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i

Catalog of Global Emissions Inventories and

Emissions Inventory Tools for Black Carbon

Draft Report

January 2002

Prepared for:

Thompson G. Pace, D205-01

U.S. Environmental Protection Agency

Research Triangle Park, North Carolina 27711

EPA Contract No. 68-D-98-046

Work Assignment No. 5-03

Prepared by:

William Battye

Kathy Boyer

EC/R Incorporated

Chapel Hill Office

1129 Weaver Dairy Road

Chapel Hill, NC 27514

(919) 933-9501
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ii

RTP Area Office

2327 Englert Drive

Durham, NC 27713

(919) 484-0222

Disclaimer

Although the research described in this document has been funded

wholly or in part by the United States Environmental Protection

Agency contract 68-D-98-046 to EC/R Incorporated, it has not

been subject to the Agency's review and therefore does not

necessarily reflect the views of the Agency, and no official

endorsement should be inferred.
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iii

Contents

Page

Introduction .................................................................. 1

Catalog of Global Emissions Inventories and Methods ................................. 2

Regional and Global Greenhouse Gas Inventories .............................. 2

CORINAIR 94 .................................................... 2

EDGAR V2.0 database ............................................. 2

European Environment Agency (EEA) ................................ 11

U.S. Department of Energy Energy Information Administration (EIA) ....... 11

Global Emission Inventory Activity (GEIA) ............................ 11

Convention for Long-Range Transboundary Air Pollution (LRTAP) ......... 11

RAINS-ASIA .................................................... 12

World Bank ..................................................... 12

Country-specific Greenhouse Gas Inventories ................................. 12

United Nations Convention on Climate Change (UNFCCC) ............... 12

Climate Change Asia–ALGAS ...................................... 13

CHINA-MAP .................................................... 13

Black Carbon and Organic Carbon Inventories ................................ 13

CHINA-MAP .................................................... 15

Cooke and Wilson ................................................ 15

ECHAM4 ....................................................... 15

GEIA .......................................................... 15

Liousse et al. .................................................... 15

Health-Related Emissions Inventories ....................................... 16

Pollutant Release Transfer Registers .................................. 16

World Bank ..................................................... 16

Opportunities for Technology Transfer ...................................... 17

Tools for Estimating Emissions of Black Carbon Particulate Matter ..................... 20

Factors Used in Existing BC Inventories ..................................... 30

Cooke and Wilson ................................................ 30

ECHAM4 ....................................................... 30

GEIA .......................................................... 30

CHINA-MAP .................................................... 31

Louisse et al. .................................................... 31

Other Tools for Estimating Black Carbon Emissions ........................... 31

Ranking Opportunities for Improving Black Carbon Emissions Inventories ......... 32

References .................................................................. 39
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iv

Figures

Page

Tables

Page

Table 1. Emissions Inventories for Climate Forcing Pollutants .......................... 3

Table 2. Global and Regional Emissions Inventories for Black Carbon (BC) and Organic Carbon

(OC) Particulate Matter .................................................. 14

Table 3. Other Emissions Inventories for Health Related Pollutants ..................... 18

Table 4. Comparison of Emission Factors and other Tools for Estimating BC and OC ...... 21

Table 5. Source Categories Covered by Black Carbon Emissions Inventories .............. 32

Table 6. Estimated Breakdown of BC Emissions in the U.S. ........................... 34

Table 7. Ranking of U.S. Source Categories by BC Emissions and Uncertainty ............ 35

Table 8. Estimated Breakdown of Global BC Emissions .............................. 36

Table 9. Ranking of Source Categories by Global BC Emissions and Uncertainty .......... 37
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1

Introduction

Emissions inventories for climate change studies have focused primarily on greenhouse

gases: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and various halogenated

compounds.1 However, various components of fine particulate matter (PM2.5) in the atmosphere

also have climate-forcing impacts, either contributing to or offsetting the effects of greenhouse

gases.2 In particular, black carbon particulate matter (BC) has recently been identified as an

important contributor to radiative heating of the atmosphere.3 Organic carbon particulate matter

(OC), which is often emitted along with BC, may act to offset some of the global warming

impact of BC emissions.2 

Most of the important sources of greenhouse gases also are important sources of healthrelated pollutants. Likewise, BC and OC particulate matter are emitted primarily from

combustion sources, which are also important sources of health-related pollutants. Because of

these overlaps, measures to reduce climate-forcing emissions often would have collateral benefits

by reducing emissions of health-related pollutants, and vice versa. Improved emissions inventory

methodologies and tools developed for health-related pollutants can also provide opportunities

for improving climate change emissions inventories (and vice versa).

A better characterization of worldwide emissions of climate forcing and health-related

pollutants is needed to improve the understanding of overlapping benefits for emission reduction

strategies both in the U.S. and abroad. The improved characterization of emissions will help in

two ways: (1) by quantifying the most efficient strategies for improving both global warming and

health-related problems, and (2) by identifying sources that are under-reported or missing from

different emissions inventories. A full characterization of these under-reported or missing

sources could alter the priorities of programs designed to mitigate both health-related and global

climate impacts. 

The purpose of the report is to twofold. First, a catalog is developed of country-specific

and regional inventories for climate forcing pollutants and health related pollutants. The purpose

of this catalog is to assist in the identification of opportunities for transfer of factors and methods

among the different types of emissions inventories. Second, a more in depth review is made of

tools and factors available to estimate particulate BC emissions. The purpose of this review is to

assist in identifying and prioritizing opportunities for improving U.S. and worldwide inventories

of BC emissions. It must be noted that this report represents only a cursory first look at the

available data. A CD-ROM companion to the report compiles available data from the catalogued

emissions inventories.
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2

Catalog of Global Emissions Inventories and Methods

Regional and Global Greenhouse Gas Inventories

Several organizations have produced regional and/or global greenhouse gas inventories in

varying degrees of resolution, ranging from a global 1o x 1o latitude/longitude grid (about 100

kilometers x 100 kilometers near the equator) to country-wide inventories. Many of these

inventories were developed to fulfill various international obligations, such as the United Nations

Framework Convention on Climate Change (UNFCCC).1 This convention is discussed in detail

in later sections. In order to be compatible with the UNFCCC reporting format, many

organizations used the Intergovernmental Panel on Climate Change (IPCC)4 or comparable

methodologies. Others, however, relied on unique models and methods. Table 1 summarizes

these inventories, including the sectors and pollutants covered. 

The following sections discuss background information and methodologies used to

produce these inventories. It must be noted that the table does not represent a comprehensive

list, but rather catalog based on readily available information. A CD-ROM companion to this

report compiles available data from the catalogued emissions inventories.

CORINAIR 94

The Core Inventory Air (CORINAIR) 94 database is the air emissions inventory for

participating European countries in 19945. The European Environment Agency (EEA)

designated the European Topic Centre on Air Emissions (ETC/AE) to assist participating

countries in reporting their national emissions estimates as required under various international

obligations. These obligations include those under the UNFCCC, as well as the Convention on

Long-Range Transboundary Air Pollution (LRTAP)6. In both cases, many European countries

used the CORINAIR methodology to produce their greenhouse gas, criteria pollutant, and toxic

air pollutant inventories. 

The database included in this report presents a summary of emission estimates according

to the LRTAP guidelines, described below. The greenhouse gases and other climate forcing

emissions include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), carbon monoxide

(CO), nitrogen oxides (NOx), non-methane volatile organic carbon (NMVOC), sulfur dioxide

(SO2), and ammonia (NH3). The inventory also includes nine heavy metals and ten persistent

organic pollutants (POPs).

EDGAR V2.0 database

The Emission Database for Global Atmospheric Research (EDGAR)7 was developed

jointly by the Netherlands Organization for Applied Scientific Research (TNO) and RIVM. This

comprehensive database stores inventories of direct and indirect anthropogenic greenhouse gas

emissions both on a per country basis as well as on a 1o x 1o grid. Using 1990 as its base year,

EDGAR inventories CO2, CH4, halocarbons (HC), N2O, CO, NOx, VOC, and SO2 for sectors

including fossil fuel and biofuel combustion, industrial processes, solvent use, land use, and

waste treatment. 
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a

These sectors include industrial and non-industrial combustion, energy combustion, production processes,

extraction, solvents, road transport, other mobile sources, waste treatment, and agriculture and forestry.

bCountries include Armenia, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Canada,

Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Macedonia, Georgia, Germany, Greece,

Hungary, Iceland, Ireland, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Moldova, Monaco, Netherlands,

Norway, Poland, Portugal, Romania, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey,

Ukraine, United Kingdom, United States, Yugoslavia, European Community.

cCountries include Bangladesh, Bhutan, Brunei, Cambodia, China, Hong Kong, India, Indonesia, Japan,

North and South Korea, Laos, Malaysia, Mongolia, Myanmar, Nepal, Pakistan, Philippines, Singapore, Sri Lanka,

Taiwan, Thailand, Vietnam, Asia

* Year of most recent inventory.

** Unless otherwise specified, the Energy sector includes all stationary and mobile combustion sources, and

Agriculture includes biomass burning.

3

Table 1. Emissions Inventories for Climate Forcing Pollutants

Inventory and

description Year * Pollutants included Source Categories ** Reference

Regional and Global Inventories

CORINAIR 94 1994 CO2, CH4, N2O, CO,

NOx, VOC, SO2,

Particulate Organic

Carbon

SNAP level 1 sectorsa CORINAIR website5

EDGAR – 1o

latitude x 1o

longitude

1990 CO2, CH4, N2O, CO, NOx,

VOC, SO2,

Fossil Fuel (combustion, noncombustion), Biofuel, Industrial

Processes, Land Use, Waste

EDGAR online

database7

EEA – EU15

countries,

Latvia

1999 CO2, CH4, N2O, HFC,

PFC, SF6

Energy, Industry, Transport,

Agriculture, Waste, Other

EEA online database8

EIA

international

report

1998 CO2 Coal, Natural Gas, Petroleum, Other EIA document9

GEIA – 1o lat

x 1o long

1994 CO2, CH4, CFC, N2O,

CO, NOx, VOC, SO2,

Reactive Cl

Fossil Fuels, Biomass GEIA online

database10

LRTAP –

Europe and

N. Amer.b

1998 CO2, CO, NOx, VOC,

SO2,

Annual Aggregated Emissions,

SNAP level 1 sectors

LRTAP website on

UNECE6

RAINS-ASIA

– 23 Asian

countriesc

1990 CO2, CH4, CO, NOx,

NMHC, SO2

Biofuels (including Firewood, Crop

Residue, Animal Waste), Fossil

Fuels

Streets, David 11,12,13
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Table 1. Emissions Inventories for Climate Forcing Pollutants (continued)

Inventory and

description Year * Pollutants included Source Categories ** Reference

dCountries include: Australia, Austria, Belgium, Bulgaria, Canada, Czech Republic, Denmark Estonia,

Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Latvia, Liechtenstein, Lithuania,

Luxembourg, Monaco, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russian Federation,

Slovakia, Slovenia, Spain, Switzerland, Ukraine, United Kingdom, United States

* Year of most recent inventory.

