of traffic markings was made to the Mexicali Public Works, but data were not available 

(Reynoso Ambriz, 2008). 

• 	 Mexicali emission estimates were used from the Mexico NEI. 

• 	 Emissions from traffic markings were assumed to be unchanged from 1999 to 2005. 

SAMPLE CALCULATIONS: 

Estimate the total annual emissions from traffic markings in Mexicali. Calculations from 1999 Mexico NEI. 

National level emissions: 
 

Total VOC emissions = 3,031.8 Mg/year 
 

State level emissions: 
 

National paved road length = 237,635 km 
 

Paved road length in Baja California = 6,805.7 km 
 

Annual VOC emissions = (6,805.7 km/237,635 km) × 3,031.8 Mg = 86.8 Mg 

Municipality level emissions – Mexicali: 
 

State paved road length = 6,805.7 km 
 

Paved road length in Mexicali = 4,057.4 km 
 

Annual VOC emissions = (4,057.4 km/6,805.7 km) × 86.8 Mg = 51.8 Mg = 57.1 tons 

A-13
 

SOURCE TYPE: Area SOURCE CATEGORY: Dry Cleaning 

DESCRIPTION: 

Solvent evaporation during dry cleaning process, from leaks in the equipment, and from solvent recovery or disposal 

systems. 

POLLUTANTS: 

VOC 

METHOD: 

Per employee emission factors. 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• 	 2005 Mexicali employee count (NAICS 81221x) (INEGI, 2005) 

EMISSION FACTORS: 

• 	 VOC – 700.66 lbs/employee-yr (ERG, 2006) 

NOTES AND ASSUMPTIONS: 

• 	 Only dry cleaning facilities using petroleum-based organic solvents for cleaning are included in this category; dry 

cleaning facilities using perchloroethylene are excluded. 

• 	 Mexico-specific emission factor derived as part of the development of the Mexico National Emissions Inventory using 

data from CANALAVA (CANALAVA, 2002). 

SAMPLE CALCULATIONS: 

Estimate annual emissions from dry cleaning in Mexicali. 

Activity data: 

Mexicali employee count = 416 

Municipality-level emissions: 

Annual VOC emissions = 700.66 lbs/employee-yr × (416 employees) = 145.7 tons 

Total VOC emissions = 145.7 tons 

A-14
 

SOURCE TYPE: Area SOURCE CATEGORY: Degreasing 

DESCRIPTION: 

Volatile organic compound emissions from surface cleaning operations. Surface cleaning operations involve the use of 

solvent liquids or solvent vapors to remove water-insoluble contaminants such as grease, oils, waxes, carbon deposits, 

fluxes, and tars from metal, plastic, glass, and other surfaces. 

POLLUTANTS: 

VOC 

METHOD: 

Per employee emission factors 

SPATIAL ALLOCATION: 

Employee data 

ACTIVITY DATA: 

• 	 2005 Mexicali employee counts (INEGI, 2005) 

• 	 Cold cleaning (auto repair) (NAICS 8111xx) 

• 	 Cold cleaning (manufacturing) (NAICS 31xxxx-33xxxx) 

• 	 Vapor and in-line cleaning (electronics and electrical) (NAICS 334xxx-335xxx) 

• 	 Vapor and in-line cleaning (other) (NAICS 31xxxx-33xxxx; 8111xx) 

EMISSION FACTORS: 

• 	 Per employee emission factors (EIIP, 1997) 

• 	 VOC – Cold cleaning (auto repair) – 270 lbs/employee-yr 

• 	 VOC – Cold cleaning (manufacturing) – 24 lbs/employee-yr 

• 	 VOC – Vapor and in-line cleaning (electronics and electrical) – 29 lbs/employee-yr 

• 	 VOC – Vapor and in-line cleaning (other) – 9.8 lbs/employee-yr 

NOTES AND ASSUMPTIONS: 

• 	 It was assumed that Mexican degreasing operations were similar to those in the U.S. 

• 	 Reconciliation with the point source inventory was conducted by subtracting point source emission estimates from any 

point sources with NAICS codes covered by the degreasing area source category. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from degreasing in Mexicali. 

Activity data: 
 

Mexicali employee count = 4,186 (cold cleaning – auto repair) 
 

Mexicali employee count = 62,015 (cold cleaning – manufacturing) 
 

Mexicali employee count = 19,677 (vapor and in-line cleaning – electronics and electrical) 
 

Mexicali employee count = 66,201 (vapor and in-line cleaning – other) 
 

Annual municipality-level emissions: 

VOC emissions = 270 lbs/employee-yr × (4,186 employees) = 565.1 tons – 0.0 tons = 565.1 tons 

VOC emissions = 24 lbs/employee-yr × (62,015 employees) = 744.2 tons – 0.0 tons = 744.2 tons 

VOC emissions = 29 lbs/employee-yr × (19,677 employees) = 285.3 tons – 0.6 tons = 284.7 tons 

VOC emissions = 9.8 lbs/employee-yr × (66,201 employees) = 324.4 tons – 0.9 tons = 323.5 tons 

Total VOC emissions = 565.1 tons + 744.2 tons + 284.7 tons + 323.5 tons = 1,917.4 tons 

A-15
 

SOURCE TYPE: Area SOURCE CATEGORY: Consumer Solvent Use 

DESCRIPTION: 

Consumer use of various solvent-containing products, including: personal care products, household products, 

automotive aftermarket products, adhesives and sealants, FIFRA-regulated products, coatings and related products, 

and miscellaneous products 

POLLUTANTS: 

VOC 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• 	 2005 Mexicali population (INEGI, 2006) 

EMISSION FACTORS: 

• 	 VOC (personal care products) – 2.32 lbs/person-yr (EIIP, 1996) 

• 	 VOC (household products) – 0.20 lbs/person-yr (EIIP, 1996; note 1) 

• 	 VOC (automotive aftermarket products) – 0.16 lbs/person-yr (EIIP, 1996; note 2) 

• 	 VOC (adhesives and sealants) – 0.57 lbs/person-yr (EIIP, 1996) 

• 	 VOC (FIFRA-regulated products – 1.78 lbs/person-yr (EIIP, 1996) 

• 	 VOC (coatings and related products) – 0.96 lbs/person-yr (EIIP, 1996) 

• 	 VOC (miscellaneous products) – 0.07 lbs/person-yr (EIIP, 1996) 

NOTES AND ASSUMPTIONS: 

• 	 Note 1 – EIIP emission factor for household products adjusted by a factor of 0.25 based upon the pervasive use of 

bleach as a cleaning agent instead of typical solvent-containing products (Fierro and O’Rourke, 2007; Fierro, 2007). 

• 	 Note 2 – EIIP emission factor for automotive aftermarket products adjusted by a factor of 0.12 based upon the use of 

water instead of windshield fluids from the area source residual quantities (BTS, 2007; U.S. Census, 2007; GonzalezAyala, 2007). 

SAMPLE CALCULATIONS: 

Estimate annual emissions from consumer products in Mexicali. 

Activity data: 
 

Mexicali population = 855,962 
 

Municipality-level emissions: 

Annual VOC emissions (personal care products) = 2.32 lbs/person-yr × (855,962 people) = 993.2 tons 
 

Annual VOC emissions (household products) = 0.20 lbs/person-yr × (855,962 people) = 84.6 tons 
 

Annual VOC emissions (automotive aftermarket products) = 0.16 lbs/person-yr × (855,962 people) = 67.6 tons 
 

Annual VOC emissions (adhesives and sealants) = 0.57 lbs/person-yr × (855,962 people) = 243.0 tons
 


Annual VOC emissions (FIFRA-regulated products) = 1.78 lbs/person-yr × (855,962 people) = 761.1 tons
 


Annual VOC emissions (coatings and related products) = 0.96 lbs/person-yr × (855,962 people) = 409.6 tons 
 

Annual VOC emissions (miscellaneous products) = 0.96 lbs/person-yr × (855,962 people) = 29.6 tons 
 

Total VOC emissions = 993.2 tons + 84.6 tons + 67.6 tons + 243.0 tons +761.1 tons + 409.6 tons + 29.6 tons = 2,588.7 

tons 

A-16
 

SOURCE TYPE: Area SOURCE CATEGORY: Asphalt Application 

DESCRIPTION: 

Asphalt application to surfaces and pavements results in hydrocarbon emissions due to evaporation. 

POLLUTANTS: 

VOC 

METHOD: 

Use of 1999 Mexico National Emissions Inventory (ERG, 2006) 

SPATIAL ALLOCATION: 

Paved road network 

ACTIVITY DATA: 

• National-level emissions (ERG, 2006) 

• National and municipality level paved road lengths (INEGI, 2002) 

EMISSION FACTORS: 

• Not applicable 

NOTES AND ASSUMPTIONS: 

• Cutback fraction of total asphalt is assumed to be similar to the U.S. (i.e., 3.06% of total asphalt). 

• Asphalt is assumed to be a medium-cure cutback. 

• Diluent content is assumed to be 35% (EIIP, 2001d). 

• Diluent density is assumed to be 0.8 kg/liter (EIIP, 2001d). 

• Evaporated fraction of diluent is assumed to be 75% (EIIP, 2001d). 

• Emissions from hot-mix and emulsified asphalts are assumed to be negligible (EIIP, 2001d). 

SAMPLE CALCULATIONS: 

Estimate annual emissions from asphalt application in Baja California. 

