Protocol for Applying and Validating the

CMB Model for PM2.5 and VOC

i

EPA-451/R-04-001

December 2004

Protocol for Applying and Validating the CMB Model for PM2.5 and VOC

By:

John G. Watson et al.

Desert Research Institute

University and Community College System of Nevada

Reno, NV 89512 

Prepared for:

C. Thomas Coulter and Charles W. Lewis, Project Officers

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711

Contract No. 5D1808NAEX

US. Environmental Protection Agency

Office of Air Quality Planning & Standards

Emissions, Monitoring & Analysis Division

Air Quality Modeling Group
1

Air Quality Modeling Group, Office of Air Quality Planning & Standards; Research Triangle Park, NC 27711

2

National Exposure Research Laboratory, Office of Research & Development; Research Triangle Park, NC 27711

ii

ACKNOWLEDGMENTS

This revised protocol for applying and validating the Chemical Mass Balance Model

(CMB) was originally developed by Desert Research Institute (DRI) of trhe University and

Community College System of Nevada under Contract 5D1808NAEX with EPA’s Office of Air

Quality Planning & Standards. The Project Officers were C. Thomas Coulter1

 and Charles W.

Lewis.2

 Substantial contributions to the initial draft of this protocol were made by DRI staff

members John G. Watson, Judith C. Chow, and Eric M. Fujita. Tom Coulter spent considerable

time reviewing and reformatting the protocol, and harmonizing it with the latest version of

CMB: EPA-CMB8.2. He also developed and produced its Appendixes A, B and G.

DISCLAIMER

This protocol was reviewed by EPA for publication. The information presented here

does not necessarily express the views or policies of the U.S. Environmental Protection Agency

or the State of Nevada. The mention of commercial hardware and software in this document

does not constitute endorsement of these products. No explicit or implied warranties are given

for the software and data sets described in this document.
iii

Table of Contents

Page

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.1 Protocol Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.2 CMB Model Development and History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2

1.3 Protocol Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

2. CMB Relationships with Other Air Quality Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1 Conceptual Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.2 Emissions Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.3 Meteorological Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2.4 Chemical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.5 Source Dispersion Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.6 Receptor Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

3. CMB Source and Receptor Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 Source Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1.1 Common Emissions Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1.2 Source Profile Normalization Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3.1.3 PM2.5 Source Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.1.4 VOC Source Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11

3.1.5 Source Characterization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14

3.1.6 Source Profile Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16

3.2 Receptor Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17

3.2.1 Physical and Chemical Characteristics of Receptor Concentrations . . . . . . . . 3-17

3.2.2 Receptor Characterization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19

3.2.3 Sampler Siting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28

3.2.4 Temporal Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

3.2.5 Receptor Measurement Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-31

3.3 CMB Application Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-31

4. Assumptions, Performance Measures, and Validation Procedures . . . . . . . . . . . . . . . . . . . 4-1

4.1 Fundamental Assumptions and Potential Deviations . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.2 CMB Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4

4.3 Protocol Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-14

4.3.1 Determine the Applicability of CMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-14

4.3.2 Format Input Files and Perform Initial Model Runs . . . . . . . . . . . . . . . . . . . . 4-14

4.3.3 Evaluate Outputs and Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . 4-17

4.3.4 Evaluate Deviations from Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 4-17

4.3.5 Modify Model Inputs to Remediate Problems . . . . . . . . . . . . . . . . . . . . . . . . . 4-17

4.3.6 Evaluate the Consistency and Stability of the Model Results . . . . . . . . . . . . . 4-19

4.3.7 Corroborate CMB Results with Other Modeling and Analyses . . . . . . . . . . . 4-20
iv

Table of Contents (continued)

Page

5. Example of Application and Validation for PM2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.1 Model Applicability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.2 Initial Source Contribution Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6

5.3 Model Outputs and Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15

5.4 Deviations from Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15

5.5 Identification and Correction of Model Input Errors . . . . . . . . . . . . . . . . . . . . . . . . 5-17

5.6 Consistency and Stability of Source Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 5-17

5.7 Consistency with Other Simulations and Data Analyses . . . . . . . . . . . . . . . . . . . . . 5-18

6. Example of Application and Validation for VOC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1 Model Applicability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.2 Initial Source Contribution Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.3 Examine Model Outputs and Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

6.4 Test Deviations from Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

6.5 Identify and Correct Model Input Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

6.6 Evaluate Consistency and Stability of Source Contributions . . . . . . . . . . . . . . . . . . . 6-7

6.7 Determine Consistency with Other Simulations and Data Analyses . . . . . . . . . . . . . 6-7

7. Summary, Conclusions, and Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

8. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

APPENDIX A. 54 PAMS target compounds (hydrocarbons) listed in their elution sequence A-1

APPENDIX B. Normalization for the VOC Source Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

APPENDIX C. Internet Links to Modeling Software and Data Sets . . . . . . . . . . . . . . . . . . . . C-1

APPENDIX D. CMB Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

APPENDIX E. Summary of CMB PM10 Source Apportionment Studies . . . . . . . . . . . . . . . . E-1

APPENDIX F. Summary of CMB VOC Source Apportionment Studies . . . . . . . . . . . . . . . . F-1

APPENDIX G. Procedures for Treating Secondary Particles . . . . . . . . . . . . . . . . . . . . . . . . G-1
v

List of Tables

Page

Table 3.1-1 Chemicals from Particles in Different Emissions Sources . . . . . . . . . . . . . . . . 3-8

Table 3.1-2 Organic Compounds Found in Different Source Emissions and in Ambient Air 3-9

Table 3.2-1 Chemical Compounds, Mnemonics, and Measurement Methods for Particle and

VOC Receptor Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-20

Table 4.2-1 CMB8 Outputs and Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

Table 5.1-1 Wintertime Emissions Inventory for Denver Metro Area . . . . . . . . . . . . . . . . . 5-2

Table 5.1-2 Source Composition Profiles from NFRAQS . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

Table 5.1-3 Source Composition Profiles from the 1987 Scenic Denver Study and Other

Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5

Table 5.2-1a Sensitivity of Total Carbon Apportionment to Alternative Wood Combustion

Profiles (Welby, 01/17/97 at 0600 to 1200 MST) . . . . . . . . . . . . . . . . . . . . . . . 5-7

Table 5.2-1b Sensitivity of Total Carbon Apportionment to Alternative Meat Cooking Profiles

(Welby, 01/17/97 at 0600 to 1200 MST) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8

Table 5.2-2a Sensitivity of Total Carbon Apportionment to Alternative Cold-Start Profiles

(Welby, 01/17/97 at 0600 to 1200 MST) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-9

Table 5.2-2b Sensitivity of Total Carbon Apportionment to Alternative Hot-Stabilized and

High Particle Emitter Profiles (Welby, 01/17/97 at 0600 to 1200 MST) . . . . 5-10

Table 5.2-2c Sensitivity of Total Carbon Apportionment to Fitting Species (Welby, 01/17/97 at

0600 to 1200 MST) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-11

Table 6.2-1 VOC Source Profiles for NARSTO-NE CMB . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

Table 6.2-2 PAMS Measured Species and CMB Fitting Species . . . . . . . . . . . . . . . . . . . . . 6-3

Table 6.3-1 CMB Sensitivity Tests for Vehicle Exhaust Profiles . . . . . . . . . . . . . . . . . . . . . 6-4

Table 6.3-2 CMB Sensitivity Tests for Different Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5
vi

List of Figures

Page

Figure 3.2-1 Spatial distribution of average PM2.5 source contributions from gasoline exhaust,

diesel exhaust, suspended dust, vegetative burning, secondary ammonium sulfate,

secondary ammonium nitrate, and primary coal-fired power station fly ash in and

near Denver, CO during winter, 1996-97. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-29

Figure 3.2-2 PM2.5 source contributions at the Welby site north of Denver, CO during winter of

1996-97. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

Figure 4.2-1 CMB8 source contribution display. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

Figure 4.2-2 Eligible space collinearity display. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6

Figure 4.2-3 Species concentration display. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7

Figure 5.7-1 Average PM2.5 source contributions at the Welby site near Denver, CO during the

winter of 1996-97. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-18

Figure 6.7-1 Hourly average VOC source contributions by day of week at Lynn, MA. . . . . 6-8

Figure 6.7-2 Wind direction dependence of VOC source contributions at Lynn, MA. . . . . . 6-9
1 - 1

1. INTRODUCTION

The Chemical Mass Balance (CMB) air quality model is one of several models that have

been applied to air resources management. Receptor models use the chemical and physical

characteristics of gases and particles measured at source and receptor to both identify the

presence of and to quantify source contributions to receptor concentrations. Receptor models are

generally contrasted with dispersion models that use pollutant emissions rate estimates,

meteorological transport, and chemical transformation mechanisms to estimate the contribution

of each source to receptor concentrations. The two types of models are complementary, with

each type having strengths that compensate for the weaknesses of the other.

The CMB receptor model consists of a solution to linear equations that express each

receptor chemical concentration as a linear sum of products of source profile abundances and

source contributions. The source profile abundances (i.e., the mass fraction of a chemical or

other property in the emissions from each source type) and the receptor concentrations, with

appropriate uncertainty estimates, serve as input data to CMB. In order to distinguish among

source type contributions, the measured chemical and physical characteristics must be such that

they are present in different proportions in different source emissions and changes in these

proportions between source and receptor are negligible or can be approximated. The CMB

model calculates values for the contributions from each source and the uncertainties of those

values.

The CMB model is applicable to multi-species data sets, the most common of which are

chemically characterized particulate matter (PM) and volatile organic compounds (VOC). PM2.5

and PM10 (mass of particles with aerodynamic diameters less than 2.5 and 10:m, respectively)

are regulated by National Ambient Air Quality Standards (NAAQS, EPA, 1997a). VOC are not

specifically regulated, but they are precursors for ozone, which is subject to NAAQS (EPA,

1997a).

CMB model results are used to determine how much different sources contribute to

ambient concentrations. This knowledge is usually used with source attributions determined by

other models to justify emissions reduction strategies. 

1.1 Protocol Objectives

This protocol describes how to use the CMB model in practical applications to determine

the contributions of different sources to PM2.5 and VOC. Its objectives are to:

• Document measurement approaches and data sources for source and receptor

input data.

• Describe the seven step applications and validation protocol to be followed when

using the CMB model for source apportionment.

• Present examples for PM2.5 and VOC apportionment using contemporary data sets

and source types.
1 - 2

1.2 CMB Model Development and History

This protocol supplements and expands on the earlier protocol for applying and

validating the CMB model (EPA, 1987a; Watson et al., 1991) that was widely used to develop

State Implementation Plans for the previous PM10 NAAQS (EPA 1987b, 1987c). With the

subsequent adoption of a PM2.5 NAAQS, as well as greater interest in apportioning VOC for its

role in local formation of photochemical oxidants, EPA decided to produce this enhanced

protocol, which supersedes the earlier edition.

The CMB software has evolved over more than two decades to facilitate model

application and validation. The Chemical Mass Balance receptor model was first applied by

Hidy and Friedlander (1971), Winchester and Nifong (1971), and Kneip et al. (1972). The

original applications used unique chemical species associated with each source-type, the socalled "tracer" solution. Friedlander (1973) introduced the ordinary weighted least-squares

solution to CMB equations, and this had the advantages of relaxing the constraint of a unique

species in each source type and of providing estimates of uncertainties associated with the source

contributions. Gordon (1980, 1988) and Kowalkzyk et al. (1978) subsequently applied this

method to elemental concentrations measured in source and receptor samples. The ordinary

weighted least squares solution was limited in that only the uncertainties of the receptor

concentrations were considered; the uncertainties of the source profiles, which are typically

much higher than the uncertainties of the receptor concentrations, were neglected.

The first interactive user-oriented software for the CMB model was programmed in 1978

in FORTRAN IV on a PRIME 300 minicomputer (Watson, 1979). The PRIME 300 was limited

to 3 megabytes of storage and 64 kilobytes of random access memory. CMB versions 1 through

6 updated this original version and were subject to many of the limitations dictated by the

original computing system. CMB7 was written in a combination of the C and FORTRAN

languages for the DOS operating system. With Windows® 3.1, 95, and NT becoming the most

widely used operating systems, CMB8 created a user interface for CMB7 calculations using the

Borland Delphi object oriented language.

CMB1 was used in the Portland Aerosol Characterization Study (PACS) to develop a

State Implementation Plan for the control of Total Suspended Particulate Matter (Watson, 1979). 

This modeling was the first to identify and quantify residential wood combustion as a major

contributor to particulate levels in a U.S. urban area. CMB2 was installed on EPA's UNIVAC

system in 1980 from which it could be operated by direct dial-up from a remote terminal. CMB3

streamlined the computer code in FORTRAN 77 for the EPA UNIVAC and added a ridge

regression solution to the effective variance least-squares estimation method for solving the

CMB equations (Williamson and DuBose, 1983). The ridge regression algorithm was thought to

reduce the effects of collinearity (i.e., two or more source profiles which are too similar to be

separated from each other by the model) on source contribution estimates. Henry (1982)

showed, however, that the ridge regression solution was equivalent to changing the source

profiles from their measured values until the collinearity disappeared. Henry (1982) determined

that the source contribution estimates given by the ridge regression solution did not represent

reality, and its use for air quality modeling was abandoned.
1 - 3

CMB4, created in 1984, ported the CMB3 software to an IBM/XT microcomputer and

added the original effective variance solution of CMB1. CMB5 was an experimental version

that contained several solution methods, performance diagnostics, and output displays. CMB5

was used as a test bed for evaluating model performance measures, and it was revised nine times

in response to recommendations and findings of these scientists and regulators. These revisions

resulted in CMB6 (EPA, 1987d) and the original protocol for applying and validating the CMB

model (EPA, 1987a). A protocol for reconciling CMB source contribution estimates with those

determined by dispersion modeling (EPA, 1987e) was also published.

While CMB7 (Watson et al., 1990a; EPA, 1990) improved the ease of use, it did not

appreciably modify the model validation performance measures. CMB8 (Watson et al., 1997a)

has major changes in the collinearity measures (Henry, 1992) that have resulted from more than

ten years of experience in using the CMB6 and CMB7 methods for model evaluation. EPACMB8.2 (EPA, 2004; Coulter and Scalco, 2005) incorporates the upgrade features that CMB8

has over CMB7, but also corrects errors/problems identified with CMB8, updates the linear

algebra library, adds enhancements for a more robust and user-friendly system, and employs

code that has been reorganized, refactored, and is well-documented.

Although the protocol is applicable to earlier versions of the software (e.g., CMB7), the

examples given are specific to Windows®-based versions developed since CMB7. Throughout

this protocol, reference is made to CMB as well as to CMB8. For practical purposes, the

protocol applies ideally to either CMB8 or EPA-CMB8.2. As implied in this protocol’s title, our

intention is to make this protocol “generic”, applicable to any of the latest Windows® versions

that have evolved since CMB7.

1.3 Protocol Overview

The CMB modeling procedure requires: 1) identification of the contributing sources

types; 2) selection of chemical species or other properties to be included in the calculation; 3)

estimation of the fraction of each of the chemical species which is contained in each source type

(source profiles); 4) estimation of the uncertainty in both ambient concentrations and source

profiles; and 5) solution of the chemical mass balance equations. The CMB model is implicit in

all factor analysis and multiple linear regression models that intend to quantitatively estimate

source contributions (Watson, 1984). These models attempt to derive source profiles from the

covariation in space and/or time of many different samples of atmospheric constituents that

originate in different sources. These profiles are then used in a CMB calculation to quantify

source contributions to each ambient sample. Section 3 describes the types of data needed to

apply and validate the CMB model.

The CMB model is intended to complement rather than replace other data analysis and

modeling methods. The CMB model explains observations that have already been taken, but it

does not predict the future. When source contributions are proportional to emissions, as they

often are for PM and VOC, then a source-specific proportional rollback is used to estimate the

effects of emissions reductions. Similarly, when a secondary compound (a substance formed in

the atmosphere rather than directly emitted by sources) apportioned by CMB is known to be

limited by a certain precursor, a proportional rollback is used on the controlling precursor.
1 - 4

The most widespread use of CMB over the past decade has been to justify emissions

reduction measures in PM10 non-attainment areas. More recently, CMB has been coupled with

extinction efficiency receptor models to estimate source contributions to light extinction and

with aerosol equilibrium models to estimate the effects of ammonia and oxides of nitrogen

emissions reductions on secondary nitrates. Section 2 describes how CMB relates to other air

quality models and Appendix C identifies Internet web sites where more information about these

models may be obtained.

Several solution methods have been proposed for the CMB equations: 1) single unique

species to represent each source (tracer solution) (Miller et al., 1972); 2) linear programming

solution (Hougland, 1983); 3) ordinary weighted least squares, weighting only by precision

estimates of ambient measurements (Friedlander, 1973; Guardrail and Friedlander, 1975); 4)

ridge regression weighted least squares (Williamson and Daboecia, 1983); 5) partial least

squares (Larson and Long, 1989; Long et al., 1988); 6) neural networks (Song and Hopke,

1996); 7) Britt and Luecke (1973) least squares; and 8) effective variance weighted least squares

(Watson et al., 1984). CMB8 software allows solutions 1, 3, 7, and 8 to be implemented, and

this facilitates tests of the effect of solution method on model results. Appendix D shows how

these solution methods relate to each other and documents the mathematical basis for CMB

performance measures.

The effective variance weighted least squares solution is almost universally applied

because it: 1) theoretically yields the most likely solutions to the CMB equations, providing

model assumptions are met; 2) uses all available chemical measurements, not just so-called

“tracer” species; 3) analytically estimates the uncertainty of the source contributions based on

the uncertainty of both the ambient concentrations and source profiles; and 4) gives greater

influence to chemical species with lower uncertainty estimates in both the source and receptor

measurements than to species with higher uncertainty estimates. The effective variance is a

simplification of a more mathematically exact, but less practical, generalized least squares

solution proposed by Britt and Luecke (1973).

CMB model assumptions are: 1) compositions of source emissions are constant over the

period of ambient and source sampling; 2) chemical species do not react with each other (i.e.,

they add linearly); 3) all sources with a potential for contributing to the receptor have been

identified and have had their emissions characterized; 4) the number of sources or source

categories is less than or equal to the number of species; 5) the source profiles are linearly

independent of each other; and 6) measurement uncertainties are random, uncorrelated, and

normally distributed.

The degree to which these assumptions are met in applications depends to a large extent

on the particle and gas properties measured at source and receptor. CMB model performance is

examined generically, by applying analytical and randomized testing methods, and specifically

for each application by following an applications and validation protocol. The six assumptions

are fairly restrictive and they will never be totally complied with in actual practice. Fortunately,

the CMB model can tolerate reasonable deviations from these assumptions, though these

deviations increase the stated uncertainties of the source contribution estimates. Section 4

explains these assumptions and summarizes the results of tests that evaluate deviations from

them.
1 - 5

The seven-step applications and validation protocol: 1) determines model applicability;

2) selects a variety of profiles to represent identified contributors; 3) evaluates model outputs

and performance measures; 4) identifies and evaluates deviations from model assumptions;

5) identifies and corrects of model input deficiencies; 6) verifies consistency and stability of

source contribution estimates; and 7) evaluates CMB results with respect to other data analysis

and source assessment methods. This protocol is illustrated for a PM2.5 example in Section 5 and

for a Volatile Organic Compound (VOC) example in Section 6. These examples contain

sufficient detail that the protocol can be followed for other source apportionment studies.

Appendix A lists the 54 PAMS (Photochemical Assessment Monitoring Sites) target

compounds (hydrocarbons) listed in their elution sequence. Appendix B describes the

normalization procedure for the VOC source profile. Appendix C lists Internet links (URLs) for

modeling software and data sets. Appendix D describes the CMB mathematical formulations. 

Appendices E & F summarize applications of CMB to PM and VOC source apportionment. 

These are related to a comprehensive bibliography of methodological and application examples

that can be consulted for greater detail. Appendix G explains procedures for treating secondary

particles within the constraints of CMB.

2 - 1

2. CMB RELATIONSHIPS WITH OTHER AIR QUALITY MODELS

Most excessive pollutant to which large populations are exposed result from various

source emissions that are transported and transformed by the atmosphere. In some cases, the

emissions emit visible plumes that can be seen to traveling toward a receptor. It is more often

the case, however, that plumes are invisible, or that many slightly visible plumes mix together

and disperse over wide areas. Different models of emissions and the atmosphere are used to

integrate science and measurements to determine the contributions from specific sources or

source types. These models are imperfect representations of reality, making many assumptions

and operating on limited data bases.

As much effort is needed to evaluate their veracity as to apply them. For this reason,

several different and independent models are commonly applied, linked to one another and

independent of each other, to quantify source. Discrepancies between model results helps to

identify and improve their weakness and to apply uncertainty bounds that should be used when

designing control strategies. Commonly used air quality models are: 1) conceptual models; 2)

emissions models; 3) meteorological models; 4) chemical models; 5) source-oriented models;

and 6) receptor models.

2.1 Conceptual Models

Conceptual models describe the relevant physical and chemical processes that affect

emissions, transport, and transformation. They are the starting point for any source

apportionment process. Conceptual models take advantage of the large body of scientific

knowledge already acquired. They identify the sources that are likely to be present and eliminate

those that are not. They examine meteorological conditions that affect concentrations and focus

further modeling on the conditions conducive to the high concentrations. Although the

conceptual models described earlier in this chapter are consistent with current information, they

are not yet verified. Field study measurements are designed to test them as hypotheses, and they

will likely change.

A conceptual model should be formulated prior to designing a CMB source

apportionment study. This should include a conception of the sources, their zones of influence,

transport from distant areas, timing of emissions throughout the day, and meteorology that

affects transport, dispersion, and transformation. This conceptual model should be used to guide

the location of monitoring sites, the time of samples, the selection of samples for laboratory

analysis, and the species that are quantified in those samples.

2.2 Emissions Models

Emissions models estimate temporal and spatial emission rates based on activity level,

emission rate per unit of activity, and meteorology (EPA, 1996). Emissions models are often

empirically derived from tests on representative source types, such as paved and unpaved roads,

motor vehicle exhaust, biota, and industries. Emissions models are used to construct emissions

inventories that are used as the basis for control strategy.
2 - 2

Emissions models and their results are used to identify initial sources types for inclusion

in a CMB analysis. When emissions rates are chemically speciated, the same profiles used for

that speciation might also be applicable to the CMB apportionment. The CMB is often used to

evaluate emissions models and to identify areas where they need improvement (e.g., Fujita et al.,

1994, 1995a, 1995b).

Emissions inventory models are often used to develop control strategies by linear

rollback (Barth, 1970; deNevers and Morris, 1975; Cass, 1981; Cass and McRae, 1981, 1983). 

Rollback assumes that atmospheric concentrations in excess of background are proportional to

aggregate emission rates. Reducing excessive concentrations of a pollutant to levels below a

pre-set standard requires emissions reductions that are proportionally equal to the relative

amount by which the standard is exceeded.

Linear rollback does not consider the effects of meteorological transport between source

and receptor or the differences in gas-to-particle conversion for different precursor emitters. It is

most valid for spatial and temporal averages of ambient concentrations that represent the entire

airshed containing urban-scale sources. The effect of transport from distant sources located

outside the airshed is compensated by subtracting background concentrations, measured nearby

but outside the airshed, from ambient levels prior to determining needed emissions reductions. 

Linear rollback also assumes for secondary particles, such as ammonium nitrate and ammonium

sulfate, that one of the precursors limits particle formation.

CMB is often used in conjunction with linear rollback to determine the contribution of

source categories to excessive concentrations. The linear rollback is then performed on a

category specific basis, starting with the largest contributors. This is often considered to be a

more accurate method of justifying emissions reductions because the relative emissions from

individual sources within a category are believed to be more accurate than the absolute emissions

within the category or the relative emissions between categories.

2.3 Meteorological Models

 Meteorological models describe transport, dispersion, vertical mixing, and moisture in

time and space. Meteorological models consist of straight line, interpolation (termed

diagnostic), and first principle (termed prognostic) formulations, with increasing levels of

complexity and requirements for computational and data resources.

The straight line model is applied to hourly wind directions from a single monitor,

assuming an air mass travels a distance equal to the wind velocity in the measured direction,

regardless of the distance from the monitoring site. This model is applicable for a few hours of

transport in flat terrain, typically for evaluating a single emission source. Interpolation models

integrate wind speed and directions from multiple measurement locations, including upper air

measurements provide by remote sensors or balloon launches. The more advanced of these

models allow barriers, such as mountains, to be placed between monitors. Wind fields,

therefore, show different directions and velocities at different horizontal and vertical positions. 

Interpolation wind models are applicable to domains with a large number of well-placed

monitors and for estimating the movement of air masses from many sources over transport times

of more than half a day. The number and placement of monitors, especially upper air monitors,

is especially important in mountainous terrain and in coastal areas where winds are unusual.
2 - 3

First principle models (Stauffer and Seaman, 1994; Seaman et al., 1995; Koracin et al.,

1993, Koracin and Enger, 1994) embody scientists’ best knowledge of atmospheric physics and

thermodynamics, employing basic equations for conservation and transfer of energy and

momentum. Also known as “prognostic models,” first principle models purport to need no data

other than values from a sparse upper air network for interpolation. They are computationally

intensive, often requiring supercomputers but are becoming more practical and cost-effective as

workstation and desktop computers become more powerful. Modern versions use “fourdimensional data assimilation” that compare model-calculated wind, humidity, and temperature

fields with measurements and “nudge” model outputs toward observations. A more complex

meteorological model is not necessarily a better model for a specific application. The MM5

meteorological model has been adopted as the platform for central California air quality studies

(Seaman et al., 1995).

Meteorological models are useful in conjunction with a CMB analysis to determine

where contributions might have come from. These models can often be used to determine the

relative contributions from individual sources within a source category to better focus control

strategies. These models are also useful adjunct analyses applicable to the seventh step of the

applications and validation protocol.

2.4 Chemical Models

Chemical models describe transformation of directly emitted particles and gases to

secondary particles and gases. Chemical models also estimate the equilibrium between gas and

particle phases for volatile species. Chemical models have been or are being developed for: 1)

photochemical formation of ozone, sulfate, nitrate, and organic particles in clear air (Seinfeld

and Pandis, 1998); 2) sulfate and nitrate formation in fogs and clouds (Seinfeld and Pandis,

1998); 3) inorganic aerosol equilibrium (Kim et al., 1993a, 1993b); and 4) organic aerosol

equilibrium (Pankow, 1994a, 1994b). Chemical models are reasonably well developed for ozone

and inorganic particles, but they are still under development for organic particles and gases.

Chemical models can be embedded in source-oriented dispersion models, or they can be

applied to infer source contributions or limiting precursors as a receptor model using

measurements from a monitoring site. Chemical equilibrium models, for example, are used to

determine the extent to which ammonia or nitric acid reductions will reduce secondary

ammonium nitrate concentrations estimated by CMB (Watson et al., 1994a).

Chemical models have also been used to simulate changes between source and receptor

(Friedlander, 1981; Lin and Milford, 1994; Venkatraman and Friedlander, 1994). These models

are often overly simplified, and require additional assumptions regarding chemical mechanisms,

relative transformation and deposition rates, mixing volumes, and transport times.

2.5 Source Dispersion Models

Source-oriented dispersion models use the outputs from emissions, meteorological, and

chemical models to estimate concentrations measured at receptors. They include mathematical

simulations of transport, dispersion, vertical mixing, deposition, and chemical models to

represent transformation. The most common source dispersion models are Gaussian plume, puff,

and grid formulations. Gaussian plume models (Schulze, 1990; Freeman et al., 1986; Schwede
2 - 4

and Paumier, 1997) are most often associated with the straight line wind model and estimate a

bell-shaped concentration field in the vertical and horizontal directions from the wind direction. 

These models are commonly used to evaluate potential effects of primary emissions from ducted

sources, such as industrial stacks. Puff, or trajectory, models treat emissions from a variety of

sources as independent entities that are moved in a curvilinear wind field generated by a

diagnostic or prognostic wind model. Grid models place transfer pollutants between boxes with

pre-defined vertical and horizontal dimensions (Bowman et al., 1995; Byun and Dennis, 1995;

Yamartino et al., 1992). The 3-D grid-based photochemical SAQM-AERO model is the main

platform that has been developed for central California studies.

2.6 Receptor Models

Receptor models (Cooper and Watson, 1980; Watson, 1984; Javitz et al., 1988a, 1988b)

infer contributions from different primary source emissions or precursors from multivariate

measurements taken at one or more receptor sites. Receptor models are based on the same

scientific principles as source dispersion models, but they are inferential rather than predictive of

source contributions. They include CMB, factor analysis (and other forms of principal

component analysis), empirical orthogonal functions, multiple linear regression, enrichment

factors, neural networks, cluster analysis, Fourier Transform time series, and a number of other

multivariate methods. In each case these other receptor models are used to identify patterns in

chemical composition, time, or space.

Several of the model types described above can be used as either source-oriented or

receptor-oriented models. An ammonium nitrate chemical equilibrium model, for example, can

be used as a source model within the context of an air quality model. It can also be used as a

receptor model when ammonia, nitric acid, ammonium nitrate, temperature, and relative

humidity measurements are available at a receptor. Wind models have source-oriented forward

trajectory modes and receptor-oriented back-trajectory modes. Each of these formulations is

useful and of value in any source apportionment effort.

Analysis methods are often termed receptor models, but they serve as inputs to models. 

Carbon-14 (14C), microscopic analysis, gas chromatograms, x-ray spectra, and many other

analytical outputs are analogous to source profiles in that they represent a pattern that might

allow a source contribution to be identified and quantified. Without the receptor model

mathematics and applications framework, however, these methods cannot provide valid

quantifiable source apportionments.
3 - 1

3. CMB SOURCE AND RECEPTOR INPUT DATA

This section describes the types of measurements that are useful and available for both

source and receptor input data to CMB. It provides references to publications and data bases that

contain greater detail on these topics.

3.2 Source Profiles

Source profiles are the mass abundances (fraction of total mass) of a chemical species in

source emissions. Source profiles are intended to represent a category of source rather than

individual emitters. The number and meaning of these categories is limited by the degree of

similarity between the profiles. Mathematically, this similarity is termed “collinearity,” which

means that two or more of the CMB equations are redundant and the set of equations cannot be

solved. Owing to measurement error, however, CMB equations are never completely collinear

in a mathematical sense. When two or more source profiles are “collinear” in a CMB solution,

standard errors on source contributions are often very high. Some source contributions may be

outlandishly high, while others may be negative. Determining the degree of collinearity is one

of the main objectives of CMB validation. 

3.1.1 Common Emissions Sources

Emissions inventories need to be examined before a CMB source apportionment to

determine which source profiles will be needed and which chemical components must be

measured in local source emissions and ambient air. Emissions inventories include thousands of

individual emitters and dozens of source categories. To be useful for receptor modeling, the

categories must be grouped into more generalized categories with similar source profiles. For

example, an inventory will often contain separate entries for power generation, industrial, and

institutional coal combustion. Since these combustion processes, and often the coal, are similar

in a given airshed, it is unlikely that their contributions can be distinguished by CMB and they

must be combined into a “coal-burning category.” The actual combinations depend on the

profiles available or that are likely to be acquired for a CMB study. Other categories that are

often combined for particulate and/or VOC are:

• Vegetative burning and cooking: Fireplaces, wood stoves, prescribed burns,

wildfires, char-broiling, and meat cooking. Some of these subcategories may be

separated when appropriate organic compounds are measured.

• Diesel exhaust: Heavy and light duty cars and trucks, off-road equipment,

stationary engines for pumps and generators, and locomotives.

• Gasoline exhaust: Heavy and light duty cars and trucks, and small engines. 

Emissions inventories do not usually contain breakdowns by cold-starts and

visibly smoking vehicles, although these might be discriminated by certain

organic compounds in a profile. Since leaded fuels are no longer used in the U.S.,

there is no need to seek this separation.

• Gasoline evaporative emissions: Fueling stations, hot-soak vehicles.
3 - 2

• Fugitive dust: Paved roads, unpaved roads, agricultural tilling, construction, wind

erosion, and industrial aggregate. These can sometimes be divided into

subcategories based on single particle profiles or the measurement of specific

mineral composition.

• Solvents and coatings: Paints, degreasers, and solvents. These can also be

broken down into subcategories, not usually identified in emissions inventories,

when the specific types of solvents have been determined.

• Metals: Copper smelters, lead smelters, steel mills, and aluminum mills. These

often have similar metal emissions but in different abundances depending on the

process.