** Unless otherwise specified, the Energy sector includes all stationary and mobile combustion sources, and

Agriculture includes biomass burning.

4

UNFCCC –

Annex I

Parties d

1998 CO2, CH4, N2O, HFC,

PFC, SF6, CO, NOx

VOC, SO2

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, International

Bunkers

UNFCCC website1

World Bank –

worldwide

 1998 NOx, SO2, Particulates Major City World Bank

Document14

Country-specific Inventories

Argentina 1997 CO2, CH4, HCFC, PFC,

SF6, N2O, CO, NOx, VOC

Energy, Industry, Agriculture, Waste Argentina National

Communication15

Armenia 1996 CO2, CH4, N2O, CO, NOx,

VOC 

Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Armenia National

Communication16, 

Australia 1995 CO2, CH4, HCFC, PFC,

SF6, N2O, CO, NOx, VOC

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents 

Australia 2nd National

Communication17

1999 CO2, CH4, HCFC, PFC,

SF6, N2O, CO, NOx, VOC

Energy, Industry, Agriculture,

Solvents, Land Use/Forestry, Waste

Australia National

Greenhouse Gas

Inventory18

Austria 1995 CO2, CH4, HCFC, PFC,

SF6, N2O, CO, NOx, VOC

Energy, Transport, Industry, Small

Consumers, Agriculture, Land

Use/Forestry, Waste

Austria 2nd National

Communication19

Azerbaijan 1994 CO2, CH4, N2O, CO, NOx,

VOC 

Energy, Industry, Agriculture, Land

Use, Waste

Azerbaijan National

Communication20

Bangladesh 1990 CO2, CH4 Energy, Agriculture, Forestry Ahmed, Ahsan21

1990 CO2, CH4, N2O,CO, NOx, Energy, Industry, Agriculture, Land

Use/Forestry, Waste 

Climate Change Asia

website22

Belgium 1995 CO2, CH4, HCFC, PFC,

SF6, N2O, CO

Energy, Industry, Agriculture,

Forestry, Waste 

Belgian 2nd National

Communication23

1998 CO2, CH4, HCFC, PFC,

SF6, N2O, CO, NOx VOC,

SO2

Energy, Industry, Agriculture, 

Forestry, Waste, Solvents

Belgian Federal

Government Online24

Brazil 1994 CO2, CH4, N2O, NOx Agriculture, Industry, Energy, Waste,

Solvents

Brazil National

Communication25

Bulgaria 1995 CO2, CH4, HCFC, PFC,

SF6, N2O, CO, NOx, VOC

Energy, Industry, Transport,

Agriculture, Forestry/Land Use,

Waste

Bulgaria 2nd National

Communication 26
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Table 1. Emissions Inventories for Climate Forcing Pollutants (continued)

Inventory and

description Year * Pollutants included Source Categories ** Reference

* Year of most recent inventory.

** Unless otherwise specified, the Energy sector includes all stationary and mobile combustion sources, and

Agriculture includes biomass burning.

5

Canada 1995 CO2, CH4, HCFC, PFC,

SF6, N2O, CO, NOx,

VOC, CF4, C2F6

Energy, Industry, Transport,

Agriculture, Forestry/Land Use,

Waste

Canada 2nd National

Communication27

Cape Verde 1995 CO2, CH4, N2O Energy, Transport, Agriculture Cape Verde National

Communication28

Chile 1994 CO2, CH4, N2O, CO, NOx,

VOC 

Energy, Industry, Agriculture, Land

Use, Waste, Solvents

Chile National

Communication29

1993 CO2, CH4, N2O, CO, NOx,

SO2, NMVOC

Energy, Industry, Forestry World Energy

Council Report30

China 1990 CO2, CH4, N2O,CO, NOx, Energy, Industry, Agriculture, Waste,

Land Use/Forestry

Climate Change Asia

website22

1995 CO, NOx, SO2 Energy, Industry, Transport,

Domestic, Biofuels, Other; also by

Region

Streets, David G. and

S.T. Waldhoff31

Cook Islands 1994 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use, Waste, Solvents

Cook Islands

National

Communication32

Cote d’Ivoire 1996 CO2, CH4, N2O, CO, NOx Energy, Industry, Agriculture, Land

Use, Waste, Solvents

Cote d’Ivoire

National

Communication33

Czech

Republic

1995 CO2, CH4, HFC, PFC,

SF6, N2O, NOx, CO, VOC

Energy, Industry, Transport,

Agriculture, Land Use/Forestry,

Commercial, Residential,

Institutional

Czech Republic 2nd

National

Communication34

Denmark 1996 CO2, CH4, HFC, PFC,

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture,

Solvents, Land Use, Waste,

Transport

Denmark 2nd National

Communication35

Ecuador 1990 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture, Land

Use /Forestry, Waste

Ecuador National

Communication 36

Egypt 1990 CO2, CH4, N2O Energy, Industry, Agriculture, Waste Egypt National

Communication37

El Salvador 1995 CO2, CH4, N2O, CO, NOx Energy, Industry, Agriculture, Waste El Salvador National

Communication38

Estonia 1996 CO2, CH4, N2O Energy, Industry, Transport,

Agriculture, Land Use/Forestry,

Waste

Estonia 2nd National

Communication39

European

Community

1999 CO2, CH4, HFC, PFC, 

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture,

Transport, Land Use/Forestry, Waste

EC Greenhouse Gas

Inventory40
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Table 1. Emissions Inventories for Climate Forcing Pollutants (continued)

Inventory and

description Year * Pollutants included Source Categories ** Reference

* Year of most recent inventory.

** Unless otherwise specified, the Energy sector includes all stationary and mobile combustion sources, and

Agriculture includes biomass burning.

6

Finland 1995 CO2, CH4, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Finland 2nd National

Communication41

France 1995 CO2, CH4, CO, N2O, NOx,

VOC, SO2

Energy, Industry, Agriculture, 

Domestic/Commercial

France 2nd National

Communication42

Georgia 1997 CO2, CH4, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Transport,

Agriculture, Forestry 

Georgia National

Communication43

1996 CO2, CH4, CO, N2O, NOx Energy, Industry, Agriculture,

Forestry, Waste

Georgia Climate

Change website44

Germany 1990 CO2, CH4, N2O, CO,

NOx,VOC

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents

Germany National

Communication45

Ghana 1996 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Ghana National

Communication46

Greece 1995 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture

(excluding burning)

Greece 2nd National

Communication47

Honduras 1995 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture, Land

Use, Waste

Honduras National

Communication 48

Hungary 1995 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Mobile Sources,

Forestry, Waste, Commercial,

Residential

Hungary 2nd National

Communication49

Iceland 1995 CO2, CH4, PFC, HFC, 

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents

Iceland 2nd National

Communication50

India (by

state/region)

1995 CO2, CH4, N2O, NOx, HC,

SO2

Coal, Oil products, Natural Gas,

Industry processes, Indirect

emissions

Climate Change India

website22

1995 CO2, CH4, N2O, NOx,

SOx

Coal, Petroleum, Natural Gas,

Industry Processes, Transport,

Electric Utilities, Livestock, Waste

Garg et al.5152

Indonesia 1994 CO2, CH4, N2O, CO, NOx,

NMHC, CF4, C2F6, 

Energy, Industry, Agriculture, Waste,

Solvents, Land Use/Forestry,

Biofuels

Indonesia National

Communication53

1990 CO2, CH4, N2O,CO, NOx, Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Climate Change Asia

website22

Ireland 1994 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Waste Ireland 2nd National

Communication54

Italy 1994 CO2, CH4, PFC, HFC, 

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents, 

Other (bunkers)

Italy 2nd National

Communication55
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Table 1. Emissions Inventories for Climate Forcing Pollutants (continued)

Inventory and

description Year * Pollutants included Source Categories ** Reference

* Year of most recent inventory.

** Unless otherwise specified, the Energy sector includes all stationary and mobile combustion sources, and

Agriculture includes biomass burning.

7

Jamaica 1994 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Jamaica National

Communication 56

Japan 1996 CO2, CH4, HFC, PFC, 

SF6, N2O, CO, NOx,

VOC, NMHC, SO2

Energy, Industry, Agriculture,

Bifuels, Land Use/Forestry, Waste,

Solvents, Other (bunkers)

Japan 2nd National

Communication57

Jordan 1994 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents, 

Other (bunkers)

Jordan National

Communication58

Kazakhstan 1994 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture, Waste,

Land Use/Forestry

Kazakhstan National

Communication59

Kiribati 1994 CO2, CH4, N2O, CO, NOx Energy, Industry, Agriculture, Land

Use /Forestry, Solvents

Kiribati National

Communication60

Korea 1990 CO2, CH4, N2O, CO, NOx,

VOC, NMHC

Energy, Industry, Agriculture,

Biofuels, Land Use /Forestry

Korea National

Communication61

Latvia 1990 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture, Land

Use /Forestry, Waste, Solvents

Latvia National

Communication62

Lebanon 1994 CO2, CH4, HFC, PFC, 

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Solvents,

Agriculture, Land Use /Forestry,

Waste

Lebanon National

Communication63

Lithuania 1990 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Solvents,

Agriculture, Land Use /Forestry,

Waste

Lithuania National

Communication64

Luxembourg 1995 CO2, CH4, N2O, CO, NOx,

NMHC, VOC, SO2

Energy, Mobile sources, Agriculture,

Extraction/distribution of fossil fuels,

Commercial/Residential, Production

processes, Waste, Solvents

Luxembourg National

Communication65

1994 CO2, CH4, N2O, CO, NOx,

NMVOC, SO2, NH3

SNAP Level 1 categoriesa CORINAIR

database5

Malaysia 1990 CO2, CH4, N2O,CO, NOx, Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Climate Change Asia

website22

1994 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use /Forestry, Waste

Malaysia National

Communication66

Mali 1995 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture Mali National

Communication67

Malta 1994 CO2, CH4, N2O, CO, NOx,

NMVOC, SO2, NH3

Energy, Industry, Transport,

Agriculture, Waste, Solvents, NonIndustry combustion, Production

processes, Fossil Fuel Extraction

CORINAIR94

database1
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Table 1. Emissions Inventories for Climate Forcing Pollutants (continued)

Inventory and

description Year * Pollutants included Source Categories ** Reference

* Year of most recent inventory.