National level emissions: 
 

National VOC emissions = 7,756.0 Mg/year
 


State level emissions: 
 

National paved road length = 237,635 km 
 

State paved road length = 6,805.7 km 
 

Annual VOC emissions in Baja California = 7,756.0 Mg/year × (6,805.7 km/237,635 km) = 222.1 Mg 

Municipality level emissions – Mexicali: 

 State paved road length = 6,805.7 km 

 Paved road length in Mexicali = 4,057.4 km 

 Annual VOC emissions in Mexicali = 222.1 Mg × (4,057.4 km/6,805.7 km) = 132.4 Mg = 146.0 tons 

A-17
 

SOURCE TYPE: Area SOURCE CATEGORY: Gasoline Distribution 

DESCRIPTION: 

This source category relates to the emissions occurring during the transportation and distribution of gasoline. This 

category includes Stage I loading losses (from tank truck to underground tank), Stage II loading losses (from 

underground tank to vehicle – including spillage), underground tank breathing, and tank truck transit losses. 

POLLUTANTS: 

VOC 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• Gasoline (Magna and Premium) quantity sold (Parra Armenta, 2008; Parra Armenta, 2009) 

EMISSION FACTORS: 

• VOCStage I controlled – 40 mg/liter (EIIP, 2001a) 

• VOCStage II uncontrolled – 1320 mg/liter (EIIP, 2001a) 

• VOCSpillage – 80 mg/liter (EIIP, 2001a) 

• VOCTank losses – 120 mg/liter (EIIP, 2001a) 

• VOCGas filled trucks – 1 mg/liter (EIIP, 2001a) 

• VOCVapor filled trucks – 13 mg/liter (EIIP, 2001a) 

NOTES AND ASSUMPTIONS: 

• It was assumed that all stations in Mexicali have Stage I controls (assumed to be vapor balance) (Ruiz Méndez, 2003). 

• It was assumed that all stations in Mexicali do not have Stage II controls (Ruiz Méndez, 2003). 

SAMPLE CALCULATIONS: 

Estimate annual VOC emissions from gasoline distribution in Mexicali. 

Mexicali Magna gasoline = 224,800,401 liters
 


Mexicali Premium gasoline = 63,773,310 liters 
 

Total gasoline = 224,800,401 liters + 63,773,310 liters = 288,573,711 liters
 


Mexicali emissions: 

Stage I controlled emissions = 40 mg/l x 288,573,711 liters = 11.54 Mg = 12.7 tons 
 

Stage II uncontrolled emissions = 1,320 mg/l x 288,573,711 liters = 380.92 Mg = 419.9 tons
 


Spillage emissions = 80 mg/l x 288,573,711 liters = 23.09 Mg = 25.4 tons 
 

Tank losses = 120 mg/l x 288,573,711 liters = 34.63 Mg = 38.2 tons 
 

Tank truck transit losses (gas-filled) = 1 mg/l x 288,573,711 liters = 0.29 Mg = 0.3 tons 
 

Tank truck transit losses (vapor-filled) = 13 mg/l x 288,573,711 liters = 3.75 Mg = 4.1 tons 
 

Total Mexicali gasoline distribution emissions = 12.7 tons + 419.9 tons + 25.4 tons + 38.2 tons + 0.3 tons + 4.1 tons = 

500.7 tons 

A-18
 

SOURCE TYPE: Area SOURCE CATEGORY: Agricultural Burning 

DESCRIPTION: 

Emissions from burning of agricultural residue. 

POLLUTANTS: 

NOx, SO2, VOC, CO, PM10, and PM2.5 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Agricultural land use 

ACTIVITY DATA: 

• 	 Burn acreage by crop type (Rodríguez Hernández, 2008) 

• 	 Fuel loading by crop type (wheat 1.9 tons/acre, sorghum 2.9 tons/acre, miscellaneous weeds 2.175 tons/acre) (ARB, 

2005) 

EMISSION FACTORS: 

• 	 Wheat (NOx – 4.3 lbs/ton, SO2 – 0.9 lbs/ton, VOC – 7.6 lbs/ton, CO – 123.6 lbs/ton, PM10 – 10.6 lbs/ton, PM2.5 – 10.1 

lbs/ton) (ARB, 2005) 

• 	 Sorghum (NOx – 4.5 lbs/ton, SO2 – 0.6 lbs/ton, VOC – 5.10 lbs/ton, CO – 77 lbs/ton, PM10 – 17.7 lbs/ton, PM2.5 – 16.9 

lbs/ton) (ARB, 2005) 

• 	 Irrigation canal and ditchbank weeds (NOx – 4.49 lbs/ton, SO2 – 0.61 lbs/ton, VOC – 10.73 lbs/ton, CO – 113.95 

lbs/ton, PM10 – 15.9 lbs/ton, PM2.5 – 15.18 lbs/ton) (ARB, 2005) 

NOTES AND ASSUMPTIONS: 

• 	 Only wheat, sorghum, and irrigation canal and ditch bank weeds are burned in Mexicali. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from agricultural burning of wheat. 

Area burned = 57,000 hectares = 140,847 acres 
 

Residue burned = 140,847 acres x 1.9 tons/acre = 267,609 tons 
 

Mexicali emissions: 

NOx – (267,609 tons) x 4.3 lb/ton = 575.4 tons 
 

SOx – (267,609 tons) x 0.9 lb/ton = 120.4 tons 
 

VOC – (267,609 tons) x 7.6 lb/ton = 1,016.9 tons 
 

CO – (267,609 tons) x 123.6 lb/ton = 16,538.3 tons 
 

PM10 – (267,609 tons) x 10.6 lb/ton = 1,418.3 tons 
 

PM2.5 – (267,609 tons) x 10.1 lb/ton = 1,351.4 tons 
 

Total NOx emissions = wheat emissions + sorghum emissions + ditchbank weed emissions = 575.4 tons + 38.7 tons + 

7.2 tons = 621.3 tons 

A-19
 

SOURCE TYPE: Area SOURCE CATEGORY: Agricultural Tilling 

DESCRIPTION: 

Fugitive dust emissions from agricultural tilling. 

POLLUTANTS: 

PM10 and PM2.5 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Agricultural land use 

ACTIVITY DATA: 

• Total planted area by crop type (SAGARPA, 2007a) 

EMISSION FACTORS: 

• Crop-specific emission factors (lbs/acre-yr) (ARB, 2003a). 

NOTES AND ASSUMPTIONS: 

• PM2.5 particle size fraction of PM10 is 0.2217 (ARB, 2002). 

• It is assumed that Mexican agricultural tilling practices are similar to practices in the San Joaquin Valley, California. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from agricultural tilling operations for wheat in Mexicali. 

Total planted area for wheat (average hard, soft, and crystalline varieties) = 81,111 hectares = 200,425 acres 

Annual PM10 emissions = 200,425 acres x 3.7 lbs/acre-yr = 741,572.5 lbs/yr = 370.8 tons/yr 

Annual PM2.5 emissions = 370.8 tons PM10 x 0.2217 = 82.2 tons/yr 

A-20
 

SOURCE TYPE: Area SOURCE CATEGORY: Agricultural Harvesting 

DESCRIPTION: 

Fugitive dust emissions from agricultural harvesting. 

POLLUTANTS: 

PM10 and PM2.5 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Agricultural land use 

ACTIVITY DATA: 

• 	 Total harvested area by crop type (SAGARPA, 2007a) 

EMISSION FACTORS: 

• 	 Crop-specific emission factors (lbs/acre-yr) (ARB, 2003b). 

NOTES AND ASSUMPTIONS: 

• 	 PM2.5 particle size fraction of PM10 is 0.2217 (ARB, 2002). 

• 	 It is assumed that Mexican agricultural harvesting practices are similar to practices in the San Joaquin Valley, 

California. 

SAMPLE CALCULATIONS: 

Estimate annual emissions from agricultural harvest operations for wheat in Mexicali. 

Total harvested area for wheat (average hard, soft, and crystalline varieties) = 80,555 hectares = 199,051 acres 

Annual PM10 emissions = 199,051 acres x 5.8 lbs/acre-yr = 1,154,498.1 lbs/yr = 577.2 tons/yr 

Annual PM2.5 emissions = 577.2 tons PM10 x 0.2217 = 128.0 tons/yr 

A-21
 

SOURCE TYPE: Area SOURCE CATEGORY: Pesticides 

DESCRIPTION: 

Volatile organic compound emissions from the active and inert ingredients in pesticides. VOC emissions can occur 

either during application of pesticides or as a result of evaporation. 

POLLUTANTS: 

VOC 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Agricultural land use 

ACTIVITY DATA: 

• 	 Municipality level crop acreage (SAGARPA, 2007a) 

• 	 Typical application rates (SAGARPA, 2007b) 

EMISSION FACTORS: 

• 	 VOC emission factors (active ingredients only) – 700 kg/Mg (vapor pressure between 0.0001 mm Hg and 0.000001 

mm Hg) and 1,160 kg/Mg (vapor pressure greater than 0.0001 mm Hg) (EIIP, 2001b) 

NOTES AND ASSUMPTIONS: 

• 	 Method of pesticide application was assumed to be surface application. 

• 	 Typical application rates were only available for alfalfa, asparagus, broccoli, cotton, garlic, tomatoes (red and green), 

and wheat. 

SAMPLE CALCULATIONS: 

Estimate the total annual emissions from pesticide application for wheat in Mexicali. 
 

Wheat acreage = 81,111 hectares = 200,425 acres 
 

Dimetoato (dimethoate) applied at a rate of 1.5 liters/hectare (converted to 1.46 lbs/acre using pesticide density)
 


43.5% active; 56.5% inert 

Active ingredient vapor pressure – 2.5 x 10-5 mm; corresponds to emission factor of 700 lbs VOC/ton of active ingredient 

Applied as an emulsified concentrate; corresponds to a inert ingredient VOC content of 56% 

Emissions from active ingredients: 

Emissions = 200,425 acres x 1.46 lbs pesticide/acre x 0.435 x 700 lbs VOC/ton of active ingredients = 44,535.2 lbs = 

22.3 tons 
 

Emissions from inert ingredients: 
 

Emissions = 200,425 acres x 1.46 lbs pesticide/acre x 0.565 x 0.56 = 92,551.4 lbs = 46.3 tons 
 

Total emissions: 
 

Total emissions = active emissions + inert emissions = 22.3 tons + 46.3 tons = 68.6 tons
 


A-22
 

SOURCE TYPE: Area SOURCE CATEGORY: Beef Cattle Feedlots 

DESCRIPTION: 

Fugitive dust emissions released because cattle movement over ground. 