• Aggregate handling: Cement, quarrying, and mining. Ores, in particular, are

often enriched in the materials being extracted and subcategories may be defined

for these cases. When low level measurements of trace elements such as copper,

zinc, and lead are made, metal processing operations that use these materials can

be classified into separate categories.

Most emissions inventories show 80% to 90% of suspended particles originating from

suspended dust. This does not imply that other particle sources can or should be ignored. 

Appendix E shows that previous PM10 source apportionment studies reported substantial

contributions from other particle emitters.

VOC emissions inventories typically show stationary sources and on-road mobile sources

contributing equally to total Reactive Organic Gases (ROG) emissions in an area. The summary

of VOC source apportionment studies in Appendix F shows that source contributions from

different vehicle components typically contribute the largest, and often the vast majority, of

ambient VOC concentrations. Vehicle-related emissions, including exhaust, evaporated fuel,

and liquid fuel are ubiquitous in all urban areas. Architectural (i.e., paints) and industrial

solvents (i.e., cleaning and process solvents, as in printing) are also common to, but highly

variable in, most urban areas. Petrochemical production and oil refining are more specific to

certain urban settings, such as the Texas coast, where these activities are numerous. Biogenic

emissions are larger in the eastern U.S., where forests are lush, in contrast to the arid west. VOC

emissions in inventories are often reported in equivalent units of methane or propane. 

Comparisons of relative CMB source attributions to emissions inventories requires appropriate

reconciliation between the inventory units and source contribution units.

3.1.2 Source Profile Normalization Options

Source profiles are created by sampling emissions from a variety of single emitters or

small groups of emitters. These samples are then subjected to a variety of chemical and physical

analyses to determine those properties that will allow contributions from the sources they

represent to be distinguished at receptors. Each of these properties must be normalized (scaled)

to some common property in the emissions from all sources. The two most widely used

normalization properties are total particle mass or total volatile organic compound emissions that

accompany the chemical components. The normalization procedure is one in which the

measured concentrations are expressed as ratios (fractional abundances), and is necessary to

construct source profile input files needed by CMB.
3 - 3

In a PM2.5 source apportionment study, the logical normalization factor is the PM2.5 mass

emission, while in a VOC source apportionment study the total VOC is the logical

normalization. One of the difficulties in combining PM2.5 and VOC source apportionment is that

there are some particle sources (e.g., suspended dust) that have negligible VOC components and

some VOC sources (e.g., solvents, evaporated gasoline, biogenics) that have negligible particle

components. However, there are many sources, such as vehicle exhaust, cooking, and wood

combustion, that have both large VOC and PM components, and profiles that are respectively

normalized to both should be considered to increase the utility of the profiles for both VOC and

PM source apportionment studies.

Individual profiles are formed from individual samples, and the uncertainty estimates of

the numerator and denominator are propagated (Watson et al., 1995) to obtain the individual

profile uncertainties. These individual profiles are further composited to obtain the source

profiles used for CMB source apportionment. The simplest composite consists of the average

and standard deviation of abundances for all individual profiles within a group. For example, if

ten tests of diesel vehicle exhaust are taken, each abundance is an average of the ten individual

abundances and the uncertainty is the standard deviation of that average. Outlier tests are often

applied to reject individual profiles that unduly bias the standard deviation of the composite

(average) profile. In general, abundances that exceed two standard deviations calculated without

the inclusion of that abundance should be omitted from a profile. There are always some outliers

in any series of source tests, usually for reasons that can never be determined. For this reason it

is important to obtain ten or more samples that run the range of operating conditions and fuels in

an area to estimate source profiles.

Particle mass is well-defined and easy to measure, so most particle profiles for a stated

size fraction are reasonably comparable, regardless of how they were measured. This is not the

case for VOC profiles, where a wide variety of normalization factors and measurement units

have been applied. Inventories employ different conventions for defining VOC. Many

published VOC profiles are not comparable to each other, or with the ambient measurements, in

terms of their normalization.

Several terms are used inconsistently but interchangeably to describe different fractions

of atmospheric organic material. Common definitions and units must be used for ambient

concentrations, source profiles, and emissions rates. The following terms are defined as they are

used throughout this protocol, and these definitions are recommended for future CMB source

apportionment projects: 

• Cx: Molecules containing x carbon atoms (e.g., C7 means the molecule contains

seven carbon atoms). This notation is useful since many sampling and analysis

techniques respond to different numbers of carbon atoms rather than to specific

compounds.

• Organic carbon: Gases and particles containing carbon and hydrogen atoms in

various ratios. Organic compounds found in ambient air may also be associated

with other elements and compounds, particularly oxygen, nitrogen, sulfur,

halogens, and metals. Various operational definitions based on measurement

method are applied to different subsets of organic compounds.
3 - 4

• Inorganic carbon: Carbon dioxide and carbon monoxide are the most abundant

inorganic gases found in the atmosphere, while amorphous graphite is the most

common particulate component. Particulate elemental carbon is operationally

defined by optical and combustion methods (Chow et al., 1993), and it contains

heavy organic material as well as inorganic carbon.

• Hydrocarbons: Organic compounds that consist only of carbon and hydrogen

atoms.

• Reactive organic gases (ROG): Organic gases with potential to react (<30 day

half-life) with the hydroxyl radical and other chemicals, resulting in ozone and

secondary organic aerosol. The most reactive chemicals are not necessarily the

largest contributors to undesirable end-products, however, as this depends on the

magnitude of their emissions as well as on their reactivity (Carter, 1990; Carter

and Lurmann, 1991).

• Total organic gases (TOG): Organic gases with and without high hydroxyl

reactivity. TOG typically includes ROG plus methane and halocarbons.

• Non-methane hydrocarbons (NMHC, also termed “light” hydrocarbons): C2

through C12 (light) hydrocarbons collected in stainless steel canisters and

measured by gas chromatography with flame ionization detection (GC-FID) by

EPA method TO-14 (EPA, 1997b). NMHC excludes carbonyls, halocarbons,

carbon dioxide, and carbon monoxide even though some of these may be

quantified by the same method. NMHC is most often used to quantify ozone

precursors.

• Halocarbons: NMHC with chlorine, fluorine, or bromine compounds attached,

quantified from canisters by gas chromatography with electron capture detection

(GC-ECD) (Farwell and Rasmussen, 1976). Methylchloride, methylchloroform,

methylbromide, and various refrigerants (Freon-12, Freon-22, SUVA) are most

commonly measured (Rasmussen et al., 1980; Khalil et al., 1985; Wang et al.,

1997). These compounds have long lifetimes and are not reactive enough to

cause major changes in tropospheric ozone and secondary organic aerosol. 

Halocarbons have been implicated in the long-term depletion of stratospheric

ozone (Lovelock et al., 1973).

• Heavy hydrocarbons: C10 through C20 hydrocarbons collected on Tenax

absorbing substrates and analyzed by thermal desorption and gas chromatography

(Pellizzari et al., 1984; Hawthorne and Miller, 1986; Walling et al., 1986;

Kamens et al., 1988, 1989; Riba et al., 1988; Zielinska and Fujita, 1994a;

Zielinska and Fung, 1994; Zielinska et al., 1996; Clausen and Wolkoff, 1997). 

These are sometimes termed “semi-volatile” compounds because the >C15

compounds are often found as both gases and particles (Hampton et al., 1982,

1983). Most of the total hydrocarbon mass is measured in the gas phase.

• Carbonyls: Aldehydes and ketones, the most common being formaldehyde,

acetone, and acetylaldehyde (Carlier et al., 1986; Altshuller, 1993). Carbonyls

are operationally defined as C1 through C7 oxygenated compounds measured by

collection on acidified 2,4-dinitrophenylhydrazine (DNPH)-impregnated C18
3

For any VOC, to convert from ppbC to ppbV, simply multiply the value by the # of carbon atoms in an individual molecule.

4

Total NMOC is directly measured using EPA’s Method TO-12 (EPA, 1985).

3 - 5

cartridges and analyzed by high performance liquid chromatography with UV

detection (HPLC/UV) (Cofer and Edahl, 1986; Zielinska and Fujita, 1994b;

Grosjean and Grosjean, 1996; Kleindienst et al., 1998).

• Non-methane organic gases (NMOG): NMHC plus carbonyls.

• Semi-volatile organic compounds (SVOC): Particles and gases collected on

filters backed with solid absorbent such as polyurethane foam (PUF) and XAD,

extracted in a variety of solvents, and analyzed by gas chromatography/mass

spectrometry or HPLC/UV (Greaves et al., 1985; Chuang et al., 1987). This class

includes compounds such as polycyclic aromatic hydrocarbons (PAHs),

methoxyphenols and lactones, pesticides, and other polar and non-polar organic

compounds. The heavy hydrocarbons are often classified as SVOC, but they are

given a separate identity here for precision and clarity.

• Volatile organic compounds (VOC): NMHC plus heavy hydrocarbons plus

carbonyls plus halocarbons, typically <C20. VOC has been imprecisely used to

describe most of the other categories defined above.

Non-standard variable definitions and units are an impediment to VOC source

apportionment using CMB. VOC concentrations are usually reported in ppbC or :g/m3 at local

temperature and pressure.3 Either unit is acceptable for CMB analysis, but the source profile

ratios must be consistent with the ambient measurements. Fortunately, the fractional

abundances of most VOCs relative to NMHC vary by only a few percent when either ppbC or

:g/m3 are used for the numerator and the denominator. However, concentrations from all

measurement methods must be in the same units. Since automated VOC analyzers inherently

measure concentrations in ppbC, this unit is probably the best/most logical one for CMB

applications.

VOC fractional abundances have been reported in ppbC or :g/m3 and normalized by 1)

total NMHC, as described above consisting of only the ROG including the unidentified fraction4;

2) the sum of the quantified or most abundant compounds in the chromatogram, which varies

depending on the investigator; 3) the sum of all canister measurements, including non-reactive

gases such as halocarbons; and 4) NMOG, the sum of all VOCs measured from all applied

methods. These profile differences preclude comparability and use of profiles from different

studies. Meaningful comparison of CMB results with emission inventories requires a common

reference (Watson et al., 2001). Since the TO-14 method is applied to PAMS (Lewis et al.,

1998a) at all severe ozone nonattainment areas, it is recommended that the sum of the 54 PAMS

target hydrocarbons (Appendix A) should be the common normalization standard for source

profiles. Measurements from other canister analyses, Tenax, and DNPH should also be

normalized to the sum of the PAMS species. With this standard convention, renormalization to

NMOG or other categories is straightforward. A discussion of the normalization procedure for a

typical VOC source is provided in Appendix B.
3 - 6

Note (however) that in many available source profiles, not all of the PAMS target

compounds will be represented. They are usually best represented in vehicle exhaust profiles. 

Note also that most source profiles available in the literature (e.g., SPECIATE; see Appendix C)

will not list uncertainties. Because CMB requires that uncertainties be input for its computation,

nominal values of 5-10% may be assumed if there is no better information.

3.1.3 PM2.5 Source Characteristics

Table 3.1-1 identifies typical abundances of elements, ions, and carbon in different

source emissions that have been found useful for CMB. Table 3.1-2 shows several of the

organic aerosol compounds that are present in ambient aerosol and that are believed to originate

in different source emissions. Note that many of these organic compounds are semi-volatile and

may be predominantly in the gas or particle phase, depending on ambient temperature and other

factors that affect equilibrium.

In geological material, aluminum (Al), silicon (Si), potassium (K), calcium (Ca), and iron

(Fe) have large abundances with low variabilities. The total potassium (K) abundance is 15 to

30 times the abundance of soluble potassium (K+

). Aluminum (Al), potassium (K), calcium

(Ca), and iron (Fe) abundances are similar among the profiles, but the silicon (Si) abundances

range from 14% in unpaved road dust to 20% in paved road dust. Lead (Pb) is sometimes

abundant in paved road dust, but it is as low as 0.004% in the other geological profiles, probably

due to deposition from previously emitted leaded-gasoline vehicle exhaust or remnants of lead

from the exhaust trains of older vehicles. Elemental carbon (EC) abundances are highly variable

in geological material, and are often negligible in natural soil samples. Organic carbon (OC) is

typically 5% to 15% in geological emitters. It is most abundant in paved road and agricultural

dusts, although the specific compounds are probably quite different for these two sources (Chow

et al., 1994). Motor vehicle emissions (e.g., brake and tire wear, oil drips) could result in greater

abundances of Pb, EC, and OC in paved road dust. Soluble sulfate, nitrate, and ammonium

abundances are low, in the range of 0 to 0.3%. Sodium and chloride are also low, with less than

0.5% in abundance. Larger abundances of these materials may be found temporarily soon after

roadway de-icing, however.

Organic and elemental carbon are the most abundant species in motor vehicle exhaust,

accounting for over 95% of the total mass. Watson et al. (1996a) found the lead (Pb) abundance

is negligible and highly variable (0.024 ± 0.036%) in 1995 motor vehicle exhaust profiles from

northwestern Colorado. The abundance of bromine (Br) was also low, in the range of 0.01% to

0.05%. Zinc was present in most exhaust profiles, usually at levels of 0.05% or less. The

abundances of organic and total carbon can be quite variable in motor vehicle exhaust profiles. 

Organic carbon abundances ranged from 36% in highway vehicle emissions to 70% in local

traffic emissions.

The ratio of organic to total carbon (OC/TC) was 0.58 in the composite vehicle profile

for northwestern Colorado. This OC/TC ratio is similar to those reported by Watson et al.

(1994b) in Phoenix, AZ, with 0.69 for gasoline-fueled vehicle exhaust, 0.55 for diesel-fueled

vehicle exhaust, and 0.52 for a mixture of vehicle types in roadside tests. Earlier measurements

in Denver, CO (Watson et al., 1990b) reported an OC/TC ratio of 0.39 for the cold transient

cycle and 0.81 for the cold stabilized cycle.
3 - 7

Watson et al. (1996a) also compared residential wood combustion (RWC), residential

coal combustion (RCC), and forest fire PM2.5 profiles. Average OC abundances ranged from

~50% in RWC and the forest fire profiles to ~70% in the RCC profile. EC averaged 3% in forest

fire, 12% in RWC, and 26% in RCC. The OC/TC ratio was highest in the forest fire profile

(OC/TC = 0.94) and similar for the two residential combustion profiles, with 0.73 in RCC and

0.81 in RWC. Chow and Watson (1997c) measured profiles for asparagus field burning in

California’s Imperial Valley with OC/TC ratios of 0.93, similar to the 0.94 ratio found in the

forest fire emissions. A similar observation was made for charbroil cooking emissions, with

60% to 70% OC abundances and high (>0.95) OC/TC ratios.

The K+/K ratios of 0.80 to 0.90 in burning profiles (Calloway et al., 1989) are in large

contrast to the low soluble to total potassium ratios found in geological material. Sulfate, nitrate,

and silicon abundances in RCC are 2 to 4 times higher than those in the RWC and forest fire

profiles. The ammonium abundance is highly variable, with an average of 1.4% in RCC and

0.1% in the RWC and forest fire profiles.
3 - 8

Table 3.1-1 Chemicals from Particles in Different Emissions Sources

Source Type

Dominant Particle Size Chemical Abundances in Percent Mass

< 0.1% 0.1 to 1% 1 to 10% 

Paved Road Dust Coarse Cr, Sr, Pb, Zr SO4=, Na+, K+, P, S, Cl, Mn, Zn,

Ba, Ti

Elemental Carbon (EC),Al, K, Ca, Fe

Unpaved Road Dust Coarse NO3–. NH4+, P, Zn, Sr, Ba SO4=, Na+, K+, P, S, Cl, Mn, Ba,

Ti

OC, Al, K, Ca, Fe 

Construction Coarse Cr, Mn, Zn, Sr, Ba SO4=, K+, S, Ti, OC, Al, K, Ca, Fe 

Agricultural Soil Coarse NO3–. NH4+, Cr, Zn, Sr SO4=, Na+, K+, S, Cl, Mn, Ba, Ti OC, Al, K, Ca, Fe 

Natural Soil Coarse Cr, Mn, Sr, Zn, Ba Cl–, NA+, EC, P, S, Cl, Ti OC, Al, Mg, K, Ca, Fe 

Lake Bed Coarse Mn, Sr, Ba K+, Ti SO4=, Na+, OC, Al, S, Cl,

K, Ca, Fe

Motor Vehicle Fine Cr, Ni, Y NH4+, Si, Cl, Al, Si, P, Ca, Mn,

Fe, Zn, Br, Pb

Cl–, NO3–. SO4=, NH4+, S 

Vegetative Burning Fine Ca, Mn, Fe, Zn, Br, Rb, Pb NO3–. SO4=, NH4+, Na+, S Cl–, K+, Cl, K 

Residual OilCombustion

Fine K+, OC, Cl, Ti, Cr, Co, Ga,

Se

NH4+, Na+, Zn, Fe, Si V, OC, EC, Ni 

Incinerator Fine V, Mn, Cu, Ag, Sn K+, Al, Ti, Zn, Hg NO3–. Na+, EC, Si, S, Ca,

Fe, Br, La, Pb

Coal-Fired Boiler Fine Cl, Cr, Mn, Ga, As, Se, Br,

Rb, Zr

NH4+, P, K, Ti, V, Ni, Zn, Sr,

Ba, Pb

SO4=, OC, EC, Al, S, Ca,

Fe

Oil-Fired Power Plant Fine V, Ni, Se, As, Br, Ba Al, Si, P, K, Zn NH4+, OC, EC, Na, Ca,

Pb

Steel Blast Furnace Fine V, Ni, Se, Al, Si, P, K, Zn Mn, OC, EC Smelter Fire Fine V, Mn, Sb, Cr, Ti Cd, Zn, Mg, Na, Ca, K, Se Fe, Cu, As, Pb Antimony Roaster Fine V, Cl, Ni, Mn SO4=, Sb, Pb S 

Marine Fine 

and Coarse

Ti, V, Ni, Sr, Zr, Pd, Ag, Sn,Sb, Pb

Al, Si, K, Ca, Fe, Cu, Zn, Ba,La

NO3–. SO4=, OC, EC 
3 - 9

Particle-Gas Phase

Species Predominant Sources Distribution 

PAH, for example

naphthalene Motor vehicles, wood smoke Gas Phase

methylnaphthalenes Motor vehicles, wood smoke Gas Phase

dimethylnaphthalenes Motor vehicles, wood smoke Gas Phase

biphenyl Motor vehicles, wood smoke Gas Phase

acenaphthylene Motor vehicles, wood smoke Gas Phase

acenaphthene Motor vehicles, wood smoke Gas Phase

fluorene Motor vehicles, wood smoke Gas Phase

phenanthrene Motor vehicles, wood smoke Particle-Gas Phase

anthracene Motor vehicles, wood smoke Particle-Gas Phase

fluoranthene Motor vehicles, wood smoke Particle-Gas Phase

pyrene Motor vehicles, wood smoke Particle-Gas Phase

retene Wood smoke -softwood Particle-Gas Phase

benzo[b]naphtho[2,1]thiophene Motor vehicles Particle Phase

benz[a]anthracene Motor vehicles, wood smoke Particle Phase

chrysene Motor vehicles, wood smoke Particle Phase

benzo[b+j+k]fluoranthene Motor vehicles, wood smoke Particle Phase

benzo[e]pyrene Motor vehicles, wood smoke Particle Phase

benzo[a]pyrene Motor vehicles, wood smoke Particle Phase

indene[123-cd]pyrene Motor vehicles, wood smoke Particle Phase

dibenzo[ah+ac]anthracene Motor vehicles, wood smoke Particle Phase

benzo[ghi]perylene Motor vehicles, wood smoke Particle Phase

coronene Motor vehicles, wood smoke Particle Phase

Hopanes and Sterenes

Cholestanes Motor vehicles Particle Phase

Trisnorhopanes Motor vehicles Particle Phase

Norhopanes Motor vehicles Particle Phase

Hopanes Motor vehicles Particle Phase

Guaiacols, for example

4-methylguaiacol Wood smoke Gas Phase

4-allylguaiacol Wood smoke Particle-Gas Phase

isouegenol Wood smoke Particle-Gas Phase

Acetovanillone Wood smoke Particle Phase

Syringols, for example

Syringol Wood smoke, mostly hardwood Particle-Gas Phase

4-methylsyringol Wood smoke, mostly hardwood Particle-Gas Phase

Syringaldehyde Wood smoke, mostly hardwood Particle Phase

Lactons, for example

Caprolactone Meat cooking Gas Phase

Decanolactone Meat cooking Particle-Gas Phase

Undecanoic-G-Lactone Meat cooking Particle-Gas Phase

Sterols, for example

Cholesterol Meat cooking Particle Phase

Sitosterol Meat cooking, wood smoke Particle Phase

Table 3.1-2 Organic Compounds Found in Different Source Emissions

and in Ambient Air
3 - 10

Coal-fired power generation profiles differ substantially from residential coal burning,

even though the fuels are similar, owing to the different emissions control technologies. Sulfate

is one of the most abundant constituents in the particle phase and sulfur dioxide can be hundreds

to thousands of time higher than the particle mass. Sulfur dioxide is a good indicator of

contributions from nearby coal-fired power stations for which it has not reacted or deposited

significantly during transport to a receptor. Crustal elements such as silicon (Si), calcium (Ca),

and iron (Fe) in the coal-fired boiler profiles are present at 30% to 50% of the corresponding

levels in geological material with the exception of aluminum (Al) which is present at similar or

higher levels than those found in geological material. Other elements such as phosphorus (P),

potassium (K), titanium (Ti), chromium (Cr), manganese (Mn), strontium (Sr), zirconium (Zr),

and barium (Ba) are present at less than 1% levels.

Watson et al. (1996a) detected selenium (Se) at the level of 0.2% to 0.4% in coal-fired

power station emissions with no scrubbers or wet scrubbers, but not in emissions from a unit

with a dry limestone scrubber. Selenium is usually in the gaseous phase within hot stack

emissions, and it condenses on particles when air is cooled in the dilution chamber. Abundances

of calcium (15%), chloride (1%), and nitrate (1%) in the limestone-scrubbed unit were a few

times higher than in the other units. These differences may have resulted from the dry lime

scrubber, which added some calcium and absorbed the selenium in the vapor phase.

Sulfate, nitrate, and ammonium abundances in directly emitted particles are not sufficient

to account for the concentrations of these species measured in the atmosphere. Ambient mass

concentrations contain both primary and secondary particles. Primary particles are those which

are directly emitted by sources; these particles often undergo few changes between source and

receptor. Atmospheric concentrations of primary particles are, on average, proportional to the

quantities that are emitted. Secondary particles are those that form in the atmosphere from gases

that are directly emitted by sources.

Sulfur dioxide, ammonia, and oxides of nitrogen are the precursors for sulfuric acid,

ammonium bisulfate, ammonium sulfate, and ammonium nitrate particles (Seinfeld, 1986;

Watson et al., 1994a). Several VOCs may also change into particles; the majority of these

transformations result from intense photochemical reactions that also create high ozone levels

(Grosjean and Seinfeld, 1989). Several of these particles, notably those containing ammonium

nitrate, are volatile and transfer mass between the gas and particle phase to maintain a chemical

equilibrium (Stelson and Seinfeld, 1982a-c). This volatility has implications for ambient

concentration measurements as well as for gas and particle concentrations in the atmosphere.

Dust suspended from bare land, roadways, agricultural fields, and construction sites is

predominantly a primary pollutant, but it does play a role in secondary particle formation (Chow

and Watson, 1992; Chow et al., 1994). Some components of dust, such as ammonium nitrate

fertilizer, may volatilize into ammonia and nitric acid gases, thereby contributing to secondary

aerosol. Alkaline particles, such as calcium carbonate, may react with nitric and hydrochloric

acid gases while on the ground, in the atmosphere, or on filter samples to form coarse particle

nitrates and chlorides. Ammonium sulfate fertilizers and minerals such as gypsum (calcium

sulfate) may be mistaken for secondary sulfates when particle samples are chemically analyzed.
3 - 11

These examples show that although there are similarities in chemical compositions for

different sources, using source profiles from one airshed or time period may not provide a valid

CMB apportionment for ambient samples in another airshed or in another time period. Source

emissions of precursor gaseous and primary particles are highly variable due to differences in

fuel use, operating conditions, and sampling methods. Source and ambient measurements must

be paired in time to establish reasonable estimates of source/receptor relationships. Trace metals

acquired from elemental analysis of Teflon-membrane filters are only abundant in the geological

and some industrial profiles. Elemental measurements by themselves are necessary, but

insufficient, for a receptor modeling study. Chemical speciation must also include ammonium,

sulfate, nitrate, organic carbon, and elemental carbon. Simultaneous gas measurements as well

as other characteristics of suspended particles will be needed as more refined control strategies

are developed using CMB.

3.1.4 VOC Source Characteristics

The largest body of knowledge about organic gas source compositions is related to

mobile source emissions (Sampson and Springer, 1973; Black et al., 1980; Carey and Cohen,

1980; Hampton et al., 1982, 1983; Jensen and Hites, 1983; Nelson and Quigley, 1983, 1984;

Kawamura et al., 1985; Booker et al., 1986; Sigsby et al., 1987; Hlavinka and Bullin, 1988;

Zweidinger et al., 1988, 1990; McClenny et al., 1989; Snow et al., 1989; Stump et al., 1989,

1990a, 1990b, 1992, 1996; Bailey et al., 1990a, 1990b; Japar et al., 1990, 1991; Trier et al.,

1990; Williams et al., 1990; Chan et al., 1991; Kaiser et al., 1991; Wallington and Japar, 1991,

1993; Chock and Winkler, 1992; Corchnoy et al., 1992; Hoekman, 1992; McCabe et al., 1992;

Siegl et al., 1992; Stedman, 1992; Bailey and Eggleston, 1993; Diehl et al., 1993; Chock et al.,

1994; Haszpra and Szilagyi, 1994; Zielinska and Fung, 1994; Conner et al., 1995; Duffy and

Nelson, 1996; Pierson et al., 1996; Sagebiel et al., 1996, 1997; Sjoren et al., 1996; Zielinska et

al., 1996; Fujita et al., 1997a, 1997b; Gelencsar et al., 1997; Gertler et al., 1997a, 1997b;

Guicherit, 1997; Simo et al., 1997). These tests include emissions from spark-ignition (gasolinefueled) vehicle exhaust, compression ignition (diesel-fueled) vehicle exhaust, liquid gasoline,

and evaporative gasoline emissions from fuel handling and vehicle operation.

With only the light hydrocarbons measured, the heavy-duty diesel and light-duty gasoline

exhaust profiles are similar, and are often collinear in CMB calculations. Ethene, acetylene, 1-

butene, iso-butene, propane, propene, isopentane, n-pentane, 2,2 dimethylbutane, 2-

methylpentane, n-hexane, benzene, 3-methyhexane, toluene, ethyl benzene, m- & p-xylene, methyltoluene, and 1,2,4-trimethylbenzene, are the most abundant compounds in either or both of

these emissions. Several of these are short-lived and are only used in CMB calculations where

fresh emissions are expected, as during early morning. Major differences between diesel and

gasoline exhaust profiles are evident for acetylene, iso-butene, isopentane, n-hexane, and 2-

methylhexane, which are most abundant in gasoline exhaust and for propene, propane, 2,2

dimethylbutane, n-decane, and n-undecane which are more abundant in diesel exhaust. Gertler

et al. (1995) show that the CMB discrimination between diesel and gasoline exhaust is

distinctive when the heavy hydrocarbons are included. Most of these compounds are highly

enriched in diesel exhaust while having negligible abundances in normal-running gasoline

vehicle exhaust.
3 - 12

Liquid gasoline contains many compounds in common with gasoline-vehicle exhaust. It

is depleted in combustion products such as ethane, ethene, and acetylene. Evaporated gasoline is

also depleted in these combustion compounds, as well as heavier hydrocarbons that volatilize

more slowly from liquid fuels. Isobutane, –butane, t-2 butene, and especially isopentane are

enriched in evaporated gasoline. Methyl tertiary butyl ether (MTBE) stands out as a large

constituent of all gasoline-related emissions that clearly separates these from diesel in areas

where it is used as an additive. These differences are sufficient for CMB separation of gasoline

exhaust from liquid and evaporated gasoline, and often from diesel exhaust, in ambient air. 

Gasoline compositions vary with location and time of year. Liquid gasoline and headspace

evaporated gasoline samples should be analyzed at times and places consistent with ambient

VOC measurements.

Petrochemical production, especially the refining of gasoline and other fuel oils (Sexton

and Westberg, 1979, 1983; Fujita et al., 1995a), can be a large contributor in areas such as

Houston, TX. Ethane, propene, propane, n-pentane, t-2 hexene, benzene, n-heptane, toluene, and

n-octane are abundant species. Most of these overlap with liquid and evaporated gasoline

vapors. Refinery VOC measurements often contain a large fraction of unidentified NMHC that

includes real, but unreported, chemical compounds that are not in the other profiles. If properly

quantified, these could probably assist CMB resolution of refinery and other petrochemical

sources.

Although solvents from paints and industrial uses are large components of all ROG

inventories, their reported profiles are few (Kitto et al., 1997; Guo et al., 1998). Censullo et al.

(1996) recently evaluated a large number of different solvent uses in southern California. These

profiles are depleted in the species common to fuel use and production, with larger abundances

of styrene, n-decane, and especially “other” compounds. The “other” VOCs are quantified and

differ substantially among the different coatings tested. These are sufficient to separate various

coating and solvent emissions from other contributors. California requires special solvent and

coating formulations to comply with air quality emissions requirements, so these profiles are

likely to be very specific to a particular area.

Printing ink solvents from offset (Wadden et al., 1995a, 1995b) and rotogravure are

commonly identified in emissions inventories. Most of these emissions are captured, condensed,

and re-used by modern printing facilities, especially the toluene used for thin rotogravure inks. 

These may be enriched in styrene, n-nonane, and 1,2,4 trimethylbenzene, similar to the other

solvents. Again, there is a large “other” fraction of identified compounds that allow the

separation of solvent contributions to ambient VOC.

In addition to these common emissions sources, landfills are sometimes identified as

large TOG emitters owing to their prodigious production of methane (Brosseau and Heitz, 1994;

Eitzer, 1995). A variety of reactive organic gases may accompany the methane, depending of

the nature of the landfill wastes and disposal practices. Brosseau and Heitz (1994) summarize

measurements from many landfills, finding acetone, alpha terpinene, benzene, butyl alcohol,

dichlorobenzene, dichloromethane, ethylbenzene, ethyl mercaptan, limonene, furans, terpenes,

toluene, vinyl acetate, vinyl chloride, and xylene to be among the most abundant components of

ROG.
3 - 13

Several of these compounds, such as vinyl chloride, are not common to widespread area

sources and might be used to determine landfill source contributions by CMB. Kalman (1986)

identifies several VOCs outgassed by plastics when they are heated. Acetone was consistently

the most abundant ROG found in emissions from the surveyed landfills, probably resulting from

the anaerobic decay of discarded organic material. Similar reactions in dumpsters and trash

cans, as well as in the natural environment, may account for a portion of the unexplained acetone

observed by Fujita et al. (1994) in Los Angeles and by Singh et al. (1994) at more remote

locations. Acetone is also a product of photochemistry. Shonnard and Bell (1993) document

substantial quantities of benzene emanating from contaminated soil, a situation that will

presumably improve as modern amelioration methods are applied to these dumpsites (Fox,

1996).

Garcia et al. (1992) found small quantities of VOC emitted by several French coal-fired

power stations, with benzene, toluene, ethylbenzene, xylenes, tetrachloroethane, benzaldehyde,

and phenol being the most abundant compounds. Abundances of these compounds were

substantially enriched over their abundances in the fuel, indicating that these compounds do not

combust as well as other fuel components or that they form as part of the combustion process. 

Some data have also been reported for petroleum fires (Booher and Janke, 1997), food and

beverage production (Passant et al., 1993), household products and indoor building materials

(Sack et al., 1992, Sanchez et al., 1987), ferry boats (Cooper et al., 1996), hot asphalt application

(Kitto et al., 1997), fish rendering (Ohira et al., 1976), and phytoplankton in the ocean (McKay

et al., 1996).