** Unless otherwise specified, the Energy sector includes all stationary and mobile combustion sources, and

Agriculture includes biomass burning.

8

Marshall

Islands

1996 CO2, CH4 Energy, Agriculture, Waste Marshall Islands

National

Communication68

Mauritius 1995 CO2, CH4, N2O, CO, NOx,

NMVOC, SO2

Energy, Industry, Solvent,

Agriculture, Land Use, Waste, Other

Mauritius National

Communication69

Mexico 1997 CO2, CH4, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Mobile Sources,

Agriculture, Land Use /Forestry,

Waste, Gas production

Mexico National

Communication70

1994 CO, NOx, HC, SO2, PM Industry, Transport, Services Data from Mexico

climate change

website71

Moldova 1998 CO2, CH4, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use /Forestry, Waste, Solvents

Moldova National

Communication 72, 

Mongolia 1990 CO2, CH4, N2O, CO, NOx Energy, Industry, Agriculture, Land

Use /Forestry, Waste

Dagvadorj, Damdin73

(hard copy only)

Nauru 1994 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use /Forestry, Waste, Solvents

Nauru National

Communication74

Netherlands 1995 CO2, CH4, HFC, PFC,

FIC, SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use /Forestry, Waste

Netherlands’

National

Communication75

New Zealand 1995 CO2, CH4, HFC, PFC,

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use /Forestry, Solvents

New Zealand 2nd

National

Communication 76

Niger 1997 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use /Forestry, Waste

Niger National

Communication77

Norway 1995 CO2, CH4, HFC, PFC,

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use, Waste, Solvents

Norway 2nd National

Communication78

Pakistan 1990 CO2, CH4, N2O,CO, NOx, Energy, Industry, Agriculture, Waste,

Land Use/Forestry

Climate Change Asia

website22

Panama 1994 CO2, CH4, CO, NOx,

VOC

Energy, Industry, Agriculture, Land

Use, Waste, Solvents

Panama National

Communication79

Philippines 1994 CO2, CH4, CO, N2O, NOx,

VOC, NMHC, SO2

Energy, Industry, Agriculture,

Biofuels, Waste, Land Use /Forestry

Philippines National

Communication80

1990 CO2, CH4, N2O,CO, NOx, Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Climate Change Asia

website22

Poland 1994 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use /Forestry, Waste

Poland 2nd National

Communication81
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Table 1. Emissions Inventories for Climate Forcing Pollutants (continued)

Inventory and

description Year * Pollutants included Source Categories ** Reference

* Year of most recent inventory.

** Unless otherwise specified, the Energy sector includes all stationary and mobile combustion sources, and

Agriculture includes biomass burning.

9

Portugal 1994 CO2, CH4, N2O, CO, NOx,

NMVOC

Energy, Industry, Land Use/Forestry,

Waste, Solvents

Portugal 2nd National

Communication82

Romania 1991 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Mobile Sources,

Waste

Romania 2nd National

Communication83

Russian

Federation

1990 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture, Waste Russia National

Communication84

Samoa 1994 CO2, CH4, N2O Energy, Industry, Agriculture, Land

Use /Forestry, Waste

Samoa National

Communication 85

Senegal 1994 CO2, CH4, CFC, N2O,

CO, NOx

Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Senegal National

Communication86

Seychelles 1995 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Seychelles National

Communication87

Singapore 1994 CO2, CH4, N2O, CO,

NMHC

Energy, Industry, Biofuels, 

Agriculture, Land Use/Forestry,

Waste

Singapore National

Communication88

Slovak

Republic

1994 CO2, CH4, HFC, PFC,

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Waste Slovak Republic 2nd

National

Communication89

Spain 1994 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents

Spain 2nd National

Communication90

Sri Lanka 1990 CO2, CH4, N2O,CO, NOx, Energy, Industry, Agriculture, Waste,

Land Use/Forestry

Climate Change Asia

website22

St. Vincent

and

Grenadines

1997 CO2, CH4, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use/Forestry, Waste

St. Vincent and

Grenadines National

Communication91

Sweden 1995 CO2, CH4, HFC, PFC,

SF6, N2O, CO, NOx VOC,

SO2,

Power/district heating plants, Other

Energy, Industry, Transport,

Agriculture, Waste, Solvents,

Residential/Commercial

Sweden 2nd National

Communication92

Switzerland 1995 CO2, CH4, HFC, PFC,

SF6, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents

Switzerland 2nd

National

Communication 93

Taiwan 1996 CO2 Energy Young, RT94

Thailand 1994 CO2, CH4, N2O, CO, NOx,

VOC

Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Thailand National

Communication95

1990 CO2, CH4, N2O,CO, NOx, Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Climate Change Asia

website22
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Table 1. Emissions Inventories for Climate Forcing Pollutants (continued)

Inventory and

description Year * Pollutants included Source Categories ** Reference

* Year of most recent inventory.

** Unless otherwise specified, the Energy sector includes all stationary and mobile combustion sources, and

Agriculture includes biomass burning.

10

Turkmenistan 1994 CO2, CH4, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use /Forestry, Waste, Solvents

Turkmenistan

National

Communication96

United

Kingdom

1994 CO2, CH4, HFC, PFC,

SF6, N2O, CO, NOx, VOC

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents

UK 2nd National

Communication97

United States 1995 CO2, CH4,HFC, PFC, SF6,

N2O, CO, NOx, VOC

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents

US 2nd National

Communication98

Uruguay 1994 CO2, CH4, N2O, CO,

VOC

Energy, Industry, Agriculture, Land

Use/Forestry, Waste, Solvents

Uruguay National

Communication99

Uzbekistan 1994 CO2, CH4, N2O, CO, NOx,

VOC, SO2

Energy, Industry, Agriculture, Land

Use/Forestry, Waste

Uzbekistan National

Communication100

Vanatua 1994 CO2, CH4, N2O, CO Energy, Agriculture, Land

Use/Forestry

Vanatua National

Communication 101

Zimbabwe 1994 CO2, CH4, N2O, CO, NOx Energy, Industry, Agriculture, Land

Use/Forestry

Zimbabwe National

Communication 102
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11

European Environment Agency (EEA)

The European Environment Agency (EEA)8 provides an environmental information

system to help the European Community attain sustainable development and integrate

environmental aspects into economic policies. As part of its services, the agency’s website

provides downloadable data sets used in technical reports. Available data include emissions

inventories from the European Community on a country-specific as well as regional basis. These

inventories were reported to the LRTAP as well as the UNFCCC conventions. 

U.S. Department of Energy Energy Information Administration (EIA)

The Energy Information Administration (EIA)9, created by Congress in 1977, is a

statistical agency of the U.S. Department of Energy. They provide policy-independent data,

forecasts, and analyses to promote sound policy making, efficient markets, and public

understanding regarding energy and its interaction with the economy and the environment. They

obtain data primarily from surveys of companies engaged in energy-related businesses such as

domestic natural gas and electric utilities, oil refineries, petroleum marketers, coal mines,

government agencies, and energy end users such as manufacturers and households.

Global Emission Inventory Activity (GEIA)

The Global Emission Inventory Activity (GEIA)10 was created in 1990 to develop and

distribute global natural and anthropogenic emissions inventories of gases and aerosols. It is a

component of the International Globa Atmospheric Chemistry (IGAC) project, which is a

network of several hundred scientists working together to understand and predict issues such as

ozone depletion and greenhouse warming.

Data sets must undergo extensive peer review in order to be accepted into the GEIA

database. Such data sets reported here include CO2, CH4, CFC, N2O, CO, NOx, VOC, SO2, and

reactive Cl. Black carbon and particulate organic carbon were also inventoried, as discussed

below. Data are presented in a 1o x 1o grid for all pollutants, and references for each inventory

are documented on the GEIA website. 

Convention for Long-Range Transboundary Air Pollution (LRTAP)

The Convention for Long-Range Transboundary Air Pollution (LRTAP)6 in Europe was

the first internationally legally binding instrument to deal with problems of air pollution on a

broad regional basis. It was signed in 1979 and entered into force in 1983. It has greatly

contributed to the development of international environmental law and created the essential

framework for controlling and reducing the damage to human health and the environment of

transboundary air pollution (UNECE website).

As part of the initial LRTAP and five subsequent Protocols, European countries agreed to

inventory greenhouse gas pollutants and precursors, sulfur emissions, ground-level ozone, heavy

metals, persistant organic pollutants, and volatile organic compounds. Most countries use the

CORINAIR methodology for their inventories, and report emissions using the Selected

Nomenclature for sources of Air Pollution (SNAP). 
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12

RAINS-ASIA

The RAINS-ASIA project, sponsored by the World Bank and the Asian Development

Bank, originally simulated acid deposition from fossil fuels and ambient pollutant concentrations

in 94 Asian regions and 23 countries. The model has now expanded to incorporate biofuels, such

as fuel wood, crop residue, and animal waste. Scientists such as David Streets, John A van

Aardenne, and Jitendra Shah used this model to inventory greenhouse gases and precursors from

biofuel11 and fossil fuel combustion.12, 13

World Bank

In their World Development Indicators 2001 publication, the World Bank inventoried

nitrogen dioxide, sulfur dioxide, and particulates in the major cities of 53 countries. Data are

based on reports from urban monitoring sites and reported as annual means.14

Country-specific Greenhouse Gas Inventories

In addition to regional and global inventories, we found more than 80 country-specific

inventories. The majority of these were developed as part of the UNFCCC using IPCC4 or

comparable methods. Recent efforts to assess and reduce greenhouse gas emissions in Asia have

also resulted in country-specific inventories for that region. Table 1 summarizes these country

inventories, including sectors and pollutants covered. 