POLLUTANTS: 

PM10 

METHOD: 

Emission factor 

SPATIAL ALLOCATION: 

Agricultural land use 

ACTIVITY DATA: 

• Number of cattle slaughtered (Rodríguez Hernández) 

• Residence time in feedlot – 90 days (Sukarne, 2008) 

EMISSION FACTORS: 

• 28.9 lbs PM10/1000 head-day (ARB, 2004) 

NOTES AND ASSUMPTIONS: 

• 

SAMPLE CALCULATIONS: 

Estimate the total annual emissions from beef cattle feedlots. 

Cattle slaughtered = 223,545 

Residence time = 90 days 

Emissions = 223,545 head x 90 days x 28.9 lbs PM10/1000 head-day = 581,440.5 lbs PM10 = 290.7 tons PM10 

A-23
 

SOURCE TYPE: Area SOURCE CATEGORY: Livestock Ammonia and VOC 

DESCRIPTION: 

The sources of emissions in this category are livestock and domesticated farm and dairy animals. 

POLLUTANTS: 

VOC and NH3 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Agricultural land use 

ACTIVITY DATA: 

• 	 Livestock population (SAGARPA, 2007c) 

EMISSION FACTORS: 

• 	 VOC: Beef cattle – 12.8 lb/head-year; dairy cows – 12.8 lb/head-year; pigs – 4.6 lb/head-year; sheep – 0.96 lb/headyear; goats – 0.96 lb /head-year (ARB, 2004) 

• 	 NH3: Beef cattle – 9.0 kg/head-year; dairy cows – 25.0 kg/head-year; pigs – 7.0 kg/head-year; sheep – 1.34 kg/head

year; goats – 0.58 kg/head-year (Chinkin et al., 2003) 

ASSUMPTIONS: 

• 	 Livestock statistics represent year-round livestock populations. 

• 	 Poultry and horse populations were not found in Mexicali. 

­

SAMPLE CALCULATIONS: 

Estimate the total annual livestock ammonia emissions in Mexicali. 

Municipality level VOC emissions – Mexicali: 

Emissions from beef cattle = (160,221 head) × (12.8 lb VOC/head-year) = 1,025.4 tons 
 

Emissions from dairy cows = (50,432 head) × (12.8 lb VOC/head-year) = 322.8 tons 
 

Emissions from pigs = (4,750 head) × (4.6 lb VOC/head-year) = 10.9 tons
 


Emissions from sheep = (9,227 head) × (0.96 lb VOC/head-year) = 4.4 tons 
 

Emissions from goats = (5,781 head) × (0.96 lb VOC/head-year) = 2.8 tons 
 

Total annual VOC emissions = 1,025.4 + 322.8 + 10.9 + 4.4 + 2.8 = 1,366.3 tons 

Municipality level NH3 emissions – Mexicali: 

Emissions from beef cattle = (160,221 head) × (9.0 kg NH3/head-year) = 1,441,989 kg = 1,589.5 tons 

Emissions from dairy cows = (50,432 head) × (25.0 kg NH3/head-year) = 1,260,800 kg = 1,389.8 tons 

Emissions from pigs = (4,750 head) × (7.0 kg NH3/head-year) = 33,250 kg = 36.7 tons 

Emissions from sheep = (9,227 head) × (1.34 kg NH3/head-year) = 12,364 kg = 13.6 tons 

Emissions from goats = (5,781 head) × (0.58 kg NH3/head-year) = 3,353 kg = 3.7 tons 

Total annual NH3 emissions = 1,589.5 + 1,389.8 + 36.7 + 13.6 + 3.7 = 3,033.3 tons 

A-24
 

Area SOURCE CATEGORY: Fertilizer Application 

DESCRIPTION: 

Ammonia emissions from the application of nitrogen based fertilizers. The amount of ammonia emissions depend on the 

nitrogen content of the fertilizer. 

POLLUTANTS: 

NH3 

METHOD: 

Emission factors 

ACTIVITY DATA: 

• 	 Municipality level crop acreage (SAGARPA, 2007a) 

• 	 Typical application rates (SAGARPA, 2007b) 

EMISSION FACTORS: 

• 	 NH3 emission factors (kg NH3/kg fertilizer): anhydrous ammonia – 0.04857; urea – 0.18214; NPK, calcium nitrate, 

diammonium phosphate – 0.02428 (EEA, 2002) 

NOTES AND ASSUMPTIONS: 

• 	 Typical application rates were only available for alfalfa, asparagus, broccoli, cotton, garlic, tomatoes (red and green), 

and wheat. 

SAMPLE CALCULATIONS: 

Estimate the total annual fertilizer emissions for wheat in Mexicali. 

Planted wheat area = 81,111 hectares 
 

Application rates = anhydrous ammonia 125 kg/hectare, urea 252 kg/hectare, 11-52-00 (N-P-K) 100 kg/hectare 
 

NH3 emissions = (81,111 hectares x 125 kg/hectare x 0.04857 kg NH3/kg fertilizer) + (81,111 hectares x 252 kg/hectare 

x 0.18214 kg NH3/kg fertilizer) + (81,111 hectares x 100 kg/hectare x 0.02428 kg NH3/kg fertilizer) = 4,412,319 kg NH3 = 

4,863.7 tons 

A-25
 

SOURCE TYPE: Area SOURCE CATEGORY: Paved Road Dust 

DESCRIPTION: 

Re-entrained road dust from vehicle activity on paved roads. 

POLLUTANTS: 

PM10, PM2.5 

METHOD: 

Emission factor equations 

SPATIAL ALLOCATION: 

Paved road network 

ACTIVITY DATA: 

• Paved road VMT (Gonzalez-Ayala, 2008a) 

EMISSION FACTORS: 

• Predictive emission factor equation (U.S. EPA, 1995) 

• k = particle size multiplier – 0.016 lb/VMT (PM10); 0.0024 lb/VMT (PM2.5) 

• sL = silt loading – derived from Mexicali paved road dust sampling at 10 locations 

• W = average vehicle weight 

• C = exhaust emission correction – 0.00047 lb/VMT (PM10); 0.00036 lb/VMT (PM2.5) 

• P = number of wet days (>0.01 in) = 21 

• N = number of days in averaging period = 365 

NOTES AND ASSUMPTIONS: 

• It was assumed that the average vehicle weight was 3.18 tons. 

• Emission factors were calculated for each of the 10 locations and then averaged together. 

• Negative emission factors calculated with the predictive emission factor equation were set to zero. 

SAMPLE CALCULATIONS: 

Estimate PM10 emission factor for Avenida Oaxaca between Calle Uxmal and Heroico Colegio Militar 

Length sampled = 216 feet 

Width sampled = 10 feet 

Sample weight = 681.5 g 

Silt content = 2.68% 

Emission factor = [k(sL/2)0.65(W/3)1.5 – C] x (1 – P/4N) 

sL = silt/area = (681.5 g x 0.0268)/(216 feet x 10 feet) = 0.091025 g/m2 

EF(PM10) = [(0.016) x (0.091025/2)0.65 x (3.18/3)1.5 – 0.00047] x (1 – 21/[4 x 365]) = 0.00185 lbs/VMT 

A-26
 

SOURCE TYPE: Area SOURCE CATEGORY: Unpaved Road Dust 

DESCRIPTION: 

Re-entrained road dust from vehicle activity on unpaved roads. 

POLLUTANTS: 

PM10, PM2.5 

METHOD: 

Emission factor equations 

SPATIAL ALLOCATION: 

Unpaved road network 

ACTIVITY DATA: 

• 	 Unpaved road VMT (Gonzalez-Ayala, 2008b) 

EMISSION FACTORS: 

• 	 Predictive emission factor equation (U.S. EPA, 1995) 

• 	 k = empirical constants – 1.8 lb/VMT (PM10); 0.18 lb/VMT (PM2.5) 

• 	 a = empirical constant – 1 (PM10 and PM2.5) 

• 	 c = empirical constant – 0.2 (PM10 and PM2.5) 

• 	 d = empirical constant – 0.5 (PM10 and PM2.5) 

• 	 s = surface material silt content (%) – derived from Mexicali unpaved road dust sampling at 10 locations 

• 	 S = mean vehicle speed (mph) – 11 mph urban; 18 mph suburban; 25 mph rural 

• 	 M = surface material moisture content average vehicle weight (%) 

• 	 C = exhaust emission correction – 0.00047 lb/VMT (PM10); 0.00036 lb/VMT (PM2.5) 

• 	 P = number of wet days (>0.01 in) = 21 

• 	 N = number of days in averaging period = 365 

NOTES AND ASSUMPTIONS: 

• 	 Urban, suburban, and rural emission factors were calculated for each of the 10 locations and then averaged to obtain 

average urban, suburban, and rural emission factors. 

• 	 Negative emission factors calculated with the predictive emission factor equation were set to zero. 

SAMPLE CALCULATIONS: 

Estimate PM10 emission factor (urban at 11 mph) for Calle Texcoco between Calzada Laguna de Xochimilco and 

Avenida Acolman 

Silt content = 1.66% 

Moisture content = 0.29% 

Emission factor = {[k(s/12)a

(S/30)d] / [(M/0.5)c] – C} x (1 – [P/N]) 
 

EF(PM10) = {[(1.8) x (1.66/12)1 x (11/30)0.5] / [(0.29/0.5)0.2] – 0.00047} x (1 – [21/365]) = 0.15802 lbs/VMT
 


A-27
 

SOURCE TYPE: Area SOURCE CATEGORY: Structure Fires 

DESCRIPTION: 

Structure fires include the unintentional burning of the structure material and building contents. 