Biogenic VOC emissions from trees and shrubs (Tingey et al., 1978, 1981; Arnts and

Meeks, 1981; Tingey, 1981; Arnts et al., 1982; Altshuller, 1983; Hov et al., 1983; Shaw et al.,

1983; Lamb et al., 1984, 1985, 1986, 1987, 1993; Oliver et al., 1984; Roberts et al., 1985; Gay,

1987; Riba et al., 1987; Chameides et al., 1988; Juttner, 1988; Yokouchi and Ambe, 1988; Das,

1992; Hewitt and Street, 1992; Khalil and Rasmussen, 1992; Nondek et al., 1992; Winer et al.,

1992; Zhang et al., 1992; Grosjean et al., 1993a, 1993b, 1993c; Guenther et al., 1993, 1994,

1996; Jobson et al., 1994; Tanner and Zielinska, 1994, Ciccioli et al., 1995, 1997a, 1997b;

Fuentes et al., 1996; Kempf et al., 1996; Benjamin et al., 1997; Bertin et al., 1997; Cao et al.,

1997; Owen et al., 1997; Pier et al., 1997; Schuh et al., 1997; Street et al., 1997; Young et al.,

1997) are typically reported for isoprene and monoterpenes such as "-pinene and $-pinene. 

These compounds are very reactive and are usually detected only in forested areas. Isidorov et

al. (1985) found a wide variety of heavy hydrocarbons in air dominated by different types of

plants and trees that might be more stable indicators of biogenic contributions to ambient VOC.

Variations in biogenic emissions source profiles are difficult to quantify due to the

variability in vegetation types, ambient temperature, seasonal growth cycles, and degree of

drought. Despite its high reactivity, isoprene is commonly used as marker for biogenic

emissions. Terpenes are not often quantified in ambient samples owing to measurement

difficulties. Although the effects of photochemical reactions on the source contributions can be

minimized for other major hydrocarbon sources by using fitting species with lifetimes

comparable to air mass residence times, this is not possible for a single-species biogenic profile

based upon isoprene with input data from conventional VOC measurement methods.

Fujita and Lu (1997) estimated an adjustment to biogenic contributions based on changes

in the ratios of reactive hydrocarbons (e.g., isomers of xylene) to a relatively unreactive
3 - 14

hydrocarbon (e.g., benzene) between morning and afternoon samples to account for the loss of

isoprene due to photochemical reactions. The average ratios of afternoon to morning

xylenes/benzene ratios reflect the net fractional loss of xylenes due to atmospheric reactions. 

This fractional loss is adjusted to isoprene by applying the ratio of the OH*

 radical reaction rate

constants for xylenes and isoprene. Adjustment factors of 6.6 to 10.0 were derived by this

method for the biogenic contribution of ambient hydrocarbon in Phoenix, AZ (Fujita and Lu,

1997).

Biogenic contributions can be distinguished from fossil fuel contributions to ambient

VOC by the 14C isotope which is much more abundant in recently-living organisms than in

ancient coal, oil, and natural gas fuels (Conny and Currie, 1996; Klouda et al., 1996; Rasmussen

et al., 1996; Lewis et al., 1999). 14C is conserved with chemical transformations, thereby

enabling the participation of biogenic emissions in photochemistry to be quantified by analysis

of VOC end-products. Vegetative burning (Darley et al., 1966; Rahmdal et al., 1982; Khalil et

al., 1983; Rahmdahl, 1983; Ramdahl and Moller, 1983; Edgerton et al., 1984, 1985, 1986;

Edgerton, 1985; Isidorov et al., 1985; Hawthorne et al., 1988, 1989; Rau and Khalil, 1989; Ward

and Hardy, 1989; Hurst et al., 1994; Koppmann et al., 1997) has also been identified by its

contributions of methyl chloride and retene in ambient air, but the compounds in its NMHC and

NMOG emissions are poorly characterized.

3.1.5 Source Characterization Methods

Several methods have been devised to extract samples from sources which will have

chemical and physical properties similar to those found at a receptor (Gordon et al., 1986; Chow

et al., 1988). In each of these methods, emitted particulate matter or gases are collected on

substrates or in containers that are subsequently analyzed for chemical content in a laboratory.

The ideal source sampling method would allow for chemical and physical

transformations of source emissions to occur prior to sample collection. Lacking this ideal, the

sampling would at least quantify the precursors of the receptor profile so that a theoretically or

empirically derived transformation could be applied. Methods used to sample source emissions

in receptor model studies include: 1) hot exhaust sampling; 2) diluted exhaust sampling; 3)

plume sampling from airborne platforms; 4) ground-based sampling of single-source dominated

air; and 5) grab sampling and resuspension.

Hot exhaust sampling is well established for determining the emission rates of criteria

pollutants, including primary particulate matter and some VOCs. Hot exhaust does not permit

the condensation of vapors into particles prior to sampling, and it sometimes interferes with the

sampling substrate or container. In vegetative burning, for example, many of the vapors do not

condense until they are near ambient temperatures. In coal-fired station emissions, the selenium

does not condense on other particles until temperatures approach ambient. Hot exhaust samples

are not often taken on substrates or in containers amenable to extensive chemical analysis. 

Components of these compliance-oriented methods have been incorporated into other exhaust

sampling procedures. Although most commonly applied, hot exhaust sampling rarely yields

profiles that represent profiles as detected at receptors because it does not account for

transformations which take place when the emissions cool. Hot exhaust sampling is not

appropriate for receptor modeling studies.
3 - 15

Several dilution samplers have been developed to bring hot exhaust effluents to ambient

temperature by mixing with clean, cool air (Cooper et al., 1988, 1989; Heinsohn and Davis,

1980; Hildemann et al.,1989; Houck et al., 1982; Hueglin et al., 1997; McCain and Williamson,

1984; McDonald et al., 1998; Merrill and Harris, 1987; Sousa et al, 1985; Westerholm et al.,

1988; Williamson and Smith, 1979 ). Dilution samplers draw hot exhaust gases into a chamber

where they are mixed with filtered ambient air. After an aging period, the particles are drawn

through a size-selective inlet and onto substrates or into sample containers. Multiple samples for

different chemical analyses are obtained simultaneously or via sequential sampling of the same

gas stream. Stainless steel or Teflon-coated chambers are used where species might be reactive. 

Recent sampling systems acquire gaseous as well as particulate samples that can be used to

apportion both particles and VOC (McDonald et al., 1998; Zielinska et al., 1998) and measure

emission rates as well as source profiles.

Diluted exhaust samplers lend themselves to laboratory simulations of emissions from

individual sources. Dynamometer simulations of motor vehicle driving with exhaust sampled

from a dilution tunnel can provide examples of aggregate emissions for a large number of

separate vehicles. Similarly, wood stoves and fireplaces can be operated under different burning

cycles with emissions sampled from a dilution tunnel.

Source sampling from airborne platforms to characterize the chemical and physical

properties of emissions has been performed from airplanes (Small et al., 1981; Richards et al.,

1981, 1985), tethered balloons (Armstrong et al., 1980; Shah et al., 1989) and helicopters. 

Sampling components of appropriate weight and packaging are elevated above the emissions,

usually on the order of 100 to 500 meters, to draw samples of the effluent.

The major advantage of airborne sampling for source characterization is that source

profile fractionation might be determined if the sample can be taken at a time after emission (i.e.,

distance) sufficient to have allowed transformations to take place. The drawbacks of airborne

plume sampling are: 1) it is difficult to know when the sampler is in the plume and when it is in

ambient air; 2) it is difficult to stay in the plume long enough to obtain a sample; and 3) ambient

air mixes with the plume, so the source profile is really a combination of emissions and ambient

air.

Ground-based source sampling is identical to receptor sampling, but it is applied in

situations for which the air being sampled is known to be dominated by emissions from a given

source. The requirements of this method are: 1) meteorological conditions and sampling times

conducive to domination by a particular source; 2) samples short enough to take advantage of

those conditions; and 3) a minimum of other interfering source contributions.

Tunnels, parking garages, vehicle staging areas, and isolated but heavily traveled

roadways are often used to obtain samples for motor vehicle exhaust. Tunnels are especially

useful for this because a large number of vehicles can be evaluated with little interference from

sources other than suspended road dust (Benner et al., 1989; Bishop et al., 1996; Chang et al.,

1981; Dannecker et al., 1990; Duffy and Nelson, 1996; Fraser et al., 1998; Gertler and Pierson,

1996; Gertler et al., 1997a; Gillies et al., 1998; Hering et al., 1984; Ingalls, 1989; Khalili et al.,

1995; Barrefors, 1996; Lonneman et al., 1986; Miguel, 1984; Moeckli et al., 1996; Pierson and

Brachaczek, 1976, 1983; Pierson et al., 1990, 1996; Rogak et al., 1998; Staehelin et al., 1998;

Weingartner et al., 1997; Zielinska and Fung, 1994).
3 - 16

Using source-dominated samples, Rheingrover and Gordon (1980) and Annergarn et al.

(1992) characterized several point sources using ambient virtual impactor measurements when

the sampling was downwind of the source. Chow (1985) examined the effects of an elevated

coal-fired power plant emission on ground-based samples in a rural environment. The presence of

the plume from corresponding SO2 and wind direction measurements could be discerned, but it was

not possible to discern other chemical concentrations contributed by the power plant owing to an

overwhelming abundance of geological material in her 24-hour sample. This method may be much

better for fugitive and area sources, however, because their influence is more constant over time.

The advantages of ground-based sampling are: 1) it is representative of fractionated

(presuming transformations are complete) and composite (for area sources such as home heating,

motor vehicles, and resuspended dust) source profiles; 2) it is relatively economical; and 3) it is

compatible with other receptor samples. The disadvantages are: 1) sampling times may be too

short to obtain an adequate deposit; and 2) contributions from other source types interfere with

the source profile.

Grab sampling and resuspension in the laboratory (Chow et al., 1994) is most often

applied to fugitive dust sources that are usually not ducted and require numerous samples to

represent a large population. Grab sampling and resuspension involves: 1) removal of a

precipitated residue of the emissions; 2) resuspension and sampling onto substrates through sizeselective inlets; and 3) analysis for the selected species. A simple sample swept, shoveled, or

vacuumed from a storage pile, transfer system, or roadbed can be taken to represent these source

types. Five to ten different samples from the same source are averaged to obtain a representative

source profile. This method is semi-established, or at least as established as the chemical and

physical analyses applied to it, because procedures are widely accepted and results are

reproducible within a method, though not necessarily among methods. The main advantages of

grab sampling and resuspension are simplicity, reliability, and low cost. 

3.1.6 Source Profile Data Bases

Several compilations of particle profiles have been produced that might be applicable to a

Level 1 source assessment described in Section 3.3 (Watson, 1979; EPA, 1988; Sheffield and

Gordon, 1985; Core and Houck, 1987; Cooper et al., 1987; Houck et al., 1989; Chow and

Watson, 1994a; Watson et al., 1994a, 1996a, 1996b; Chow and Watson, 1997a, 1997b; Chow et

al., 1997). These include chemical abundances of elements, ions, and carbon for geological

material (e.g., paved and unpaved road dust, soil dust, storage pile), motor vehicle exhaust (e.g.,

diesel-, leaded-gasoline-, and unleaded-gasoline-fueled vehicles), vegetative burning (e.g., wood

stoves, fireplaces, forest fires, prescribed burning), industrial boiler emissions, and other aerosol

sources. More modern, research-oriented profiles include specific organic compounds or

functional groups, elemental isotopes, and microscopic characteristics of single particles.

As fuels, technologies, and use patterns have changed from 1970 to the present, so have

the chemical profiles for many emissions sources. Lead has been phased out of U.S. and

Canadian fuels, but it is still used in some Mexican gasolines that might affect PM2.5 in border

areas. Catalytic converters on spark-ignition vehicles, improved compression-ignition engines

(Pierson et al., 1996), and newly-designed wood combustion appliances have substantially

reduced carbon abundances in emissions from these small but numerous sources.
3 - 17

Similarly, process improvements and new source performance standards have resulted in

changes in chemical component emissions from large industrial emitters. Source profiles must

be paired in time with ambient PM2.5 chemical species measurements to establish a reasonable

estimate of what is expected in ambient air.

Several compilations of VOC source profiles have also been assembled (Shah and Singh,

1988; EPA, 1988; Scheff et al., 1989a, 1989b; Shah et al., 1989; Doskey et al., 1992; Harley et

al., 1992; Fujita et al., 1997a) from original measurements and a combination of published and

unpublished test results. Most of these profiles are limited for CMB use because: 1) they

represent older technology and fuels that are different today; 2) documentation is lacking or

insufficient; 3) compound abundances are normalized to different definitions of NMOG or

NMHC and are derived from a variety of measurement units; and 4) reported VOCs are not the

same among profiles.

The most complete and available compilation of organic speciation profiles are those

associated with the example in Section 6. These are available with the CMB8 software.

3.2 Receptor Measurements

Receptor measurements need to be a subset of the source profile measurements. They

must include at least those species in the source profiles that allow sources to be separated. 

3.2.1 Physical and Chemical Characteristics of Receptor Concentrations

Several characteristics of VOC and particle emissions were discussed above. Major

chemical components of PM2.5 or PM10 mass in urban and non-urban areas consist of geological

material, carbon, nitrate, sulfate, ammonium, sodium chloride, and liquid water:

• Geological Material: Suspended dust consists mainly of oxides of aluminum,

silicon, calcium, titanium, iron, and other metal oxides (Chow and Watson, 1992). 

The precise combination of these minerals depends on the geology of the area and

industrial processes such as steel-making, smelting, mining, and cement

production. Geological material is mostly in the coarse particle fraction, and

typically constitutes ~50% of PM10 while only contributing 5 to 15% of PM2.5

(Chow et al., 1992a; Watson et al., 1994b).

• Organic Carbon: Particulate organic carbon consists of hundreds, possibly

thousands, of separate compounds. The mass concentration of organic carbon can

be accurately measured, as can carbonate carbon, but only about 10% of specific

organic compounds that it contains have been measured. Vehicle exhaust (Rogge

et al., 1993a; Zielinska et al., 1998), residential and agricultural burning (Rogge

et al., 1998; Zielinska et al., 1998), meat cooking (Rogge et al., 1991; Zielinska et

al., 1998), fuel combustion (Rogge et al., 1993b, 1997), road dust (Rogge et al.,

1993c), and particle formation from heavy hydrocarbon (C8 to C20) gases (Pandis

et al., 1992) are the major sources of organic carbon in PM2.5. Because of this

lack of molecular specificity, and owing to the semi-volatile nature of many

carbon compounds, particulate “organic carbon” is operationally defined by the

sampling and analysis method (Chow et al., 1993; Hering et al., 1985).
3 - 18

• Elemental Carbon: Elemental carbon is black, often called “soot.” Elemental

carbon contains pure, graphitic carbon, but it also contains high molecular weight,

dark-colored, non-volatile organic materials such as tar, biogenics, and coke. 

Elemental carbon usually accompanies organic carbon in combustion emissions

with diesel exhaust (Watson et al., 1994c) being the largest contributor.

• Nitrate: Ammonium nitrate (NH4NO3) is the most abundant nitrate compound,

resulting from a reversible gas/particle equilibrium between ammonia gas (NH3),

nitric acid gas (HNO3), and particulate ammonium nitrate. Because this

equilibrium is reversible, ammonium nitrate particles can easily evaporate in the

atmosphere, or after they have been collected on a filter, owing to changes in

temperature and relative humidity (Stelson and Seinfeld, 1982a, 1982b; Allen et

al., 1989). Sodium nitrate (NaNO3) is found in the PM2.5 and coarse fractions

near sea coasts and salt playas (e.g., Watson et al., 1994b) where nitric acid vapor

irreversibly reacts with sea salt (NaCl).

• Sulfate: Ammonium sulfate ((NH4)2SO4), ammonium bisulfate ((NH4HSO4), and

sulfuric acid (H2SO4) are the most common forms of sulfate found in atmospheric

particles, resulting from conversion of gases to particles. These compounds are

water-soluble and reside almost exclusively in the PM2.5 size fraction. Sodium

sulfate (Na2SO4) may be found in coastal areas where sulfuric acid has been

neutralized by sodium chloride (NaCl) in sea salt. Though gypsum (Ca2SO4) and

some other geological compounds contain sulfate, these are not easily dissolved

in water for chemical analysis. They are more abundant in the coarse fraction

than in PM2.5, and are usually classified in the geological fraction.

• Ammonium: Ammonium sulfate ((NH4)2SO4), ammonium bisulfate (NH4HSO4),

and ammonium nitrate (NH4NO3) are the most common compounds. The sulfate

compounds result from irreversible reactions between sulfuric acid and ammonia

gas, while the ammonium nitrate can migrate between gases and particle phases

(Watson et al., 1994a). Ammonium ions may coexist with sulfate, nitrate, and

hydrogen ions in small water droplets. While most of the sulfur dioxide and

oxides of nitrogen precursors of these compounds originate from fuel combustion

in stationary and mobile sources, most of the ammonia derives from living beings,

especially animal husbandry practiced in dairies and feedlots.

• Sodium Chloride: Salt is found in suspended particles near sea coasts, open

playas, and after de-icing materials are applied. Bulk sea water contains 57±7%

chloride, 32±4% sodium, 8±1% sulfate, 1.1±0.1% soluble potassium, and

1.2±0.2% calcium (Pytkowicz and Kester, 1971). In its raw form (e.g., deicing

sand), salt is usually in the coarse particle fraction and classified as a geological

material (Chow et al., 1996a, 1996b). After evaporating from a suspended water

droplet (as in sea salt or when resuspended from melting snow), it is abundant in

the PM2.5 fraction. Sodium chloride is often neutralized by nitric or sulfuric acid

in urban air where it is often encountered as sodium nitrate or sodium sulfate

(Pilinis et al., 1987).

• Liquid Water: Soluble nitrates, sulfates, ammonium, sodium, other inorganic
3 - 19

ions, and some organic material (Saxena and Hildemann, 1997) absorb water

vapor from the atmosphere, especially when relative humidity exceeds 70% (Tang

and Munkelwitz, 1993). Sulfuric acid absorbs some water at all humidities. 

Particles containing these compounds grow into the droplet mode as they take on

liquid water. Some of this water is retained when particles are sampled and

weighed for mass concentration. The precise amount of water quantified in a

PM2.5 depends on its ionic composition and the equilibration relative humidity

applied prior to laboratory weighing.

3.2.2 Receptor Characterization Methods

A variety of sampling and analysis methods have been applied to acquire measurements

at source and receptor for both particles (Chow, 1995; Chow and Watson, 1994b, 1998) and

VOC (Zielinska et al., 1994, 1996). Table 3.2-1 specifies gas and particle chemical compounds

that are quantified by these methods and are being reported in source profiles.

A mnemonic is given for each chemical species that is used by CMB8 to identify the

compound. As can be seen in Table 3.2-1, most of these mnemonics bear a resemblance to the

chemical compound names. These mnemonics are reasonably straightforward for elemental

species, but they can be complex for organic species.

Several compounds can be measured by different methods, and it is a good idea to

designate these mnemonics differently. For example, the elements in Table 3.2-1 might also be

quantified by proton induced x-ray emission spectroscopy (PIXE), instrumental neutron

activation analysis (INAA), inductively couple plasma emission spectroscopy (ICP/ES) in

addition to or in place of x-ray fluorescence (XRF). The “X” in the third place of the mnemonic

could be replaced with another identifier to designate these methods. As noted above, water

soluble potassium (KPA) and total potassium (KPX) are measured by different methods, but also

represent different characteristics that distinguish among source contributions. These need to be

designated by different mnemonics.
Table 3.2-1 Chemical Compounds, Mnemonics, and Measurement Methods 

for Particle and VOC Receptor Modeling

3 - 20

Mnemonic Species Methoda Groupb

MSG Mass GRAV N

CO Carbon monoxide NDIR G

HNO3 Nitric Acid NACL/IC G

NO2 Nitrogen Dioxide TEA/AC G

SO2 Sulfur dioxide KOH/IC G

NH3 Ammonia CA/AC G

CLI Chloride Q/IC IP

N3I Nitrate Q/IC IP

S4I Sulfate Q/IC IP

N4C Ammonium Q/AC IP

KPA Soluble Potassium Q/AA IP

TCT Total Carbon Q/TOR OP

OCT Organic Carbon Q/TOR OP

ECT Elemental Carbon Q/TOR OP

NAX Sodium T/XRF IP

MGX Magnesium T/XRF IP

ALX Aluminum T/XRF IP

SIX Silicon T/XRF IP

PHX Phosphorus T/XRF IP

SUX Sulfur T/XRF IP

CLX Chlorine T/XRF IP

KPX Potassium T/XRF IP

CAX Calcium T/XRF IP

TIX Titanium T/XRF IP

VAX Vanadium T/XRF IP

CRX Chromium T/XRF IP

MNX Manganese T/XRF IP

FEX Iron T/XRF IP

COX Cobalt T/XRF IP

NIX Nickel T/XRF IP

CUX Copper T/XRF IP

ZNX Zinc T/XRF IP

GAX Gallium T/XRF IP

ASX Arsenic T/XRF IP

SEX Selenium T/XRF IP

BRX Bromine T/XRF IP

RBX Rubidium T/XRF IP

SRX Strontium T/XRF IP

YTX Yttrium T/XRF IP

ZRX Zirconium T/XRF IP

MOX Molybdenum T/XRF IP

PDX Palladium T/XRF IP

AGX Silver T/XRF IP

CDX Cadmium T/XRF IP

INX Induium T/XRF IP

SNX Tin T/XRF IP

SBX Antimony T/XRF IP

BAX Barium T/XRF IP
Table 3.2-1 Chemical Compounds, Mnemonics, and Measurement Methods 

for Particle and VOC Receptor Modeling

Mnemonic Species Methoda Groupb

3 - 21

LAX Lanthanum T/XRF IP

AUX Gold T/XRF IP

HGX Mercury T/XRF IP

TLX Thallium T/XRF IP

PBX Lead T/XRF IP

URX Uranium T/XRF IP

NAPHTH Naphthalene GC/MS OG

MNAPH2 2-menaphthalene GC/MS OG

MNAPH1 1-menaphthalene GC/MS OG

DMN267 2,6+2,7-dimenaphthalene GC/MS OG

DM1367 1,7+1,3+1,6-dimenaphthalene GC/MS OG

D14523 2,3+1,4+1,5-dimenaphthalene GC/MS OG

DMN12 1,2-dimenaphthalene GC/MS OG

DMN18 1,8-dimenapthalene GC/MS OG

BIPHEN Biphenyl GC/MS OG

M_2BPH 2-Methylbiphenyl GC/MS OG

M_3BPH 3-Methylbiphenyl GC/MS OG

M_4BPH 4-Methylbiphenyl GC/MS OG

ATMNAP A-Trimethylnaphthalene GC/MS OG

EM_12N 1-Ethyl-2-methylnaphthalene GC/MS OG

BTMNAP B-Trimethylnaphthalene GC/MS OG

CTMNAP C-Trimethylnaphthalene GC/MS OG

EM_21N 2-Ethyl-1-methylnaphthalene GC/MS OP

ETMNAP E-Trimethylnaphthalene GC/MS OP

FTMNAP F-Trimethylnaphthalene GC/MS OP

GTMNAP G-Trimethylnaphthalene GC/MS OP

HTMNAP H-Trimethylnaphthalene GC/MS OP

TM128N 1,2,8-Trimethylnaphthalene GC/MS OP

ACNAPY Acenaphthylene GC/MS OP

ACNAPE Acenaphthene GC/MS OP

PHENAN Phenanthrene GC/MS OP

FLUORE Fluorene GC/MS OP

A_MFLU A-Methylfluorene GC/MS OP

M_1FLU 1-Methylfluorene GC/MS OP

B_MFLU B-Methylfluorene GC/MS OP

C_MFLU C-Methylfluorene GC/MS OP

A_MPHT A-Methylphenanthrene GC/MS OP

M_2PHT 2-Methylphenanthrene GC/MS OP

B_MPHT B-Methylphenanthrene GC/MS OP

C_MPHT C-Methylphenanthrene GC/MS OP

M_1PHT 1-Methylphenanthrene GC/MS OP

DM36PH 3,6-Dimethylphenanthrene GC/MS OP

A_DMPH A-Dimethylphenanthrene GC/MS OP

B_DMPH B-Dimethylphenanthrene GC/MS OP

C_DMPH C-Dimethylphenanthrene GC/MS OP

DM17PH 1,7-Dimethylphenanthrene GC/MS OP

D_DMPH D-Dimethylphenanthrene GC/MS OP

E_DMPH E-Dimethylphenanthrene GC/MS OP
Table 3.2-1 Chemical Compounds, Mnemonics, and Measurement Methods 

for Particle and VOC Receptor Modeling

Mnemonic Species Methoda Groupb

3 - 22

ANTHRA Anthracene GC/MS OP

M_9ANT 9-Methylanthracene GC/MS OP

FLUORA Fluoranthene GC/MS OP

PYRENE Pyrene GC/MS OP

A_MPYR A-Methylpyrene GC/MS OP

B_MPYR B-Methylpyrene GC/MS OP

C_MPYR C-Methylpyrene GC/MS OP

D_MPYR D-Methylpyrene GC/MS OP

E_MPYR E-Methylpyrene GC/MS OP

F_MPYR F-Methylpyrene GC/MS OP

RETENE Retene GC/MS OP

BNTIOP Benzonaphthothiophene GC/MS OP

BAANTH Benz(a)anthracene GC/MS OP

M_7BAA 7-Methylbenz[a]anthracene GC/MS OP

CHRYSN Chrysene GC/MS OP

BBJKFL Benzo(b+j+k)FL GC/MS OP

BEPYRN BeP GC/MS OP

BAPYRN BaP GC/MS OP

M_7BPY 7-Methylbenzo[a]pyrene GC/MS OP

INCDPY Indeno[123-cd]Pyrene GC/MS OP

DBANTH Dibenz(ah+ac)anthracene GC/MS OP

BBCHRN Benzo(b)chrysene GC/MS OP

BGHIPE Benzo(ghi)Perylene GC/MS OP

CORONE Coronene GC/MS OP

GCAPLA A-Caprolactone GC/MS OP

GUACOL Guaiacol GC/MS OP

M4GUCL 4-Methylguaiacol GC/MS OP

E4GUCL 4-Ethylguaiacol GC/MS OP

SYRGOL Syringol GC/MS OP

PPGUCL Propylguaiacol GC/MS OP

A4GUCL 4-Allylguaiacol GC/MS OP

GNONLA G-Nonanoic Lactone GC/MS OP

F4GUCL 4-Formylguaiacol GC/MS OP

M4SYRG 4-Methylsyringol GC/MS OP

E4SYRG 4-Ethylsyringol GC/MS OP

ISOEUG Isoeugenol GC/MS OP

GDECLA G-Decanolactone GC/MS OP

ACETVA Acetovanillone GC/MS OP

UNGLAC Undecanoic-G-Lactone GC/MS OP

SYRALD Syringaldehyde GC/MS OP

ERGOS Ergostane GC/MS OP

SITOS Sitostane GC/MS OP

C27SDS Diasterane-1 GC/MS OP

C27RDS Diasterane-2 GC/MS OP

C27RCH Cholestane-1 GC/MS OP

C27SBC Cholestane-2 GC/MS OP

C27RAC Cholestane-3 GC/MS OP

AABTNH Trisnorhopane-1 GC/MS OP
Table 3.2-1 Chemical Compounds, Mnemonics, and Measurement Methods 

for Particle and VOC Receptor Modeling

Mnemonic Species Methoda Groupb

3 - 23

AB_TNH Trisnorhopane-2 GC/MS OP

AB30NH Norhopane-1 GC/MS OP

CHLSRL Cholesterol GC/MS OP

BA30NH Norhopane-2 GC/MS OP

AB_HOP Hopane-1 GC/MS OP

STEROW Steroid-w GC/MS OP

BA_HOP Hopane-2 GC/MS OP

SABHHP Homohopane-1 GC/MS OP

RABHHP Homohopane-2 GC/MS OP

SITOST Sitosterol GC/MS OP

BB_HOP Hopane-3 GC/MS OP

STEROM Steroid-m GC/MS OP

SABBHH Bishomohopane-1 GC/MS OP

RABBHH Bishomohopane-2 GC/MS OP

IDNMHC Total Identified NMHC F N

UNID Unidentified1 F

METHAN methane F P

ACETYL acetylene F Y

CO_PPM carbon monoxide F

ETHENE ethene F O

MEACRO methacrolein F,D AL

ETHANE ethane F P

METOH methanol F OH

FORMAL formaldehyde D AL

PROPE propene F O

CO2PPM carbon dioxide F

ACETAL acetaldehyde F AL

N_PROP propane F P

ETHOH ethanol F OH

BUDI13 1,3-butadiene F O

BUTYN 1&2-butyne F Y

ACETO acetone F,D K

ACROLN acrolein D AL

BEABYL 1-butene&i-butene F O

C2BUTE c-2-butene F O

LBUT1E 1-butene F O

LIBUTE iso-butene F O

T2BUTE t-2-butene F O

PROAL propionaldehyde D AL

I_BUTA isobutane F P

N_BUTA –butane F P

CPENTE cyclopentene F O

I_PREN isoprene F O

CROTON crotonaldehyde D AL

B1E2M 2-methyl-1-butene F O

B1E3ME 3-methyl-1-butene F O

B2E2M 2-methyl-2-butene F O

CPENTA cyclopentane F P
Table 3.2-1 Chemical Compounds, Mnemonics, and Measurement Methods 

for Particle and VOC Receptor Modeling

Mnemonic Species Methoda Groupb

3 - 24

PENTE1 1-pentene F O

C2PENE c-2-pentene F O

T2PENE t-2-pentene F O

PRAL2M 2-methylpropanal F AL

BUAL butanal F,D AL

BUONE butanone F K

IPENTA isopentane F P

N_PENT n-pentane F P

BENZE benzene F A

CPENE1 1-methylcyclopentene F O

CYHEXE cyclohexene F O

C2HEXE c-2-hexene F O

C3HEXE c-3-hexene F O

C6OLE1 C6 olefin F O

CYHEXA cyclohexane F P

HEX1E 1-hexene F O

MCYPNA methylcyclopentane F P

P1E2ME 2-methyl-1-pentene F O

P1E3ME 3-methyl-1-pentene F O

P1E4ME 4-methyl-1-pentene F O

P2E2ME 2-methyl-2-pentene F O

P2E3MC cis-3-methyl-2-pentene F O

P2E3ME 3-methyl-2-pentene F O

P2E3MT trans-3-methyl-2-pentene F O

T2HEXE t-2-hexene F O

T3HEXE t-3-hexene F O

MECL2 methylene chloride E X

VALAL valeraldehyde D AL

BU22DM 2,2-dimethylbutane F P

BU23DM 2,3-dimethylbutane F P

N_HEX n–hexane F P

PENA2M 2-methylpentane F P

PENA3M 3-methylpentane F P

MTBE methyl tertiary butyl ether (ppbv) F E

TOLUE toluene F,T A

PHENOL phenol T AL

MEBR methylbromide E X

C12DCE cis-1,2,-dichloroethylene E X

T12DCE trans-1,2-dichloroethylene E X

VINECL vinylidenechloride E X

C7OLE1 C7 olefin F O

CPA13M 1,3-dimethylcyclopentane F A

MECYHX methylcyclohexane F P

T3HEPE t-3-heptene F O

ETDC12 1,2-dichloroethane E X

HEXAL hexanal F,D AL

BU223M 2,2,3-trimethylbutane F A
Table 3.2-1 Chemical Compounds, Mnemonics, and Measurement Methods 

for Particle and VOC Receptor Modeling

Mnemonic Species Methoda Groupb

3 - 25

HEXA2M 2-methylhexane F P

HEXA3M 3-methylhexane F P

HEXE4M 4-methylhexene F P

N_HEPT n-heptane F P

PEN22M 2,2-dimethylpentane F P

PEN23M 2,3-dimethylpentane F P

PEN24M 2,4-dimethylpentane F P

PEN33M 3,3-dimethylpentane F P

PA3ET 3-ethylpentane F P

STYR styrene F,T A

HEPAL heptanal F A

BENZAL benzaldehyde F,D,T AL

ETBZ ethylbenzene F,T A

MP_XYL m - & p-xylene F,T A

O_XYL o-xylene F,T A

CHX11M 1,1-dimethylcyclohexane F P

OCT1E octene-1 F O

P1E244 2,4,4-trimethyl-1-pentene F O

N_OCT n–octane F,T P

HEP2ME 2-methylheptane F P

HEP3ME 3-methylheptane F P

HEX24M 2,4-diemthylhexane F P

HEX25M 2,5-diemthylhexane F P

HX23DM 2,3-dimethylhexane F P

PA224M 2,2,4-trimethylpentane F P

PA234M 2,3,4-trimethylpentane F P

INDENE indene F,T A

INDAN indan F,T A

CCL3 chloroform E X

ACPHONE acetophenone T K

TOLUAL tolualdehyde D AL

BZ123M 1,2,3-trimethylbenzene F,T A

BZ124M 1,2,4-trimethylbenzene F,T A

BZ135M 1,3,5-trimethylbenzene F,T A

IPRBZ isopropylbenzene F,T A

M_ETOL m-ethyltoluene F,T A

MEOCT methyloctane T P

N_PRBZ n-propylbenzene F,T A

O_ETOL o-ethyltoluene F,T A

P_ETOL p-ethyltoluene F,T A

F12 Freon 12 E X

IPCYHX isopropylcyclohexane F P

NONE1 1-nonene T O

OCTAL octanal F AL

NAPHTH naphthalene F A

HEP24D 2,4-dimethylheptane F P

HEP25D 2,5-dimethylheptane F P
Table 3.2-1 Chemical Compounds, Mnemonics, and Measurement Methods 

for Particle and VOC Receptor Modeling

Mnemonic Species Methoda Groupb

3 - 26

HEP26D 2,6-dimethylheptane F P

HEP33D 3,3-dimethylheptane F P

HEP44D 4,4-dimethylheptane F P

HEP4ME 4-methylheptane F P

HEX225 2,2,5-trimethylhexane F P

HEX235 2,3,5-trimethylhexane F P

N_NON n-nonane F,T P

OCT2ME 2-methyloctane F P

OCT3ME 3-methyloctane F P

TCENE trichloroethylene E X

IND_1M 1-methylindan F A

IND_2M 2-methylindan F A

TCE112 1,1,2-trichloroethane E X

MECCL3 methyl chloroform E X

BZ1234 1,2,3,4-tetramethylbenzene T A

BZ1235 1,2,3,5-tetramethylbenzene T A

BZ1245 1,2,4,5-tetramethylbenzene F A

BZDME 1,3-dimethyl-4-ethylbenzene F A

DETBZ1 m-diethylbenzene F A

DETBZ2 p-diethylbenzene F A

DETBZ3 o-diethylbenzene F A

DMETBZ dimethylethylbenzene T A

I_BUBZ isobutylbenzene F A

IPRTOL isopropyltoluene F A

N_BUBZ n-butylbenzene F A

S_BUBZ sec-butylbenzene F A

A_PINE alpha-pinene F O

B_PINE beta-pinene F O

LIMONE limonene T O

F11 Freon 11 E X

NONAL nonanal F AL

NAP_1M 1-methylnaphthalene T A

NAP_2M 2-methylnaphthalene T A

DMOCT dimethyloctane T P

N_DEC n-decane F P

OCT26D 2,6-dimethyloctane F P

OCT36M 3,6-dimethyloctane F P

INDDM1 dimethylindan T A

MDCBZ m-dichlorobenzene E X

ODCBZ o-dichlorobenzene E X

PDCBZ para-dichlorobenzene T X

DETMBZ diethylmethylbenzene T A

ACNAPY acenaphthylene T A

CCL4 carbon tetrachloride E X

ACENPE acenaphthene T A

DMN12 1,2-dimethylnaphthalene T A

DMN13 1,3-dimethylnaphthalene T A
Mnemonic Species Methoda Groupb

3 - 27

DMN14 1,4-dimethylnaphthalene T A

DMN15 1,5-dimethylnaphthalene T A

DMN18 1,8-dimethylnaphthalene T A

DMN23 2,3-dimethylnaphthalene T A

DMN26 2,6-dimethylnaphthalene T A

DMN27 2,7-dimethylnaphthalene T A

NAP1ET 1-ethylnaphthalene T A

NAP2ET 2-ethylnaphthalene T A

N_UNDE n-undecane T,F P

PERC perchloroethylene E X

N_DODE n-dodecane F P

DBRME 1,3-dibromomethane E X

PHENA phenanthrene T A

N_TRID n-tridecane T P

F113 Freon 113 E X

F114 Freon 114 E X

ETDB12 1,2-dibromoethane E X

N_TETD n-tetradecane T P

CLDBRM chlorodibromomethane E X

N_PEND n-pentadecane T P

N_HEXD n-hexadecane T P

N_HEPD n-heptadecane T P

N_OCTD n-octadecane T P

N_NOND n-nonadecane T P

N_EICO n-eicosane T P

N_HENE n-heneicosane T P

a AC=Automated colorimetry KOH/IC=Potassium hydroxide filter & ion 

CA/AC=Citric acid filter and automated chromatography

 colorimetry NACL/IC=Sodium chloride filter & ion 

D=DNPH with HPLC/UV chromatography

E=Canister with GC/ECD NDIR=Non-Dispersive Infrared

F=Canister with GC/FID T=Tenax with GC/FID, 

GC/MS=Gas chromatography mass TEA/IC=Triethanolamine filter & automated 

 spectrometry colorimetry

GRAV=Gravimetric, XRF= X-ray fluorescence

IC=Ion chromatography

b

 Group codes: 