United Nations Convention on Climate Change (UNFCCC)

Country-specific greenhouse gas inventories are prepared under the UNFCCC1. The

Convention was opened for signature in 1992, at the UN Conference on Environment and

Development, or the “Earth Summit,” in Rio de Janeiro. Currently, 181 governments and the

European Community are Parties to the UNFCCC. Parties meet regularly at the annual

Conference of the Parties (COP) to review the implementation of the Convention.

In Article 4 of the UNFCCC, all Parties agreed to compile greenhouse gas inventories and

publish them in periodic reports, or National Communications, to the COP. However, the

Convention recognized that countries contribute differently to greenhouse gas emissions as well

as their abilities to produce inventories. The Parties were therefore divided into three categories

according to socio-economic status: Annex I, Annex II, and non-Annex I. Annex I includes both

the relatively wealthy countries that were members of the Organization for Economic

Cooperation and Development (OECD) in 1992, and countries with “economies in transition.” 

They are the industrialized countries who have historically contributed the most anthropogenic

greenhouse gas emissions. These countries agreed to produce inventories within six months of

“entry into force” of the Convention. They also agreed to adopt programs and policies that will

limit anthropogenic emissions and enhance emissions sinks and carbon reservoirs.

The OECD members of Annex I are also listed in Annex II. These countries, including

the United States, provide financial, technological, and research assistance to developing

countries (non-Annex I) in fulfilling their obligations under the Convention. The non-Annex I

countries may report greenhouse gases in more general terms, and they may submit their
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13

emissions inventories within a broader time frame than Annex I countries. They may, however,

accept the responsibilities of Annex I parties at any time. 

Countries inventoried the following greenhouse gases and precursors to greenhouse gas

formation: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), sulfur dioxide (SO2),

nitrogen oxides (NOx), carbon monoxide (CO), and volatile organic carbon (VOC). Countries

did not inventory sources of black carbon as part of the Convention, and very few inventoried

particulate matter. 

The Convention specified that inventories should include those greenhouse gases not

included in the Montreal Protocol on Substances that Deplete the Ozone Layer, as those

pollutants are already specifically reported and controlled. Since some pollutants included in the

Protocol are also greenhouse gases, some countries included them in their inventories. These

pollutants include chlorofluorocarbons (CFCs), hydrofluorocarbons (HFCs), perfluorocarbons

(PFCs), and sulphur hexafluoride (SF6). 

Climate Change Asia–ALGAS

The Asia Least-Cost Greenhouse Gas Abatement Strategy (ALGAS)22, developed by the

United Nations Development Program/Global Environmental Facility (UNDP/GEF), assessed the

present levels of greenhouse gas emissions and reviewed options to reduce emissions in Asia. 

Participating countries included China, India, Indonesia, Korea, Mongolia, Myanmar, Pakistan,

Philippines, Thailand, and Vietnam. The project inventoried CO2, CH4, N2O,CO, and NOx, and

emissions for each country using IPCC methods. Results of the study were reported to the

UNFCCC as part of the convention agreements. 

CHINA-MAP

CHINA-MAP was an international and multi-disciplinary program that assessed the

effects of economic development and the regional environmental impacts it made on agriculture

in China.103 The program was led by the Chinese Academy of Sciences and the Georgia Institute

of Technology, and sponsored by NASA. As part of the study, David Streets and Stephanie

Waldhoff developed emissions profiles for SO2, NOx, and CO for China for the years 1995 and

2020.31 In this study, all sectors of the Chinese economy were considered, including biofuel

combustion in rural homes. Much of the data needed to construct the inventory was obtained

through the RAINS-ASIA model, which is described above. 

Black Carbon and Organic Carbon Inventories

Most of the inventories identified in Table 1 focus on greenhouse gases. We also

identified five regional and/or global emissions inventories that include black carbon (BC)

particulate matter, and two that include organic carbon (OC) particulate matter. These

inventories were published in scientific papers, and each one has been developed using a unique

methodology. Table 2 specifies the source categories covered by each inventory, divided into

residential, industrial, utility, mobile, industrial processes, and fire. We further discuss the

methods used to develop these emissions factors in a later section.
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14

Table 2. Global and Regional Emissions Inventories for Black Carbon (BC) and

Organic Carbon (OC) Particulate Matter

Black Carbon Inventories

CHINA-MAP 1995 Residential, Industry, Transport, and

Power Fossil Fuel Combustion, Field

Combustion

Streets, David G. et al104

Cooke and Wilson –

Regional; global with 10o

latitude x 10o longitude

resolution 

1996 Fossil Fuel and Biomass Combustion Cooke and Wilson105

ECHAM4 – worldwide

and regional/country with

3.75o

 latitude x 3.75o

longitude resolution

Fossil Fuel Combustion Cooke et al.106

GEIA – worldwide, 1o

latitude x 1o longitude 

1994 Fossil Fuel combustion, Biomass

burning

GEIA online database10

Liousse et al. – global, 4.7o

longitude x 7.5o latitude

Biomass burning of savannahs,

tropical forests, agricultural fires, and

domestic fuels; Fossil fuel combustion

Liousse et al.107

Organic Carbon Inventories

ECHAM4 – worldwide

and regional/country with

3.75o latitude x 3.75o

longitude resolution

Fossil Fuel Combustion Cooke et al.106

Liousse et al. – global,

4.7o longitude x 7.5o

latitude

Biomass burning of savannahs,

tropical forests, agricultural fires,

and domestic fuels; Fossil fuel

combustion

Louisse et al.107
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15

CHINA-MAP

As another component of the CHINA-MAP program, David Streets et al.104 produced an

inventory for black carbon for the years 1995 and 2020. This inventory included 37 source

categories, covering all fuel combustion activities in China except the use of gaseous fuels. The

authors calculated emissions using fuel consumption data by sector and fuel type within the

framework of the RAINS-Asia model. They compiled emissions factors from previously

published data where available. If sufficient information existed, they also derived fractions of

BC and fine particulate matter from total PM emissions. The central and high emission factors

used in this study resulted from combining these data.

Cooke and Wilson

William Cooke and Julian Wilson105 constructed a 10o latitude x 10o longitude global

inventory for black carbon emissions. The inventory covers fossil fuel combustion and biomass

burning. The authors assumed that all emissions were anthropogenic, and they did not account

for differences between developed and less developed countries. 

ECHAM4

William Cooke et al.106 developed global inventories of black and organic carbon from

fossil fuel combustion. They were calculated using the fourth generation atmospheric general

circulation model, ECHAM4, from the Max Planck Institute. The version used in the study, T30,

has an approximate resolution of 3.75o longitude x 3.75o latitude.108

The authors used published emissions factors and data sets to calculate both bulk

(particles with diameters of greater than a few microns) and submicron emissions. They obtained

fossil fuel consumption data from the United Nations (1993), and used 1984 as the reference

year. The authors also distinguished between developed and less developed countries in order to

characterize the combustion efficiency and emissions controls used in each country. Developed

countries included those in the Organization for Economic Cooperative Development (OECD) in

1984. The semi-developed countries included those in the former Eastern European Bloc, the

former USSR, South Africa, Israel, Hong Kong, Singapore, and Taiwan. All other countries

were placed in the group of developing countries.

GEIA

As mentioned above, the GEIA database included an inventory for black carbon

developed by J.E. Penner and H. Eddleman.10 This inventory was developed using two distinct

methods. The first method used measured ambient concentration ratios of BC and SO2 at

locations throughout the world. The authors demonstrated that BC to SO2 ratios are well

correlated at most sites and are relatively constant in any one economic region. The inventory

was constructed using these ratios and previously published sulfur inventories. The second

method used estimated emission factors and published fuel production for wood and bagasse

burning, diesel fuel, and domestic and industrial coal use. 

Liousse et al.107

These authors developed emissions inventories for black and organic carbon from

biomass burning and fossil fuel combustion. The purpose of the study was to calculate global
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quote.

distribution of carbonaceous particles in the three-dimensional 4.5o o longitude

published inventories. 

Health-Related Emissions Inventories

pollutants, including SO2 X, and CO. However, many countries and non-government

pollutants. These are not as readily available as climate change inventories because of the lack of

a central system for organizing health-related inventories (as the UNFCCC does for climate

sponsored by the United Nations Institute for Training and Research (UNITAR).109

provides a repository for all countries with a national PRTR inventory, in an online version or on

CD-ROM. However, very few countries have compiled an inventory of this magnitude. The

Table 3 lists separate health-related emissions inventories that we have identified in the

current project. However, this is a cursory first look at available information on international

following sections provide background information on the major databases listed in Table 3.

Pollutant Release Transfer Registers

Nations Conference on Environment and Development. Countries agreed at this conference that

the public should have access to environmental data, and that governments and industries should

Council for the Organization for Economic Cooperation and Development (OECD)

recommended that countries develop a Pollutant Release Transfer Register (PRTR). Registers

and are compiled in a common reporting format based on the OECD Guidance to Governments

Manual for PRTRs.

The following countries have compiled a PRTR to date: Australia, Canada, Mexico, the

Netherlands, Slovak Republic, United States, and the United Kingdom. These inventories are

World Bank

The World Bank’s Industrial Pollution Prevention System (IPPS) is a database that

Toxic Intensities database, and pollution intensities from Mexico and China.

The toxic intensities database provides pollution intensities, or pollution per unit of

from the USEPA’s Toxic Release Inventory. 

The databases for Mexico and China estimate various pollution intensities at the 2, 3, and
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17

4-digit ISIC levels. Mexico’s inventory was provided by the Instituto Nacional de Ecologia. 

China’s inventory was produced using the China Environmental Yearbooks for 1992, 1994, and

1996. The yearbooks were produced by China’s State Environmental Protection Administration

(SEPA).111

U.S. Emissions Inventories

The U.S. Environmental Protection Agency (EPA) publishes a number of databases and

inventories for health related pollutants. In particular, the criteria pollutant subsystem of the

National Emissions Inventory (NEI) provides emissions estimates for SO2, VOC, NOX, CO,

PM10, PM2.5, ammonia (NH3), and secondary organic aerosols (SOA)112. The NEI also includes a

National Toxics Inventory (NTI) that gives emissions estimates for 188 hazardous air pollutants

(HAPs).112 Both the criteria pollutant section of the NEI and the NTI are designed to provide

detailed comprehensive inventories, giving precise locations of major emission sources and

county-level emissions estimates where precise locations cannot be provided. The EPA also

produces a Toxics Release Inventory (TRI) that lists industrial emissions of about 650 toxic

chemicals.