POLLUTANTS: 

NOx, VOC, CO, PM10, and PM2.5 

METHOD: 

Emission factors 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• Fires (Ruiz Burgueño, 2008) 

EMISSION FACTORS: 

• NOx – 1.4 lbs/ton of material (EIIP, 2001c) 

• VOC – 11 lbs/ton of material (EIIP, 2001c) 

• CO – 60 lbs/ton of material (EIIP, 2001c) 

• PM10 – 10.6 lbs/ton of material (EIIP, 2001c) 

• PM2.5 – 9.9 lbs/ton of material (EIIP, 2001c) 

• CH4 – 2.9 lbs/ton of material (EIIP, 2001c) 

NOTES AND ASSUMPTIONS: 

• Average content loss was assumed to be 7.3% (EIIP, 2001c). 

• Average amount of combustible contents in the structure was assumed to be 38.62 kg/m2 (EIIP, 2001c). 

• Average area of a structure was assumed to be 100 m2

. 

• Only residential building fires were considered. 

• Combustible building material was assumed to be 0 Mg (masonry construction). 

• PM2.5 size fraction of PM10 = 0.9327 (ARB, 2002). 

SAMPLE CALCULATIONS: 

Estimate the total annual emissions from structure fires in Mexicali. 

Annual municipality level emissions: 

Number of fires = 498 

Total combustible material = number of fires × structural loss × (combustible building material + combustible building 

contents) = 498 × (0.073) × (0 + 100 m2 × 38.62 kg/m2

) = 140,399 kg = 154.8 tons 

NOx emissions = 154.8 tons × 1.4 lbs/ton = 217 lbs = 0.1 tons 
 

VOC emissions = 154.8 tons × 11 lbs/ton = 1,703 lbs = 0.9 tons 
 

CO emissions = 154.8 tons × 60 lbs/ton = 9,288 lbs = 4.6 tons 
 

PM10 emissions = 154.8 tons × 10.6 lbs/ton = 1,641 lbs = 0.8 tons 
 

PM2.5 emissions = 154.8 tons × 9.9 lbs/ton = 1,533 lbs = 0.8 tons 
 

CH4 emissions = 154.8 tons × 2.9 lbs/ton = 449 lbs = 0.2 tons 
 

A-28
 

SOURCE TYPE: Area SOURCE CATEGORY: Border Crossings 

DESCRIPTION: 

Emissions from motor vehicles idling at border crossings. 

POLLUTANTS: 

NOx, SO2, VOC, CO, PM10, PM2.5, NH3 

METHOD: 

Emission factors derived from MOBILE6-Mexico (ERG, 2003) 

SPATIAL ALLOCATION: 

Border crossing location 

ACTIVITY DATA: 

• Monthly average minimum and maximum temperatures 

• Number of vehicles at border crossing points (BTS, 2008) 

• Vehicle wait times at various border crossing ports (BTS, 2008) 

EMISSION FACTORS: 

• MOBILE6-Mexico (ERG, 2003) 

NOTES AND ASSUMPTIONS: 

• Average vehicle speed at border crossing points is assumed to be 4 km/hr. 

• Emissions were only estimated for vehicles entering from Mexico into the United States. 

SAMPLE CALCULATIONS: 

Estimate NOx emissions from Calexico border crossing in January 

Number of LDGVs = 268,892
 


Number of LDGTs = 250,583
 


Number of MCs = 7,261 
 

Number of HDDVs = 125 
 

Speed – 0.0414 miles/minute (4 km/hour) 
 

Wait times – 43 minutes for LDGVs, LDGTs, and MCs; 2 minutes for HDDVs 
 

LDGV emission factor = 1.08 g NOx/mile 
 

LDGT emission factor = 1.328 g NOx/mile 
 

MC emission factor = 1.34 g NOx/mile 
 

HDDV emission factor = 15.959 g NOx/mile 
 

Emissions = (268,892 x 43 min x 0.0414 miles/min x 1.08 g/mile) + (250,583 x 43 min x 0.0414 miles/min x 1.328 g/mile) 

+ (7,261 x 43 min x 0.0414 miles/min x 1.34 g/mile) + (125 x 2 min x 0.0414 miles/min x 15.959 g/mile) = 

1.127 Mg = 1.243 tons 

A-29
 

SOURCE TYPE: Area SOURCE CATEGORY: Brick Kilns 

DESCRIPTION: 

Emissions released during the firing of brick kilns. 

POLLUTANTS: 

NOx, SO2, VOC, CO, PM10, PM2.5, NH3 

METHOD: 

Utilization of previous emission estimates from 2005 San Luis Río Colorado (SLRC)/Mexicali emissions inventory 

(ENVIRON and ERG, 2008) 

SPATIAL ALLOCATION: 

Brick kiln location 

ACTIVITY DATA: 

• 	 Not applicable 

EMISSION FACTORS: 

• 	 Not applicable 

NOTES AND ASSUMPTIONS: 

• 	 The 2005 SLRC/Mexicali inventory included three brick kiln areas: Colonia Mezquite in SLRC (the largest brick kiln 

area), Colonia La Grullita in SLRC (a smaller brick kiln area), and south of Algodones in northeast Mexicali (firing bricks 

and tile shingles). 

• 	 Mexicali includes two brick kiln areas – Algodones and Colonia Campestre 

• 	 Accurate activity data could not be collected at Colonia Campestre 

• 	 The emissions from the Algodones brick kiln area were used. 

• 	 The emissions from the Colonia La Grullita area in SLRC were assumed to be equivalent to the Colonia Campestre 

area in Mexicali. 

SAMPLE CALCULATIONS: 

Not applicable 

A-30
 

SOURCE TYPE: Area SOURCE CATEGORY: Charbroiling/Street Vendors 

DESCRIPTION: 

Emissions from charbroiling of meat. 

POLLUTANTS: 

NOx, VOC, CO, PM10, PM2.5 

METHOD: 

Estimates from Mexico NEI (ERG, 2006) 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• Not applicable 

EMISSION FACTORS: 

• Not applicable 

NOTES AND ASSUMPTIONS: 

• Data request for meat quantities, but adequate data were not available. 

• Mexicali emission estimates were used from the 1999 Mexico NEI. 

• Emissions were grown from 1999 to 2005 using population. 

SAMPLE CALCULATIONS: 

Estimate the total annual emissions from charbroiling in Mexicali. 
 

1999 annual CO emissions = 121.7 Mg/yr 
 

1999 population 764,602 
 

2005 population 855,962 

Growth factor = 855,962/764,602 = 1.1195 

2005 annual CO emissions = 121.7 Mg x 1.1195 = 136.2 Mg/yr = 150.2 tons/year 

A-31
 

SOURCE TYPE: Area SOURCE CATEGORY: Construction Dust 

DESCRIPTION: 

Dust emitted from earthmoving and other construction activities. 

POLLUTANTS: 

PM10 and PM2.5 

METHOD: 

Estimates from 1999 Mexico NEI (ERG, 2006) 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• Not applicable 

EMISSION FACTORS: 

• Not applicable 

NOTES AND ASSUMPTIONS: 

• Mexicali emission estimates were used from the 1999 Mexico NEI. 

• Emissions were grown from 1999 to 2005 using population. 

SAMPLE CALCULATIONS: 

Estimate the total annual emissions from construction dust in Mexicali. 
 

1999 annual PM10 emissions = 110.2 Mg/yr 
 

1999 population 764,602 
 

2005 population 855,962 

Growth factor = 855,962/764,602 = 1.1195 

2005 annual PM10 emissions = 110.2 Mg x 1.1195 = 123.4 Mg/yr = 136.0 tons/year 

A-32
 

SOURCE TYPE: Area SOURCE CATEGORY: Bakeries 

DESCRIPTION: 

Emissions from bakeries. 

POLLUTANTS: 

VOC 

METHOD: 

Estimates from 1999 Mexico NEI (ERG, 2006) 

SPATIAL ALLOCATION: 

Population 

ACTIVITY DATA: 

• Not applicable 

EMISSION FACTORS: 

• Not applicable 

NOTES AND ASSUMPTIONS: 

• Mexicali emission estimates were used from the 1999 Mexico NEI. 

• Emissions were grown from 1999 to 2005 using population. 

SAMPLE CALCULATIONS: 

Estimate the total annual emissions from bakeries in Mexicali. 
 

1999 annual PM10 emissions = 95.6 Mg/yr 
 

1999 population 764,602 
 

2005 population 855,962 

Growth factor = 855,962/764,602 = 1.1195 

2005 annual PM10 emissions = 95.6 Mg x 1.1195 = 107.0 Mg/yr = 117.9 tons/year 

A-33
 

SOURCE TYPE: Area SOURCE CATEGORY: Landfills 

DESCRIPTION: 

Landfill gases emission by anaerobic processes in landfills. 

POLLUTANTS: 

VOC and CH4 

METHOD: 

Theoretical first-order kinetic model of methane production 

SPATIAL ALLOCATION: 

Landfill location 

ACTIVITY DATA: 

• Average refuse acceptance rates (R) – 880 Mg/day or 321,200 Mg/yr (Villegas Ibarra, 2008) 

EMISSION FACTORS: 

• Calculated values 

NOTES AND ASSUMPTIONS: 

• Methane generation potential (Lo) – 100 m3 CH4/Mg refuse. 

• Methane generation rate constant (k) – 0.02 yr-1. 

• Time since landfill closure (c) – 0 years. 

• Time since initial refuse placement (t) – 15 years (Villegas Ibarra, 2008) 

• Molecular weight of CH4 (MWCH4)– 16.04 g/gmol 

• Molecular weight of NMOC (MWNMOC) (expressed as hexane) – 86.18 g/gmol. 

• Temperature of landfill gas – 25 oC. 