A = aromatic VOC K = ketone VOC P = parafin VOC 

AL = aldehyde VOC O = alkene (olefin) VOC X = haogenatedVOC

E = ether VOC OG=organic gas Y = alkyne VOC

IG=inorganic gas OH = alcoholVOC 

IP=inorganic particle OP=organic particle

1 Sum of unidentified hydrocarbons. Excludes halogenated and oxygenated compounds.
3 - 28

3.2.3 Sampler Siting

The chemical dimension can be supplemented by spatial separation of receptors to further

define the source categories or the specific emitters represented by different source types. These

sites are classified as background, transport, gradient, and source sites that are intended to

measure the following (Watson et al., 1997b):

• Community Representative (CORE): CORE sites are intended to represent

concentrations of large populations that live, work, and play within 5 to 10 km

surrounding the site. These sites are most affected by regional and urban scale

contributions with relatively small neighborhood scale and smaller contributions.

• Background: Background sites intend to measure concentrations that are not

influenced by emissions from the regulated study area. These are located in

pristine areas, away from local or urban sources. Few background locations are

completely devoid of anthropogenic emissions.

• Interbasin transport: These sites are intended to evaluate concentrations along

established or potential transport pathways. In mountainous terrain, these are

typically located at the mountain passes through which inflows and outflows have

been documented. In flat terrain they are located between urban areas or

industrial source areas and urban areas.

• Intrabasin gradient: These sites are located in large regional areas, such as the

Great Lakes region, the northeast corridor, the Los Angeles area, and within

California’s San Joaquin Valley where urban complexes are in non-urban areas

between core sites. They are intended to evaluate the extent to which one

urbanized area in an airshed affects concentrations in another urban area, as well

as the extent to which urban contributions arrive at non-urban locations within an

airshed.

• Source: Source sites are located right next to, and downwind of, representative

and identifiable emitters. Where practical, these are located within 1 km of

gradient or core sites to further evaluate the zone of influence of these source

emissions.

Figure 3.2-1 shows how sampler siting within and between urban areas can assist in

determining which components are regional and which are nearby contributors. In this example

it is apparent that most of the primary contributions from carbon and geological material are

from urban and neighborhood sources, while secondary nitrate and sulfate are contributed from

outside the urban area. This would not be discernible from a single sampling location in the city

center. The source contributions in Figure 3.2-1 were determined by CMB applied to elemental,

ionic, and carbon measurements without use of specific organic compounds.
3 - 29

Figure 3.2-1. Spatial distribution of average PM2.5 source contributions from gasoline exhaust

(LDGV), diesel exhaust (diesel), suspended dust (road dust/geological), vegetative burning

(meat & wood), secondary ammonium sulfate, secondary ammonium nitrate, and primary coalfired power station fly ash in and near Denver, CO during winter, 1996-97 (Watson et al., 1998).
3 - 30

PM2.5 - Welby

-10

0

10

20

30

40

50

60

12/1812/1912/2012/2512/3012/3101/0101/0201/0601/0701/0801/1201/1301/1401/1501/1601/1701/1801/1901/2001/2701/2801/2901/30

Contributions (μg/m3

)

LDGV cold start LDGV hot stabilized LDGV high emitter diesel exhaust

meat cooking RW C (softwood) RW C (hardwood) road dust/geological

ammonium sulfate ammonium nitrate coal power station Unexplained

Figure 3.2-2. PM2.5 source contributions at the Welby site north of Denver, CO, during winter

of 1996-97. Organic compounds were used in these apportionments, with resulting addition of

source categories for gasoline exhaust for cold starts (LDGV cold start), normal running (LDGV

hot stabilized), and poorly maintained (LDGV high emitter) vehicles. Vegetative burning is

separated into meat cooking and residential wood combustion (RWC) for softwood and

hardwood. Samples were taken from 0600-1200, 1200-1800, and 1800-0600 MST, with the

morning sample directly over the date. 

3.2.4 Temporal Variability

Temporal variability in concentrations is important because it helps to confirm source

contributions by bracketing their emissions in time. Seasonal variations often allow vegetative

burning contributions to be attributed to prescribed burning and wildfires during summer, when

residential burning is at a minimum, and to woodstoves and fireplaces that are used during cool

weather.

Figure 3.2-2 shows the temporal variation of source contributions at a site near Denver,

CO. Motor vehicle exhaust contributions are typically largest during morning samples, and

residential wood combustion is abundant in nighttime samples, especially near New Years Day.
3 - 31

3.2.5 Receptor Measurement Data Bases

Ambient chemical concentrations are not commonly available for CMB source

apportionment. Special studies have been conducted to acquire the needed data at representative

receptors during periods where PM or VOC concentrations have been found excessive. 

Appendices E and F identify many of these studies that have adequate data bases. Lioy et al.

(1980); Chow and Watson (1989); and Watson and Chow (1992) summarize other chemically

speciated data sets for suspended particles.

The most complete chemical data base to which CMB can be applied is the Interagency

Monitoring of Protected Visual Environments (IMPROVE) network that has acquire elemental,

ionic, and carbon measurements at National Parks and Wilderness areas since 1987. The most

comprehensive VOC data base derives from the Photochemical Assessment Monitoring Sites

(PAMS) that takes canister or continuous gas chromatographic measurements at urban and

suburban sites during the summer.

New networks in support of the PM2.5 NAAQS will acquire speciated measurements at

several hundred sites throughout the United States. One of the specific purposes of these

measurements is to obtain source contributions via CMB modeling. Many of them will be

collocated with PAMS sites, thereby offering the opportunity to use VOC and PM2.5 chemical

components together in the source apportionment. Appendix C provides Internet links to data

bases containing measurements useful for source apportionment studies.

3.3 CMB Application Levels

There is no single sampling and analysis design that will permit successful CMB source

apportionment in every urban area. Since measurements can be costly, it is useful to examine

existing samples and existing data to assist in forming a conceptual model prior to designing a

full-scale source apportionment study. Three sequential levels of complexity (EPA, 1984) can

be applied, with each level being more costly, but supplying more accurate and precise

information than the previous level. The levels are useful as a shorthand notation of the general

level of comprehensiveness of a CMB study but have no regulatory significance. A given level

may not provide valid results because of data limitations. In such cases, the next higher level

may need to be undertaken to complete the CMB analysis.

The basic level of CMB application (Level I) uses existing data or data that can be

readily obtained from analyses of existing samples (Gordon et al., 1984). Source profiles that

were measured elsewhere, but that can be related to local sources, are also used. This effort

confirms the selection of contributing sources from the preliminary analysis and eliminates

minor contributors from further scrutiny. If the sources contributing to the high concentrations

of PM10 are apparent and sufficiently certain, no further work will be needed. Otherwise, this

effort serves to reduce the areas to be studied in greater detail under an intermediate (Level II)

analysis.

The intermediate (or Level II) analysis involves additional chemical analyses on existing

samples or the acquisition of additional samples from existing sampling sites. It is intended to

fill the gaps in model input data which may have been discovered in Level I so as to reduce

uncertainty in results of the Level I source apportionment. A comprehensive CMB analysis

(Level II) involves the acquisition of new data from new source and ambient sampling activities. 
3 - 32

Local dust samples are obtained and analyzed, at a minimum. Ground-based vehicle exhaust and

vegetative burning profiles are also often acquired. Industrial source profiles are usually adapted

from other studies. Light hydrocarbons are measured for a VOC apportionment study and

elements, ions, and carbon are quantified for PM2.5 or PM10. Where new sampling is possible,

sampling locations and times are selected to bracket suspected contributors.

A Level III analysis is only applied in the most complex airsheds where the costs of

emissions reduction are high and their effectiveness is uncertain. A Level III study involves

original source testing and measurements beyond the basic particulate or VOC species. Heavy

hydrocarbons and organic particles are measured at source and receptor. A Level III study

usually involves a complex and detailed application of all model types specified in Section 2.

The CMB applications and validation protocol described here is appropriate to all three

levels of PM and VOC assessment. It provides estimates of precision and validity that serve to

define the measurement requirements for the next level of analysis. These estimates can also be

used to determine whether or not the model results at a given level of PM and VOC assessment

are certain enough to eliminate the need for more extensive assessment.
4 - 1

4. ASSUMPTIONS, PERFORMANCE MEASURES, AND VALIDATION PROCEDURES 

4.1 Fundamental Assumptions and Potential Deviations

The CMB model assumptions are:

1. Compositions of source emissions are constant over the period of ambient and source

sampling.

2. Chemical species do not react with each other, i.e., they add linearly.

3. All sources with a potential for significantly contributing to the receptor have been

identified and have had their emissions characterized.

4. The source compositions are linearly independent of each other.

5. The number of sources or source categories is less than or equal to the number of

chemical species.

6. Measurement uncertainties are random, uncorrelated, and normally distributed.

Assumptions 1 through 6 are fairly restrictive and will never be totally complied with in

actual practice. Fortunately, CMB can tolerate deviations from these assumptions, though these

deviations increase the stated uncertainties of the source contribution estimates.

The CMB model has been subjected to a number of tests to determine its abilities to

tolerate deviations from model assumptions (Watson, 1979; Gordon et al., 1981; deCesar and

Cooper, 1982; Henry, 1982, 1992; Currie et al., 1984; Dzubay et al., 1984; deCesar et al., 1985a,

1985b, 1986; Javitz and Watson, 1986; Lowenthal and Rahn, 1988a, 1088b, Lowenthal et al.,

1987, 1988, 1992, 1994; Javitz et al., 1988a, 1988b; Cheng and Hopke, 1989; Kim and Henry,

1989; Henry and Kim, 1990; White and Macias, 1991). These studies all point to the same basic

conclusions regarding deviations from the above-stated assumptions.

With regard to Assumption 1, source compositions, as seen at the receptor, are known to

vary substantially among sources, and even within a single source over an extended period of

time. These variations are both systematic and random and are caused by three phenomena: 1)

transformation and deposition between the emissions point and the receptor; 2) differences in

fuel type and operating processes between similar sources or the same source in time; and 3)

uncertainties or differences between the source profile measurement methods. Evaluation

studies have generally compared CMB results from several tests using randomly perturbed input

data and from substitutions of different source profiles for the same source type. These tests

consistently demonstrate that the error in the estimated source contributions due to biases in all

of the elements of a source profile is in direct proportion to the magnitude of the biases. For

random errors, the magnitude of the source contribution errors decreases as the difference

between the number of species and sources increases.

Javitz et al. (1988b), for example, examined a simple 4-source urban airshed and a

complex 10-source urban airshed using randomly perturbed source profiles and receptor

concentrations with known source contributions. These tests with 17 commonly measured

chemical species showed that primary mobile, geological, coal-fired power plant, and vegetative

burning source types can be apportioned with uncertainties of approximately 30% when

coefficients of variation in the source profiles are as high as 50%. This performance was

demonstrated even without the presence of unique “tracer” species such as selenium for coalfired power plants or soluble potassium for vegetative burning.
4 - 2

In a complex urban airshed, which added residual oil combustion, marine aerosol, steel

production, lead smelting, municipal incineration, and a continental background aerosol, it was

found that the geological, coal-fired power plant, and background source profiles were collinear

with the measured species. At coefficients of variation in the source profiles as low as 25%,

average absolute errors were on the order of 60%, 50%, and 130% for the geological, coalburning, and background sources, respectively. All other sources were apportioned with average

absolute errors of approximately 30% even when coefficients of variation in the source profiles

reached 50%. These tests were performed with commonly measured chemical species, and

results would improve with a greater number of species that are uniquely emitted by the different

source types.

With regard to the nonlinear summation of species, Assumption 2, it is necessary to

measure source profiles, or modify them by some objective method, to account for changes in

the character between source and receptor. The conversion of gases to particles and reactions

between particles are not inherently linear processes. This assumption is especially applicable to

the end products of photochemical reactions and their apportionment to the sources of the

precursors. Further model evaluation is necessary to determine the tolerance of CMB to

deviations from this assumption.

The current practice is to apportion the primary material that has not changed between

source and receptor. The remaining quantities of reactive species such as ammonium, nitrate,

sulfate, and organic carbon are then apportioned to chemical compounds (single constituent

source type) rather than directly to sources. While this approach is not as satisfying as a direct

apportionment, it at least separates primary from secondary emitters, and the types of compounds

apportioned give some insight into the chemical pathways that formed them. As noted in

Section 3, when profiles are coupled with chemical reaction mechanisms and rates, deposition

velocities, atmospheric equilibrium, and methods to estimate transport and aging time, it is

possible to produce “aged” source profiles which will allow this direct attribution of reactive

species to sources. This apportionment requires measurements of gaseous as well as particulate

species at receptor sites, and is one of the main arguments for combining PM2.5 and VOC source

apportionment studies together.

A major challenge to the application of CMB is the identification of the primary

contributing sources for inclusion in the model, Assumption 3. Watson (1979) systematically

increased the number of sources contributing to his simulated data from four to eight

contributors while solving the CMB equations assuming only four sources. More sources were

included in the least squares solution than those that were actually contributors.

These studies found that underrepresenting the number of sources had little effect on the

calculated source contributions if the prominent species contributed by the missing sources were

excluded from the solution. When the number of sources was underrepresented, and when

prominent species of the omitted sources were included in the calculation of source

contributions, the contributions of sources with properties in common with the omitted sources

were overestimated. When source types actually present were excluded from the solution, ratios

of calculated to measured concentrations were often outside of the 0.5 to 2.0 range, and the sum

of the source contributions was much less than the total measured mass. The low

calculated/measured ratios indicated which source compositions should be included. When the

number of sources was overrepresented, the sources not actually present yielded contributions
4 - 3

less than their standard errors if their source profiles were significantly distinct from those of

other sources. The over-specification of sources decreased the standard errors of the source

contribution estimates.

Determining deviations from Assumption 4, the linear independence of source

compositions, is one of the main goals of CMB validation. The degree of collinearity depends

on the number of source categories contributing to influential fitting species, the relative

contributions from source types with similar (but not identical) profiles, the variability of species

abundances in the profiles, and the relative contribution from each category. These conditions

vary from sample to sample, so it is not possible to state that two or more profiles are overly

collinear prior to applying them to a specific sample. Similarly, the presence or absence of a

“unique” or “tracer” species does not guarantee that collinearity is eliminated, especially if the

“tracer” is at a very low abundance (e.g., <0.1%) and is highly variable. The variability of the

profile abundances is more influential than the distinctness of the chemical species, in many

cases.

Lowenthal et al. (1992), for example, showed that diesel and gasoline vehicle exhaust

were non-collinear in a simple airshed where they were the major source of carbon. When a

vegetative burning contribution was present, however, the diesel and gasoline exhaust profiles

were too collinear to allow discrimination of their contributions, and only a composite “motor

vehicle exhaust” contribution could be estimated.

Gordon et al. (1981) found instabilities in the ordinary weighted least square solutions to

the CMB equations when species presumed to be “unique” to a certain source type were

removed from the solution. Using simulated data with known perturbations ranging from 0 to

20%, Watson (1979) found that in the presence of likely uncertainties, sources such as urban

dust and continental background dust cannot be adequately resolved by least squares fitting, even

though their compositions are not identical. Several nearly unique ratios must exist for good

separation.

Several “regression diagnostics” have been proposed for least squares estimation

methods similar to the CMB effective variance solution (e.g., Belsley et al., 1980; DeCesar et

al., 1985a, 1985b). Kim and Henry (1989) show that most of these diagnostics are not

meaningful because they are based on the assumption of zero uncertainty in the source profiles. 

Kim and Henry (1989) demonstrate, through the examination of randomly perturbed model input

data, that the values for these diagnostics vary substantially with typical random changes in the

source profiles. Tests performed on simulated data with obviously collinear source compositions

typically result in positive and negative values for the collinear source types as well as large

standard errors in the collinear source contribution estimates. Unless the source compositions

are nearly identical, the sum of these large positive and negative values very closely

approximates the sum of the true contributions.

CMB8 makes the collinearity measures proposed by Henry (1992) more transparent to

identify the degree of collinearity. These measure the degree of overlap among source profiles

as if they were vectors in a multi-dimensional space. The user can set the overlap he or she is

willing to tolerate for a selected maximum uncertainty in the quantity being apportioned (i.e.,

total VOC or PM mass to which the profiles are normalized). Little guidance is given in this

protocol or elsewhere on how to select these overlaps and uncertainties, or on what the

implication of that selection might be. By having these options available in CMB8, however, it
4 - 4

is hoped that such a body of knowledge can be acquired as more source apportionment studies

are completed and the collinearity issue is studied in greater detail.

With most commonly measured species for particles (e.g., ions, elements, carbon) and

common source types (e.g., motor vehicle, geological, residual oil, sea salt, steel production,

wood burning, various industrial processes, secondary sulfuric acid, secondary ammonium

bisulfate, secondary ammonium sulfate, secondary ammonium nitrate, secondary sodium

nitrate), approximately five to seven source types are linearly independent of each other. About

the same number of VOC source types (e.g., motor vehicle exhaust, liquid gasoline, evaporated

gasoline, degreasers and coatings, graphic arts, biogenics) can be distinguished with most

commonly measured species for VOC (e.g., C2 - C10 hydrocarbons in canisters). The degree of

resolution and number of source types can be enhanced substantially, as will be shown in Section

5, when more detailed particle and gaseous organic compounds are measured at source and

receptor, and when gas and particles are measured in conjunction with each other.

With regard to Assumption 5, the true number of individual sources contributing to

receptor concentrations is generally much larger than the number of species that can be

measured. It is therefore necessary to group sources into source types of similar compositions so

that this assumption is met. For the most commonly measured species, meeting Assumption 4

practically defines these groupings.

With respect to Assumption 6 (the randomness, normality, and the uncorrelated nature of

measurement uncertainties), there are few results available from verification or evaluation

studies. Every least squares solution to the CMB equations requires this assumption, as

demonstrated by the derivation of Watson et al. (1984). In reality, very little is known about the

distribution of errors for the source compositions and the ambient concentrations. If anything,

the distribution probably follows a log-normal rather than a normal distribution. Ambient

concentrations can never be negative, and a normal distribution allows a substantial proportion

of negative values, while a log-normal distribution allows no negative values. For small errors

(e.g., less than 20%), the actual distribution may not be important, but for large errors it probably

is important. A symmetric distribution becomes less probable as the coefficient of variation of

the measurement increases. This assumption still requires further evaluation to determine the

effects of its deviations.

4.2 CMB Performance Measures

Figures 4.2-1, 4.2-2, and 4.2-3 show the three segments of CMB model output that are

displayed each time the model is applied to a set of source profiles and chemical species. These

outputs accompany each application. Table 4.2-1 describes the model outputs and performance

measures in these displays. The use of these measures to evaluate CMB solutions is explained in

subsequent sections.
4 - 5

 Chemical Mass Balance Version EPA-CMB8.2

 Report Date: 11/8/2004

SAMPLE: OPTIONS: INPUT FILES: 

SITE: WELBY BRITT & LUECKE: No INnfraqs.in8

SAMPLE DATE: 01/17/97 SOURCE ELIMINATION: No PRnfraqs.sel

DURATION: 6 BEST FIT: No SPnfraqs.sel

START HOUR: 06 .xxx

SIZE: Fine ADnfraqs.txt

 PRnfraqs.txt

Species Array: 5 

Sources Array: 1 

FITTING STATISTICS:

 R SQUARE 0.92 % MASS 93.8 

 

 CHI SQUARE 0.61 DEGREES FREEDOM 72 

 

SOURCE CONTRIBUTION ESTIMATES:

 

SOURCE 

EST CODE NAME SCE(:g/m3) Std Err Tstat 

---------------------------------------------------- 

YES N001 NVNSP 3.32689 2.06033 1.61474 

YES N007 NVNSP2 0.92216 0.41334 2.23096 

YES N010 NVSM 6.50609 3.73659 1.74118 

YES N013 NWHD 7.23091 1.83963 3.93062 

YES N050 NMc 1.86174 2.03008 0.91708 

YES N055 NWFSc 0.81836 0.47345 1.72850 

YES N067 NWSHc2 2.45221 1.30867 1.87383 

YES N074 NRDC 6.37413 1.65625 3.84852 

YES N082 AMSUL 6.84817 0.83248 8.22628 

YES N084 AMNIT 13.68479 1.33685 10.23660 

YES N124 PCHCLC1 -0.25354 1.24129 -0.20425 

----------------------------------------------------

 49.77192

 

MEASURED CONCENTRATION FOR SIZE: Fine 

 53.1+- 2.7

Figure 4.2-1. CMB8 source contribution display.
4 - 6

 Eligible Space Display 

================================================================================

ELIGIBLE SPACE DIM. = 11 FOR MAX. UNC. = 10.61606 (20.% OF TOTAL MEAS. MASS) 

 

1 / Singular Value 

--------------------------------------------------------------------------------

 0.25514 0.37985 0.75550 0.86353 0.93826 1.34737 1.36735 1.59420 

 1.86357 2.18762 4.20631 

--------------------------------------------------------------------------------

 

NUMBER ESTIMABLE SOURCES = 11 FOR MIN. PROJ. = 0.95 

 PROJ. SOURCE PROJ. SOURCE PROJ. SOURCE PROJ. SOURCE PROJ. SOURCE 

--------------------------------------------------------------------------------

1.0000 N001 1.0000 N007 1.0000 N010 1.0000 N013 1.0000 N050 

1.0000 N055 1.0000 N067 1.0000 N074 1.0000 N082 1.0000 N084 

1.0000 N124 

--------------------------------------------------------------------------------

 

ESTIMABLE LINEAR COMBINATIONS OF INESTIMABLE SOURCES 

COEFF. SOURCE COEFF. SOURCE COEFF. SOURCE COEFF. SOURCE SCE Std Err

Figure 4.2-2. Eligible space collinearity display.
4 - 7

SPECIES CONCENTRATIONS: 

 CALCULATED RESIDUAL 

 ---------- -----------

SPECIES FIT MEASURED CALCULATED MEASURED UNCERTAINTY

--------------------------------------------------------------------------------

 