Opportunities for Technology Transfer

A comparison of Table 1 and Table 3 indicates that climate change inventories are much

more widespread than specialized inventories of health-related pollutants. The country-specific

climate change inventories generally include some health related pollutants, most notably SO2,

VOC, and NOX. (see Figure 1). However, these do not generally have the same spatial resolution

as inventories of health-related pollutants.

There are opportunities for technology transfer in both directions. The widespread

compilation of climate change emissions inventories has resulted in the development of

emissions inventories for some criteria pollutants, at least at the national level. These estimates

can be allocated to smaller spatial scales using a variety of spatial surrogates (such as population

or land-use) or using raw activity data compiled for with the climate change inventory. The raw

activity data can also be used to develop emissions estimates for additional criteria pollutants

(such as PM2.5) or for toxic air pollutants.

In the other direction, some of the tools that have been developed for health-related

emissions inventories allow a broader source category coverage than is characteristic of climate

change inventories. This is particularly true for the U.S. NEI. For instance, climate change

inventories generally focus on fuel combustion and industrial sources, although they also include

agricultural sources for CH4 and N2O and non-industrial sources of halogenated compounds. The

NEI provides activity estimates for a number of additional categories, most notably forest fires

and prescribed burning. These categories are purposefully excluded from the CO2 portion of the

climate change inventory for material balance (over the long term the net CO2 flux for a forest or

other ecosystem is assumed to be zero, unless there are permanent land use changes). However,

fires do result in net emissions of CH4, N2O, and BC. These can be quantified using fire activity

data compiled for health-related emissions inventories. In addition, the spatial and temporal

allocation tools developed for health-related emissions inventories can provide more accurate

distribution of climate change emission for modeling applications.
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18

Table 3. Other Emissions Inventories for Health Related Pollutants

Inventory description Year Pollutants Sectors Reference

Australia National

Pollutant Inventory

2000 83 Toxic, Criteria, and

Heavy Metal Pollutants

Industrial Australia NPI

document113

Canada’s Nataional

Pollutant Release

Inventory 

1999 172 substance (by Chemical

Abstract Service (CAS)

Registry Number)

Industrial Canada NPRI

document114

China Pollutant

Intensities

1995 SO2, Smoke, Dust Industrial

Pollutant

Intensities

New Ideas in

Pollution Regulation

(NIPR) website111

Mexico Pollutant

Intensities

1997 PT, SOx, CO, NOX, HC Industrial (SIC

code)

New Ideas in

Pollution Regulation

(NIPR) website111

Mexico Pollutant Release

and Transfer Registry

1997-

1998

178 Toxic, Criteria, and

Climate Forcing Pollutants

Mexican PRTR

document115

Netherlands Pollutant

Release and Transfer

Registry

1995-

96

170 Toxic, criteria, climate

forcing, and heavy metal

pollutants

industry, public

utilities, traffic,

households,

agriculture

Netherlands PRTR

document116

Slovak Republic 1998 93 Toxic, criteria, climate

forcing, and heavy metal

pollutants

industry Slovak Republic

PRTR document117

Turkey, by major city 2000 9 VOC pollutants none Muezzinoglu, A.118

UK National

Atmospheric Emission

Inventory

1990-

1999

29 Toxic, Criteria, Metal,

and Climate Forcing

Pollutants

Industrial NAEI database

online119

US National Emissions

Inventory (NEI),

including the National

Toxics Inventory (NTI)

1996

and

other

years

VOC, NOX, CO, PM, SO2,

NH3, secondary organic

aerosol

industry, public

utilities, traffic,

households,

agriculture,

wildland fire

NEI112 and NTI

online

US Toxic Release

Inventory

Annual app. 650 Toxic, Criteria,

Metal, and Climate Forcing

Pollutants

Industrial US TRI document120

World Bank – Industrial

pollution intensity dataset

1987 SO2, NO2, CO, VOC, PM10, TSPIndustrial (SIC

codes at 4

digits)

World Bank’s New

Ideas in Pollution

Regulation (NIPR)

websiteJanuary 24,

2002111

World Bank – Toxic

Pollution Intensity

Dataset

246 Toxic, Criteria, and

Heavy Metal Pollutant

Intensities

Industrial (ISIC

codes at 2, 3,

and 4 digits)

World Bank’s New

Ideas in Pollution

Regulation (NIPR)

website111
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19

Pollutants cove red

Global warming ga ses only

with SO2

with VOC

with NOx

with SO2 and N Ox

with NOx and VOC

with SO2 and VOC

with SO2, VOC, and N Ox

no c limate change inventory available

Figure 1. Health Related Pollutants Included in National Climate Change Inventories
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20

Tools for Estimating Emissions of

Black Carbon Particulate Matter

This section reviews existing emissions inventories for black carbon (BC) particulate

matter, the tools used to produce these inventories, and other available tools. Uncertainties are

evaluated and opportunities for improving BC emissions inventories are identified and

prioritized. 

Any analysis of BC emissions must recognize some unavoidable ambiguities in the

definition of the pollutant. Carbon in particulate matter can take a variety of forms. Typically,

these are divided into three main components: organic carbon (OC), a refractory component also

known as elemental carbon (EC), and carbonate ion (CO3

=

). The split between EC and OC can

be measured by different methods, but is typically obtained by measuring the amount that

pyrolizes at different temperatures.121 Soot generally falls in the EC fraction. 

For the purposes of climate change emissions inventories, BC is defined as the carbon

component of particulate matter that absorbs light. However, this specific component of

particulate matter is difficult, if not impossible, to measure. Methods that measure light

absorption in particulate matter assume that BC is the only light absorbing component present,

while methods that rely on the partitioning of EC and OC use a somewhat arbitrary division

point. For the purposes of this analysis, we have treated BC as roughly equivalent to EC. 

However, some components of OC may also be light-absorbing; in this case, inventories of BC

and OC may overlap.

Table 4 compares emission factors used in existing BC inventories, as well as other

available information on BC emissions. In many cases, BC emission factors were developed by

combining BC speciation factors with appropriate particulate matter emission factors. Where

possible, the table presents the BC speciation factors, the particulate matter emission factors, and

the resultant BC emission factors. (In some cases, only an emission factor or a speciation factor

is available.) Uncertainties are also given wherever possible. In addition to BC emission factors,

Table 4 also includes OC emission factors, since the line between OC and BC is somewhat

blurred. However, while some OC components may absorb light, most researchers presume that

OC possesses optical properties that scatter solar radiation.105, 107 Where possible, emission

factors are given for BC and OC in particulate matter with an aerodynamic diameter of less than

or equal to 2.5 microns (PM2.5). In some cases, however, the literature used different measures,

such as BC in PM1.0 (submicron PM), or PM10 (less than or equal to 10 microns). 

The following subsections discuss emission factors used in existing BC emissions

inventories, and other tools for estimating BC emissions. The final subsection of this chapter

discusses methods of ranking opportunities for improving BC emissions inventories.
21

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Utility and industrial combustion - coal

 Previous inventories - China

Utility pulverized burner, with fabric filter 0.66 0.99 2.5 0.5 2 6 Streets et al, 104

Utility pulverized burner, with ESP 0.66 0.99 0.5 0.5 0.1 0.5 Streets et al, 104

Utility pulverized burner, with scrubber 0.66 0.99 2.5 0.5 0.8 4 Streets et al, 104

Utility pulverized burner, with cyclone 0.66 0.99 1.5 0.5 0.2 2 Streets et al, 104

Utility pulverized burner, uncoltrolled 0.66 0.99 2.5 0.5 3 9 Streets et al, 104

Industrial pulverized burner, with ESP 0.66 0.99 0.5 0.5 0.1 3 Streets et al, 104

Industrial pulverized burner, with scrubber 0.66 0.99 0.5 0.5 0.8 10 Streets et al, 104

Industrial pulverized burner, uncoltrolled 0.66 0.99 0.5 0.5 3 20 Streets et al, 104

Utility stoker, with scrubber 3.3 11.2 10 20 77 2,600 Streets et al, 104

Utility stoker, with cyclone 3.3 11.2 10 20 32 880 Streets et al, 104

Utility stoker, uncontrolled 3.3 11.2 10 20 320 4,400 Streets et al, 104

Industrial stoker, with scrubber 3.3 11.2 10 20 130 3,600 Streets et al, 104

Industrial stoker with cyclone 3.3 11.2 10 20 32 1,300 Streets et al, 104

Industrial stoker, uncontrolled 3.3 11.2 10 20 320 4,400 Streets et al, 104

 Previous inventories - global

Lignite, underdeveloped countries 25 1,800 25 5400 Cooke et al, 106

Lignite, semideveloped countries 25 590 25 1770 Cooke et al, 106

Lignite, developed countries 25 360 25 1080 Cooke et al, 106

Hard coal, underdeveloped countries 25 1,000 25 1000 Cooke et al, 106

Hard coal, semideveloped countries 25 325 25 325 Cooke et al, 106

Hard coal, developed countries 25 200 25 200 Cooke et al, 106

 Other information

Fingerprint test 3.3 3.8 9.4 14.8 Watson et al (a), 122

SPECIATE (uncontrolled) 0.9 0.1 SPECIATE, 123

Fabric filter 7.0 3.0 4.4 4.2 Houck et al, 124

 Best estimate (for US) 5.1 3.4 6.9 9.8
22

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Utility and industrial combustion - oil

 Previous inventories

Light distillate oil - China 0.15 0.12 55 25 70 210 Streets et al, 104

Middle distillate oil - China 0.45 0.49 55 25 250 730 Streets et al, 104

Residual oil - China 0.65 0.69 55 25 360 1,000 Streets et al, 104

Residual oil - general 10 Cooke & Wilson, 105

 Other information

SPECIATE 7.4 5.4 4.5 3.1 SPECIATE, 123

Boiler fingerprint 0.0 0.1 0.1 0.1 Watson et al (b), 125

Glass furnace fingerprint 0.78 0.82 2.4 1.9 Watson & Chow, 126

Utility and industrial combustion - natural and process gas

 Previous inventories

Natural gas 0.4 Cooke & Wilson, 105

Process gas 0.06 Cooke & Wilson, 105

Natural gas and process gas 0.013 0.026 Cooke et al, 106

Utility and industrial combustion - wood and biofuel

 Previous inventories and compilations

Biofuel, general 11 9 12 1,300 2,400 Liousse et al, 107

Biofuel - China 3.6 32.4 25 35 1,000 10,000 Streets et al, 104

 Other information

AP-42 particulate 0.03 0.15 AP-42, 127

SPECIATE 3.1 1.6 7.1 2.4 SPECIATE, 123
23

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Residential combustion - coal