• Concentration of NMOC in landfill gas (CNMOC)– 4000 ppmv. 

• VOC is assumed to be equivalent to NMOC. 

SAMPLE CALCULATIONS: 

Estimate annual VOC and CH4 emissions from the Mexicali landfill. 

QCH4 (methane generation, m3

/yr) = LoR(e-kc – e-kt) = 100 x 321,200 x (e[-0.02 x 0] – e[-0.02 x 15]) = 8,324,919 m3 CH4/yr 

MCH4 = QCH4 x {[MW(CH4) x 1 atm]/[(8.205 x 10-5 m3

-atm/gmol-K) x (1000 g/kg) x (273 + T)]} = 

8,324,919 m3

 CH4/yr x {[16.04 g/gmol x 1 atm]/[(8.205 x 10-5 m3-atm/gmol-K) x (1000 g/kg) x (298 K)]} =
 


5,461,218 kg/yr = 6,019.9 tons CH4/yr
 


QNMOC (NMOC generation, m3

/yr) = 1.82 x QCH4 x CNMOC/(1 x 106) = 
 

1.82 x 8,324,919 m3

 CH4/yr x 4000/(1 x 106) = 60,605 m3/yr 

MNMOC = QNMOC x {[MW(NMOC) x 1 atm]/[(8.205 x 10-5 m3-atm/gmol-K) x (1000 g/kg) x (273 + T)]} = 
 

60,605 m3

 NMOC/yr x {[86.18 g/gmol x 1 atm]/[(8.205 x 10-5 m3-atm/gmol-K) x (1000 g/kg) x (298 K)]} =
 


213,611 kg/yr = 235.5 tons NMOC/yr 
 

A-34
 

SOURCE TYPE: Area SOURCE CATEGORY: Wastewater Treatment 

DESCRIPTION: 

Volatile organic compound emissions from collection, contaminant treatment, and/or storage of industrial wastewater 

streams. These streams are discharged into either a receiving body of water or a municipal treatment plant. 

POLLUTANTS: 

VOC 

METHOD: 

Emission factors 

ACTIVITY DATA: 

• Municipality level municipal treatment plant statistics (CNA, 2007) 

EMISSION FACTORS: 

• TOG – 1.3 x 10-5 kg/liter (U.S. EPA, 1991) 

NOTES AND ASSUMPTIONS: 

• VOC emissions equal TOG emissions. 

SAMPLE CALCULATIONS: 

Estimate the total annual VOC emissions from wastewater treatment in Mexicali. 

Actual flow rate = 1.8494 m3/s = 58,322,678,400 liters/year 

Annual TOG emissions = 1.3 × 10-5 kg/liter × 58,322,678,400 liters/year = 758,195 kg/year = 835.8 tons/year 

A-35
 

APPENDIX B: 
 

TECHNICAL REPORT (DRAFT), ESTIMATION OF TRAVEL DEMAND 
 

AND VEHICULAR TRAFFIC CONGESTION OF THE MEXICALI REGION
 

TECHNICAL REPORT 
 

(DRAFT)
 


Estimation of Travel Demand and Vehicular Traffic Congestion of the 
 

Mexicali Region 
 

September 2008 

Prepared by: 

TransEngineering 

for Eastern Research Group, Inc. 

B-1
 

I. Introduction 

The present document describes the approach followed for estimation of motor-vehicle activity and related 

mobile source emissions for the municipality of Mexicali. As shown in Figure 1, the municipality of Mexicali is 

located in the State of Baja California, Mexico, and includes the city of Mexicali. 

City of Mexicali 

(urban area) 

Limits of the Mexicali 

municipality 

Figure 1. Geographic location of the Mexicali municipality. 

The municipality of Mexicali has an area of 5,650 square-miles, while the urban area of Mexicali has only 54 

square-miles, thus the urban area represents about 1% of the entire municipality area. Nevertheless, based 

on INEGI statistics, the 2005 population for the urban area of Mexicali was 653,000, which represents 

approximately 88% of the total municipality population1

. 

1

 According to INEGI, the population of the municipality of Mexicali was 745,000 in 2005 (Instituto Nacional de 

Estadística, Geografía e Informática/ Conteo de Población 2005). 

B-2
 
The main roadways in the Mexicali municipality have been identified, for both urban and rural areas; these 

roadways are depicted in Figure 2. In the rural areas, the most important roadways are Federal Highway 2 

(MEX 2), which connects the city of Mexicali to the city of Tijuana (to the west), and San Luis Rio Colorado (to 

the east), and Federal Highway 5 (MEX 5) which connects the city of Mexicali to San Felipe (to the south) and 

Ensenada (to the southwest). The rest of the roadways identified in Figure 2 are state highways. As part of 

the roadway infrastructure, three ports-of-entry (POEs) between Mexico and the US have been identified as 

well: the Andrade POE in northeast corner of the municipality, and the Calexico and Calexico East POEs in 

the city of Mexicali. Figure 3 presents a map with details of the urban area of Mexicali. 

Andrade POE 

Calexico POE Calexico East POE 

BC 15 BC 6 

BC 2 

BC 1 BC 10 

BC 4 

MEX 2 

MEX 2 

MEX

5 

MEX 5 

Figure 2. Main rural and urban roadways within the Mexicali municipality. 

B-3
 

Calexico POE 

Calexico East POE 

Figure 3. Detail of roadway system for the city of Mexicali (urban area). 

The rural and urban roadway systems have been converted into a travel demand model to estimate daily 

vehicle traffic volumes. The travel demand model developed for this study has three main components: 

• Trip generation 

• Trip distribution 

• Traffic assignment 

Each of these components will be briefly described in the following pages. 

B-4
 

II. Trip Generation 

As a first step to establish traffic volumes in Mexicali, the methodology takes advantage of research in the US 

indicating that the values of trip generation rates across urban area locations and sizes are fairly stable when 

disaggregated by socioeconomic conditions of the population such as household size, income, and 

employment. This stability intuitively suggests the transferability of known rates from the limited case studies 

in Mexico to other urban areas in the country. One known and documented case study is Ciudad Juarez, and 

thus its disaggregate trip rates as well as other traffic patterns can be borrowed and used as a basis for 

estimating preliminary traffic volumes in other urban areas. For the present study, such rates will also be 

assumed to take place in the rural areas of the municipality. 

Trip generation rates 

The trip generation rates developed from the Juarez’ transportation research program2

 are shown in Tables 1 

and 2. Table 1 presents production rates based on household size and household income. Table 2 presents 

attraction rates based on area type and employment by economic activity. These rates are expressed as 

person-trips per day. 

Table 1. Juarez’ disaggregate trip production rates (daily person-trips per household). 

Trip HH HH size 

purpose income 1 2 3 4 5 6+ 

(1) 0.508 1.268 1.156 1.417 1.474 2.067 

(2) 0.902 1.705 1.479 1.726 2.096 2.400 

HBW (3) 0.875 1.581 1.522 1.698 2.247 2.987 

(4) 1.000 1.847 1.708 1.800 2.438 2.762 

(5) 0.598 1.559 1.541 2.169 2.494 3.259 

(6) 0.801 2.180 2.307 2.119 2.622 3.196 

(1) 2.609 6.142 3.761 9.435 13.264 13.111 

(2) 1.647 6.005 3.792 9.294 12.064 13.730 

HBO (3) 1.933 5.767 3.921 9.221 12.955 13.430 

(4) 2.580 4.536 3.143 11.005 12.653 17.027 

(5) 1.933 7.240 4.349 10.707 10.839 12.742 

(6) 0.769 4.957 2.675 10.475 13.960 11.975 

(1) 0.435 1.484 0.560 1.252 2.604 2.396 

(2) 0.352 1.515 0.820 1.891 2.479 2.335 

NHB (3) 1.019 2.301 0.976 2.583 2.577 2.314 

(4) 1.230 1.766 1.659 3.550 3.299 3.060 

(5) 1.230 3.890 2.167 3.198 3.587 3.060 

(6) 0.609 1.726 1.882 2.929 5.643 1.995 

The income ranges are as follows: 

Range Household income range Household income range 

Code (1996 daily min wages) (1996 dollars) 

1 0 to 2 $ 0.00 to $ 1,374 /year 

2 2 to 6 $ 1,375 to $ 4,122 /year 

3 6 to 10 $ 4,123 to $ 6,870 /year 

4 10 to 12 $ 6,871 to $ 8,244 /year 

5 12 to 18 $ 8,244 to $12,367 /year 

6 over 18 $12,368 plus 

2 Technical Memo series “Estudio Integral de Transporte/Multimodal Transportation Study: Development of Travel Demand and MobileSource Emission Models for base year 1996, Juarez”, (IMIP 1998). 

B-5
 
Table 2. Juarez’ disaggregate trip attraction rates (daily person-trips per employee). 

Trip Area Household Employment type 

purpose Type Basic Retail Service 

CBD 0.125 2.000 1.557 1.899 

HBW Urban 0.125 1.859 3.351 2.953 

Suburban 0.125 1.932 1.777 2.027 

Rural 0.125 1.497 1.615 2.099 

CBD 0.569 0.031 36.758 22.417

HBO Urban 0.569 0.084 28.701 16.275

Suburban 0.569 0.036 29.457 20.086

Rural 0.569 0.195 3.423 2.881 

CBD 0.127 0.062 0.409 14.438

NHB Urban 0.127 0.505 7.179 3.568 

Suburban 0.127 0.109 2.681 4.875 

Rural 0.127 0.627 2.694 10.485

CBD 0.160 0.002 0.000 0.000 

TT Urban 0.170 0.000 0.129 0.000 

Suburban 0.180 0.012 0.549 0.102 

Rural 0.180 0.087 0.477 0.028 

As depicted in these tables, the trip rates have been disaggregated into four different trip purposes: 

1) Home-based-work (HBW) trips, which have the home as one of the trip ends, and work on the other, 

regardless of which is the origin or destination. 