MSGC MSGU 53.08030+- 2.70112 49.77192+- 2.80479 0.94+- 0.07 -0.8 

CLIC CLIU * 0.45540+- 0.05500 0.19340+- 0.16807 0.42+- 0.37 -1.5 

N3IC N3IU * 10.48780+- 0.69455 10.66432+- 1.06124 1.02+- 0.12 0.1 

S4IC S4IU * 5.03660+- 0.39970 5.09540+- 0.50542 1.01+- 0.13 0.1 

N4CC N4CU * 5.04460+- 0.26760 4.96436+- 0.37570 0.98+- 0.09 -0.2 

KPAC KPAU * 0.06260+- 0.00540 0.02193+- 0.10357 0.35+- 1.65 -0.4 

TCTC TCTU 21.55080+- 1.38265 21.47394+- 0.57545 1.00+- 0.07 -0.1 

OCTC OCTU * 13.42380+- 1.14530 13.44418+- 0.86062 1.00+- 0.11 0.0 

ECTC ECTU * 8.12700+- 0.77460 8.02976+- 0.93415 0.99+- 0.15 -0.1 

NAXC NAXU * 0.07980+- 0.04150 0.11505+- 0.10432 1.44+- 1.51 0.3 

MGXC MGXU * 0.01450< 0.04340 0.05238< 0.06781 3.61< 11.78 0.5 

ALXC ALXU * 0.13630+- 0.01320 0.34546+- 0.21019 2.53+- 1.56 1.0 

SIXC SIXU * 0.52050+- 0.02830 1.13702+- 0.52088 2.18+- 1.01 1.2 

PHXC PHXU * 0.00000< 0.01640 0.00529< 0.06631 0.00< 0.00 0.1 

SUXC SUXU 2.05200+- 0.10310 1.74683+- 0.17037 0.85+- 0.09 -1.5 

CLXC CLXU * 0.34080+- 0.02290 0.20754+- 0.15976 0.61+- 0.47 -0.8 

KPXC KPXU * 0.12110+- 0.00820 0.16756+- 0.06941 1.38+- 0.58 0.7 

CAXC CAXU * 0.21120+- 0.01240 0.17604+- 0.11150 0.83+- 0.53 -0.3 

TIXC TIXU * 0.00000< 0.02990 0.01525< 0.03837 0.00< 0.00 0.3 

VAXC VAXU * 0.00000< 0.01660 0.00097< 0.01616 0.00< 0.00 0.0 

CRXC CRXU * 0.00000< 0.00510 0.00151< 0.00388 0.00< 0.00 0.2 

MNXC MNXU * 0.01120+- 0.00180 0.00385+- 0.00311 0.34+- 0.28 -2.0 

FEXC FEXU * 0.34700+- 0.01770 0.24112+- 0.14516 0.69+- 0.42 -0.7 

NIXC NIXU * 0.00010< 0.00140 0.00035< 0.00226 3.51< 54.03 0.1 

CUXC CUXU 0.01190+- 0.00120 0.00551+- 0.00513 0.46+- 0.43 -1.2 

ZNXC ZNXU 0.09250+- 0.00480 0.01933+- 0.01895 0.21+- 0.21 -3.7 

ASXC ASXU * 0.00250< 0.00340 0.00020< 0.00464 0.08< 1.86 -0.4 

SEXC SEXU * 0.00110+- 0.00100 -0.00004+- 0.00226 -0.04+- 2.06 -0.5 

BRXC BRXU * 0.00490+- 0.00090 0.00141+- 0.00383 0.29+- 0.78 -0.9 

RBXC RBXU * 0.00020< 0.00120 0.00077< 0.00213 3.87< 25.55 0.2 

SRXC SRXU * 0.00170+- 0.00090 0.00150+- 0.00234 0.88+- 1.45 -0.1 

ZRXC ZRXU * 0.00010< 0.00190 0.00079< 0.00277 7.88< ***** 0.2 

HGXC HGXU * 0.00000< 0.00300 0.00010< 0.00404 0.00< 0.00 0.0 

PBXC PBXU * 0.01120+- 0.00280 0.00576+- 0.01204 0.51+- 1.08 -0.4 

NAPHTH NAPHTH 1.15685+- 0.06375 1.10813+- 0.70144 0.96+- 0.61 -0.1 

MNAPH2 MNAPH2 0.54554+- 0.03148 0.47372+- 0.23977 0.87+- 0.44 -0.3 

MNAPH1 MNAPH1 0.32017+- 0.01963 0.25244+- 0.11945 0.79+- 0.38 -0.6 

DMN267 DMN267 0.13343+- 0.00974 0.05868+- 0.02370 0.44+- 0.18 -2.9 

DM1367 DM1367 0.21683+- 0.01504 0.08922+- 0.03454 0.41+- 0.16 -3.4 

D14523 D14523 0.06834+- 0.00613 0.02964+- 0.01144 0.43+- 0.17 -3.0 

DMN12 DMN12U 0.02676+- 0.00310 0.01277+- 0.00481 0.48+- 0.19 -2.4 

BIPHEN BIPHEN 0.08488+- 0.00494 0.02298+- 0.00887 0.27+- 0.11 -6.1 

M_2BPH M_2BPH 0.01973+- 0.00147 0.00207+- 0.00210 0.10+- 0.11 -6.9 

M_3BPH M_3BPH 0.07953+- 0.00443 0.01531+- 0.00639 0.19+- 0.08 -8.3 

M_4BPH M_4BPH 0.04232+- 0.00275 0.00831+- 0.00350 0.20+- 0.08 -7.7 

ATMNAP ATMNAP 0.06545+- 0.00444 0.02170+- 0.00843 0.33+- 0.13 -4.6 

EM_12N EM_12N 0.02026+- 0.00192 0.00753+- 0.00304 0.37+- 0.15 -3.5 

BTMNAP BTMNAP 0.06678+- 0.00496 0.02225+- 0.00791 0.33+- 0.12 -4.8 

CTMNAP CTMNAP 0.07318+- 0.00484 0.02223+- 0.00744 0.30+- 0.10 -5.7 

EM_21N EM_21N 0.00257+- 0.00056 0.00155+- 0.00174 0.60+- 0.69 -0.6 

ETMNAP ETMNAP 0.05283+- 0.00473 0.01536+- 0.00514 0.29+- 0.10 -5.4 

FTMNAP FTMNAP 0.04885+- 0.00378 0.01568+- 0.00527 0.32+- 0.11 -5.1 

GTMNAP GTMNAP 0.02637+- 0.00279 0.00904+- 0.00316 0.34+- 0.13 -4.1 

HTMNAP HTMNAP 0.00561+- 0.00100 0.00399+- 0.00197 0.71+- 0.37 -0.7 

TM128N TM128N 0.00283+- 0.00097 0.00128+- 0.00170 0.45+- 0.62 -0.8 

ACNAPY ACNAPY 0.02183+- 0.00300 0.09141+- 0.04484 4.19+- 2.13 1.5 

ACNAPE ACNAPE 0.02829+- 0.00216 0.01991+- 0.02033 0.70+- 0.72 -0.4 

PHENAN PHENAN * 0.04598+- 0.00297 0.10035+- 0.03346 2.18+- 0.74 1.6 

Figure 4.2-3. Species concentration display.
4 - 8

FLUORE FLUORE * 0.03263+- 0.00278 0.03197+- 0.01276 0.98+- 0.40 -0.1 

A_MFLU A_MFLU * 0.01556+- 0.00144 0.01204+- 0.00448 0.77+- 0.30 -0.7 

M_1FLU M_1FLU * 0.00697+- 0.00094 0.00559+- 0.00243 0.80+- 0.37 -0.5 

B_MFLU B_MFLU * 0.00351+- 0.00057 0.00287+- 0.00189 0.82+- 0.55 -0.3 

C_MFLU C_MFLU * 0.01187+- 0.00109 0.02067+- 0.00713 1.74+- 0.62 1.2 

A_MPHT A_MPHT * 0.01087+- 0.00100 0.00989+- 0.00409 0.91+- 0.39 -0.2 

M_2PHT M_2PHT * 0.01164+- 0.00105 0.01089+- 0.00442 0.94+- 0.39 -0.2 

B_MPHT B_MPHT * 0.00065+- 0.00046 0.00330+- 0.00254 5.08+- 5.31 1.0 

C_MPHT C_MPHT * 0.00797+- 0.00078 0.00680+- 0.00308 0.85+- 0.39 -0.4 

M_1PHT M_1PHT * 0.00694+- 0.00079 0.00729+- 0.00321 1.05+- 0.48 0.1 

DM36PH DM36PH * 0.00269+- 0.00064 0.00207+- 0.00187 0.77+- 0.72 -0.3 

A_DMPH A_DMPH * 0.00325+- 0.00069 0.00268+- 0.00206 0.83+- 0.66 -0.3 

B_DMPH B_DMPH * 0.00204+- 0.00047 0.00134+- 0.00176 0.66+- 0.88 -0.4 

C_DMPH C_DMPH * 0.00650+- 0.00067 0.00426+- 0.00252 0.66+- 0.39 -0.9 

DM17PH DM17PH * 0.00286+- 0.00064 0.00211+- 0.00189 0.74+- 0.68 -0.4 

D_DMPH D_DMPH * 0.00236+- 0.00052 0.00171+- 0.00178 0.72+- 0.77 -0.4 

E_DMPH E_DMPH * 0.00222+- 0.00064 0.00185+- 0.00184 0.83+- 0.86 -0.2 

ANTHRA ANTHRA 0.00408+- 0.00098 0.02458+- 0.00835 6.03+- 2.51 2.4 

FLUORA FLUORA 0.00579+- 0.00084 0.03360+- 0.01209 5.80+- 2.25 2.3 

PYRENE PYRENE 0.00694+- 0.00064 0.04160+- 0.01530 5.99+- 2.27 2.3 

B_MPYR B_MPYR * 0.00118+- 0.00042 0.00079+- 0.00169 0.67+- 1.45 -0.2 

D_MPYR D_MPYR * 0.00077+- 0.00042 0.00107+- 0.00169 1.39+- 2.33 0.2 

F_MPYR F_MPYR * 0.00094+- 0.00042 0.00085+- 0.00170 0.90+- 1.84 -0.1 

RETENE RETENE * 0.00074+- 0.00059 0.00035+- 0.00167 0.47+- 2.30 -0.2 

BAANTH BAANTH * 0.00162+- 0.00126 0.00152+- 0.00176 0.94+- 1.30 0.0 

CHRYSN CHRYSN * 0.00177+- 0.00067 0.00111+- 0.00169 0.62+- 0.98 -0.4 

BBJKFL BBJKFL * 0.00183+- 0.00076 0.00214+- 0.00182 1.17+- 1.11 0.2 

BEPYRN BEPYRN * 0.00157+- 0.00059 0.00075+- 0.00171 0.48+- 1.11 -0.5 

BAPYRN BAPYRN * 0.00127< 0.00138 0.00093< 0.00177 0.73< 1.60 -0.2 

INCDPY INCDPY * 0.00115+- 0.00113 0.00055+- 0.00172 0.48+- 1.56 -0.3 

DBANTH DBANTH * 0.00027< 0.00163 0.00006< 0.00176 0.22< 6.74 -0.1 

BGHIPE BGHIPE * 0.00260+- 0.00147 0.00169+- 0.00220 0.65+- 0.92 -0.3 

CORONE CORONE * 0.00162< 0.00292 0.00122< 0.00216 0.75< 1.90 -0.1 

GUACOL GUACOL 0.04846+- 0.01833 0.02527+- 0.01222 0.52+- 0.32 -1.1 

M4GUCL M4GUCL 0.00080< 0.00109 0.02785< 0.01598 34.94< 51.70 1.7 

E4GUCL E4GUCL * 0.00286+- 0.00105 0.00656+- 0.00394 2.29+- 1.61 0.9 

SYRGOL SYRGOL 0.00000< 0.00171 0.04130< 0.02025 0.00< 0.00 2.0 

PPGUCL PPGUCL * 0.00000< 0.00046 0.00090< 0.00172 0.00< 0.00 0.5 

A4GUCL A4GUCL * 0.00000< 0.00054 0.00417< 0.00266 0.00< 0.00 1.5 

GNONLA GNONLA * 0.00487+- 0.00123 0.00444+- 0.00253 0.91+- 0.57 -0.2 

F4GUCL F4GUCL * 0.01388+- 0.00709 0.01459+- 0.00660 1.05+- 0.72 0.1 

M4SYRG M4SYRG * 0.00552< 0.00602 0.01250< 0.00437 2.26< 2.59 0.9 

E4SYRG E4SYRG * 0.02555+- 0.00566 0.00551+- 0.00194 0.22+- 0.09 -3.4 

ISOEUG ISOEUG * 0.02425+- 0.00359 0.01139+- 0.00457 0.47+- 0.20 -2.2 

GDECLA GDECLA * 0.00227+- 0.00113 0.00176+- 0.00190 0.78+- 0.92 -0.2 

ACETVA ACETVA * 0.00035< 0.00196 0.00408< 0.00246 11.52< 64.23 1.2 

UNGLAC UNGLAC * 0.00313+- 0.00122 0.00706+- 0.00665 2.26+- 2.30 0.6 

SYRALD SYRALD 0.06167+- 0.01311 0.00321+- 0.00207 0.05+- 0.04 -4.4 

C27SDS C27SDS * 0.00092+- 0.00067 0.00052+- 0.00168 0.57+- 1.88 -0.2 

C27RDS C27RDS * 0.00080+- 0.00050 0.00041+- 0.00167 0.51+- 2.12 -0.2 

C27RAC C27RAC * 0.00000< 0.00063 0.00081< 0.00172 0.00< 0.00 0.4 

AB30NH AB30NH * 0.00139+- 0.00075 0.00120+- 0.00174 0.86+- 1.34 -0.1 

CHLSRL CHLSRL * 0.00000< 0.00447 0.00115< 0.00190 0.00< 0.00 0.2 

BA30NH BA30NH * 0.00092+- 0.00063 0.00074+- 0.00173 0.81+- 1.97 -0.1 

AB_HOP AB_HOP * 0.00000< 0.00063 0.00095< 0.00170 0.00< 0.00 0.5 

BA_HOP BA_HOP * 0.00012< 0.00042 0.00031< 0.00172 2.61< 17.28 0.1 

SABHHP SABHHP * 0.00062+- 0.00050 0.00021+- 0.00168 0.34+- 2.72 -0.2 

RABHHP RABHHP * 0.00035< 0.00042 0.00015< 0.00168 0.43< 4.76 -0.1 

STEROM STEROM 0.00000< 0.00731 0.00035< 0.00178 0.00< 0.00 0.0 

SABBHH SABBHH * 0.00038< 0.00046 0.00015< 0.00168 0.40< 4.39 -0.1 

RABBHH RABBHH * 0.00027< 0.00042 0.00009< 0.00422 0.36< 15.86 0.0 

CO COU 3.29642+- 0.34448 7.22289+- 6.74482 2.19+- 2.06 0.6 

NOX NOXU 0.60373+- 0.06120 0.32596+- 0.21088 0.54+- 0.35 -1.3 

SO2 SO2U 9.15143+- 5.08306 -9.95237+- 5.06539 -1.09+- 0.82 -2.7 

Figure 4.2-3 (continued). Species concentration display.
Table 4.2-1 CMB8 Outputs and Performance Measures

4 - 9

Output/Statistic/Code Abbreviation Description

Source Contribution Display

Source Contribution Estimate SCE Contribution from the source type designated by the pro

the profile normalizing component (usually PM2.5 masUnits can be specified in the options menu of CMB8.

Standard Error Std Err The uncertainty of the source contribution estimate (SC

standard deviation of the most probable SCE. This is an precision or certainty of each SCE. The STD ERR is espropagating the uncertainty estimates of the receptor daprofiles through the effective variance least-squares calmagnitude is a function of the uncertainties in the input of collinearity (i.e., degree of similarity) among source three times the standard error may be taken as an upper contribution. [Target Std Err << SCE]

t-Statistic Tstat Ratio of the SCE to its Std Err. A high Tstat suggests a 

[Target > 2.0]

R-square R-SQUARE Variance in ambient species concentrations explained b

species concentrations. A low R SQUARE (<0.8) indic

source profiles have not accounted for the variance in thconcentrations. Ranges from 0 to 1.0. [Target 0.8 to 1.

Percent Mass Accounted For PERCENT

MASS or

%MASS

The sum of SCE divided by the total mass or VOC concapproaching 100% is desired. A %MASS near 100% cbecause a poor fit can force a high %MASS. [Target 10

Degrees of Freedom DF The number of species in fit minus number of sources in 

larger degrees of freedom are typically more stable and with small degrees of freedom. [Target > 5]

Chi-square CHI SQUARE Similar to R-SQUARE except that it also considers the 

calculated species concentrations. A large CHI SQUARone or more of the calculated species concentrations difmeasured concentrations by several uncertainty intervalthese statistics exceed their targets when: 1) contributinomitted from the CMB calculation; 2) one or more sourbeen selected which do not represent the contributing souncertainty estimates of receptor or source profile data and/or 4) source or receptor data are inaccurate. CHI Ssquare root of the sum of the squares of the RATIO R/U fitting species divided by the DF. [Target 0.0 to 4.0] 
Table 4.2-1 CMB8 Outputs and Performance Measures

4 - 10

Output/Statistic/Code Abbreviation Description

Source Contribution Display (cont.)

Site, sample duration, date, starthour, size 

Describes the sample being modeled by location, time, sample. Size refers to different particle size fractions, tPM2.5 (sometimes called “fine” particles).

Britt and Luecke Solution B and L A “Yes” flag indicates that the complex Britt and Lueck

has been applied. A “No” flag means the default effecthas been applied.

Source Elimination SRC ELIM The source elimination option automatically removes ne

less than the corresponding Std Err before printing the sflag means that the option is on and the default “No” mrecommended that negative and negligible source types manually as they may be indicators of collinearity that swhen interpreting the source categories represented by 

Weights for CHI SQR, R SQR,PCMASS, and FRCEST

Allows the “best” solution to be obtained automatically combinations of source profiles based on a relative weigsquare, R-Square, Percent Mass, and Estimable Sources measures. The weights can be set in the options menu.

Estimable Source Profile EST A “Yes” flag in this column indicates that the source is 

uncertainty parameters defined in the options menu. A that the source is not estimable within the uncertainty p

Code and Name The source code matches the profile with the source com

source selection file. The name corresponds to a short mdesignates the source profile.
Table 4.2-1 CMB8 Outputs and Performance Measures

4 - 11

Output/Statistic/Code Abbreviation Description

Estimable Space Display

Eligible Space Dimension andMaximum Uncertainty

Replaces U/S CLUSTERS and SUM OF CLUSTER SOThis treatment (Henry, 1992) uses two parameters, maxuncertainty and minimum source projection on the eligiset to default values of 1.0 and 0.95, respectively, in CMsource uncertainty determines the eligible space to be speigenvectors whose inverse singular values are less than maximum source uncertainty. Estimable sources are dewhose projection on the eligible space is at least the minprojection. Inestimable sources are sources that are not modify these values click in the edit boxes and edit with 

Singular Value The singular value decomposition of the source transfer Number of Estimable Sources The sources that are estimable given their source contri

propagated uncertainties. This changes with the acceptspecified in the options menu.

Estimable Linear Combinations COEFF.

SOURCE

SCE

Std Err

Show clusters of sources which the model cannot easily and that are likely to be interfering with the model’s abigood set of SCE’s. [Target - No clusters.] Estimates the sum of SCE’s of the sources in a cluster aof the sum. Not needed if source profiles of cluster souimproved. The standard error of the SCE follows the ± 
Table 4.2-1 CMB8 Outputs and Performance Measures

4 - 12

Output/Statistic/Code Abbreviation Description

Species Concentration Display

Selected Species In the Fit I A “

*” in this column indicates the species is included i

the source contribution estimate.

Missing Measurement for Species M Status: M in column indicates missing measurement. T

by –99 in the input data set.

Measured Species Concentration MEAS Ambient species concentrations (measurements and uncCalculated Species Concentration CALC Calculated chemical concentrations and propagated unc

the selected profiles and the source contribution estimatreported both for fitting and non-fitting species.

Ratio of Calculated to MeasuredSpecies

CALCULATED

MEASURED

Ratio of CALC/MEAS and its uncertainty Used to idenover/under accounted for by the model. The ratios shouthe model has accurately explained the measured concethat deviate from unity by more than two uncertainty inan incorrect set of profiles is being used to explain the mconcentrations. [Target 0.5 to 2.0.]

Ratio of Residual to Its Uncertainty RESIDUAL

UNCERTAINTY

Ratio of the signed difference between the calculated aconcentration (i.e., the residual) divided by the uncertai(i.e., square root of the sum of the squares of the uncertcalculated and measured concentrations). Used to idenover- or under-accounted for by the model. The RATIO number of uncertainty intervals by which the calculated concentrations differ. When the absolute value of the R2, the residual is significant. If it is positive, then one oprofiles is contributing too much to that species. If it is is an insufficient contribution to that species and a sourThe sum of the squared RATIO R/U for fitting species degrees of freedom yields the CHI SQUARE. The highvalues for fitting species are the cause of high CHI SQU[Target |<2.0|.]
4 - 13

Output/Statistic/Code Abbreviation Description

Command Display

Modified Psuedo Inverse Matrix MPIN Shows which species most influence the source contrib

corresponding to each profile. It is examined to determmarker species are having the most influence on the ap

Species – Source Contribution SSCONT Shows the fraction of each measured species concentra

accounted for by the calculated species for each source This can be > 1.0 for a particular source if that species for by the fit. It is used to identify the sources which arparticular species.
4 - 14

4.3 Protocol Steps

Each of the seven steps in the application and validation protocol is described below with

respect to their general application. They are illustrated in greater detail for specific examples in

Sections 5 and 6.

4.3.1 Determine the Applicability of CMB

The following conditions must be met for CMB to be applicable:

1. A sufficient number of PM or VOC receptor samples have been taken with accepted

sampling methods to fulfill study objectives. If objectives are to determine how to

attain NAAQS, samples should represent annual average and maximum

concentrations for PM2.5 and PM10 and correspond to maximum 8-hour average ozone

concentrations for VOC.

2. Samples are amenable to or have been analyzed for a variety of chemical species. As

noted above, elements, ions, and carbon are the minimal needs for PM apportionment

and light hydrocarbons in canisters or automatic gas chromatographs are the minimal

requirements for VOC apportionment.

3. Potential source contributors can be identified and grouped into source categories of

distinct chemical compositions with respect to the receptor species available from

requirement 2.

4. Source profiles are available, from the study area or from similar sources, that

represent the source compositions as they would appear at the receptors. Changes in

source composition between source and receptor must be accommodated in order for

the model to be physically meaningful.

5. The number of source types in a single application of CMB must be fewer than the

number of chemical species measured above lower quantifiable limits at the receptor.

Unless all five of the above requirements are met, the Chemical Mass Balance receptor

model is not applicable to the situation under study. These are necessary, but not sufficient,

requirements, and it may still be found that even though these requirements are met, the

precision and validity of CMB results are not adequate for control strategy decisions. The

remaining steps in the applications and validation protocol must be completed to arrive at this

conclusion.

4.3.2 Format Input Files and Perform Initial Model Runs

CMB8 allows input data files to be prepared in spreadsheet formats and, with

contemporary computer memories, has no practical limit on the number of source profiles,

chemical species, or individual samples that can be included in a single file. It is convenient,

however, to divide input data into groups by site or season when data sets are large.

The initial model runs usually contain many more profiles than are used in production

runs to determine how different composites might affect the precision and stability of the source

contribution estimates. One or more initial arrays (combinations) of source profiles are usually

examined during this step. Various arrays of fitting species are also examined.
4 - 15

During this step, it may be necessary to modify ambient data or source profiles by

making additional assumptions. This is very often the case when some profiles are obtained

from another study and may not report all of the species available in the other profiles or in the

receptor samples. A default value of zero with a standard deviation equal to an analytical

detection limit may be assigned to a species in a source profile if that species is known to be

absent from that source type from previous tests of similar sources.

When selecting fitting species, only one of the different measurements of the same

species (such as elemental carbon and total carbon or sulfur and sulfate) should be included in

the fit. If more than one measurement of the same species is included in the CMB solution, then

that species influences the source contribution estimates more than it should. This does not

apply to soluble and insoluble species (such as potassium), which are really different species that

distinguish among source types.

Concentrations with values below detection limits may be included only if their

uncertainty is also included. Minimum detection limits may be used to estimate this uncertainty

if it is not otherwise reported. If the uncertainty is underestimated or is not specified (and given

a default value of zero), then these very imprecise measurements will have an excessive

influence on the source contribution estimates.

Secondary components can be represented by their chemical form. In the simplest case, a

single constituent source type (Section 4.1), such as secondary organic carbon that contains only

an entry in the organic carbon column, may be used. These should be used sparingly, however,

because a single constituent type effectively removes the influence of that source profile species

on the source contribution estimates. A discussion and example application for PM10 is

presented in Appendix G.

Uncertainties assigned to the measurements for use in the CMB application should be

reviewed to ensure that they are realistic estimates (see Appendix B). Measurement

uncertainties should be provided as part of the measurement process. Typical measurement

uncertainties are on the order of ±5% to ±20%, with some species being more uncertain than

others because of analytical interferences and proximity to detection limits. Uncertainties in

source profiles could be much greater. The model considers these uncertainties when it develops

the “fit”. Species with high uncertainties are unlikely to be very influential in the fit.

Chemical measurements are usually reported with their measurement uncertainties

determined from error propagation of chemical analysis and flow rate uncertainties (e.g., Watson

et al., 1995). These uncertainties are determined from periodic performance tests and replicate

analyses. The reporting of these uncertainties should be specified when the measurements are

made. If chemical concentrations are available without uncertainties, typical uncertainties may

be assigned based on those reported in previous analyses. The value of the diagnostics provided

by the CMB software is substantially decreased without an adequate and accurate definition of

measurement uncertainties in receptor data.

In most cases, the individual samples should be run separately in CMB. Compositing or

combining the data from several samples will usually decrease the number of sources that CMB

can resolve. Likewise, separate analysis of different PM size fractions is preferable to a “total”

sample that combines the two size fractions. The sources contributing to these two size fractions

are generally quite different.
4 - 16

Several source profiles for each source type may be included in the source profile input

files, but only one profile from each type should be included in a fit. The set of profiles that best

explains the measurements may differ from sample to sample, both because the profiles are

different and because the source contributions change in magnitude. Several sources will nearly

always contribute, and profiles should be included to represent vehicle exhaust, suspended dust,

secondary sulfate, and possibly secondary nitrate or vegetative burning. Natural sources, such as

sea salt or wind blown dust, should be included if these are in the proximity of the receptors. 

Point sources, such as coal-fired power stations, steel mills, cement production facilities, and

other industrial sources in an emissions inventory are next in priority. These may be very

directional, depending on which way winds are oriented between source and receptor. Finally,

single constituent source types can be added as a last resort when there is no other explanation

for a chemical species. This is sometimes done for zinc and copper, which are often in excess

owing to nearby plating or metal handling operations.

In selecting source profiles for inclusion in a fit, it is helpful to review wind direction

data and eliminate sources that have virtually no chance of contributing a detectable

concentration because they are downwind of the receptor. Source types that are unlikely to be

emitting during the period of time being studied (e.g., wood smoke emissions during hot summer

months) can be omitted, or their profiles should be replaced with ones that represent wildfires or

prescribed burning that might occur during that period.

The final selection of the most appropriate source types and the profiles to represent

those source types results from interactive applications of CMB with an evaluation of the

diagnostic measures. It is possible that more than one subset of source types and source profiles

will fit the receptor data equally well. The interactive application of the model to different

source subsets will identify these cases.

Some sources have emissions that are chemically similar or consistent over time, i.e.,

although the absolute magnitude of the emissions may vary, the relative composition of many of

the measured species present in a source may be sufficiently stable. However, the chemistry of

some species could be variable if the source changes its operating conditions, feedstock, or fuel. 

This variability must be reflected in the uncertainties that are assigned to each species in the

profile. (These concerns about source profile variability are analogous to those faced by the

dispersion modeler when estimating emission rates or dispersion parameters.)

Because the CMB model uses the information provided by all species included in the fit,

mis-estimation of a single species, even so-called “tracer” species, may not appreciably affect

the source contribution estimates. This is especially true if these species have been assigned

uncertainties which reflect their variability. When these uncertainties are adequately estimated,

other, less variable species provide a larger influence on the source contribution estimates.
4 - 17

4.3.3 Evaluate Outputs and Performance Measures

Model outputs and performance measures are described in Table 4.3-1. These are

examined for different combinations of fitting profiles and fitting species to determine the

optimal fit to the data. This process will become more evident when applied to the specific

examples in Sections 5 and 6.

4.3.4 Evaluate Deviations from Model Assumptions

The CMB performance measures and tests using different profiles and fitting species can

often indicate when deviations from model assumptions may have occurred. These deviations

do not necessarily invalidate the CMB results – they merely indicate the potential for invalidity. 

This is why a separate step is necessary in the applications and validation protocol which

evaluates the effects of these deviations from assumptions and determines whether or not these

effects can be tolerated.

4.3.5 Modify Model Inputs to Remediate Problems

There are four main categories of problems which, once they have been identified, can be

addressed to improve the performance of the model. The problem categories are: 1) insufficient

receptor measurements; 2) insufficient source measurements; 3) incorrect profile combinations;

and 4) source profile collinearity. Not all “indications” must persist for a problem to be present. 

The more “indications” that persist, the more evidence of a problem. Because of the complex

interactions of all of the data in a least squares estimate, the statistics or diagnostics may not

always be adequate to conclusively isolate a problem with model input. Additional physical

evidence is also very helpful.

There may be inaccuracies in the receptor measurements that have not been uncovered in

the routine data validation. If the data are “suspect” and there are no apparent data entry or

analytical errors, the next step would be to eliminate the suspect species from the fit and rerun

the model. Examine the changes in the estimates for each source. If the estimate changes by

more than one standard error, and if the receptor concentration or a source profile value for the

removed species is suspect, then either remeasure the species or use the SCE calculated without

that species in the fit. A RATIO R/U << -2.0 for a species suggests either the ambient data are

high or the profile data are low for that species while a RATIO R/U >> 2.0 for a species would

imply that the ambient data are low or the profile data for that species are high. In this case, it is

prudent to:

1. Review the uncertainty assigned to the species with the high residuals. Make any

justifiable and appropriate changes and rerun CMB. If this improves the RATIO

R/U, Step 2 is not necessary.

2. Delete the suspect species from the list of fitting species and rerun. If the SCE

changes by at least one standard error, do not use this species in the fit until it has

been remeasured.

An unacceptable RATIO R/U can also indicate that the set of profiles is not optimized or

that the uncertainty for that species is underestimated in the receptor measurements or source

profiles.
4 - 18

A gross error in the value of one or more species in a profile might result in a high

standard error in the SCE and a high residual for those species. Therefore, one or more high

residual values suggests that the uncertain source profile (and the associated species in

particular) be checked and remeasured if necessary. This condition may be indicated by a SCE

that is inconsistent with preliminary analyses or physical evidence. If one or more species has a

“high” (positive or negative) residual which cannot be attributed to incorrect ambient data, one

should examine the SSCONT to see if one source contribution dominates that species. In this

case, review profile data for the suspect species carefully. Correct or remeasure profile if

necessary.

Missing source types are identified by a low percent mass explained (e.g., less than 80%)

and/or a RATIO R/U << -2.0 for chemical species which are in the missing source. A “high

negative” residual for one or more species and a high Chi-Square are also indicative of missing

sources. The key to identifying these sources resides in the calculated to measured chemical

concentrations listed in the species concentration display. “High negative” residuals imply that a

source is needed which will supply a larger quantity of that species. The source profiles may be

listed from CMB8 to assure that they have been properly formatted and read into the software. 

These profiles can be examined to determine which ones would supply sufficient quantities of

the missing concentrations if they were added to the set of fitting sources. The CMB model can

be reapplied as many times as is necessary to determine which source types and source profiles

best account for the underestimated receptor concentrations. A source should not be included in

the final fit just because it “explains” the data, however; there must be a physical justification for

the source's contribution at a receptor if it is to be included in the fit.

Noncontributing source types, i.e., source types with contributions lower than detection

limits, are identified by Tstat values below 2. Such source types may be eliminated from the fit

if the source contribution is indeed small. If the source is present but with a very small

contribution to total mass, it should only be removed from the fit if the SSCONT shows that

none of the species in the source account for more than 5% to 10% of the ambient concentration

for those species.

Estimable linear combinations (of inestimable sources) may occur owing to high profile

uncertainty or excessive collinearity with low profile uncertainty. To determine if the

uncertainty in the SCE is due to high profile uncertainty, reduce the uncertainties in the profile to

levels that might be reasonable to achieve if the source profiles were measured more precisely;

then, rerun CMB – if the clusters containing those sources are no longer listed, it is likely that

collinearity per se is not significant. Remeasurement of the profile will probably improve the

uncertainties of the source contribution estimates. It is possible that reducing the uncertainty

will not eliminate the clusters but the SCE uncertainty will likely be improved somewhat. This

would suggest that collinearity is also present.

Remedies for unacceptably high uncertainties due to collinearity can take five forms,

ranked from most to least desirable.

1. The profile of one or more of the cluster sources could be improved by measuring

additional species.

2. Reduce the uncertainties in the source profiles of the cluster sources. If the Tstat

becomes > 2.0, and if these profile uncertainties are realistically achievable by

remeasurement, then the “apparent” collinearity can be improved in large part by
4 - 19

improving the uncertainty in the profiles. Ideally, the cluster for that group of sources

would disappear. Remeasure and rerun CMB with the improved measurements. 

More precise source profile measurements must be obtained before reapplying the

model.

3. The estimate of the SCE of the source categories that are estimable linear

combinations of inestimable sources. Obtain independent estimates of the

contributions of the individual source categories and use them to apportion the SCEs

into the source categories.

4. Combine the profiles of the collinear source profiles into a single profile of a

“composite source category” that chemically represents the source categories

identified by the estimable linear combinations of inestimable sources. For example,

resuspended road dust and windblown soil dust are chemically similar, and some

modelers include a single term to represent “crustal material” instead of the two

individual source types. This would result in improved source estimates of the crustal

component, which can then serve as an estimate of the combined impact of the two

sources. This aggregate source contribution estimate might then be partitioned into

its components by another method (e.g., dispersion modeling, microscopy, or wind

trajectory analysis).

5. Species that are causing the similarity in source profiles might be deleted from the fit. 

These species can often be determined from the display produced by the

Contributions by Species tab in the Main Report. Often one of the inestimable

sources will be >> 100% for that species and the other will be negative. 

Unfortunately, eliminating too many species from the fit may cause the model to fail

the applicability requirements. Also, the results should acknowledge that the deleted

source may be present.

4.3.6 Evaluate the Consistency and Stability of the Model Results

The CMB estimates should be tested to see how sensitive they are to the various input

data. Unstable estimates (source contribution estimates that change by more than one standard

error) are an indication that the model may not be providing stable results. For CMB validation,

the term “model stability tests” is usually taken to mean the evaluation of model estimates to

changes in input parameters, such as the selected sources and their profiles, as well as selection

of fitting species used to reach a solution with the CMB model.

The CMB model’s effective variance fitting procedure uses estimates of the source

profile and receptor concentration uncertainties to “weight” their effect in arriving at source

contribution estimates. It is helpful to explore how sensitive the source contribution estimates

are to changes in the source profiles and these uncertainties. This can be done by introducing

changes into the source profiles and rerunning the model for each change.

The model user can select several species from a source(s) of particular regulatory

interest and assign worst case values to those species in the profile. The model can then be rerun

with the worst case profile(s). A practical way to accomplish this sensitivity analysis is to

include a “worst case” source profile along with the “best estimate” profile in the data file. The

resulting source estimate(s) can be considered “brackets” to the source contribution estimates

and can be compared to the uncertainty intervals calculated for each run. If the bracketing
4 - 20

interval is greater than the calculated uncertainty interval, then the model may be sensitive to

changes in the source profiles.

The stability of source contribution estimates with respect to receptor concentrations is

best tested with collocated chemical measurements from one of the sampling sites. These

collocated measurements are usually included as part of the quality assurance plan for a subset of

all samples. If nearly equivalent source contribution estimates are derived from these two

independent measurements of the same ambient air, then the receptor data are not likely causing

instabilities in the CMB results.

Lacking these collocated data, portions of the input data may be perturbed randomly or

systematically in proportion to their uncertainty. The source contribution estimates for the

sources of regulatory interest should not change by more than one standard error in response to

small perturbations if the results are stable. (A “small” perturbation is defined as one standard

error of the ambient species concentrations.) If the results are not stable, the validity of the CMB

result for that particular data set are questionable.

The stability of CMB model results to the fitting species can be evaluated by identifying

a species which SSCONT attributes in large part to a single source. Eliminate this species from

the fit and examine how much the corresponding source contribution changes. If this change is

greater than the Std Err, then that species must be greatly influencing the “fit”. Review the

quality of both the source and ambient measurements for that species carefully because of its

influence on the model estimates.

4.3.7 Corroborate CMB Results with Other Modeling and Analyses

If the CMB model is determined to be applicable, the summary statistics and diagnostics

are generally within target ranges, there are no significant deviations from model assumptions,

and the sensitivity tests reveal no unacceptable instability or consistency problems, the CMB

analysis is considered valid. If uncertainties associated with source estimates are too high for

decision-making purposes even after taking the steps recommended in this protocol, then the

source compositions being used are not representative of the sources in the airshed, or they

contain too much uncertainty associated with the influential species.

It is recommended that both a dispersion model and receptor model be used in a

collaborative manner to perform an apportionment, provided that the dispersion model is

applicable and the receptor model is valid for the particular application. Spatial and time series

distributions, similar to the examples in Section 3, should be examined to establish that source

contribution magnitudes are consistent with the locations and timing expected from those

sources.
5 - 1

5. EXAMPLE OF APPLICATION AND VALIDATION FOR PM2.5

This example demonstrates how the CMB applications and validation protocol is applied

to PM2.5 measurements from the Northern Front Range Air Quality Study (NFRAQS, Watson et

al., 1998). NFRAQS is a preview of future PM2.5 source apportionment studies that have high

stakes in terms of control strategy development. NFRAQS used a variety of the models

described in Section 2, including CMB, to determine the source categories and individual

emitters contributing to excessive contributions of primary suspended dust, carbon, and

secondary ammonium, sulfate, and nitrate.