 Previous inventories

Hard coal - China 7.2 51.3 50 30 3,700 20,000 Streets et al, 104

Hard coal - developing countries 25 4,550 25 9450 Cooke et al, 106

Hard coal - developed countries 2,780 5830 Cooke et al, 106

Hard coal - general 10,000 Cooke & Wilson, 105

Lignite - developing countries 8,180 24500 Cooke et al, 106

Lignite - developed countries 5,000 15000 Cooke et al, 106

Lignite - general 10,000 Cooke & Wilson, 105

 Other information

Fingerprint test 26.1 15.6 69.5 19.2 Watson et al (a), 122

Residential wood combustion

 Fireplace combustion

Pine 9.5 1.0 1.4 0.1 130 160 56 2.8 5300 6200 Shauer et al (a), 128

Paper birch 2.7 0.0 22.0 2.9 590 670 86.8 6 2300 2500 Shauer et al (a), 128

Eastern hemlock 3.7 0.4 5.4 0.9 200 260 102.3 6.4 3800 4500 Shauer et al (a), 128

Red maple 3.3 0.3 6.7 1.9 220 310 85.5 5.8 2800 3300 Shauer et al (a), 128

Balsam fir 4.8 0.5 7.0 0.8 340 410 106.3 6.5 5100 6000 Shauer et al (a), 128

Oak 5.1 0.5 3.2 0.2 160 190 59 3 3000 3500 Shauer et al (a), 128

Northern red oak 5.7 0.6 3.8 0.7 220 280 87.5 5 5000 5800 Shauer et al (a), 128

Fireplace combustion Eastern White Pine 11.4 2.0 31.3 2.8 3,600 4,600 73.4 6.4 8400 10700 Shauer et al (a), 128

Eucalyptus 8.5 0.8 2.6 0.2 220 260 43.7 2.2 3700 4300 Shauer et al (a), 128

Fireplaces profiles - SPECIATE 21.2 23.2 45.2 7.8 SPECIATE, 123

 Other residential wood

Woodstoves profiles - SPECIATE 8.4 3.7 40.9 7.6 SPECIATE, 123

Average fingerprint, Denver CO 12.4 4.2 51.4 11.7 Watson et al (a), 122

Average fingerprint 19 7 64 7 Houck et al, 124

 Overall - best estimate 6.1 2.9 11.1 9.7 675 1,879 69.4 22.4
24

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Residential combustion - other fuels

 Oil - previous inventories

Heavy oil - China 0.65 0.69 55 25 360 1,000 Streets et al, 104

Kerosene - China 0.15 0.118 55 25 70 210 Streets et al, 104

Oil - general 80 Cooke & Wilson, 105

Oil - general 2,000 Cooke & Wilson, 105

 Natural gas - previous inventories

General 50 Cooke & Wilson, 105

General 0.137001 0.685004 Cooke et al, 106

 Natural gas - other information

Appliances - SPECIATE 6.7 84.9 SPECIATE, 123

 Charcoal - previous inventories

Briquettes - China 0.4 1.2 30 20 120 740 Streets et al, 104

Charcoal - general 10 15 1,500 1500 Liousse et al, 107

 Charcoal - other information

Meat charbroiling 18.8 2 0 0.5 0 100 33.8 2 6,400 7,400 Shauer (b), 129

Charcoal cooking 2.8 1.5 64.6 9.2 Watson & Chow, 126

 Other fuels

Dung burning 1,000 Liousse et al, 107

Peat 670 6,070 Cooke et al, 106
25

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Gasoline vehicles and trucks

 Previous inventories

Underdeveloped countries 15 730 Cooke et al, 106

Semideveloped countries 15 730 Cooke et al, 106

Developed countries 30 70 Cooke et al, 106

China 0.221 0.14 32 0 80 210 Streets et al, 104

 Dynamoneter tests

High emitting vehicles - southern California 0.72 1.34 29 18 210 980 71 18 510 1,830 Cadle et al (a), 130

Light duty vehicles 1986-96 models - Denver 0.15 27 42 50 77 Cadle et al (b), 131

Light duty vehicles - high emitters - Denver 3.26 9 290 44 1,400 Cadle et al (b), 131

MOBILE model - light duty vehicles 0.031 EPA NEI, 111

MOBILE model - heavy duty vehicles 0.048 EPA NEI, 111

MOBILE model - light duty trucks 0.022 EPA NEI, 111

 Field tests

Tunnel, 1993, Switzerland - light duty 0.097 0.006 19 3.5 18 20 Weingartner et al, 132

Tunnel, 1997, California - light duty vehicles0.110 0.01 32 6 35 38 48 12 53 61 Kirchstetter et al, 133

Tunnel, 1993, California - light duty vehicles0.068 0.024 60 38 41 49 73 32 50 51 Allen et al, 134

Tunnel, 1993, California - light duty vehicles0.073 0.051 21 218 15 86 53 132 39 61 Allen et al, 134

Tunnel test 1993, California 157 174 Fraser, 135

Fingerprint - Arizona 14 8.0 30 12 Watson et al (b), 125

Fingerprint - Imperial county, California 18 5.8 49 19 Watson & Chow, 126

Fingerprint - Arizona 37 11 39 19 Watson et al (b), 125

Fingerprint - Colorado 40 16 57 26 Watson et al (a), 122

Fingerprint - Mexicali, Mexico 19 7.9 50 29 Watson & Chow, 126

Fingerprint 1997 - Paris 43 20 Ruellan et al, 136

Fingerprint - Australia 27 9.4 Chan et al, 137

SPECIATE 18 14 56 39 SPECIATE, 123

Average of fingerprints 0.031 27 15 8 13 41 30 13 22

 Best estimates

Gasoline vehicles, developed countries 27 33 45 174 48 37 46 100
26

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Diesel vehicles and trucks

 Previous inventories

Underdeveloped countries 66 13 10,000 5000 Cooke et al, 106

Semideveloped countries 66 13 10,000 5000 Cooke et al, 106

Developed countries 66 13 2,000 1000 Cooke et al, 106

China 1.43 2.7 52 0 1,100 2,500 Streets et al, 104

 Dynamoneter tests

Diesel truck (1999 Ford F250) 850 884 408 442 Moosmuller et al, 138

Diesel truck (1996 Dodge Ram 2500) 329 340 397 431 Moosmuller et al, 138

1995 turbocharged intercooled engine 36.4 32 Shi et al, 139

Medium duty diesel trucks - 1999 1.6 0.2 30.8 2.6 480 580 19.7 1.6 310 370 Shauer (c), 140

Light duty diesel 1996 - Denver 6.0 27 1,600 18 1,100 Cadle et al (b), 131

MOBILE model average 0.53 EPA NEI, 111

 Field tests

Tunnel test 1997 - heavy duty diesels (CA) 2.5 0.2 52 16 1,300 1,600 20 3 500 540 Kirchstetter et al, 133

Tunnel test 1993 - heavy duty vehicles (CA) 2.68 0.19 56 6.3 1,518 1,582 32 2.6 848 857 Allen et al, 134

Tunnel test 1993 - heavy duty vehicles (CA) 1.53 0.28 61 38 936 1,330 39 16 588 713 Allen et al, 134

Tunnel, Switzerland, 1993 - diesel 2.6 0.1 31 3.4 823 880 Weingartner et al, 132

Tunnel, Switzerland, 1993 - heavy duty* 2.1 0.1 32 2.4 687 719 Weingartner et al, 132

Fingerprint - Arizona 33 8.0 174 40 6.6 213 Watson et al (b), 125

SPECIATE - heavy duty diesel 50 7.2 31 7.1 SPECIATE, 123

SPECIATE - light duty diesel 60 12 30 10 SPECIATE, 123

 Best estimates

Diesel vehicles, developed countries 48 18 1,100 1,600 29 10 645 900
27

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Aircraft

 Previous inventories

Jet fuel 1,000 450 Cooke et al, 106

Aviation gasoline 100 1150 Cooke et al, 106

 Other information

Jet engines 110 150 Petzold et al, 141

Jet engines - SPECIATE 70.1 25.9 SPECIATE, 123

Wildland fires

 Previous inventories

Forest fires - general 18 10 8.5 1,500 2,400 Liousse et al, 107

Tropical and subtropical 1,100 Cooke & Wilson, 105

Cold-deciduous forest with evergreens 750 Cooke & Wilson, 105

Cold-deciduous woodland 500 Cooke & Wilson, 105

Cold-deciduous forest without evergreens 600 Cooke & Wilson, 105

Temperate, various 500 Cooke & Wilson, 105

Evergreen broad-leaved sclerophyllous 1,100 Cooke & Wilson, 105

Xeromorphic (cactus) 1,100 Cooke & Wilson, 105

Grassland 500 Cooke & Wilson, 105

Savannah fires 8.1 5.5 10 810 1,360 Liousse et al, 107

 Field tests

Prescribed burning, Pacific Northwest 6.6 4.8 792 1,655 55 7.6 6,552 12,976 Ward & Hardy (a), 142

Chaparral, Southern California 9.0 3.4 610 739 51 7.8 3,675 4,441 Ward & Hardy (b), 143

Cerrado, Brazil 3.3 130 Kaufman et al, 144

Forest, Brazil 10 1,050 Kaufman et al, 144

Forest, fingerprint 3.2 1.8 47 15.7 Watson et al (a), 122

SPECIATE 6.9 5.5 56 8.7 SPECIATE, 123

 Best estimates 7.2 4.5 730 1,430 54 7.7 5,800 11,500
28

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Agricultural burning

 Previous inventories

Sugar cane burning - developing countries 7.0 11.0 770 770 Liousse et al, 107

Surgar cane burning - developed countries 5.0 15.5 780 780 Liousse et al, 107

Corn straw burning - China 720 7,200 Streets et al, 104

Corn and sugar cane - developing countries 12.0 8.0 960 960 Liousse et al, 107

Corn and sugar cane - developed countries 5.0 14.0 700 700 Liousse et al, 107

Wheat straw burning - China 900 9,000 Streets et al, 104

Wheat and other grains - developing countries 1.2 10.0 120 120 Liousse et al, 107