2) Home-based-other (HBO) trips, which have the home as one of the trip ends, and a non-work activity 

on the other, regardless of which is the origin or destination. 
 

3) Non-home-based (NHB) trips, which have non-home trip ends. 
 

4) Truck-taxi (TT) trips, which are delivery type NHB trips. 
 

Table 2 shows in addition four different categories of Area Type. This is a measure of urban activity 

concentration also known as activity density. Such attribute needs to be computed for the different component 

zones of each urban area. Activity density is a function of the population and employment per unit area, as 

follows: 

Activity Density = [Zone Population + (Zone Employment * F)] / Zone Acres (Eq. 1) 

...where the normalization factor 

F = Total study area Population / Total study area Employment (Eq. 2) 

Area Type thus is categorized as follows: 

Code

 4 

3 

2 

1 

 Area Type

Rural 

Suburban 

Urban 

CBD 

 Activity density

up to 26 

27 to 62 

63 to 135 

over 136 

Zone structure 

In order to develop trip generation patterns from expansion of the disaggregate trip rates, a zone structure with 

the required demographic variables like population, household size, income, and employment was established 

for the municipality of Mexicali. In this regard the Instituto Nacional de Estadística Geografía e Informática 

(INEGI) has demographic and socio-economic information, disaggregated in census tracts called Areas 

Geoestadísticas Básicas (AGEBs). These AGEB arrangements were used as reference to develop a traffic 

analysis zone (TAZ) structure for the urban and rural areas of the municipality. Figure 4 shows the resulting 

TAZ structure, which follows the alignment of the main roadway system. This TAZ structure was developed 

into a geographic information system (GIS) coverage, to store zonal attributes of the municipality. 

B-6
 
Detail of TAZ structure 

for urban Mexicali 

Figure 4. Traffic Analysis Zone (TAZ) structure for the Mexicali municipality. 

Disaggregate household information
 


To develop total productions by TAZ, the trip generation model requires as input the total number of 
 

households under each category of size and income for each TAZ. 
 

Regarding income, INEGI reports such variable on a per capita basis, so under the Juarez case study a linear 
 

regression was developed to correlate per capita income to a zonal household average income value, 
 

specially requested to INEGI from the 1990 census. The regression has the following form: 
 

RelHHinc = 3.230 – 0.125*Wa – 0.078*Wb – 0.059*Wc (Eq. 3) 

 where: 

RelHHinc: Zonal average household income/Regional average household income

 Wa: Zonal Pop earning less than 1 minimum wage x100/Total zone pop 

Wb: Zonal Pop earning between 1 and 2 minimum wages x100 /Total zone pop 

Wc: Zonal Pop earning between 2 and 5 minimum wages x100/Total zonal pop 

Household size is reported by INEGI as a zonal average at the AGEB level; this number was converted to a 

relative household size (RelHHsiz), dividing the zonal average household size value by the regional average 

household size value. 

B-7
 
In order to convert the previously described zonal averages to actual number of households on each income 

and size category, the marginal curves in Figures 5 and 6 were developed. These are used by entering the 

relative zonal average (zonal average/regional average) on the X axis, and obtaining the percentage of 

households under each category, depicted by the corresponding marginal curves. The percentages are then 

applied to the total number of households in the TAZ. 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 

HH inc 1 

Adj 1 

HH inc 2 

Adj 2 

HH inc 3 

Adj 3 

HH inc 4 

Adj 4 

HH inc 5 

Adj 5 

HH inc 6 

Adj 6 

Relative HH income 

Figure 5. Distribution of households by relative household income (RelHHinc). 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 

HH size 1 

Adj 1 

HH size 2 

Adj 2 

HH size 3 

Adj 3 

HH size 4 

Adj 4 

HH size 5 

Adj 5 

HH size 6 

Adj 6 

Relative HH size 

Figure 6. Distribution of households by relative household size (RelHHsiz). 

%HHs %HHs 

The marginal curves were also developed under the Juarez’ transportation research program, using 

extensively detailed databases specially requested to INEGI from the 1990 census. 

B-8
 
Disaggregate employment information 

Employment by economic activity for each TAZ is required by the trip generation model to establish trip 

attraction patterns. This information was obtained directly from the “Censos económicos 2004” (INEGI 2004) 

for the Mexicali area. Since this information is available only for urban areas of 50,000+ population, there is no 

employment information for rural areas in Mexico, and thus employment distribution for the rural area of the 

municipality was assumed to follow similar patterns as those observed in the urban setting; therefore an 

employee was allocated for every 4 people living on every rural TAZ. 

Balanced trip generation totals by AGEB 

As previously indicated, the production trip rates are disaggregated by household size and income, therefore 

the total productions for a zone were obtained simply by multiplying the production trip rates (Table 1) times 

the number of households under each category of household size and income. 

Similarly, total attractions for each zone were obtained by multiplying the attraction trip rates (Table 2) times 

the number of employees under the corresponding category of area type and economic activity. 

The end result of this process is a production-attraction table by zone, for each trip purpose. In theory these 

productions and attractions under the same trip purpose need to add up to the same number, but since their 

development are based on different sources (household survey for productions, and workplace survey for 

attractions), it is common practice to “balance” or factor one to the other, usually to the more reliable one. For 

the Juarez’ case, and thus for the study domain, attractions were balanced to productions. 

In the case of the TT trip purpose, the attractions were balanced to a control total equal of POP/20.9, which 

was the validated relationship in Juarez. 

Table 3 shows a summary of raw production and attraction totals by trip purpose as well as the resulting 

balance factors. 

Table 3. Resulting P-A (person-trips/day) totals for the Mexicali municipality. 

unbalanced FA balanced 

HBW P 390,000 390,000 

A 393,676 0.9907 390,000 

HBO P 364,950 364,950 

A 311,433 1.1718 364,950 

NHB P 1,820,513 1,820,513 

A 1,352,227 1.3463 1,820,513 

TT P 35,477 35,477 

A 55,374 0.6407 35,477 

Total Ps 2,610,940 

Total As 2,610,940 

The balanced P-A by TAZ was stored in the zonal (TAZ) GIS. 

B-9
 

III. Vehicle-Trip Distribution 

At the trip generation step, the total number of trips produced from, and attracted to each TAZ were estimated 

and presented in tabular format. This step provides a rough estimate of the trip-making potential for each 

zone, but does not determine the actual trip exchange with other zones. 

The trip distribution step focuses on this trip exchange between zones, identifying from where and to where 

trips take place. Trip exchange between zones is conventionally presented as a two-dimensional matrix array, 

where each cell represents the number of trips produced at zone i (row i) and attracted to zone j (column j). 

Next, this Production-Attraction matrix (also referred to as the “P-A matrix”) is transformed to OriginDestination (O-D), simply by reconfiguring the cell values to produce a matrix symmetric around the main 

diagonal. In this regard it is important to remember that only in the case of NHB trips, Production is 

considered synonymous to Origin and Attraction to Destination. 

The traditional gravity model 

The traditional gravity model was originally developed from analogies with the physical world (Newton’s gravity 

formulations), but its real strength comes from later improvements and derivation through entropy-maximizing 

considerations, which makes the model closely related to information theory, to error measures, and to 

maximum likelihood in statistics. 

The doubly constrained version of the traditional gravity model has the following form: 

Tij = βi*Pi*αj*Aj*f(tij) (Eq. 4) 

 Where:

 Tij: Trips produced in zone i and attracted to zone j. 

Pi: Total trips produced in zone i. 

βi: Balancing factor for row i (production constraint). 

Aj: Total trips attracted to zone j. 

αj: Balancing factor for column j (attraction constraint) 

f(tij): Impedance (decreasing) function, based on the travel 

time between zone i and zone j. 

The two constraints that the model is required to meet are 1) that the sum of trips in any specific row of the 

matrix should equal the total number of trips produced in that zone, and 2) that the sum of trips in any specific 

column should correspond to the number of trips attracted to that zone (refer back to Figure 5). The two 

conditions can be written as: 

Σ Tij = Pi (Eq. 5) 

j 

Σ Tij = Aj (Eq. 6) 

i 

B-10
 
The expression of both balancing factors βi and αj can thus be derived through simple algebraic 

manipulations of Equations 4 to 6. These have the following forms: 

1 

βi = (Eq. 7)

Σ αj*Aj*f(tij)

j 

1 

αj = (Eq. 8)

Σβi*Pi*f(tij)

i 

As shown here, the balancing factors are interdependent, meaning that the calculation of one set requires the 

values of the other set, furthermore suggesting an iterative process until convergence is achieved. Thus, the 

practical approach to solving this formulation is to specify separate singly constrained models to both 

productions (Eq. 9) and to attractions (Eq. 10). The first one is obtained by making αj = 1 since in this case 

the columns are not being balanced. Similarly, the second one is obtained by making βi = 1 since in this other 

case the rows are the ones not being balanced. 

Aj*f(tij) Tij = Pi* (Eq. 9)

Σ Aj*f(tij)

j 

Pi*f(tij) Tij = Aj* (Eq. 10)

Σ Pi*f(tij)

i 

The solution for the doubly constrained model can then be converged upon by iteratively applying Eq. 9 to 

balance the productions (rows), and Eq. 10 to balance attractions (columns). 

Transportation networks 

In order to perform trip distribution, a roadway network needs to be developed for each urban area selected. 

This network is a simplified version of the current roadway infrastructure layout, including only the main 

roadways, federal and state highways in the rural setting, and from principal arterials to collector roads in the 

urban setting. Local roadways and streets are modeled by artificial links called connectors, which allow for the 

channeling of flows between the TAZs (TAZ centroids) and the network system. The networks were drawn 

from available maps, and organized in a roadway GIS. Figure 7 shows such primary roadway network 

developed for the Mexicali municipality. 