The PM2.5 CMB data set is used for this example because it is one of the few data sets

that contains specific organic compounds in both source and receptor measurements. It is

anticipated that, as organic aerosol measurement methods are standardized and the cost of their

application decreases, these measurements will become more standard in future source

apportionment studies.

Each of the seven steps in the protocol is illustrated using examples from this data set. 

The CMB input files are available with the other test data sets on EPA’s website

(www.epa.gov/scram001 ; Appendix C). The NFRAQS source profiles may be useful for Level

I or Level II assessments in other airsheds in preparation for a more comprehensive source

apportionment study. 

5.1 Model Applicability

The requirements for CMB model applicability are: 1) a sufficient number of receptor

samples is taken with an accepted method to evaluate temporal and spatial variations; 2) samples

are analyzed for chemical species which are also present in source emissions; 3) potential source

contributors have been identified and chemically characterized; and 4) the number of

non-collinear source types is less than or equal to the number of measured species.

In NFRAQS, aerosol samples were taken by well-characterized methods and

measurements were fully evaluated (Chow et al., 1998). Samples were analyzed for 20 days

throughout the winter of 1996-97. Two (i.e., Welby and Brighton) of the nine sampling sites

acquired samples for organic aerosol analysis.

Table 5.1-1 shows an inventory that was especially compiled for NFRAQS, using

published emission factors (not specific to the Denver area) and different activity estimates. 

According to this inventory, the major sources were: 1) normal hot-stabilized gasoline-powered

vehicle exhaust; 2) gasoline-powered vehicle emitting visible smoke; 3) diesel exhaust; 4) meat

cooking; 5) wood combustion; 6) road dust and sanding; 7) secondary ammonium sulfate; 8)

secondary ammonium nitrate; and 9) industrial point sources, including coal-fired power

stations, refineries, etc. Owing to previous source testing (Watson et al., 1990b), it was believed

that cold starts of gasoline vehicles might be a significant contributor and that soft wood (used 

mostly in fireplaces because it offers a nice flame) and hard wood (used in wood stoves because

it heats more efficiently) might be distinguishable if the appropriate organic compounds were

measured. Samples were acquired by dilution sampling of vehicles on dynamometers, wood

burning on laboratory stoves, meat cooking in a laboratory kitchen, and grab sampling of

suspendable dust. These samples were analyzed in the laboratory for elements, ions, carbon,

organic aerosol compounds, and 14C using the same methods applied at the receptors. Profiles

from coal-fired electrical generation were available from a previous study in the area (Watson et

al., 1988).
5 - 2

Table 5.1-1 Wintertime Emissions Inventory for Denver Metro Area

Source emission rate estimates (tons/day)a

PM10 PM2.5 SO2 NOx NH3 VOC CO

Gas Exhaust 1.7 1.6 3.3 137.7 157.6 1340.8

Visibly Smoking Gas Exhaust 0.2 0.2

Diesel Exhaust 5.0 4.9 1.5 36.1 8.4 30.9

Off-Road Exhaust 1.8 1.8 1.7 27.4 14.3 111.7

Wood Burning 1.8 1.8 0.0 0.0

Road Dust & Sand 49.6 7.4 0.0 0.0

Coal Power Stations 1.3 0.7 62.1 64.3

Other Industries 7.8 2.6 16.7 47.8

Natural Gas 0.5 0.5 0.0 28.4

Unpaved Road Dust 28.2 4.2 0.0 0.0

Restaurant Cooking 1.4 1.4 0.0 0.0

Construction Dust 2.2 0.3 0.0 0.0

Biogenic 3.0 31.3 0.0

Industrial 34.8 22.3

Area Sources 1.0 89.8 72.3

Total 101.6 27.5 85.2 345.7 336.2 1578.0

a

Regional Air Quality Council "Review of Blueprint for Clean Air Emissions Inventories"; April 8, 1998.

Tables 5.1-2 and 5.1-3 identify and describe the profiles available in the NFRAQS data

base. A subset of these profiles was used for testing. The number of fitting species used in

CMB (about 80-85 species with organic species, about 20-25 species with conventional element,

ion, and carbon species) exceeds the number of source types (up to 11 source types). The CMB

model is applicable to source apportionment of this PM2.5 data base.
5 - 3

Table 5.1-2 Source Composition Profiles from NFRAQSPNO Mnemonic Size Type DescriptionN001 NWNSP F Composite Winter, non-smoker, Phase 1 minus Phase 2, L2, ML1, M1, M2, M3, H1N002 NWLP F Composite Winter, non-smoker, Phase l - Phase 2, L2, ML1, ML2N003 NWHP F Composite Winter, non-smoker, Phase l - Phase 2, M1, M3, H1N004 NWNSPI F Composite Winter, non-smoker, Phase 1 -UPI, MLIP1, MIPl, M2P1, M3P1, HIP1N005 NWNSP1mC F Composite Winter, non-smoker, Phase 1 - UPI, MLIP1, MIP1, M2P1, M3P1, HIP1 minus backup carN006 NWNSPIpC F Composite Winter, non-smoker, Phase 1 - L2P1, MLIP1, MIP1, M2P1, M3P1, HIP1 plus backup carbN007 NWNSP2 F Composite Winter, non-smoker, Phase 2-LlP2, L2P2, MLIP2, MIP2, M2P2, M3P2, HIP2, H2P2N008 NWNSP2mC F Composite Winter, non-smoker, Phase 2 - LlP2, L2P2, MLIP2, MIP2, M2P2, M3P2, HIP2, H2P2 minN009 NWNSP2pC F Composite Winter, non-smoker, Phase 2 - LIPI, L2P2, MLIP2, MIP2, M2P2, M3P2, HIP2, H2P2 plu

N010 NWSM F Composite Winter, smokers, S2P1, S2P2, S2P3, S3P1, S3P2, S3P3N011 NWSMmC F Composite Winter, smokers, S2P1, S2P2, S2P3, S3P1, S3P2, S3P3 minus backup carbonN012 NWSMpC F Composite Winter, smokers, S2P1, S2P2, S2P3, S3P1, S3P2, S3P3 plus backup carbonN013 NWHD F Composite Winter, heavy-duty diesel, runs 2-15N014 NWHDmC F Composite Winter, heavy-duty diesel, runs 2-15 minus backup carbonN015 NWHDpC F Composite Winter, heavy-duty diesel, runs 2-15 plus backup carbonN016 NWLCPI F Composite Winter, low emitter, phase 1 - UPI, MLIP1, M2P1N017 NWLCP2 F Composite Winter, low emitter, phase 2 - L2P2, MLIP2, M2P2N018 NWLCP3 F Composite Winter, low emitter, phase 3 - L2P3, MLIP3, M2P3N019 NWLCPC F Composite Winter, low emitter, FTP composite - L2PC, MLIPC, M2PCN020 NWL2PI F Individual Winter, low emitter, phase 1 - L2PIN021 NWL2P2 F Individual Winter, low emitter, phase 2 - L2P2N022 NWL2P3 F Individual Winter, low emitter, phase 3 - L2P3N023 NWL2C F Individual Winter, low emitter, FTP composite - L2PCN024 NWHCPI F Composite Winter, high emitter, phasel - MlP1, M3P1, HIPIN025 NWHCP2 F Composite Winter, high emitter, phase 2 - MIP2, M3P2, HIP2N026 NWHCP3 F Composite Winter, high emitter, phase 3 - M1 P3, M3P3, H1 P3N027 NWHCPC F Composite Winter, high emitter, FTP composite - MIPC, M3PC, H1 PCN028 NWHIPI F Individual Winter, high emitter, phase 1, HIPIN029 NWHIP2 F Individual Winter, high emitter, phase 2, HIP2N030 NWHIP3 F Individual Winter, high emitter, phase 3, HIP3N031 NWHIC F Individual Winter, high emitter, FTP composite, HIPCN032 NWnSPI F Composite Winter, non-smoker, phase 1, UPI, MLIP1, ML2P1, M2P1, MIP1, M3P1, HIP1N033 NWnSP2 F Composite Winter, non-smoker, phase 2, L2P2, MLIP2, ML2P2, M2P2, MIP2, M3P2, HIP2N034 NWnSP3 F Composite Winter, non-smoker, phase 3, L2P3, MLIP3, ML2P3, M2P3, MIP3, M3P3, HIP3N035 NWnSPC F Composite Winter, non-smoker, FT? composite, L2PC, MLIPC, ML2PC, M2PC, MIPC, M3PC, HIPN036 NWSCPI F Composite Winter, smoker, phase 1, SIP1, S2P1, S3P1N037 NWSCP2 F Composite Winter, smoker, phase 2, SIP2, S2P2, S3P2N038 NWSCP3 F Composite Winter, smoker, phase 3, SIP3, S2P3, S3P3N039 NWSCPC F Composite Winter, smoker, FTP composite, SIPC, S2PC, S3PCN040 NWSaPI F Composite Winter, smoker, phase 1, S2P1, S3P1N041 NWSaP2 F Composite Winter, smoker, phase 2, S2P2, S3P2N042 NWSaP3 F Composite Winter, smoker, phase 3, S2P3, S3P3N043 NWSaPC F Composite Winter, smoker, FTP composite, S2PC, S3PCN044 NWLDCPI F Composite Winter, light-duty diesel, phase 1, LDIPI, LD2P1, LD3P1, LD4Pl, LD5P1

N045 NWLDCP2 F Composite Winter, light-duty diesel, phase 2, LDIP2, LD2P2, LD3P2, LD4P2, LD5P2N046 NWLDCP3 F Composite Winter, light-duty diesel, phase 3, LDIP3, LD2P3, LD3P3, LD4P3, LD5P3N047 NWLDCPC F Composite Winter, light-duty diesel, FTP composite, LDIPC, LD2PC, LD3PC, LD4PC, LD5PC

N048 NWHDc F Composite Winter, heavy-duty diesel, runs 2-15 (all)N049 NWHDOc F Composite Winter, heavy-duty diesel, runs 2,5,8,10,16,17,24,32 (others)N050 NMc F Composite Composite of NMAHa, NMCH, NMCCa, and NMCKN051 NMAHa F Average 3 replicate samples, automated charbroiler, hamburger, samples MAHI, 2, and 3
5 - 4

Table 5.1-2 (cont.) Source Composition Profiles from NFRAQSPNO Mnemonic Size Type DescriptionN052 NMCH F Individual Charbroiled hamburger, sample MCHIN053 NMCCa F Average Charbroiled chicken w/skin, samples MCC1 and MCC2N054 NMCK F Individual Charbroiled steak, sample MCKIN055 NWFSc F Composite Fireplace burning soft woodsN056 NWFGPDa F Average Fireplace, Pine, samples WFGPD1, 2 and 3 with Grate, DryN057 NWFEND F Individual Fireplace, pinon, sample WFENDI, Empty, DryN058 NWFGAMD F Individual Fireplace, Apple/Mesquite, sample WFGAMDI, with Grate, DryN059 NWFGBD F Individual Fireplace, Bundled wood, sample WFGBDI, with Grate, DryN060 NWFHc F Composite Fireplace burning hard woodsN061 NWFEHD F Individual Fireplace, mixed Hardwood, sample WFEHDI, Empty, DryN062 NWFGHD F Individual Fireplace, mixed Hardwood, sample WFGHD2, with Grate, DryN063 NWFGOD F Individual Fireplace, Oak, sample WFGODI, with Grate, DryN064 NWFGOW F Individual Fireplace, Oak, sample WFGOWI, with Grate, WetN065 NWFGDD F Individual Fireplace, Duraflame, sample WFGDDI, with Grate, DryN066 NWSHc F Composite Woodstove burning hardwoodN067 NWSHDHH F Individual Woodstove, mixed hardwood, sample WSHDHHI, Dry, High fuel, High burnN068 NWSHDHL F Individual Woodstove, mixed hardwood, sample WSHDHLI, Dry, High fuel, Low burnN069 NWSHDLH F Individual Woodstove, mixed hardwood, sample WSHDLHI, Dry, Low fuel, High burnN070 NWSHDLL F Individual Woodstove, mixed hardwood, sample WSHDLLI, Dry, Low fuel, Low burnN071 NWSODHLa F Individual Woodstove, Oak, sample WSODHLI, Dry, High fuel, Low burnN072 NWSOWHL F Individual Woodstove, Oak, WSOWHL2, Wet, High fuel, Low burnN073 NRDC F Composite Composite roaddust, NRD01 to 05N074 NRDOI F Individual Jewell, w of Kendall on 10/25/96, sample 717N075 NRD02 F Individual Kipling at Federal on 3/3/97, sample 818N076 NRD03 F Individual Kipling at Federal on 3/2/97, sample 819N077 NRD04 F Individual Speer, Bannock to 11th on 12/21/96, sample 831N078 NRD05 F Individual Jewell, w of Kendall on 12/24/96, sample 800N079 AMSUL F Calculated Secondary ammonium sulfateN080 AMBSUL F Calculated Secondary ammonium bisulfateN081 AMNIT F Calculated Secondary ammonium nitrateN135 NSLCPI F Composite Summer, Light-Duty, GasoSummer, LineL2P1N136 NSLCP2 F Composite Summer, Light-Duty, GasoSummer, LineL2P2N137 NSLCP3 F Composite Summer, Light-Duty, GasoSummer, LineL2P3N138 NSLCPC F Composite Summer, Light-Duty, GasoSummer, LineL2PCN139 NSMCP1 F Composite Summer, Light-Duty, GasoSummer, LineMIPlN140 NSMCP2 F Composite Summer, Light-Duty, Gasosummer, LineM I P2N141 NSMCP3 F Composite Summer, Light-Duty, GasoSummer, LineMIP3N142 NSMCPC F Composite Summer, Light-Duty, GasoSummer, LineMIPCN143 NSHCPI F Composite Summer, Light-Duty, GasoSummer, LineHIPIN144 NSHCP2 F Composite Summer, Light-Duty, GasoSummer, LineHIP2N145 NSHCP3 F Composite Summer, Light-Duty, GasoSummer, LineHIP3N146 NSHCPC F Composite Summer, Light-Duty, GasoSummer, LineHIPCN147 NSSCPI F Composite Summer, Light-Duty, GasoSummer, LineSIPlN148 NSSCP2 F Composite Summer, Light-Duty, GasoSummer, LineSIP2N149 NSSCP3 F Composite Summer, Light-Duty, GasoSummer, L1neSIP3N150 NSSCPC F Composite Summer, Light-Duty, GasoSummer, L1neSIPCN151 NSLDCP1 F Composite Summer, Light-Duty, DieseILDIP1N152 NSLDCP2 F Composite Summer, Light-Duty, DieseILDIP2N153 NSLDCP3 F Composite Summer, Light-Duty, D1eseILDIP3N154 NSLDCPC F Composite Summer, Light-Duty, DieseILDIPC
5 - 5

Table 5.1-3 Source Composition Profiles from the 1987 Scenic Denver Study and Other StudiesPNO Mnemonic Size Type DescriptionN082 BRAKE B Individual radial tire (195/608-15 Toyo, 7200 milesN083 TRDST B IndividualN084 GPHWYCI F Composite 3 samples. Interstate highway.N085 GCYSTC3 F Composite 2 samples. City street near civic center.N086 GSCRDC4 F Composite 2 samples. Secondary paved roads.N087 GRSDMC5 F Composite 2 samples. Road sanding material.N088 GHWYTC6 F Composite 2 samples. Stapleton Tunnel.N089 GHWSTC7 F Composite 3 samples. Stapleton Tunnel and adjacent dirt.N090 GPRDVC8 F Composite 6 samples. Paved roads (general).N091 GUADVC9 F Composite 5 samples. Unpaved roads (general).N092 GAGSLC2 F Composite 2 samples. Agricultural soil.N093 MNDCC F Composite 9 samples. Dynamometer diesel, cold start.N094 MNDCS F Composite 9 samples. Dynamometer diesel, cold stabilized.N095 MNDCH F Composite 9 samples. Dynamometer diesel, hot transient.N096 MLCC F Composite 10 samples. Dynamometer leaded, cold start.N097 MLCS F Composite 10 samples. Dynamometer leaded, cold stabilized.N098 MLCH F Composite 9 samples. Dynamometer leaded, hot transient.N099 MUCCC F Composite 8 samples. Dynamometer unleaded, closed - loop catalyst, cold start.N100 MUCCS F Composite 4 samples. Dynamometer unleaded, closed - loop catalyst, cold stabilized.N101 MUCCH F Composite 3 samples. Dynamometer unleaded, closed - loop catalyst, hot transient.N102 MUOCC F Composite 14 samples. Dynamometer unleaded, oxidation catalyst, cold start.N103 MUOCS F Composite 8 samples. Dynamometer unleaded, oxidation catalyst, cold stabilized.N104 MUOCH F Composite 5 samples. Dynamometer unleaded, oxidation catalyst, hot transient.N105 MUCC F Composite 22 samples. Dynamometer unleaded, both catalyst types, cold start.N106 MUCS F Composite 12 samples. Dynamometer unleaded, both catalyst types, cold stabilized.N107 MUCH F Composite 8 samples. Dynamometer unleaded, both catalyst types, hot transient.N108 MD50U50S F Composite 21 samples. Dynamometer, 50% diesel, 50% unleaded, cold stabilizedN109 MD75U25S F Composite 21 samples. Dynamometer, 75% diesel, 25% unleaded, cold stabilizedN110 MD95U5S F Composite 21 samples. Dynamometer, 95% diesel, 5% unleaded, cold stabilizedNI I I ML50U50S F Composite 22 samples. Dynamometer, 50% leaded, 50% unleaded, cold stabilizedN112 ML25U75S F Composite 22 samples. Dynamometer, 25% leaded, 75% unleaded, cold stabilizedN113 ML5U95S F Composite 22 samples. Dynamometer, 5 % leaded, 95% unleaded, cold stabilizedN114 MD5L2U3S F Composite 31 samples. Dynamometer, 50% diesel, 20% leaded, 30% unleaded, cold stabilized.N115 MD7515US F Composite 31 samples. Dynamometer, 75% diesel, 15% leaded, 10% unleaded, cold stabilized.N116 MD851OUS F Composite 31 samples. Dynamometer, 85% diesel, 10% leaded, 5% unleaded, cold stabilized.N117 MD3035US F Composite 31 samples. Dynamometer, 30% diesel, 35% leaded, 35% unleaded, cold stabilized.N118 MD305OUS F Composite 31 samples. Dynamometer, 30% diesel, 50% leaded, 20% unleaded, cold stabilized.N119 PCHKC04 F Cherokee Pow. Pt., #4 boiler burning coal, mech. collector., elec. precipitator, wet scrubbeN120 PCOOROI F Adolph Coors Co., #5 boiler burning coal and brewery sludge, wet scrubber.N121 PCHKG03 F Cherokee Pow. Pt., #3 boiler burning natural gas, no control equipment.N122 PCHKC03 F Cherokee Pow. Pt., #3 boiler burning coal, bag house.N123 PCHCLCI F Composite, PCHKC03 & PCHKC04, boilers burning coal.N124 PCOALC2 F Composite, PCHKC03, PCHKC04 & PCOOROI, boilers burning coal.N125 CCRCSCI F Composite, 3 samples. Fluidized Catalytic Cracker (FCC) regenerator.N126 WFPLLOI F Fireplace, low burn rate.N127 WFPLH02 F Fireplace, high burn rate.N128 WWSIN03 F Fireplace insert (treated as woodstove).N129 WWSTC04 F Woodstove, thermostatically controlled.N130 WWSLF05 F Woodstove, large firebox.N131 WWSSF06 F Woodstove, small firebox.N132 WFIRECI F Composite of two fireplace tests.N133 WSTOVC2 F Composite of four woodstove tests.N134 WRWCBC3 F Composite of all six tests.
5 - 6

5.2 Initial Source Contribution Estimates

Initial CMB tests were performed to select a default combination of source profiles and

fitting species for the ambient data. These tests were performed to apportion carbon, as this was

a major focus of the NFRAQS. A preliminary set of source profiles consisting of at least one

source profile from each source category was applied to NFRAQS Winter 97 organic speciated

measurements. These initial apportionments were calculated in CMB’s batch mode. No attempt

was made to manually improve the apportionment. These tests were used to select a default set

of fitting species and examine the sensitivity of total carbon and PM2.5 apportionment to

alternative source profiles within source categories. Tables 5.2-1 and 5.2-2 show the results of

the sensitivity tests for meat cooking and wood combustion profiles, and motor vehicle profiles,

respectively, for the sample collected from the Welby sampling site on 01/17/97 at 0600 to 1200

MST. This sample had the highest PM2.5 and total carbon concentration during the study and

showed detectable contributions from all major sources in the initial CMB tests.

The range of the carbonaceous fraction explained by alternative individual wood stove

profiles (using hardwoods) was 5.7% to 21.7% of particulate carbon. Moisture content is the

primary reason for the variability in apportionment. The composite profile that was used in the

NFRAQS CMB as the default profile for this source category (NWSHc2) gives an average

contribution of 14.0%.

Fireplace combustion profiles (using softwoods) yield contributions of 1.2% carbon for

NWFGPDa and 4.4% for NWFEND. The composite of these two profiles (WFSc) gives a

carbon contribution that is equal to the variability in the apportionments for individual softwood

profiles.

With the exception of NWFGHD, the individual fireplace/hardwood profiles yield similar

apportionments in the range of 0.7% to 0.8% carbon. The NWFGHD apportionment is a

statistical outlier, and is excluded from the composite profile (NWFHc) for this source category. 

This composite profile gives an average apportionment of 0.8% carbon, which is similar to all

other profiles within the composite. Apple and mesquite are both hardwoods, and their profile

yields a carbon apportionment consistent with the majority of the hardwood profiles.

Synthetic log yields the highest apportionment among the fireplace profiles, with an

average of 47% carbon. This profile is composed of an abundance of elemental carbon, with a

lack of other key "marker" compounds. The high elemental carbon content of the synthetic log

causes it to be collinear with the diesel profiles, which is why the apportionment for this profile

is so high.

Several wood combustion profiles were used together in order to examine the potential

for collinearity among subcategories of wood combustion. The softwood composite (NWFSc)

gives similar apportionments with all alternative hardwood profiles regardless of the type of

appliance, wood stove or fireplace. This softwood composite profile is not collinear with any

other wood combustion profiles. Using the fireplace/hardwood composite profile with the wood

stove/hardwood composite profile results in negative source contributions for the

fireplace/hardwood profile. This fireplace/hardwood profile also causes an overall increase in

the average predicted apportionment for wood stove. This indicates collinearity between the

profiles for hardwood combustion in fireplaces and wood stoves. Because the contribution to
5 - 7

Test Base W01 W02 W03 W04 W05 W06 W07 Concentration of TC (ug/m3) 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21R-squared 0.93469 0.93289 0.92899 0.93299 0.93631 0.92211 0.92392 0.93393 0Chi-squared 0.48714 0.49703 0.52859 0.49595 0.46708 0.57092 0.56866 0.48505 Percent mass 99.56697663 99.40744659 99.32327338 99.14931232 99.51277911 99.08973217 99.23200067 98.82333834 98Absolute Contribution (ugTC/m3) Profile

 LDGV, cold start nvnsp 3.21 ± 0.28 3.24 ± 0.28 3.14 ± 0.27 3.46 ± 0.30 3.82 ± 0.33 3.44 ± 0.30 3.31 ± 0.29 3.43 ± 0.30 3. LDGV, hot stabilized nvnsp2 0.80 ± 0.05 0.76 ± 0.05 0.71 ± 0.05 0.71 ± 0.05 0.81 ± 0.05 0.67 ± 0.04 0.64 ± 0.04 0.67 ± 0.04 0. LDGV, high particle emitter nvsm 5.23 ± 0.21 5.65 ± 0.23 6.72 ± 0.27 5.53 ± 0.22 3.07 ± 0.12 7.35 ± 0.30 7.33 ± 0.30 7.41 ± 0.30 7. Diesel Exhaust nwhdc 7.03 ± 0.28 6.92 ± 0.27 7.02 ± 0.28 6.87 ± 0.27 6.45 ± 0.26 6.86 ± 0.27 6.41 ± 0.25 6.89 ± 0.27 6. Meat composite nmc 1.94 ± 0.15 1.98 ± 0.16 2.02 ± 0.16 2.03 ± 0.16 2.07 ± 0.16 2.19 ± 0.17 2.17 ± 0.17 2.18 ± 0.17 2. Road or geologic dust nrdc 0.56 ± 0.18 0.57 ± 0.18 0.57 ± 0.18 0.57 ± 0.18 0.55 ± 0.18 0.59 ± 0.19 0.59 ± 0.19 0.57 ± 0.18 0. Coal power stations pchclc1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0. Fireplace, softwood composite nwfsc -0.33 ± 0.11 Wood stove hardwood composite nwshc2 3.01 ± 0.14 2.32 ± 0.11 Wood stove hardwood nwshdhl 1.22 ± 0.08 Wood stove oak nwsodhla 2.20 ± 0.15 Wood stove wet oak nwsowhl 4.68 ± 0.30 Fireplace, pine average nwfgpda 0.26 ± 0.02 Fireplace, pinion-no grate nwfend 0.94 ± 0.05 Fireplace, apple/mesquite nwfgamd 0.14 ± 0.01 Fireplace, bundled wood nwfgbd 0. Fireplace, hardwood composite nwfhc Test Base W10 W11 W12 W13 W14 W15 W16 Concentration of TC (ug/m3) 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21R-squared 0.93469 0.9299 0.93048 0.93243 0.93403 0.93187 0.93396 0.93392 0Chi-squared 0.48714 0.51275 0.51143 0.49398 0.48274 0.54105 0.49936 0.50149 0Percent mass 99.56697663 98.91832322 98.90180411 98.81614604 98.79851328 101.3780927 99.12935947 98.99405126 100Absolute Contribution (ugTC/m3) Profile LDGV, cold start nvnsp 3.21 ± 0.28 3.47 ± 0.30 3.34 ± 0.29 3.47 ± 0.30 3.44 ± 0.30 2.96 ± 0.26 3.39 ± 0.29 3.42 ± 0.30 3. LDGV, hot stabilized nvnsp2 0.80 ± 0.05 0.67 ± 0.04 0.68 ± 0.04 0.67 ± 0.04 0.67 ± 0.04 0.82 ± 0.05 0.72 ± 0.05 0.69 ± 0.05 0. LDGV, high particle emitter nvsm 5.23 ± 0.21 7.36 ± 0.30 7.50 ± 0.30 7.30 ± 0.30 7.37 ± 0.30 4.49 ± 0.18 6.34 ± 0.26 6.69 ± 0.27 3. Diesel Exhaust nwhdc 7.03 ± 0.28 6.88 ± 0.27 6.93 ± 0.27 6.88 ± 0.27 6.89 ± 0.27 -0.50 ± 0.02 6.97 ± 0.28 6.91 ± 0.27 1. Meat composite nmc 1.94 ± 0.15 2.18 ± 0.17 2.15 ± 0.17 2.22 ± 0.18 2.19 ± 0.17 3.31 ± 0.26 2.09 ± 0.17 2.12 ± 0.17 2. Road or geologic dust nrdc 0.56 ± 0.18 0.58 ± 0.19 0.58 ± 0.18 0.58 ± 0.19 0.59 ± 0.19 0.60 ± 0.19 0.57 ± 0.18 0.56 ± 0.18 0. Coal power stations pchclc1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0. Fireplace, softwood composite nwfsc -0.33 ± 0.11 -0.24 ± 0.08 -0.09 ± 0.03 -0 Wood stove hardwood composite nwshc2 3.01 ± 0.14 1.43 ± 0.07 0.92 ± 0.04 2. Fireplace, apple/mesquite nwfgamd 0.10 ± 0.01 Fireplace, bundled wood nwfgbd Fireplace, hardwood composite nwfhc 0.10 ± 0.01  Fireplace, hardwood-no grate nwfehd 0.17 ± 0.01 Fireplace, hardwood nwfghd 0.13 ± 0.01 Fireplace, oak nwfgod 0.17 ± 0.01 Fireplace, wet oak nwfgow 0.15 ± 0.01 Fireplace, synthetic log nwfgdd 10.18 ± 0.60 8.