Wheat and other grains - developed countries 7.0 13.0 910 910 Liousse et al, 107

Rice straw burning- China 580 5,800 Streets et al, 104

Rice - developing countries 5.3 16.3 860 860 Liousse et al, 107

Rice - developed countries 2.5 24.0 600 600 Liousse et al, 107

 Fingerprint tests

Asparagus field burning 4.4 1.6 56 15.0 Watson & Chow, 126

Field burning - San Joaquin Valley, CA 12.0 7.0 44 10.0 Houck et al, 124

Field burning - Southern California, PM-10 10.9 3.3 35 8.0 Watson et al (b), 125

Field burning - SPECIATE data base 7.6 1.9 35 6.7 SPECIATE, 123

 Best estimates 12.2 5.9 713 961 42 9.9
29

Table 3. Emission Factors and Speciation Factors for BC and OC Fine Particulate Matter

Category

PM-2.5

(g/kg)

Uncertainty

BC

content

(%)

UncertaintyBC-2.5

(mg/kg)

Upper

limit

OC

content

(%)

UncertaintyOC-2.5

(mg/kg)

Upper

limit Reference

Geological

 Paved road dust

Denver, CO 0.8 0.5 7.4 2.8 Watson et al (a), 122

San Joaquin Valley, CA 2.0 1.0 18.0 2.0 Houck et al, 124

Southern CA 2.5 1.4 31.4 11.6 Watson et al (b), 125

SPECIATE 1.5 0.9 12.0 5.5 SPECIATE, 123

Best estimate 1.7 1.1 17.2 10.6

 Unpaved road dust

Denver, CO 0.5 0.2 4.6 2.2 Watson et al (a), 122

SPECIATE - Pocatello, Idaho 1.5 1.3 2.1 1.6 SPECIATE, 123

Best estimate 1.0 0.9 3.4 2.3

 Agricultural soil

Imperial Valley, CA <0.2 1.5 1.1 Houck et al, 124

Denver, CO <0.6 1.7 1.8 Houck et al, 124

 Confined Animal Feeding Operations

San Joaquin Valley, CA 6 2 31 13 Houck et al, 124

 Other

Soil dust - SPECIATE 0.6 0.72 4.3 3.6 SPECIATE, 123

Construction 0 0.51 0 1.4 Watson et al (b), 125

Construction 0 0.51 0 1.4 Watson et al (b), 125
Draft.

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30

Factors Used in Existing BC Inventories

Cooke and Wilson

In developing their black carbon inventory, Cooke and Wilson used published emissions

factors and data sets for fossil fuel and biomass combustion. Emission factors for biomass

combustion were calculated using a GISS surface-type data set and country-specific data on areas

of forests and grasslands burnt per year. Fossil fuel emission factors used 1950-1991 data from

the UN, and used 1984 as the reference year.

The authors assumed several things in calculating this inventory. When reliable data

were unavailable, emissions were assumed to be zero. All emissions were assumed to be

anthropogenic, due to lack of data from lightning-initiated fires. They did not account for

combustion efficiency differences between sectors, nor did they consider economic differences

between countries. 

ECHAM4

As described in a previous section, Cook, Liousse, and Cachier used published emissions

factors and data sets to calculate both bulk (particles with diameters of greater than a few

microns) and submicron black and organic carbon emissions. The methods used in this paper

were based on those used in the Cooke and Wilson study described above. The authors

combined total particulate matter and BC emission factors in order to extrapolate black carbon

emission factors for various sectors. Because OC emission factors had previously only been

reported for developed countries, the authors used a ratio of OC to BC for each fuel type to scale

the emission factor of OC for the same consumption sector for other countries. 

This study differed from the previous one in that the authors distinguished between

countries, combustion processes, and particle sizes. As described above, developed countries

included those in the OECD, semi-developed countries were former members of the Eastern

European Bloc, and all others were classified as developing countries. In addition, the authors

calculated both bulk and submicron emissions factors in this study. They hypothesized that these

two emissions types would better represent the upper and lower limits of global emissions

impacts, since submicron emissions are capable of long-range transport. Bulk emission factors

were derived from uncontrolled emission factors, and submicron factors were developed

assuming an average of 50% of emissions reductions from control devices in developed

countries. 

The authors averaged data from EPA and other authors to develop submicron and bulk

emissions for industrial, domestic, and combined/mobile combustion sources. The average

emission factor represented domestic and industrial fossil fuel use in developed countries. The

maximum in the range of factors represented domestic fuel use in semi-developed and

underdeveloped countries. Industrial emission factors for underdeveloped countries were

assumed to be five times that of developed countries. In all cases, the combined sector was

calculated as the square root of the product of the domestic and industrial emission factors.

GEIA

Penner and Eddleman developed their global BC inventory using two distinct methods. 

The first method used measured ambient concentration ratios of BC and SO2. The authors
Draft.

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31

determined that BC and SO2 emissions are highly correlated and fairly constant within distinct

economic regions. They multiplied known SO2 emissions in a particular region by the BC/SO2

ratio for that region in order to determine BC emissions. The second method used estimated

emission factors and published fuel production and use data.

CHINA-MAP

 Streets and Waldhoff developed a black carbon inventory for China using fuel

consumption data and detailed BC emission factors. Fuel data by sector and fuel type was

obtained from the RAINS-Asia model. These data are provincial-level estimates compiled from

official Chinese statistics. 

The authors conducted a literature survey to obtain emission factors. They expanded

upon these by calculating BC factors from total particulate matter emission factors. When

sufficient information existed, they derived the submicron fraction of total particulate emissions

from uncontrolled burning, and the BC fraction of the submicron particles. The authors then

calculated central and high values from the tabulated factors, and produced controlled emission

factors for each sector by applying removal efficiencies for each type of control device to the

uncontrolled emission factor. Central emission factors were calculated using a geometric

average of the measurements. High values represented the highest reported measurements, and

indicated a worst-case scenario for China’s BC emissions.

Louisse et al.

In developing black and organic carbon emissions factors for biomass burning and fossil

fuel combustion, the authors reevaluated factors from previously published studies using recent

analyses of fuel type, fuel amount, and combustion efficiency. 

Other Tools for Estimating Black Carbon Emissions

A great deal of work has been done over the last 20 years to analyze the chemical makeup

of respirable particulate matter, and to identify the important emission sources contributing to

elevated levels of respirable particulate matter. As part of these efforts, researchers have taken

“fingerprint” measurements of the chemical composition of particulate matter emitted by a wide

array of sources. These fingerprints consist of speciation factors for different atomic elements

and other components, expressed in terms of the mass fraction of a given component in

particulate matter emissions. Most fingerprints include EC and OC, and as noted earlier, BC is

roughly equal to EC. 

EPA’s SPECIATE database includes almost 200 measurements of the percentage of EC,

OC, and other components of PM2.5. Other sources of particulate fingerprint data include: the

California Air Resources Board (CARB) speciation manual,145 measurements carried out under

the Northern Front Range Air Quality Study,146 and data compiled by Desert Research Institute.147

BC emission factors can be estimated by applying EC speciation factors to PM2.5 emission

factors, from sources such as EPA’s Compilation of Emission Factors (AP-42). Alternatively,

speciation factors can be applied directly to the PM2.5 emissions inventories to estimate BC

emissions. In addition, separate EC emissions estimates have been published for a number of

emission source types as a result of fingerprint analyses. 
Draft.

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32

Ranking Opportunities for Improving Black Carbon Emissions Inventories

As the scientific community becomes more aware of the importance of global BC

emissions, the need for more complete and comprehensive inventories of this pollutant increase. 

Table 5 summarizes the emission source categories covered by current BC emissions inventories. 

The table illustrates that the existing inventories focus on industrial, utility, and residential

combustion sources. Less attention is devoted to industrial process emissions, nonroad mobile

source emissions, and categories such as open burning. These categories are candidates for

additional attention in any effort to improve BC emissions inventories. However, the broad

ranges variation in emission factors shown in Table 4 illustrate that considerable uncertainties

exist even for stationary combustion sources. In many cases, these uncertainties could be

reduced without additional measurements, by drawing on existing data to develop more detailed

emission factors for specific subcategories of emission sources. In some cases, additional

measurements may be needed.

Table 5. Source Categories Covered by Black Carbon Emissions Inventories

Source Category

Inventory

Cooke

and

Wilson

ECHAM4

(Cooke)

GEIA

(Penner)

Louisse

et al. 

Streets et

al. 

Residential fuel Coal T T

a T T T

Oil/diesel T T T T

Biofuel T T T T T

Natural Gas T T

Industrial and utility

fuel

Coal T T T T

Oil T T T

Biofuel T T T T

Gas T

b T

Petroleum T T

Kerosene T T

On-road mobile Diesel T T

Gasoline T T T

Non-road mobile Marine vessels T

Aircraft T T

Other T

Fire Wildland fire T T

Agricultural burning T T

a also includes briquettes, coke oven coke, gas coke, brown coal coke, lignite brown coal

b includes natural, blast furnace, coke-oven, gasworks, and refinery gases
Draft.

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33

The factors in Table 4 were used to make general calculations of U.S. and global BC

emissions, in order to identify areas where improved emission factors would have the greatest

impact. These calculations focused both on the relative magnitude of estimated BC emissions,

and the relative uncertainty the estimates. 