Each link in the network was initially provided with attributes of functional class (generic type of infrastructure 

facility), and direction of flow. This information on the urban setting was provided by Mexicali’s IMIP (Instituto 

Municipal de Investigación y Planeación), while for the rural setting this information was established through 

visual interpretation of satellite images. From this information, a second set of network link attributes was 

established, based on average conditions developed for the Ciudad Juarez case study. This information was 

link capacity and average daily speed, corresponding to functional class and area type where individual links 

are located, as depicted in Table 4. Link travel time was then computed using the assigned link speed. 

B-11
 

Detail of roadway network 

for urban Mexicali 

Figure 7. Roadway network model for the Mexican domain. 
 

Table 4. Speed & capacity (daily per lane) lookup table developed for Juarez. 
 

Speed (mph) Area Type 

Capacity (vpl) CBD 

(1) 

Urban 

(2) 

Suburban 

(3) 

Rural 

(4) 

Conn 15 15 25 35 

(0) 30,000 30,000 30,000 30,000 

Fwy 29 29 31 38 

tion 

(2) 17,250 17,250 14,750 11,750 

Expy 25 25 29 36 

ifica

(3) 13,100 13,100 11,750 10,250 

PartD 17 17 26 33 

Class

(4) 8,350 8,350 7,500 6,250 

PartU 17 17 25 32 

ctional

(5) 7,550 7,550 6,800 5,600 

MartD 14 14 24 28 

(6) 7,250 7,250 6,500 4,050 

MartU 14 14 24 28 

Fun

(7) 6,600 6,600 5,950 3,750 

MartUnp 11 11 18 25 

(8) 6,200 6,200 5,550 3,350 

Ramp 17 17 24 34 

(12) 18,000 18,000 18,000 18,000 

B-12
 

Roadway networks are needed at this stage of the modeling process mainly to establish travel times between 

TAZs, since this will feed into the gravity-model evaluation. Using the network travel times, a skim matrix was 

developed for the domain. This skim matrix depicts the shortest travel time (in minutes) between all TAZs in 

the study area. 

Evaluation of gravity model 

The end result of the gravity-model estimation-calibration process, is the definition of the impedance function 

(the f(tij) term). For the Juarez case study, doubly-constrained gravity models were estimated and calibrated 

for the four trip purposes, yielding impedance functions in the form of friction factor (FF) tables. Under the 

current project, these FF tables were borrowed to evaluate gravity models for each of the selected urban 

areas, using the corresponding set of skim matrices and P-A tables. The result of this evaluation, was the 

development of trip distribution P-A matrices by trip purpose, for the Mexicali municipality. 

The FF tables for each trip purpose are shown in Appendix A. 

Development of vehicle-trip OD matrices 

As a final task of the trip distribution process, the all-mode person-trip P-A matrices needed to be converted 

into vehicle-trip O-D matrices. 

First, this requires the application of mode shares (specifically AUTO mode), to the total number of persontrips by trip purpose. 

Secondly, person-trips on AUTO need to be converted into vehicle-trips, by applying a passenger occupancy 

rate to the person-trip figure. 

From the Juarez research, these AUTO shares and occupancy rates were observed as follows: 

 person-trip average 

AUTO share veh occupancy

 HBW purpose 57.7% 1.09 pax/veh

 HBO purpose 44.4% 1.39 pax/veh

 NHB purpose 66.1% 1.22 pax/veh 

TT purpose 100.0% 1.00 pax/veh 

In the Juarez Case study, vehicle-miles traveled (VMT) from TRANSIT mode represents in the order of 2% of 

the total daily VMT, thus such vehicle activity has been emulated in the VMT mix at the emission modeling 

stages. The same approach has been followed for the initial six urban area categories. 

Finally, in addition to the four generic trip purposes characterizing local (internal to the area) trips, an additional 

trip purpose was added at this stage, depicting external trips (EXT). This trip purpose actually includes all 

external-external and external-local trips, that is, trips that have at least one of their trip-ends outside of the 

study area. In order to develop the corresponding O-D matrix for EXT trip purpose, counts were obtained as 

control totals for the two POEs3

 and the most important highways connecting the Mexican municipality to other 

points outside the study area. Table 5 presents a summary of daily vehicle-trips estimated for the Mexican 

domain. 

Table 5. Summary of daily vehicle-trips estimated for the Mexicali municipality. 

daily vehicle-trips 

HBW 193,189 

HBO 582,286 

NHB 211,303 

TT 35,477 

EXT 104,200 

TOTAL 1,126,455 

3

 In year 2005, the Calexico, Calexico East and Andrade POEs had daily averages of 17000, 9900 and 2000 northbound 

vehicle crossings, respectively. 

B-13 
IV. Traffic Assignment 

The final step in the travel demand modeling process and traffic congestion estimation is traffic assignment. In 

this step, the final OD vehicle-trip matrix for each urban area was assigned to the corresponding network 

system, using a user-equilibrium (UE) algorithm. Such algorithm assigns volumes to the network links, and 

through iteration balances congestion levels between similar cost (time) alternatives. The process involves recomputation of travel speeds to account for congestion levels as traffic volumes accumulate. The result of this 

step yielded “loaded” networks for the selected urban areas with traffic volumes and speeds at each of the 

links of the network. 

A separate assignment was obtained for weekday and weekend conditions. The weekend assignment was 

established by applying a factor of 0.7 to the weekday OD matrix. Figure 8 shows a thematic map of the 

loaded network for the Mexicali municipality under weekday conditions. 

Detail of roadway loaded network 

for urban Mexicali 

Figure 8. Loaded roadway network under weekday conditions for the Mexican domain. 

This information was used to estimate on-road mobile source emissions by link, an then aggregated at the 

municipality level. 

B-14
 
V. On-road mobile source emissions 

Having completed the travel demand modeling part of the study, the final task requires converting the resulting
 


daily vehicle activity (motorized travel) into total daily on-road mobile source emissions, for the entire Mexicali
 


municipality. . 
 

Base emission rates
 


Emission rates in grams/mile-of-travel (or grams/VMT) for VOC, CO, NOx, SO2, NH3, PM2.5, and PM10, have
 


been obtained through the use of Mobile6-Mexico, for a combination of vehicle speeds going from 3 to 65mph. 
 

In addition, emission rates have been obtained for a range of temperatures depicting average ambient 
 

conditions at four different time-of-day periods, during four generic seasons of the year. 
 

The time-of-day periods considered were: 

 period 1 7am to 9am 

 period 2 9am to 4pm 

 period 3 4pm to 8pm 

 period 4 8pm to 7am 

The four generic seasons were designed to represent spring, summer, fall, and winter. 

Emissions by link 

Having developed the base emission rates for the resulting combinations of speeds and temperatures, these 

were assigned to each link in the transportation networks, based on the prevailing “congested” speed at a 

specific point in time during the day. For this, the daily non-directional traffic and 24-hour average speeds 

assigned to each link in the travel models needed to be converted to time-of-day directional traffic and time-ofday directional speeds. To do this a special computer program labeled PrepinPlus was coded in Visual Basic 

(VB) language, following the methodology developed by the Texas Transportation Institute on its software 

PREPIN. The following sequence summarizes the process, which needs to be done for each of the four timeof-day periods: 

a) 	 The 24-hour non-directional traffic assigned to each network link is distributed over the 

four time-of-day periods, based on information from ground counts. For Mexican roadway 

networks the daily traffic volumes have been distributed as follows, based on the Juarez 

case study: 

period 1 9.8% over a 2-hour period 

period 2 39.8% over a 7-hour period 

period 3 26.0% over a 4-hour period 

period 4 24.4% over an 11-hour period 

100.0% 

b) 	 To establish the directional split, the period non-directional traffic volume on each link is 

then applied a factor, which depends on the specific time-of-day period being analyzed. 

These factors vary also depending on the area type and functional classification of the 

roadway. Table 6 shows the average directional factors used for the AB link direction; the 

complement to 1 is applied to the BA direction of each link. At this point then every link in 

the network has directional traffic volumes for the time-of-day period being evaluated. 

B-15
 

Table 6. Directional split factors used for Juarez. 

period 1 

Functional classification 

0 1 2 3 4 5 6 7 8 9 10 11 12 

Area Type

1 0.540 0.650 0.500 0.650 0.650 0.650 0.580 0.580 0.645 0.645 0.500 0.500 0.500 

2 0.540 0.650 0.500 0.650 0.650 0.650 0.580 0.580 0.645 0.645 0.500 0.500 0.500 

3 0.870 0.600 0.500 0.600 0.600 0.600 0.590 0.590 0.630 0.630 0.500 0.500 0.500 

4 0.760 0.620 0.630 0.665 0.665 0.665 0.650 0.650 0.738 0.738 0.630 0.630 0.630 

periods 2 and 4 

Functional classification 

0 1 2 3 4 5 6 7 8 9 10 11 12 

Area Type

1 0.540 0.550 0.510 0.550 0.550 0.550 0.550 0.550 0.545 0.545 0.510 0.510 0.510 

2 0.540 0.550 0.510 0.550 0.550 0.550 0.550 0.550 0.545 0.545 0.510 0.510 0.510 

3 0.540 0.540 0.510 0.540 0.540 0.540 0.550 0.550 0.530 0.530 0.510 0.510 0.510 

4 0.540 0.565 0.510 0.565 0.565 0.565 0.565 0.565 0.555 0.555 0.510 0.510 0.510 

period 3 

Functional classification 

0 1 2 3 4 5 6 7 8 9 10 11 12 

Area Type

1 0.550 0.620 0.540 0.620 0.620 0.620 0.520 0.520 0.575 0.575 0.540 0.540 0.540 

2 0.550 0.620 0.540 0.620 0.620 0.620 0.520 0.520 0.575 0.575 0.540 0.540 0.540 

3 0.720 0.590 0.540 0.590 0.590 0.590 0.530 0.530 0.645 0.645 0.540 0.540 0.540 

4 0.715 0.615 0.665 0.615 0.615 0.615 0.645 0.645 0.648 0.648 0.665 0.665 0.665 

c) 	 Next, the average directional 24-hour link capacities provided as attributes of the roadway 

network are converted into directional hourly capacities. This is done through another set 

of conversion factors that also depend on the area type and functional classification of the 

link. Table 7 shows the factors used; these remain constant during the day. 