Table 5.2-1a Sensitivity of Total Carbon Apportionment to Alternative Wood Combustion Profile

01/17/97 at 0600 to 1200 MST)
5 - 8

Test Test Base M01 M02 M03 Concentration of TC (ug/m3) conc 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 21.55 ± 1.38 R-squared rsquar 0.93 0.93 0.93 0.94 Chi-squared chisquar 0.49 0.50 0.49 0.48 

Percent mass pcmass 99.6 99.9 99.6 99.5 Absolute Contribution (ugTC/m3)

 LDGV, cold start nvnsp 3.21 ± 0.28 3.23 ± 0.28 3.40 ± 0.29 3.09 ± 0.27  LDGV, hot stabilized nvnsp2 0.80 ± 0.05 0.82 ± 0.05 0.82 ± 0.05 0.78 ± 0.05  LDGV, high particle emitter nvsm 5.23 ± 0.21 4.51 ± 0.18 4.45 ± 0.18 5.99 ± 0.24 

 Diesel Exhaust nwhdc 7.03 ± 0.28 7.08 ± 0.28 6.96 ± 0.28 7.09 ± 0.28  Fireplace, softwood composite nwfsc -0.33 ± 0.11 -0.32 ± 0.11 -0.33 ± 0.11 -0.33 ± 0.11  Wood stove hardwood composite nwshc2 3.01 ± 0.14 2.93 ± 0.14 3.04 ± 0.14 3.02 ± 0.14  Road and geologic dust nrdc 0.56 ± 0.18 0.55 ± 0.17 0.55 ± 0.18 0.57 ± 0.18  Coal power station pchclc1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00  Meat composite nmc 1.94 ± 0.15 Hamburger, automated charbroiler nmaha 2.72 ± 0.18 Hamburger, under-fired charbroiler nmch 2.58 ± 0.17 Chicken, under-fired charbroiler nmcca 1.24 ± 0.09 Steak, under-fired charbroiler nmck 

Table 5.2-1b Sensitivity of Total Carbon Apportionment to Alternative Meat Cooking Prof

(Welby, 01/17/97 at 0600 to 1200 MST)
5 - 9

Profile Base CS1 CS2 CS3 CS4 

Concentration of TC (ug/m3) 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 R-square 0.96 0.95 0.95 0.96 0.96 Chi-square 0.27 0.47 0.38 0.31 0.31 Percent mass 99.1 102.1 100.3 99.5 99.6 Absolute Contribution (ug/m3)

 LDGV (cold start, l) nvcsl2 1.37 ± 0.11 LDGV (cold start, l,ml) nvlp 1.61 ± 0.18 LDGV (cold start, l,ml,m) nvcslm 2.67 ± 0.25 LDGV (cold start, l,ml,m) nvcslml 2.13 ± 0.24 LDGV (cold start, l,ml,m,h) nvnsp 3.50 ± 0.30 LDGV (cold start, m,h) nvhp  LDGV (hot stabilized, l,ml,m,h) nvnsp2 0.68 ± 0.04 0.58 ± 0.04 0.67 ± 0.04 0.69 ± 0.04 0.66 ± 0.04  LDGV (phase 1,2&3, s) nvsm 6.54 ± 0.27 9.78 ± 0.40 8.88 ± 0.36 7.09 ± 0.29 7.61 ± 0.31  HD diesel nwhdc 6.80 ± 0.27 7.80 ± 0.31 7.75 ± 0.31 7.40 ± 0.29 7.45 ± 0.30  Meat cooking nmc 2.17 ± 0.17 0.91 ± 0.07 1.19 ± 0.09 1.93 ± 0.15 1.97 ± 0.16  Wood (fireplace, softwood) nwfsc 0.07 ± 0.02 0.12 ± 0.04 0.16 ± 0.05 0.09 ± 0.03 0.10 ± 0.03  Wood (woodstove, hardwood) nwshc2 1.08 ± 0.05 0.86 ± 0.04 0.81 ± 0.04 1.06 ± 0.05 1.05 ± 0.05  Road dust/geological nrdc 0.50 ± 0.16 0.56 ± 0.18 0.55 ± 0.17 0.51 ± 0.16 0.51 ± 0.16  Coal-fired power station pchclc1 0.00 ± 0.00 0.02 ± 0.02 0.01 ± 0.01 0.01 ± 0.01 0.01 ± 0.01 

Profile CS6 CS7 CS8 CS9 CS10 

Concentration of TC (ug/m3) 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 R-square 0.96 0.96 0.96 0.97 0.96 Chi-square 0.34 0.28 0.28 0.21 0.28 Percent mass 102.9 102.8 100.0 98.6 103.8 Absolute Contribution (ug/m3)

 LDGV (cold start, h) nvcsh1 3.08 ± 0.12 LDGV (cold start, s,h) nvcssh 8.99 ± 0.44 LDGV (cold start, s) nvcss 12.61 ± 0.48 LDGV (phase 1, l,ml,m,h) nvnsp1 7.49 ± 0.31 LDGV (phase 1, s,h) nvp1sh 9.71 ± 0.43 LDGV (phase 1, s) nvp1s  LDGV (hot stabilized, l,ml,m,h) nvnsp2 0.91 ± 0.06 0.63 ± 0.04 0.54 ± 0.04 0.66 ± 0.04  LDGV (phase 1,2&3, s) nvsm 10.02 ± 0.41 6.58 ± 0.27 2.05 ± 0.08 4.99 ± 0.20 8.36 ± 0.34  HD diesel nwhdc 5.92 ± 0.23 5.26 ± 0.21 6.50 ± 0.26 4.62 ± 0.18 4.86 ± 0.19  Meat cooking nmc 0.83 ± 0.07 -0.51 ± 0.04 -1.32 ± 0.10 2.52 ± 0.20 -2.04 ± 0.16  Wood (fireplace, softwood) nwfsc 0.23 ± 0.07 0.25 ± 0.08 0.29 ± 0.10 0.03 ± 0.01 0.40 ± 0.13  Wood (woodstove, hardwood) nwshc2 0.61 ± 0.03 0.41 ± 0.02 0.30 ± 0.01 1.14 ± 0.05 -0.15 ± 0.01  Road dust/geological nrdc 0.56 ± 0.18 0.54 ± 0.17 0.55 ± 0.18 0.47 ± 0.15 0.57 ± 0.18  Coal-fired power station pchclc1 0.01 ± 0.01 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 

Table 5.2-2a Sensitivity of Total Carbon Apportionment to Alternative Cold-Start Profiles

(Welby, 01/17/97 at 0600 to 1200 MST)
5 - 10

Profile Base HS1 HS2 HE1 HE2 

Concentration of TC (ug/m3) 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 R-square 0.96 0.96 0.97 0.97 0.96 Chi-square 0.27 0.29 0.20 0.18 0.26 Percent mass 99.1 100.9 99.2 92.6 93.2 Absolute Contribution (ug/m3)

 LDGV (cold start, l,ml,m,h) nvnsp 3.50 ± 0.30 5.60 ± 0.49 2.74 ± 0.24 2.90 ± 0.25 5.60 ± 0.49  LDGV (phase2, l) nvhslc 0.87 ± 0.07 LDGV (phase2, l,ml,m,h) nvnsp2 0.68 ± 0.04 -0.94 ± 0.06 0.78 ± 0.05  LDGV (phase2, h) nvhshc 2.42 ± 0.15 LDGV (phase2, h,s) nvp2sh 8.83 ± 0.39 LDGV (phase2, s) nvp2s 0.13 ± 0.01 LDGV (cold & phase2, s) nvcshs  LDGV (phase123, s) nvsm 6.54 ± 0.27 6.36 ± 0.26 6.39 ± 0.26 HD diesel nwhdc 6.80 ± 0.27 5.32 ± 0.21 5.99 ± 0.24 4.67 ± 0.19 5.97 ± 0.24  Meat cooking nmc 2.17 ± 0.17 2.03 ± 0.16 2.17 ± 0.17 2.76 ± 0.22 5.61 ± 0.44  Wood (fireplace, softwood) nwfsc 0.07 ± 0.02 0.07 ± 0.02 0.09 ± 0.03 0.03 ± 0.01 -0.07 ± 0.02  Wood (woodstove, hardwood) nwshc2 1.08 ± 0.05 1.01 ± 0.05 1.06 ± 0.05 1.19 ± 0.06 1.59 ± 0.07  Road dust/geological nrdc 0.50 ± 0.16 0.48 ± 0.15 0.51 ± 0.16 0.50 ± 0.16 0.47 ± 0.15  Coal-fired power station pchclc1 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 

Profile HE4 HE5 HE6 HE7 HE8 

Concentration of TC (ug/m3) 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 R-square 0.96 0.97 0.97 0.97 0.97 Chi-square 0.27 0.18 0.16 0.14 0.15 Percent mass 93.3 95.8 94.6 94.6 97.0 Absolute Contribution (ug/m3) LDGV (cold start, l,ml,m,h) nvnsp 4.95 ± 0.43 0.12 ± 0.01 0.13 ± 0.01 0.20 ± 0.02 -0.04 ± 0.00  LDGV (phase2, l,ml,m,h) nvnsp2 0.75 ± 0.05 0.66 ± 0.04 0.71 ± 0.05 -0.60 ± 0.04 0.75 ± 0.05  LDGV (cold & phase3, s) nvscsp2 1.03 ± 0.04 LDGV (cold & phase23, s) nvcshsa 11.76 ± 0.45 LDGV (phase12, s) nvp1hs 11.27 ± 0.43 LDGV (phase123, h,s) nvsh 13.51 ± 0.62 LDGV (phase123, h,s) nvp1hsa 12.29 ± 0.47 LDGV (phase123, s) nvs  HD diesel nwhdc 6.20 ± 0.25 5.39 ± 0.21 5.50 ± 0.22 4.01 ± 0.16 5.49 ± 0.22  Meat cooking nmc 5.22 ± 0.41 1.21 ± 0.10 1.26 ± 0.10 1.71 ± 0.14 0.91 ± 0.07  Wood (fireplace, softwood) nwfsc -0.10 ± 0.03 0.12 ± 0.04 0.14 ± 0.05 0.07 ± 0.02 0.14 ± 0.05  Wood (woodstove, hardwood) nwshc2 1.59 ± 0.07 0.87 ± 0.04 0.81 ± 0.04 1.01 ± 0.05 0.81 ± 0.04  Road dust/geological nrdc 0.47 ± 0.15 0.51 ± 0.16 0.56 ± 0.18 0.48 ± 0.15 0.54 ± 0.17  Coal-fired power station pchclc1 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 

Table 5.2-2b Sensitivity of Total Carbon Apportionment to Alternative Hot-Stabilized and 

Particle Emitter Profiles (Welby, 01/17/97 at 0600 to 1200 MST)
5 - 11

Profile Base no OC no EC no OC & EC no H

Concentration of TC (:g/m3) 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 2

R-square 0.96 0.96 0.96 0.95 Chi-square 0.27 0.25 0.27 0.26 Percent mass 99.1 80.5 83.2 72.9 Absolute Contribution (:g/m3)

 LDGV, cold start nvnsp 3.50 ± 0.30 4.62 ± 0.40 3.38 ± 0.29 4.14 ± 0.36 3

 LDGV, hot stabilized nvnsp2 0.68 ± 0.04 0.82 ± 0.05 0.70 ± 0.05 0.79 ± 0.05 0

 LDGV, high particle emitter nvsm 6.54 ± 0.27 1.49 ± 0.06 6.67 ± 0.27 3.28 ± 0.13 6

 Diesel Exhaust nwhdc 6.80 ± 0.27 6.55 ± 0.26 2.50 ± 0.10 2.83 ± 0.11 6 Meat Cooking nmc 2.17 ± 0.17 2.22 ± 0.18 3.03 ± 0.24 3.01 ± 0.24 2 Wood combustion, softwood nwfsc 0.07 ± 0.02 0.06 ± 0.02 0.08 ± 0.03 0.07 ± 0.02 0 Wood combustion, hardwood nwshc2 1.08 ± 0.05 1.12 ± 0.05 1.06 ± 0.05 1.11 ± 0.05 1 Road dust/geological nrdc 0.50 ± 0.16 0.48 ± 0.15 0.51 ± 0.16 0.49 ± 0.16 0 Coal-fired power station pchclc1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0Test Profile no MeO-Phenols no spec organics with PAH (g) with brake wConcentration of TC (:g/m3) 21.6 ± 1.4 21.6 ± 1.4 21.6 ± 1.4 21.55 ± 1.38 21

R-squared 0.97 1.00 0.88 0.93502 Chi-squared 0.27 0.04 0.58 0.5028 Percent mass 99.2 108.8 99.54957589 99Absolute Contribution (:gTC/m3)

 LDGV, cold start nvnsp 3.29 ± 0.29 -10.65 ± 0.93 12.19 ± 1.06 3.09 ± 0.27 3

 LDGV, hot stabilized nvnsp2 0.65 ± 0.04 11.67 ± 0.76 -0.34 ± 0.02 0.79 ± 0.05 0

 LDGV, high particle emitter nvsm 7.20 ± 0.29 47.54 ± 1.93 5.61 ± 0.23 5.77 ± 0.23 3

 Diesel Exhaust nwhdc 6.31 ± 0.25 -73.95 ± 2.93 3.33 ± 0.13 7.04 ± 0.28 6 Meat composite nmc 2.15 ± 0.17 -31.50 ± 2.49 2.06 ± 0.16 1.45 ± 0.12 1 Fireplace, softwood composite nwfsc 1.73 ± 0.57 223.95 ± 73.84 0.09 ± 0.03 -0.33 ± 0.11 -0

 Wood stove hardwood composite nwshc2 -0.45 ± 0.02 1.7977E+308 0.01 ± 0.00 3.04 ± 0.14 3 Road and geologic dust nrdc 0.51 ± 0.16 0.57 ± 0.18 0.51 ± 0.16 0.52 ± 0.17 0 Coal power station pchclc1 0.00 ± 0.00 -0.03 ± 0.03 -0.01 ± 0.01 -0.01 ± 0.01 0 Brake wear brake 0.10 ± 0.02 Tire wear trdst 2

Table 5.2-2c Sensitivity of Total Carbon Apportionment to Fitting Species (Welby, 01/17/97 

to 1200 MST)
5 - 12

ambient carbon predicted from the fireplace/hardwood profile is negligible (0.5%), the wood

stove/hardwood profile was selected as the default hardwood profile.

The fractions of ambient fine particles attributed to five alternative meat cooking profiles

range from 5.7% to 12.6% of total ambient PM2.5 carbon with an average of 9.7%. This average

is consistent with the apportionment results from the composite of all of these meat profiles

(nmc), which was 9.0%. Because each of the alternative meat cooking profiles is collinear with

each other, the composite profile, NMc, was selected as the default meat cooking profile.

Sensitivity tests were performed to examine the effect of alternative LDGV cold start

profiles (Table 5.2-2a) and alternative LDGV hot stabilized and high particle emitter profiles

(Tale 5.2-2b) on the apportionment using a common set of default profiles for non-vehicular

sources. The base case represents the set of default profiles used in the NFRAQS "extended

species" CMB runs.

Each of the alternative LDGV cold start profiles was used individually with the default

set of profiles. Tests CS1 to CS8 in Table 5.2-2 show the results for alternative incremental cold

start profiles (i.e., FTP Phase 1 minus Phase 3), and are arranged according to increasing

composite emission rates. The apportionment of total PM2.5 carbon ranges from 6.2% for

incremental cold starts for low emitters to a high of 59% for visible smokers. The increase in

contribution of cold starts with the inclusion of visible smokers is mostly at the expense of the

high particle emitter category, which is comprised of visible smoking vehicles in both cold start

and hot stabilized modes. Among the alternative incremental cold start profiles for non-smoking

vehicles, the range in apportionment is 6.2% to 25.2%. The default profile for this category

(nvnsp), which is an average of all non-smoking vehicles, gives an apportionment of 16.4% for

cold start emissions.

In general, the apportionment (SCE) for other carbon sources varies less with alternative

cold start profiles. The corresponding ranges in LDGV hot stabilized emissions, LDGV high

particle emitters, diesel exhaust, meat cooking and wood combustion are 2.7% to 4.1%, 26.3% to

45.2%, 27% to 36%, 3.7% to 10.2%, 3.8% to 5.3%, respectively. In comparison to the default

incremental cold start profile, the corresponding Phase 1 profile yields 35.2% contribution with

zero hot stabilized emissions and lower contributions for LDGV high particle emitters and diesel

exhaust. Compared to the profile for Phase 1, the incremental cold start is more distinguishable

(chemically) from high emitters and non-smoking hot stabilized emissions.

All non-smoker Phase 2 profiles give lower carbon contributions than any of the Phase 1

profiles, regardless of the emitter category. With the exception of one sample, all of the

alternative smoker profiles give about the same apportionment regardless of phase. The

differences in apportionment between Phases 1 and 2 are within 15% and even lower with Phase

3. While the relative apportionment among the three spark-ignition profiles (i.e., Phase 1, Phase

2, and smoker) vary with alternative profiles for smokers, the amount apportioned to the default

meat cooking profile is relatively insensitive to the use of alternative smoker profiles.

The initial CMB tests done for heavy-duty diesel exhaust showed that apportionment

(SCE) of diesel exhaust is relatively insensitive to the differences in elemental carbon (EC)

abundances in the profile. Diesel exhaust with relative abundances of EC of 86% and 63% show

differences in apportionment (SCE) of total carbon of 10%. In addition to EC, the CMB
5 - 13

sensitivity matrix shows that particulate PAHs, especially methyl- and dimethyl-phenanthrene,

have strong influence on the apportionment.

Table 5.2-2c shows the effects of using alternative sets of fitting species on the

apportionment. The default fitting species included inorganic species and particle-phase organic

species (particulate PAH, methoxy phenols, lactones, sterols, hopanes, and steranes) with R/U

ratios between -2 and +2. The tests included the following changes to the default set of fitting

species: 1) no organic carbon (OC); 2) no EC; 3) no OC and EC; 4) no hopanes and steranes; 5)

no lactones and sterols; 6) no phenols; 7) no organic species; and 8) addition of gas-phase PAHs.

Removing OC from the set of fitting species reduces the apportionment of LDGV high

particle emitter from 30.6% to 8.6%. However the PERCENT MASS is reduced from 99.1% to

80.5%, and a slight increase results in the other three vehicle exhaust profiles. Although organic

carbon is the major component in the LDGV high emitter profile, it is incorrect to assume that

the apportionment of this source is keyed simply on organic carbon. This statement would be

true if the smoker profile is the only organic carbon source used in the fit. There are seven other

sources of organic carbon that are used in the apportionment for NFRAQS.

The CMB sensitivity matrices show that organic carbon has the greatest influence on

apportionment of LDGV high particle emitter and has little effect on the other sources of organic

carbon. However, contributions of LDGV cold starts are strongly influenced by phenanthrene,

fluorene, methylfluorene isomers, and heavy PAHs. Hot-stabilized particulate emissions from

non-smokers are influenced by methylfluorene isomers, and methylphenanthrene and

dimethylphenanthrene isomers. Lactones influence the apportionment for meat cooking and

guiacols and syringols affect apportionments of softwood and hardwood combustion,

respectively. Diesel exhaust and road dust are predominantly influenced by elemental carbon

and crustal elements, respectively. The attributions of carbon to the other seven sources are all

influenced by species other than organic carbon.

Removing EC from the set of fitting species reduces the apportionment of diesel exhaust

from 31.9% to 13.9%. As with the previous case with OC, the PERCENT MASS is reduced (to

83.2%) and contributions of LDGV high particle emitter and meat cooking increase by small

margins. Removing both OC and EC cause decreases in contributions of both LDGV high

particle emitter and diesel exhaust and correspondingly larger decrease in the percent of mass

attributed (72.9%).

Removing hopanes and steranes has negligible effect on the apportionment. The

effective variance weighted solutions in CMB8 gives greater influence to chemical species with

lower uncertainty in both source and ambient measurements. As ambient levels approach

detection levels for marker compounds, as was the case for these species, their influence on the

CMB fit decreases.

According to the CMB sensitivity matrix, the expected marker species (i.e., lactones and

sterols) have the greatest influence on the apportionment of meat cooking. Removing them

resulted in an unexpected increase in apportionment for meat cooking and offsetting decrease in

the prediction for LDGV high particle emitters. The long-chain g-lactones and cholesterol are

considered “marker” species for meat cooking. However, motor vehicles were also found to

emit “lactones”, which raised questions regarding their proper identification. Since some of the

light-duty gasoline and heavy-duty diesel vehicle exhaust samples, when analyzed by electron
5 - 14

impact/mass spectrometry (EI/MS), showed an m/z 85 ion (characteristic of lactones) at the same

gas chromatographic retention time that correspond to some of the lactones, they were

re-analyzed by chemical ionization/mass spectrometry (CI/MS) in order to confirm their identity.

None of the light-duty gasoline vehicle exhaust samples contains detectable amounts of

lactones. All four lactones were found in heavy-duty diesel exhaust samples, but in much lower

amounts than quantified by EI/MS. Six ambient samples (three from the Welby site and three

from the Brighton site) were also re-analyzed for lactones using the CI/MS technique. All

lactones, previously quantified by EI/MS in the ambient samples, were also identified and

quantified by the CI/MS technique. Since all ambient samples were quantified by the EI/MS

technique using the m/z 85 ion, some of the compounds emitted by motor vehicles could

“artifactually” contribute to the intensity of this ion. Thus, lactones were retained in all motor

vehicle profiles, whether or not they were truly lactones. This situation is analogous to the

application of “organic” carbon in CMB which contain a variety of unidentified organic

compounds. The lactones are also imprecisely quantified, as reflected in measurement

uncertainty estimates that approach or exceed 30% of their concentrations. Owing to these large

uncertainties, the CMB8 effective variance solution reduces their influence on the apportionment

relative to more precisely measured components. When lactones are removed as fitting species,

however, the standard error of source contribution estimates for meat cooking increases.

Removing methoxyphenols from the fit results in a shift of the attribution from hardwood

to softwood. This is expected since there are other markers for softwood (e.g., retene and 1,7

dimethylphenanthrene) while syringols are the primary markers for hardwoods. Including

gas-phase PAH in the set of fitting species causes significant increase in the cold start

contribution and an overestimation of mass because gas-phase organic species are generally not

correlated with particulate mass.

Removing all organic species from the fit results in complete breakdown of CMB fit due

to significant collinearity that results among the subcategories of motor vehicle as well as wood

and meat combustion profiles. Tests were conducted using “conventional” species, which

include only total organic carbon, elemental carbon, inorganic ions (nitrate, sulfate, ammonium),

and elements. LDGV profiles were combined into one composite profile and meat cooking and

wood combustion were combined for the conventional CMB. The effective variance weighted

solutions in CMB8 uses all available chemical measurements, not just “tracer” species, and gives

greater influence to chemical species with lower uncertainty in both source and ambient

measurements.

As ambient levels approach detection levels for marker compounds, as was the case for

many of the samples from Brighton, their influence on the CMB fit decreases. This situation

could lead to overestimation of LDGV high particle emitter and underestimation of other sources

of organic carbon, and could explain the differences that exist between the “extended” and

“conventional” CMB results for Brighton. The low-concentration samples from Brighton

contain less than 1 or 2 :g/m3

 of total carbon and are associated with transport from the north

rather than from the Denver area, and are not representative of the urban source mix. In contrast,

the samples from Welby are more representative of the urban source mix, and typically contain

levels of total particulate carbon that allow for quantitative determination of organic markers. 

The “extended” and “conventional” CMB results are consistent with each other, and

comparisons with isotopic carbon measurements are more consistent for this site than for
5 - 15

Brighton. The “conventional” CMB results for CAMP and Highlands indicate that results for

Welby are likely representative of the Denver urban area.

Because tire wear and brake wear were not tested as part the NFRAQS study, chemical

composition profiles were developed for these source from data published by Hildemann et al.

(1991) and Rogge et al. (1993c). Hildemann et al. (1991) reports elemental data for tire wear

and brake wear. Organic data were obtained from Rogge et al (1993b). The profiles were

derived by converting the emission data into weight fractions normalized to total measured fine

particle mass. A nominal uncertainty of 20% was applied to the weight fractions. Table 5.2-2c

shows the average source contribution estimates for tire dust and brake wear for the sample that

was examined as part of the sensitivity tests of alternative source profiles. The contributions of

brake wear is negligible (<1%). Tire wear is about 10% of the total carbon. However, the

apportionment of PM2.5 has high standard errors, which indicates that there is a high degree of

uncertainty and/or collinearity in these profiles. These profiles were not used in the final

NFRAQS apportionment.

5.3 Model Outputs and Performance Measures

Nearly 1,000 individual CMB calculations were performed for NFRAQS in various

sensitivity tests. Apportionment of the NFRAQS ambient data included 132 apportionments

using the “extended” data sets that include specific organic compounds measured at the Welby

and Brighton sites of 6-hour or 12-hour durations for the Winter 97 samples. The CMB model

was also applied to 150 24-hour average “conventional” data sets from all seven NFRAQS

Winter 97 sites that included the elemental, ionic, and elemental/organic carbon concentrations

that are most commonly measured on source and receptor samples. This allowed for comparison

of source contribution estimates derived from the “extended” and “conventional” CMB

calculations for the Welby and Brighton data.

For these source apportionments, R-SQUARE typically exceeded 0.9 and CHI-SQUARE

values typically ranged from 0.3 and 0.6. PERCENT MASS values for organic carbon,

elemental carbon, total carbon, and PM2.5 were within one standard deviation of 100% most of

the time. 

5.4 Deviations from Model Assumptions

Assumptions 1 and 2 of the CMB model specify that the compositions of source

emissions are constant over the period of ambient and source sampling, and that chemical

species do not react with one another. Once released into the atmosphere, primary emissions are

subjected to dispersion and transport and, at the same time, to various physical and chemical

processes that determine their ultimate environmental fate. Primary emissions from motor

vehicles, residential wood combustion, meat cooking, etc., are complex mixtures containing

thousands of organic and inorganic constituents in the gas and particulate phases.

These compounds have different chemical reactivities and are removed by dry and wet

deposition processes at varying rates. Some of the gaseous species, by a series of chemical

transformations, are converted into particles, forming secondary aerosol. Sulfates and nitrates

are the most common secondary particles, though a fraction of organic carbon can also result

from VOC via atmospheric reactions.
5 - 16

While the mechanisms and pathways for inorganic secondary particles are fairly well

known, those for secondary organic aerosols are not well understood. Hundreds of precursors

are involved in these reactions, and the rates at which these particles form are highly dependent

on the concentrations of other pollutants and meteorological variables. Organic compounds

present in the gas phase undergo atmospheric transformation through reactions with reactive

gaseous species such as OH* radicals, NO3

*

 radicals, or O3.

Atmospheric lifetimes can be estimated for several organic compounds in direct

gas-phase emissions due to known tropospheric chemical removal reactions (Atkinson, 1988). 

These lifetimes (i.e., the time for the compound to decay to 1/e or 37% of its original

concentration) are calculated from the corresponding measured reaction rate constants and the

average ambient concentration of the tropospheric species involved. Although the individual

rate constants are known to a reasonable degree of accuracy (in general, to within a factor of

two), the tropospheric concentrations of these key reactive species are much more uncertain.

For example, the ambient concentrations of OH*

 radicals at any given time and/or

location are uncertain to a factor of at least five, and more likely ten (Atkinson, 1988). The

tropospheric diurnally and annually averaged OH*

 radical concentrations are more certain, to

possibly a factor of two. For this reason, calculated lifetimes are approximate only for those

reactive species concentrations that are listed in the footnotes. However, these data permit one

to estimate the contribution of each of these atmospheric reactions to the overall removal rates of

most pollutants from the atmosphere. The major atmospheric loss process for most of the direct

emission constituents is by daytime reaction with OH* radicals.

For some pollutants, photolysis, reactions with ozone, and reactions with NO3 radicals

during nighttime hours are also important removal routes. For alkanes, the atmospheric lifetimes

calculated from the corresponding measured reaction rate constant and the average ambient

concentration of OH* radicals, ranges from ~19 days for propane (C3H8) to ~1 day for

n-pentadecane (C15H32). For aromatic hydrocarbons, lifetimes range from 18 days for benzene to

a few hours for methylnaphthalenes (assuming average 12-hour daylight OH*

 radical

concentration of 1 x 106 molecule/cm3).

Secondary organic compounds in particulate matter include aliphatic acids, aromatic

acids, nitro aromatics, carbonyls, esters, phenols, and aliphatic nitrates (Grosjean, 1992;

Grosjean and Seinfeld, 1989). However, these compounds can also be present in primary

emissions (e.g., Rogge, 1993), thus they are not unique tracers for atmospheric transformation

processes.

It has been reported that, in the presence of NOx, the OH*

 radical reactions with

fluoranthene and pyrene present in the gas phase lead to the formation of specific nitroarene

isomers different from those present in the direct emissions (Arey et al., 1986, 1989; Atkinson et

al., 1990; Zielinska et al., 1990). The nighttime reactions with NO3 radicals lead to the same

product as OH*

 radical reactions which form nitro-fluoranthene and nitro-pyrene isomers

(Zielinska et al., 1986). In contrast, the electrophilic nitration reaction of fluoranthene, or

pyrene, involving an NO2

+

 ion, produces mainly 3-nitrofluoranthene from fluoranthene and

1-nitropyrene from pyrene and these isomers are present in direct emissions from combustion

sources.
5 - 17

In order to assess the importance of atmospheric formation of secondary aerosol, the

concentration of 2-nitrofluoranthene and 2-nitropyrene was measured during the NFRAQS at the

Welby site (Watson et al., 1998). Although 2-nitrofluoranthene and 2-nitropyrene are present in

low amounts in daytime samples collected at the Welby site, their concentrations are not

significantly different during sunny and cloudy days. Secondary organic aerosol formation was

therefore considered negligible during the NFRAQS Winter 97 intensive operating period.

With respect to Assumption 3 involving the inclusion of all source types, it appears from

the PERCENT MASS performance measures that all of the significant contributors have been

included in most of the CMBs.

With respect to Assumption 4 concerning number of species and number of sources, 85

species and up to 11 source profiles were used in each calculation. The number of chemical

species always exceeded the number of source types.

With respect to Assumption 5 concerning collinearity, this was largely eliminated by the

inclusion of specific organic species in the extended data sets. These were sufficient to separate

contributions from fireplaces, wood stoves, meat cooking, diesel exhaust, gasoline smoker

exhaust, gasoline cold-start exhaust, and gasoline hot-stabilized exhaust. For the conventional

data sets, wood burning and meat cooking were collinear and the gasoline exhaust contributors

were collinear. Source-types for suspended dust, secondary ammonium sulfate and ammonium

nitrate, and specific coal-fired power station contributions could not be resolved. Profiles for

other industrial point sources were lacking, and their primary particle contributions could not be

explicitly estimated by CMB.

The effects of deviations from Assumption 6 on the randomness and normality of

measurement errors remain to be studied. For this study, all of the CMB assumptions are met to

the extent that the source contribution estimates can be considered valid. 

5.5 Identification and Correction of Model Input Errors

Many Level III validation deficiencies in the processing, formatting, compositing, and

reporting of ambient concentration and source profile measurements were identified and corrected or

flagged as a result of CMB8 source apportionment. Corrections and flags have been incorporated

into the NFRAQS data base, and the results presented by Watson et al. (1998) reflect these changes. 

Some chemical species concentrations were physically unreasonable, as indicated by large

CHI-SQUARE values with a large R/U value for the related species. The trimethylnaphthalenes and

biphenyls consistently showed large R/U values. The reason is not readily apparent. In these cases

the suspect species was removed from the fit. In general, the CMB modeling was robust enough

that, when performance measures were within acceptable ranges around target values, there was

little effect of suspect concentrations on the source contribution estimates.

5.6 Consistency and Stability of Source Contributions

The source contribution estimates and the statistics and diagnostic information were

reviewed to determine the validity of the initial model results. The analysis was repeated by

eliminating source profiles that gave negative source contribution estimates or standard errors that

exceed the source contribution estimates. The good agreement between the calculated source
5

This SIP-planning inventory was compiled externally to the NFRAQS.

5 - 18

Dust

16%

Softwood Burning

2%

Diesel

10%

Meat Cooking

4%

Hardwood 

Burning

3%

Gas 

High Emitter

13%

Gas 

Normal

3%

Gas 

Cold Start

12%

Coal Power

 Station

2%

Ammonium 

Nitrate

25%

Ammonium

 Sulfate

10%

contributions and the measured ambient concentrations indicate that all major source types were

included in the calculations, that ambient and source profile measurements are reasonably accurate,

and that the source profiles are reasonably representative of actual emissions.

5.7 Consistency with Other Simulations and Data Analyses

Watson et al. (1998) demonstrate that the CMB8 source contribution estimates for carbon

and PM2.5 are consistent with other NFRAQS data analysis and simulations. Figure 5.7-1 shows the

CMB source apportionment at the Welby site. This chart shows substantial discrepancies with

respect to the inventory5 in Table 5.1-1, in which diesel exhaust emissions are four times gasoline

exhaust emissions. The cold start and high emitter portions of the gasoline exhaust reverse these

proportions. These discrepancies should be further investigated.

Figure 5.7-1. Average PM2.5 source contributions at the Welby site near Denver, CO during the

winter of 1996-97.
6

Note that as of March 1998, the 2-Methyl-1-Pentene was removed; the list is now 54 species (see Appendix AA).

6 - 1

6. EXAMPLE OF APPLICATION AND VALIDATION FOR VOC

This example of VOC source apportionment is taken from the NARSTO-NE ozone study

that took place between June 1 and August 31, 1995 in the region between Washington DC and

Boston, MA (Fujita et al., 1998). While several different types of VOC samples were taken, this

example focuses on hourly measurements acquired with automated gas chromatographs at PAMS.

6.1 Model Applicability

The data set includes hydrocarbon measurements for 55 species6 measured hourly at eight

PAMS sites in six source areas (E. Hartford, CT; McMillan Reservoir, DC; Chicopee, MA; Lynn,

MA; Lake Clifton, MD; Bronx, NY) and two downwind (Type 1, 3, or 4) PAMS sites (Lums Pond,

DE and Rider College, NJ). The speciated hydrocarbon data are hourly measurements by automated

gas chromatographs providing more than 15,000 hydrocarbon samples covering the period from

June 1, 1995 to August 31, 1995. The sites, number of samples, and number of species measured

are sufficient to perform a CMB source apportionment.

6.2 Initial Source Contribution Estimates

Table 6.2-1 lists the mnemonic of the profiles that were considered in this study with short

descriptions. The actual profiles are reported by Fujita et al. (1998) and are included as one of the

CMB8 test data sets. As discussed in Section 3.1.2, the profiles are expressed as mass fractions of

total NMHC. Compounds other than the PAMS target VOCs (Appendix A) that are in the profiles

have been grouped into a category named “others”. The 28 species that were used as fitting species

in the CMB analysis are identified in Table 6.2-2 with asterisks. Compounds reported as

“unknown” were grouped into a category named “UNID”.

The PAMS target compounds typically account for about 80% of the ambient hydrocarbons

in urban areas. Although MTBE is a major component in reformulated gasoline and in the exhaust

of vehicles using reformulated gasoline, it was not included in the profiles because MTBE is not

measured in the PAMS program. The source profile data reported in units of ppbC were converted

to g/m3

 prior to calculating the mass fractions (expressed as percentages) using species-specific

conversion factors (Section 3.1.2). One-sigma uncertainties were derived from variations among

multiple measurements for a particular source type or a nominal analytical uncertainty of ±15%. 

The assigned uncertainties are the larger of the two values.