We estimated BC emissions for the U.S. by applying speciation factors for different

emission categories (from Table 4) to PM2.5 emissions estimates from the EPA’s National

Emissions Inventory.112 These calculations are summarized in Table 6. The table shows the NEI

PM2.5 emissions estimate for each category, a best estimate and upper end estimate for the BC

speciation factor, and a best estimate and upper end estimate for BC emissions. Table 7 ranks

different emission source categories according to their contribution to total U.S. emissions, based

both on the best estimate and the upper end estimate of emissions. Categories are also ranked

based on the approximate magnitude of the uncertainty of BC emissions. The following

equations were used in these evaluations:

C(B) i = 

100 × E(B) i

' k E(B) k

C(U) i = 

100 × E(U) i

E(U) i + ' k Ö i E(B) k

Unc i = E(U) i – E(B) i

where:

subscripts i and k refer to source categories contributing to BC emissions

C(B) = best estimate of the contribution of a particular source category to total

emissions (%)

C(U) = upper end estimate of the contribution of the particular source category to total

emissions (%)

Unc = uncertainty of emissions estimate for the particular source category

E(B) = best estimate of emissions from a given category

E(U) = upper end estimate of emissions from a given category

' k refers to the sum over all source categories

' k Ö i refers to the sum over all categories except the particular source category i

Tables 8 and 9 show similar calculations for global BC emissions. We estimated global

emissions in Table 8 by applying BC emission factors to fuel consumption figures, which for

most categories were calculated from CO2 emissions reported in the EDGAR climate change

database.7 In the case of wildland fires, fuel consumption was taken from an analysis by Hao and

Ward.148 It must be noted that the emissions in Table 8 are rough estimates developed without

detailed activity data, for the purposes of ranking order of magnitude uncertainties. Table 9 
Draft.

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34

Table 6. Estimated Breakdown of BC Emissions in the U.S.

Reported

US PM2.5

emissions BC to PM2.5 ratio (%)

Estimated BC emissions

(1000 Mg/yr)

Category

(1000

Mg/yr)

Best

estimate

High

estimate

Best

estimate

High

estimate

Electric utilities - coal 92 3.7 7.1 3.0 7.0

Electric utilities - petroleum 4.0 7.4 13 <0.1 <0.1

Electric utilities - natural gas 17 6.7 15 1.0 3.0

Electric utilities - wood/other 3.0 9.3 31 <0.0 1.0

Industrial combustion - coal 21 3.7 7.1 1.0 2.0

Industrial combustion - petroleum 22 7.4 13 2.0 3.0

Industrial combustion - natural gas 49 6.7 15 3.0 7.0

Industrial combustion - wood 44 9.3 31 4.0 14

Commercial combustion - coal 7.0 3.7 7.1 <0.1 <0.1

Commercial combustion - petroleum 4.0 7.4 13 <0.1 <0.1

Commercial combustion - natural gas 7.0 6.7 15 <0.1 1.0

Residential combustion - natural gas 13 6.7 15 1.0 2.0

Residential combustion - wood 340 6.1 9.0 21 31

Onroad diesel vehicles 151 43 59 65 89

Onroad gasoline vehicles 58 27 60 16 35

Marine transportation 36 43 59.a 16 21

Locomotives - diesel 25 43 59.a 11 15

Aircraft 25 70 17 25

Nonroad - gasoline 75 27 60.a 20 45

Nonroad - diesel 211 43 59.a 91 125

Miscellaneous fuel combustion 73 14 24 10 18

Wildfires 212 7.2 12 15 25

Prescribed forest burning 478 7.2 12 34 56

Agricultural burning 85 12 13 10 11

Open burning - residential 157 12 13.b 19 21

Open burning - other 275 12 13.b 34 36

Incineration - residential 28 28

Incineration - other 15 15

Industrial - metals processing 94 10 30 9.0 28

Industrial - asphalt manufacture 4.0 4.0

Industrial - peteoleum refining 12 0.3 0.4 <0.1 <0.1

Oil and gas production 1.0 1.0

Rubber and plastics products 2.0 2.0

Fugitive dust - unpaved roads 1,283 1.0 1.9 12 24

Fugitive dust - paved roads 620 1.7 2.8 11 17

Fugitive dust - construction 355 0.5 2.0

Fugitive dust - other 133 0.6 1.3 1.0 2.0

Agriculture - tilling 782 0.6 5.0

Agriculture - livestock 81 6.0 8.0 5.0 6.0

 Total 5,894 433 727

a - Estimated based on information for onroad vehicles.

b - Estimated based on information for agricultural burning.
Draft.

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35

Table 7. Ranking of U.S. Source Categories by BC Emissions and Uncertainty

Contribution to total U.S.

BC emissions (%) Rankings of importance

Category

Based on

best

estimate

Based on

upper end

estimate

Uncertainty

of BC

estimate

(1000 Mg/yr)

Based on share

of BC Based on

uncertaintyBest

estimate Upper

Electric utilities - coal 0.8 1.5 3.1

Electric utilities - petroleum 0.1 0.1 0.2

Electric utilities - natural gas 0.3 0.6 1.4

Electric utilities - wood/other 0.1 0.2 0.6

Industrial combustion - coal 0.2 0.3 0.7

Industrial combustion - oil 0.4 0.6 1.2

Industrial combustion - gas 0.8 1.7 4.1

Industrial combustion - wood 1.0 3.1 9.8

Commercial combustion - coal 0.1 0.1 0.2

Commercial combustion - oil 0.1 0.1 0.2

Commercial combustion - gas 0.1 0.2 0.5

Residential combustion - gas 0.2 0.4 1.1

Residential combustion - wood 4.8 6.9 9.8 5 7 10

Onroad diesel vehicles 15 19 24 2 2 4

Onroad gasoline vehicles 3.6 7.7 19 10 6 6

Marine transportation 3.6 4.9 5.8 9

Locomotives - diesel 2.5 3.3 4.0

Aircraft 4.0 5.6 7.4 8

Nonroad - gasoline 4.7 9.8 25 6 4 3

Nonroad - diesel 21 27 34 1 1 1

Miscellaneous fuel combustion 2.4 4.0 7.3

Wildfires 3.5 5.6 9.5 10

Prescribed forest burning 7.9 12 22 3 3 5

Agricultural burning 2.4 2.6 0.8

Open burning - residential 4.4 4.7 1.4 7

Open burning - other 7.7 8.3 2.5 4 5

Incineration - residential 6.1 28 9 2

Incineration - other 3.2 15 8

Industrial - metals processing 2.2 6.2 19 8 7

Industrial - asphalt manufacture 0.9 3.8

Industrial - peteoleum refining <0.1 <0.1 <0.1

Oil and gas production 0.1 0.6

Rubber and plastics products 0.5 2.0

Fugitive dust - unpaved roads 2.9 5.4 12 9

Fugitive dust - paved roads 2.4 3.9 6.8

Fugitive dust - construction 0.4 1.8

Fugitive dust - other 0.2 0.4 1.0

Agriculture - tilling 1.1 4.7

Agriculture - livestock 1.1 1.5 1.6
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Table 8. Estimated Breakdown of Global BC Emissions

Reported

global

BC emission factors

(mg/kg)

Estimated BC

emissions (106 Mg) Notes on

Source category

 CO2

emissions

Best

estimate

Upper

end

Best

estimate

Upper

end

 emission

factors

Fossil fuel combustion

Industrial boilers 4,590 130 770 0.60 3.5 a

Power generation 6,638 130 770 0.86 5.1 b

Industrial process fuel 1,624 130 770 0.21 1.3 a

Residential, etc. 3,343 1,350 7,000 4.5 23 c

Road transport 3,261 570 1,360 1.9 4.4 d

Non-road land transport 343 500 1,600 0.17 0.55 e

Air (domestic and int’l.) 545 110 150 0.06 0.08 f

International shipping 350 200 1,150 0.07 0.40 g

Fossil fuel: non-combustion

Gas flaring 260 0.01 0.03 <0.01 <0.01 h

Biofuel

Industrial boilers 118 1,000 10,000 0.12 1.18 i

Other industry fuel use 1 1,000 10,000 0.00 0.01 i

Residentials etc. 427 675 1,880 0.29 0.80 j

Other

Deforestation 1,837 730 1,430 1.3 2.6 j

Savannah fires (k) 5,200 130 500 0.68 2.6 j

Forest fires (k) 620 730 1,430 0.45 0.89 j

 Total 21 90

a Average of coal stoker, residual oil, and natural gas from Streets et al

b Average of coal stoker, pulverized coal, and residual oil from Streets et al

c Average of coal, oil, and natural gas from Streets et al 

d Best estimate is average of best estimates from gasoline and diesel from Table 4, upper end estimate

is average of gasoline and diesel from Streets et al

e Average of coal stoker, residual oil, and diesel from Streets et al

f Based on jet fuel factors from Petzold et al

g Average of coal stoker and residual oil from Streets et al

h Based on Cooke et al for natural gas combustion

i Based on Streets et al

j Based on averages from Table 4

k Biomass burned in wildland fire from Hao and Ward, 1993
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Table 9. Ranking of Source Categories by Global BC Emissions and Uncertainty

Contribution to global

BC emissions (%) Rankings of importance

Category

Based on

best

estimate

Based on

upper end

estimate

Uncertainty

of BC

estimate

(106 Mg/yr)

Based on share of

BC Based on

uncertaintyBest

estimate Upper

Fossil fuel: combustion

Industrial boilers 5.3 25 2.9 6 4 3

Power generation 7.7 33 4.2 4 2 2

Industrial process fuel 1.9 10 1.0 8 7 8

Residentials etc. 40 78 19 1 1 1

Road transport 17 32.1 2.6 2 3 4

Non-road land transport 1.5 4.7 0.4 9 10 10

Air (domestic + intern.) 0.5 0.7 <0.1

International shipping 0.6 3.5 0.3

Fossil fuel: non-combustion

Gas flaring <0.1 <0.1 <0.1

Biofuel

Industrial boilers 1.0 9.6 1.1 10 8 7

Other industry fuel use <0.1 0.1 <0.1

Residentials etc. 2.6 6.8 0.5 7 9 9

Other

Deforestation 12 21 1.3 3 5 6

Savannah fires 6.0 20 1.9 5 6 5

Forest fires 4.0 7.6 0.4

ranks different emission source categories according to their estimated contribution to total

global emissions, and to their uncertainties. 

Based on the calculations in Tables 6 and 7, the largest BC emission source categories in

the U.S. are nonroad and onroad diesel emissions and prescribed forest burning. However, the

most important categories in terms of the magnitude of uncertainty are nonroad diesel vehicle,

residential incineration, and nonroad gasoline vehicles. 

The global inventory categories available from EDGAR are much less detailed than the

categories in the U.S. NEI. From Tables 8 and 9, the largest BC emission categories on a global

basis are residential combustion, onroad vehicles, power generation, and deforestation (by

burning). The most important categories based on the magnitude of uncertainty are residential

combustion, industrial combustion, and utility combustion.
Draft.

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Draft.

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