Table 7. Conversion factors for 24-hour to hourly capacities. 

Functional classification 

0 1 2 3 4 5 6 7 8 9 10 11 12 

Area Type 

1 N/A 0.063 0.104 0.063 0.066 0.066 0.076 0.076 0.073 0.070 0.070 0.066 0.061 

2 N/A 0.063 0.104 0.063 0.066 0.066 0.076 0.076 0.073 0.070 0.070 0.066 0.061 

3 N/A 0.077 0.081 0.077 0.080 0.081 0.092 0.092 0.086 0.083 0.083 0.080 0.067 

4 N/A 0.106 0.166 0.106 0.116 0.121 0.173 0.167 0.164 0.159 0.159 0.112 0.078 

By multiplying the directional hourly capacities by the hours in each period, these are then
 

turned into directional link capacities for the entire time-of-day period. 
 

Having established both directional traffic volume and capacity for each link, for a specific 
 

time-of-day period, the link’s v/c ratio on each direction can be computed. 
 

d) To establish the directional time-of-day speed, the DFW algorithm in the PREPIN 

methodology was used. For this algorithm the free-flow speed for each link needs to be 

established, and for that purpose Table 8 provide average values depending on the link’s 

specific area type and functional classification. For each link, this free-flow speed goes 

into the DFW algorithm together with the average 24-hour speed, and the directional v/c 

ratio, thus yielding the directional congested speed. 

Table 8. Free-flow speeds in mph. 

Functional classification 

0 1 2 3 4 5 6 7 8 9 10 11 12 

Area Type 

1 15.00 50.00 55.00 50.00 11.90 11.90 11.90 11.90 11.90 11.90 11.90 11.90 16.90 

2 15.00 50.00 55.00 50.00 11.90 11.90 11.90 11.90 11.90 11.90 11.90 11.90 16.90 

3 25.00 50.00 55.00 50.00 25.40 25.40 20.00 20.00 19.40 19.40 25.40 25.40 30.40 

4 35.00 51.00 57.00 51.00 36.40 36.40 31.30 31.30 30.00 30.00 30.00 36.40 41.40 

B-16
 

Converting link congestion into emissions by-link 

The directional speed on each link (established for specific time-of-day periods) is then used to establish the 

link’s emissions rate to use for VOC, CO, NOx, SO2, NH3, PM2.5, and PM10. These Mobile6-Mexico rates are 

provided as a lookup table on the VB code and used to assign the resulting rates to each link. 

Having established the emission rates for each link, these are then multiplied by the link’s length and traffic 

volume (i.e. link VMT) in the period to obtain the link’s emissions concentration per time-of-day. 

Annual mobile source emissions estimation 

The PrepinPlus code was run using the traffic loaded networks, under each of the seasonal temperatures, as 

well as for weekday and weekend conditions. The resulting emissions-by-link were added for the four time-ofday periods to establish daily emissions-by-link. These total daily emissions-by-link were aggregated for the 

entire Mexicali municipality. Finally the weekday and weekend emissions were aggregated by season, 

considering 13 weeks per season, and within each week, 5 weekdays and 2 weekend days. The seasonal 

emissions for spring, summer, fall and winter were added to establish annual emissions. 

The annual on-road mobile source emissions estimated for the Mexicali municipality are summarized by 

pollutant on Table 9. 

Table 9. Annual on-road mobile source emissions (8 vehicle classes) for the Mexican municipality (2005) 

VOC* 

CO 

NOx 

SO2 

NH3 

CH4 

PM2.5 

PM10 

LDGV 

[Tm/year] 

LDGT12 

[Tm/year] 

LDGT34 

[Tm/year] 

HDGV 

[Tm/year] 

LDDV 

[Tm/year] 

LDDT 

[Tm/year] 

HDDV 

[Tm/year] 

MC 

[Tm/year] 

ALL 

[Tm/year] 

4,128 2,163 1,098 357 9 5 309 75 8,143 

26,709 15,943 8,234 2,246 17 10 1,428 392 54,979 

1,045 1,019 363 340 11 7 4,956 34 7,775 

23 21 9 4 0 0 96 0 154 

113 79 24 3 0 0 10 0 229 

53 39 17 6 0 0 12 4 132 

107 104 37 6 2 1 347 0 604 

130 123 44 8 2 1 385 1 694 

* Emissions for VOC include exhaust and evaporative 

B-17
 

VI. Paved and Unpaved Road Dust 

In addition to the particulate matter (PM) emissions estimated from vehicle exhaust and tire/break wear 

(shown in Table 9), in a separate exercise PM was also estimated from roadway dust airborne by vehicle 

movement. The amount of airborne dust is a function of the amount of dust on the roadway, and also of the 

vehicle traffic volume (VMT: vehicle-miles of travel) and average vehicle speed of a roadway, which are 

conditions estimated from the travel demand model. 

In most cases, the functional class of each link will indicate if it is paved or unpaved (FUNCL=8 indicates 

unpaved condition), and therefore which PM emission factor is appropriate. Yet, in the special case of 

centroid connector links (FUNCL=0), this requires an additional task, since centroid connectors are virtual links 

representing multiple local roadways within a TAZ. If a proportion of such local roadways in a given TAZ are 

unpaved, this needs to be represented in the selected emission factors for the TAZ’s centroid connector link. 

Figure 9 presents a map of the TAZs with categories of roadway pavement coverage4

. 

Pavement coverage detail for 

urban Mexicali 

Figure 9. Roadway pavement coverage of TAZs in the Mexicali municipality. 

4

 Coverage by zone provided by the Mexicali IMIP. 

B-18
 
Paved and unpaved VMT 

For proper characterization of PM from paved and unpaved roadways, the VMT obtained with the travel 

demand model was also identified by the paved/unpaved categories. 

On the one hand, the VMT on all centroid connector links (red links in Figure 9) was divided between paved 

and unpaved conditions proportionally to the roadway pavement coverage of the corresponding TAZ. In 

addition, some of the on-system links (gray links in Figure 9) were identified as unpaved as well (refer to 

Figure 2), and therefore the VMT over on-system links was also categorized as paved or unpaved. Table 10 

provides a summary of VMT over paved and unpaved roadways in the Mexicali municipality. 

Table 10. Summary of modeled VMT on paved and unpaved roadways for the Mexciali municipality (2005) 

area 

[sq-miles] daily VMT %VMT 

paved unpaved 

Urban Mexicali 54 3,965,327 98.0% 2.0% 

Rural Mexicali 5,563 3,911,214 89.7% 10.3% 

Mexicali Municipality 5,617 7,876,541 93.9% 6.1% 

Even though the apparent surface of TAZs with no paved roadways seems to dominate over the study area, 

the actual miles of roadways in these TAZs is minimal. In terms of vehicle activity the effect is equivalent, and 

thus over 90% of the VMT in the Mexicali municipality takes place over paved roadways. 

Paved and unpaved PM emissions from roadway dust 

Based on the dust sampled from roadways in the Mexicali municipality, the following emission factors were 

estimated with Mobile6-Mexico: 

Paved roads, at any vehicle speed: 

- PM10 of 0.00377 lbs/VMT5 

- PM2.5 of 0.00034 lbs/VMT 

Unpaved roads, at 11mph (average speed on urban settings): 

- PM10 of 0.35442 lbs/VMT 

- PM2.5 of 0.03515 lbs/VMT 

Unpaved roads, at 18mph (average speed on suburban settings): 

- PM10 of 0.45350 lbs/VMT 

- PM2.5 of 0.04505 lbs/VMT 

Unpaved roads, at 25mph (average speed on rural settings): 

- PM10 of 0.53453 lbs/VMT 

- PM2.5 of 0.05316 lbs/VMT 

Once every roadway link in the municipality was assigned and emission factor, and multiplied by its 

corresponding VMT, total PM for weekday and weekend day was estimated, as summarized in Tables 11 and 

12. 

5

 Link VMT is estimated by multiplying the daily traffic volume of each link by the link’s length (in miles). 

B-19
 
Table 11. Weekday PM emissions from roadway dust in the Mexicali municipality (2005) 

VMT PM10 

[metric Tons] 

PM2.5 

[metric Tons] 

paved roadways 7,395,242 12.5 1.1 

unpaved roadways 481,299 115.0 11.4 

all roadways 7,876,541 127.5 12.6 

Table 12. Weekend day PM emissions from roadway dust in the Mexicali municipality (2005) 

VMT 

PM10 

[metric Tons] 

PM2.5 

[metric Tons] 

paved roadways 5,158,478 8.7 0.8 

unpaved roadways 337,933 80.8 8.0 

all roadways 5,496,411 89.5 8.8 

Using the daily emissions from Tables 11 and 12, yearly PM emissions from roadway dust is summarized on 

Table 13. 

Table 13. Yearly PM emissions from roadway dust in the Mexicali municipality (2005) 

PM2.5 
 

PM10 
 

paved roadways 

[metric Tons/yr] 

unpaved roadways 

[metric Tons/yr] 

ALL roadways 

[metric Tons/yr] 

383 3,819 4,202 

4,182 38,403 42,586 

It can be seen that the PM emission factors for unpaved roadways are considerably higher than paved 

roadways, thus explaining why the amount of PM from unpaved roadways overpasses by far the PM from 

paved roadways, even though the VMT on paved roadways is much larger. 

Compared to PM from vehicle exhaust (Table 9), PM from roadway dust is considerably higher. 

B-20