In urban locations, motor vehicle exhaust and evaporative emissions of gasoline are often the

major sources of hydrocarbon emissions. Composites of dynamometer measurements of vehicles of

varying age and mileage are commonly used to represent fleet-averaged exhaust profiles. For these

profiles to represent the actual fleet-average exhaust near ambient monitoring sites, the fuels in the

dynamometer tests should resemble the fuels used in the study region and the mix of test vehicles

should reflect the relative influence of non-catalyst vehicles or high emitters and catalyst-equipped

normal emitters. 
6 - 2

Table 6.2-1 VOC Source Profiles for NARSTO-NE CMB

No. Mnemonic Description

1 Tu_TusHD Tuscarora Tunnel, Heavy duty emissions

2 Tu_MchHD Ft. McHenry Tunnel, Heavy duty emissions

3 Exh_Cal0 Callahan Tunnel emissions with diesel contributions removed.

4 Exh_Lin0 Lincoln Tunnel emissions with diesel contributions removed.

5 Exh_Cal1 Callahan Tunnel emissions with diesel and 5~10% of running loss contributions removed.

6 Exh_Lin1 Lincoln Tunnel emissions with diesel and 5~10% of running loss contributions removed.

7 Exh_Cal2 Callahan Tunnel emissions with diesel and 15~25% of running loss contributions removed.

8 Exh_Lin2 Lincoln Tunnel emissions with diesel and 15~25% of running loss contributions removed.

9 WA_Tu1 Mt. Baker Tunnel emissions with diesel and 5~10% of running loss contributions removed.

10 Tu_Calla Callahan Tunnel emissions

11 Tu_Lin Lincoln Tunnel emissions

12 Tu_TusLD Tuscarora Tunnel, Light duty emissions

13 Tu_MchLD Ft. McHenry Tunnel, Light duty emissions

14 BoCS_Tip Tip O’Neill Garage emissions, Boston, cold start

15 Exh801a Derived from the FTP tests of Sigsby et al.

16 Bogl01 Boston liquid gasoline composite.

17 LA_liqGs LA liquid gasoline composite.

18 WA_Liq Washington liquid gasoline composite of 15 samples, weighted by brands and grades.

19 Bogv01 Boston headspace vapor composite

20 LA_Hsvap LA headspace vapor composite

21 WA_Vap Washington headspace composite of 15 samples, weighted by brands and grades.

22 COATcomp Composite of various coating emissions, weighted by total emissions.

23 CNG Commercial natural gas

24 GNG Geogenic natural gas

25 LPG Liquified petroleum gas

26 Biogenic Constructed biogenic profile

27 Unid Sum of unidentified species.

Previous studies showed that source attributions between tailpipe and evaporative emissions

from receptor modeling can vary greatly depending on the particular profile chosen for tailpipe

emissions (Harley et al., 1992; Fujita et al., 1994; Pierson et al., 1996). This is because tailpipe

emissions are a mixture of hydrocarbons produced during combustion along with unburned gasoline

resulting from incomplete combustion. In the CMB calculation, liquid gasoline represents the

additional unburned gasoline (due to misfiring and other engine malfunctions) that is not included in

the exhaust profile, plus evaporative emissions from gasoline spillage, hot soaks, and some portion

of resting losses (leaks, permeation). The profile for gasoline headspace vapor is taken to represent

fuel tank vapor losses (e.g., migration of fuel vapor from the canister). Measuring exhaust in onroad tunnels is one way to obtain a composite profile for a larger mix of vehicles.

While tunnel measurements are reasonable approximations for exhaust profiles of the lightduty fleet, they also include varying amounts of diesel exhaust and running evaporative losses. The

composite light-duty exhaust profiles that were derived by Fujita et al. (1997a) from measurements

by Gertler et al. (1997a) in the Lincoln and Callahan Tunnels were used in this study.
6 - 3

Table 6.2-2 Measured PAMS Species and CMB Fitting Species

PAMS Species CMB Fit PAMS Species CMB Fit

ETHANE * N_HEPT *

ETHENE MECYHX *

ACETYL * PA234M *

LBUT1E TOLUE *

PROPE HEP2ME *

N_PROP * HEP3ME *

I_BUTA * N_OCT *

N_BUTA * ETBZ

T2BUTE MP_XYL

C2BUTE STYR

IPENTA * O_XYL

PENTE1 N_NON *

N_PENT * IPRBZ

I_PREN * N_PRBZ

T2PENE M_ETOL

C2PENE P_ETOL

B2E2M BZ135M

BU22DM * O_ETOL

CPENTE BZ124M

P1E4ME N_DEC *

CPENTA * BZ123M

BU23DM * DETBZ1

PENA2M * DETBZ2

PENA3M * N_UNDE *

P1E2ME UNID

N_HEX *

T2HEXE

C2HEXE

MCYPNA *

PEN24M *

BENZE *

CYHEXA *

HEXA2M *

PEN23M *

HEXA3M *

PA224M *
6 - 4

6.3 Examine Model Outputs and Performance Measures

The tunnel-derived exhaust profiles, uncorrected tunnel profiles, and dynamometer-derived

exhaust profiles were applied to the same ambient samples to determine the sensitivity of the CMB

model to alternative exhaust profiles. Table 6.3-1 shows the effect of alternative vehicle exhaust

profiles on the average source contributions for a set of 65 ambient samples from the PAMS site at

Bronx, NY. Samples for this test were collected during the 0700 to 0800 EDT in the summer of

1995. Each of the ambient samples were apportioned with the diesel profile, TU_MCHHD, plus

twelve alternative gasoline vehicle exhaust profiles (Exh801a, Exh_Cal0, Exh_Cal1, Exh_Cal2,

Exh_Lin0, Exh_Lin1, Exh_Lin2, Tu_Calla, Tu_Lin, Tu_Mchld, Tu_Tusld, and Wa_Tu1) using

only fitting species that are enriched in diesel and spark-ignition vehicle exhaust (ethene, acetylene,

propene, benzene, nonane, decane, and undecane).

Table 6.3-1 CMB Sensitivity Tests for Vehicle Exhaust Profiles

# of TNMOC % of

samplesa (:g/m3

) r2 NMHC HD Profiles LD Profiles HD (%) LD (%)

65 144.2 0.88 3.27 65.2 Tu_Mchhd EXH801A 22.2 43.0

65 144.2 0.92 2.86 70.5 Tu_Mchhd EXH_CAL0 22.0 48.6

65 144.2 0.92 2.81 66.6 Tu_Mchhd EXH_CAL1 21.8 44.8

65 144.2 0.92 2.75 61.2 Tu_Mchhd EXH_CAL2 21.9 39.3

65 144.2 0.93 2.40 78.8 Tu_Mchhd EXH_LIN0 21.8 57.0

65 144.2 0.93 2.40 75.1 Tu_Mchhd EXH_LIN1 21.6 53.5

65 144.2 0.93 2.28 68.7 Tu_Mchhd EXH_LIN2 21.6 47.1

65 144.2 0.92 2.87 70.5 Tu_Mchhd TU_CALLA 19.3 51.2

65 144.2 0.93 2.77 73.0 Tu_Mchhd TU_LIN 14.5 58.5

65 144.2 0.88 4.83 66.6 Tu_Mchhd TU_MCHLD 22.7 43.9

65 144.2 0.86 5.04 57.5 Tu_Mchhd TU_TUSLD 24.3 33.2

65 144.2 0.91 3.46 52.2 Tu_Mchhd WA_TU1 22.8 29.3

a

 Samples collected between 0700 and 0800 EDT at Bronx, NY were used in the test.

Source contribution estimates using alternative gasoline vehicle exhaust profiles range from

50% to 70% of total NMHC. Exhaust profiles for relatively cleaner fleets (e.g., Tuscarora and

Mount Baker Tunnels) yield lower contributions. Exhaust contributions varied by no more than

10% for the three levels of assumed headspace vapor contributions for both Lincoln and Callahan

Tunnels profiles. The profiles corresponding to the maximum level of evaporative correction gave

exhaust contributions about 5% to 6% greater than profiles corresponding to averages between no

correction and maximum correction. Profiles derived from the tunnel measurements at the Lincoln

Tunnel consistently yielded the best model performance.

Table 6.3-2 shows the effect of alternative gasoline profiles on the average source

contributions for the same set of 65 ambient samples from the PAMS sites in Bronx, NY during the

0700 to 0800 EDT sampling period. Use of the vapor profiles for gasoline samples from either

Boston or Los Angeles results in large overestimation of total NMHC. In contrast, the vapor profile

for the Washington samples yield total predicted NMHC contributions that are, on average, about

90% of the observed ambient NMHC. Less than 100% is expected as only vehicle-related source

profiles were included in these sensitivity tests. Adding the other default source profiles does not
6 - 5

Table 6.3-2 CMB Sensitivity Tests for Different Profiles

No.

samplesa

TNMOX

r

2

% of

Liq. Gaso. Gaso. Vapor

HDb LDb Liquid

Gaso.

Gaso.

(:g/m Vapor Coating CNG GNG LPG Biogenic 3

) NMHC (%) (%)

65 144.2 0.80 6.63 124.0 BOgl01 BOgv01 21.6 44.9 0.0 57.4

65 144.2 0.86 4.61 115.7 BOgl01 LA_HsVap 21.6 43.3 0.0 50.8

65 144.2 0.82 6.03 90.1 BOgl01 WA_Vap 22.0 44.6 0.0 23.5

65 144.2 0.82 6.52 139.4 LA_liqGS BOgv01 19.7 44.7 53.7 18.5

65 144.2 0.86 4.54 114.6 LA_liqGS LA_HsVap 22.1 42.7 0.0 51.1

65 144.2 0.82 6.30 94.8 LA_liqGS WA_Vap 21.7 44.8 6.5 21.8

65 144.2 0.80 6.63 124.0 WA_liq BOgv01 21.6 44.9 0.0 57.4

65 144.2 0.86 4.61 115.7 WA_liq LA_HsVap 21.6 43.3 0.0 50.8

65 144.2 0.82 6.03 90.1 WA_liq WA_Vap 22.0 44.6 0.0 23.5

65 144.2 0.89 2.81 109.2 WA_liq WA_Vap 7.7 50.9 5.0 18.0 13.5 8.8 2.9 1.4 0.9

a

 Samples collected between 0700 and 0800 EDT at Bronx, NY were used in the test.

b

 Tu_Mchhd and Exh_Lin2 were used to represent HD and LD, respectively.

significantly alter the contributions among the tailpipe and evaporative emissions for gasoline

vehicles, but reduces the contribution of diesel exhaust from 22.0% to 7.7%. The difference is

assigned to surface coating because decane and undecane are major components of both diesel

exhaust and surface coatings. Because the sensitivity tests shown in Table 6.3-1 indicate that diesel

exhaust is the correct source of the higher molecular weight species at the Bronx site, the surface

coating profiles were not used in the default set of profiles in order to avoid potential for collinearity

between these two profiles.

6.4 Test Deviations from Model Assumptions

Assumptions 1 and 2 of the CMB model specify that the compositions of source emissions

are constant over the period of ambient and source sampling, and that chemical species do not react

with one another. The CMB model was applied to the ten alternative diesel and evaporative

emissions-corrected samples for each tunnel run with diesel exhaust and evaporative emissions as

source profile. The model performance parameters and comparisons of calculated and measured

amounts of total NMHC, isobutane, –butane, and isopentane were examined to determine the level

of evaporative corrections that yield the best fit. These tests showed that the fit deteriorates rapidly

beyond a certain level of assumed headspace vapor contribution. This level is typically 15% to

25%. The predicted vapor contributions do not increase above these levels of assumed vapor

contribution. This is consistent with the expectation since there is a limit to the fractional

contribution of running losses to hydrocarbons mixing ratios in roadway tunnels. Because the

performance parameters for various levels of assumed headspace vapor contributions are similar up

to the level at which the fit deteriorates, three sets of corrected profiles were derived for each tunnel

run. One profile corresponded to no evaporative correction (i.e., only diesel correction), and a

second set of profiles corresponded to the maximum level of evaporative correction before the fit

begins to deteriorate (15% to 20%). In the 3rd profile, a composite of the best fitting diesel corrected

profiles was made for the uncorrected tunnel measurements.

For PAMS sites, the significant contributors to the average 24-hour ambient NMHC are

gasoline vehicle exhaust (40%), gasoline vapor (17%), and diesel exhaust (17%). Natural gas leak

(9%), liquid gasoline (7%), liquefied petroleum gas (4%), and biogenic emissions (4%) are minor
6 - 6

contributors to NMHC. On average, only 4% of the identified NMHC are unexplained. Three of

the Type 2 sites (Chicopee and Lynn, MA and Bronx, NY) account for the relatively high average

contribution of diesel exhaust. Higher diesel contributions are possible at the Bronx site due to

diesel buses.

However, there are no obvious sources of the high levels of heavy hydrocarbon at Chicopee

and Lynn that result in high diesel contributions at those sites. Contributions of liquid gasoline and

gasoline vapors are also much higher at Chicopee than for a typical Type 2 site. Removing

Chicopee and Lynn from the average, decreases the average contribution of diesel exhaust for the

remaining sites to 12% and increases gasoline vehicle exhaust to 46%.

The significant contributors to the average 24-hour ambient NMHC at downwind PAMS

sites (Lums Pond, DE and Rider College, NJ) are gasoline vehicle exhaust (30%), gasoline vapor

(18%), liquefied petroleum gas (18%), and natural gas (11%). Diesel exhaust (9%), liquid gasoline

(6%), biogenic emissions (5%) are minor contributors to NMHC.

Biogenic emissions are a significantly larger fraction of total NMHC in suburban and rural

areas than in urban area. The contributions of isoprene over a 24-hour average range from 4% to

12% in suburban and rural areas and are less than 2% in urban areas. Because isoprene is emitted

only during daylight hours with peak emission rates occurring at midday, it is one of the larger

sources of NMHC during the day in suburban and rural areas. For the CMB calculations performed

in this study, only species with summertime lifetimes greater than that of toluene (~9 hours) were

used as fitting species. An exception to this is isoprene. It was included as a fitting species despite

its high reactivity because it serves as a marker for biogenic emissions. The source contribution

estimates underestimated the actual source contributions of biogenic emissions, i.e., they provide a

lower limit to biogenic contributions.

The actual contributions of isoprene may be estimated by examining changes between

morning and afternoon samples in the ratios of reactive hydrocarbons (e.g., isomers of xylene) to a

relatively unreactive hydrocarbon (e.g., benzene) from a common source (i.e., vehicle exhaust) as an

indicator of the net fractional loss of reactive hydrocarbon between the two sampling periods. These

ratios are invariant to atmospheric dispersion and include continuous injections of fresh emissions

into the air parcel during its transport to the sampling site. The ratio of afternoon to morning

xylenes/benzene ratios reflects the net fractional loss of xylenes due to atmospheric reactions. This

fractional loss is adjusted to isoprene by applying the ratio of the OH*

 radical reaction rate constants

for xylenes (18.8) and isoprene (101.0). Based on this approach, Fujita et al. (1997b) estimated that

the actual daytime contributions of isoprene to total NMHC emissions may be 5 to 10 times greater

than CMB estimates.

Unidentified compounds are not considered in the apportionment because a large fraction of

these compounds are not quantified in the PAMS program. These compounds include terpenes and

higher molecular weight aldehydes, which are relatively more abundant in non-urban areas.
6 - 7

6.5 Identify and Correct Model Input Errors

Substantial validation was done on the profiles and ambient data sets, and no major

discrepancies were found as part of the source apportionment.

6.6 Evaluate Consistency and Stability of Source Contributions

The source contribution estimates and the statistics and diagnostic information were

reviewed to determine the validity of the initial model results. The analysis was repeated by

eliminating source profiles that gave negative source contribution estimates or standard errors that

exceed the source contribution estimates. The good agreement between the calculated source

contributions and the measured ambient concentrations indicate that all major source types were

included in the calculations, that ambient and source profile measurements are reasonably

accurate, and that the source profiles are reasonably representative of actual emissions. 

6.7 Determine Consistency with Other Simulations and Data Analyses

Hourly data offers substantial opportunities to evaluate consistency with other analyses,

especially expected diurnal, weekly, and spatial variations in source emissions. Figure 6.7-1

shows the diurnal variations of the absolute source contributions for each source category by day

of the week at the Lynn, MA site. While motor vehicle exhaust contributions generally peak

during morning and afternoon commute periods on weekday, the average contributions are

significantly lower during weekend mornings. These patterns provide confidence in the proper

apportionment of vehicle emissions. The diurnal and day-of-the-week patterns in the liquid

gasoline contributions are essentially identical to motor vehicle exhaust, which suggests that a

large fraction of the liquid gasoline contribution may be associated with tailpipe emissions rather

than evaporative emissions from either vehicle or industrial sources.

The diurnal variations in the contribution of natural gas correlate with diurnal variations in

vertical mixing. This diurnal pattern and lack of day-of-the-week variations are consistent with

constant leakage of natural gas. Liquefied petroleum gas (LPG) generally shows the same

diurnal variations. However, lower contributions for LPG during weekend mornings suggests

some correlation with the vehicle exhaust profile since the latter profile is derived from roadside

ambient measurements.
6 - 8

Lynn - HD Exhaust

0

10

20

30

40

50

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

μg/m3

Lynn - Biogenic

0

2

4

6

8

10

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Monday Tuesday Wednesday Thursday

Friday Saturday Sunday

Lynn - LD Exhaust

0

10

20

30

40

50

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Lynn - Gasoline Vapor

0

5

10

15

20

25

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

μg/m3 μg/m3 μg/m3

Figure 6.7-1. Hourly average VOC source contributions by day of week at Lynn, MA.
6 - 9

Lynn (18-24 EDT)

0

5

10

15

20

25

30

N

NE

E

SE

S

SW

W

NW

HD Exhaust

LD Exhaust

Liq. Gas.

Gaso. Vap.

CNG

Biogenic

Figure 6.7-2 shows the average source contributions to NMHC by wind sector (centered

on N, NE, E, SE, S, SW, W, NW) at the Lynn, MA site for the evening period. Contributions of

gasoline vehicle exhaust are predominantly from the southeast, south, and southwest. In

contrast, the contribution of diesel exhaust is more or less uniform from all directions. This

pattern suggests a very strong local source that dominates the ambient VOC composition near

the sampling site. It also indicates that the source of the heavy hydrocarbons that are ascribed to

diesel exhaust is some source other than diesel vehicles.

Figure 6.7-2. Wind direction dependence of VOC source contributions at Lynn, MA.

7 - 1

7. SUMMARY, CONCLUSIONS, AND FUTURE PROSPECTS

This applications and validation protocol has summarized knowledge on using the

Chemical Mass Balance receptor model to determine source contributions to suspended particles

and VOC measured at receptors. It describes new performance measures incorporated into

CMB8 modeling software that facilitate the evaluation of similarity among different profiles.

Examples are given for PM2.5 source apportionment in Denver, CO, and for VOC source

apportionment in the northeast corridor of the U.S. These examples demonstrate how the

applications and validation steps can be used to build confidence in the source apportionment

results.

New measurement methods, especially for organic aerosol and heavy hydrocarbons, will

expand the ability of CMB to better distinguish sources from each other. Organic aerosol

measurements allow cold-starts and high emitting gasoline exhaust to be distinguished from

normal running vehicle exhaust. Initial indications are that emissions inventories do not

adequately account for these emissions. Hourly gas chromatographic data allow the diurnal

cycles and dominant wind directions of VOC sources to be estimated. These add confidence to

the CMB apportionments, as well as elucidating temporal and spatial relationship between

emissions and ambient concentrations.

Collocation of PM2.5 speciation sites with PAMS VOC sites will allow gas and particle

properties to be used together in a single CMB apportionment. This holds the potential to

provide more accurate source apportionments for a wider variety of chemical components.

CMB8 provides a myriad of options that can be applied to better understand the CMB

source apportionment method. The new collinearity measures need to be better characterized to

provide more specific guidance for their use in practical situations. Furthermore, the

Britt-Luecke algorithm, as implemented in CMB8, has not undergone comprehensive testing. It

is therefore not recommended for inexperienced users. Its inclusion as an option is mainly

intended to provide the opportunity for interested advanced users to perform research

investigations needed to establish its future viability.

This protocol will surely be revised as more and better data become available, and we

gain more experience and skill in applying CMB to source apportionment studies.

8 - 1

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Aronian, P.F., P.A. Scheff and R.A. Wadden (1989). Wintertime source-reconciliation of

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1

Note that because 1-Butene and Isobutene elute at about the same time, they are difficult to resolve. The coeluting isomers are assigned AIRS Parameter Code 43127. Isobutene is assigned AIRS Parameter Code 43270 and CAS 115117.

2

These isomers of xylene are also difficult to resolve. Individually, their AIRS Parameter Codes are 45205 & 45206, respectively. 

Respective CAS numbers are provided in the table.

3

Also named 1,3-Diethylbenzene.

A - 1

APPENDIX A. 54 PAMS TARGET COMPOUNDS (HYDROCARBONS) LISTED IN

THEIR ELUTION SEQUENCE.

Hydrocarbon AIRS CAS

1. Ethylene 43203 74851

2. Acetylene 43206 74862

3. Ethane 43202 74840

4. Propylene 43205 115071

5. Propane 43204 74986

6. Isobutane 43214 75285

7. 1-Butene1 43280 106989

8. n-Butane 43212 106978

9. t-2-Butene 43216 624646

10. c-2-Butene 43217 590181

11. Isopentane 43221 78784

12. 1-Pentene 43224 109671

13. n-Pentane 43220 109660

14. Isoprene 43243 78795

15. t-2-Pentene 43226 646048

16. c-2-Pentene 43227 627203

17. 2,2-Dimethylbutane 43244 75832

18. Cyclopentane 43242 287923

19. 2,3-Dimethylbutane 43284 79298

20. 2-Methylpentane 43285 107835

21. 3-Methylpentane 43230 96140

22. n-Hexane 43231 110543

23. Methylcyclopentane 43262 96377

24. 2,4-Dimethylpentane 43247 108087

25. Benzene 45201 71432

26. Cyclohexane 43248 110827

27. 2-Methylhexane 43263 591764

28. 2,3-Dimethylpentane 43291 565593

29. 3-Methylhexane 43249 589344

30. 2,2,4-Trimethylpentane 43250 540841

31. n-Heptane 43232 142825

32. Methylcyclohexane 43261 108872

33. 2,3,4-Trimethylpentane 43252 565753

34. Toluene 45202 108883

35. 2-Methylheptane 43960 592278

36. 3-Methylheptane 43253 589811

37. n-Octane 43233 111659

38. Ethylbenzene 45203 100414

39. m & p-Xylene2 45109 108383/106423

40. Styrene 45220 100425

41. o-Xylene 45204 95476

42. n-Nonane 43235 111842

43. Isopropylbenzene 45210 98828

44. n-Propylbenzene 45209 103651

45. m-Ethyltoluene 45212 620144

46. p-Ethyltoluene 45213 622968

47. 1,3,5-Trimethylbenzene 45207 108678

48. o-Ethyltoluene 45211 611143

49. 1,2,4-Trimethylbenzene 45208 95636

50. n-Decane 43238 124185

51. 1,2,3-Trimethylbenzene 45225 526738

52. m-Diethylbenzene3 45218 141935

53. p-Diethylbenzene 45219 105055

54 n-Undecane 43954 1120214

B - 1

APPENDIX B. NORMALIZATION FOR THE VOC SOURCE PROFILE

As we stressed in Section 3.1.2, the different ways in which source profiles are

constructed make it difficult in practice to compare and use VOC profiles from different studies. 

Furthermore, inventories employ different conventions for defining VOC. Meaningful

comparison of CMB results with emission inventories requires a common reference. In this

discussion we explain how normalized profiles are constructed for input to CMB, and also point

out some differences to be seen in different normalization approaches.

In Table B-1 are shown data collected from the 1990 Atlanta Ozone Precursor

Monitoring Study, in which motor vehicle emissions were sampled in canisters beside a roadway

in a tunnel-like underpass during periods of heavy traffic. Roadway tunnels are usually good

integrators of the mobile source fleet. In the first column is the average measured concentration

for the top 83 NMOC appearing in the chromatogram, based on 9 samples. Two additional

species, n-Tridecane and n-Tetradecane, were included to be consistent with the airport and

aircraft profiles also collected in this study. The average sum of the 85 peaks is 1949.4, while

the sum of all quantified NMOC chromatogram peaks is 2335.7 ppbC. Note that the example

presented here - in which multiple samples were taken of a single source type for profile

characterization, and subjected to averaging - is ideal. In some cases, only a single sample may

be taken. It is important to understand exactly how the values appearing in Table B-1 were

derived:

Average Concentration for species i = , where B-1

cik = measured concentration (ppbC) for species i in the chromatogram for sample k, and

n = number of samples available for constructing this source profile.

Fractional abundance of species i = , where B-2

fik = (where summationk = sum of the concentration peaks for sample k B-3

 

used in the normalization).

Uncertainty for fractional abundance of species i = B-4

(the standard deviation of the mean fractional abundance determined from n samples).

In applying Eqns B-2, B-3, and B-4, professional judgement should of course be used in

the matter of potential outliers. For example, if for a particular sample k, fik is greatly different

from , especially for multiple species i, the sample k should probably be deleted from the

calculations. For the VOC source profile presented in Table B-1, the uncertainties reported for
B - 2

the mean fractional abundances were statistically analyzed. The mean is 12.7% and the median

is 8.9%, with a minimum uncertainty of 2.6% (ethylbenzene) and a maximum uncertainty of

73.5% (2,3,3 trimethyl-1-butene). As indicated by these statistics, the routine practice of some

laboratories to provide only a single, overall assessment of uncertainty/precision for an analysis

is an oversimplification.

Ideally, the fractional abundances for each species i are determined individually for each

source sample, and then averaged for the suite of samples taken. Note that the mean fractional

abundance wasn’t simply determined by taking the mean concentration for species i, and then

dividing by the mean summation for the suite of samples, i.e.,

B-5

Equations B-2 and B-5 are not equivalent and the value of determined via Eqn. B-5 in fact

may be considerably biased. Note also that the uncertainty wasn’t simply determined as the

standard deviation of the mean concentration of species i for the suite of samples taken, i.e.,

B-6

Equations B-4 and B-6 are not equivalent. Eqn. B-6, patterned after Eqn. B-1, is inappropriate

because it isn’t normalized (i.e., does not account for variations in summationk). And because

Eqn. B-5 is not a robust estimate of relative species abundance, the associated uncertainty ( )

is not an accurate reflection of the uncertainty for species i.

As stated in Section 3.1.2, speciated VOC data are sometimes normalized to all the

compounds in the chromatogram, as shown in the first set of paired columns in Table B-1. In

fact, the values appearing in this set of columns are exactly as reported in Table 1 of the cited

JAPCA article, except that the latter were expressed as percentages. Normalized fractional

abundances are often presented as percentages because they are easier to read (fewer leading

decimal places). However, CMB requires that abundances be in fractional form; the ratios

represented in Table B-1 are appropriate for the source profile input file required by CMB. In

this case, the value for summationk in Eqn. B-3, with which the fractional abundances (fik) were

determined, included the sum of all peaks in the chromatogram.

The 2nd set of paired columns represents a normalization to only the PAMS

(Photochemical Assessment Monitoring Stations) species. Here, the ratio was constructed as

before, but the value for summationk in Eqn. B-3 included only the sum of the measured PAMS

peaks.
B - 3

Note that since the ratios appearing in column 2 include only 85 NMOC species, the sum

of their fractional abundances normalized to total NMOC is less than unity. Conversely, since

the ratios appearing in the 4th column are based on the sum of only the PAMS species, the sum of

fractional abundances is greater than unity. Of course, had only PAMS species been sampled

and measured, the sum would theoretically be unity in the 4th column. There is a 35% difference

in fractional abundances normalized via the two approaches shown here. The ratio of 1.123 /

0.833 is 1.35, which is precisely the same ratio as 2335.7 / 1735.7 (sum of all measured peaks /

sum of PAMS peaks). This relationship is shown most clearly in the last entry (total NMOC), in

which the respective normalized values clearly reflect the 35% difference. It is worth noting that

many published source profiles, including the source from which these data were reproduced,

include an entry for total NMOC. When published profiles include a normalized value for total

NMOC, the normalization approach becomes obvious. Beyond its practical purposes, it is useful

to include an entry for total NMOC in the source profile input file for CMB provided it is

normalized to the sum of the measured PAMS species. Ideally, the uncertainty for the

normalized total NMOC abundance should be calculated as:

, where

= normalized uncertainty computed via Eqn. B-4, and m = total number of NMOC species

measured. For convenience, the value for this uncertainty reported in Table B-1 was based on

m = 85 (only the 85 values in column 5 were used). If possible, all measured NMOC species

should be used in this calculation.

We mentioned that the VOC source type characterized for this example is in many ways

ideal. Roadway sources such as this one are likely to reflect most, if not all, of the PAMS

species. However, many VOC source types may be sampled in which all or even most of the

PAMS species will not be represented. So long as the sampling system is capable of collecting

all of the PAMS species, and so long as the analytical system is capable of detecting all of the

PAMS species, the normalization procedure described in Eqns. B-1 through B-4 should be

followed. The value for n will reflect the number of samples taken, and the value for summationk

will include the sum of as many PAMS peaks measured in the chromatogram for any sample k.
B - 4

Table B-1. Measured VOC concentrations and their normalized fractional abundances.1

(Species flagged with ‘•’ are PAMS target compounds listed in Appendix A)

 Average Concentration Ratio to TNMOC Ratio to PAMS

VOC Species (ppbC) Abundance Unc.2 Abundance Unc.2

C Ethylene 0.043400 0.004700 0.058590 0.006345

C Acetylene 0.038000 0.006400 0.051300 0.008640

C Ethane 0.015500 0.001900 0.020925 0.002565

C Propene 0.019600 0.001800 0.026460 0.002430

C Propane 0.010500 0.001800 0.014175 0.002430

C Isobutane 0.011200 0.002500 0.015120 0.003375

C 1-Butene/Isobutene 0.011810 0.000900 0.015944 0.001215

1,3-Butadiene 0.003800 0.001100 0.005130 0.001485

C n-Butane 0.041100 0.007100 0.055485 0.009585

C t-2-Butene 0.004080 0.000280 0.005508 0.000378

C c-2-Butene 0.003340 0.000300 0.004509 0.000405

3-Methyl-1-Butene 0.001583 0.000091 0.002137 0.000123

C Isopentane 0.086400 0.008400 0.116640 0.011340

C 1-Pentene 0.003200 0.001600 0.004320 0.002160

2-Methyl-1-Butene 0.006360 0.000530 0.008586 0.000716

C n-Pentane 0.026600 0.002700 0.035910 0.003645

C Isoprene 0.003200 0.001700 0.004320 0.002295

C t-2-Pentene 0.007500 0.000450 0.010125 0.000608

C c-2-Pentene 0.004110 0.000290 0.005549 0.000392

2-Methyl-2-Butene 0.009300 0.002900 0.012555 0.003915

C 2,2-Dimethylbutane 0.004950 0.000850 0.006683 0.001148

Cyclopentene 0.001720 0.000084 0.002322 0.000113

4-Methyl-1-Pentene 0.001790 0.000016 0.002417 0.000216

C Cyclopentane 0.002508 0.000077 0.003386 0.000104

C 2,3-Dimethylbutane 0.008630 0.000250 0.011650 0.000338

C 2-Methylpentane 0.024340 0.000990 0.032859 0.001336

C 3-Methylpentane 0.014180 0.000620 0.019143 0.000837

2-Methyl-1-Pentene 0.001830 0.000180 0.002471 0.000243

1-Hexene 0.001338 0.000055 0.001806 0.000074

C n-Hexane 0.010880 0.000580 0.014688 0.000783

t-3-Hexene 0.002580 0.000570 0.003483 0.000769

t-2-Hexene 0.002490 0.000210 0.003362 0.000284

c-2-Hexene 0.001270 0.000110 0.001715 0.000149

C Methylcyclopentane 0.007830 0.000380 0.010571 0.000513

C 2,4-Dimethylpentane 0.007040 0.000580 0.009504 0.000783

2,3,3-Trimethyl-1-Butene 0.003400 0.002500 0.004590 0.003375

C Benzene 0.027300 0.001900 0.036855 0.002565

C Cyclohexane 0.001660 0.000110 0.002241 0.000149

C 2-Methylhexane 0.008740 0.000530 0.011799 0.000716

C 2,3-Dimethylpentane 0.009010 0.000600 0.012164 0.000810

C 3-Methylhexane 0.008880 0.000420 0.011988 0.000567

c-1,3-Dimethylcyclopentane 0.001920 0.000130 0.002592 0.000175

3-Ethylpentane 0.002980 0.000150 0.004023 0.000203

C 2,2,4-Trimethylpentane 0.025100 0.002300 0.033885 0.003105

C n-Heptane 0.005400 0.000380 0.007290 0.000513

C Methylcyclohexane 0.003010 0.000170 0.004064 0.000230

2,5-Dimethylhexane 0.003290 0.000330 0.004442 0.000446

2,4-Dimethylhexane 0.005490 0.000440 0.007411 0.000594

C 2,3,4-Trimethylpentane 0.009500 0.001100 0.012825 0.001485

C Toluene 0.065900 0.003600 0.088965 0.004860
B - 5

2,3-Dimethylhexane 0.002830 0.000350 0.003820 0.000472

C 2-Methylheptane 0.003240 0.000240 0.004374 0.000324

C 3-Methylheptane 0.003610 0.000440 0.004874 0.000594

2,2,5-Trimethylhexane 0.003720 0.000420 0.005022 0.000567

C n-Octane 0.002860 0.000120 0.003861 0.000162

2,5-Dimethylheptane 0.001888 0.000084 0.002549 0.000113

C Etylbenzene 0.012800 0.000330 0.017280 0.000446

C m/p-Xylene 0.043500 0.001400 0.058725 0.001890

4-Methyloctane 0.002460 0.000220 0.003321 0.000297

3-Methyloctane 0.001860 0.000130 0.002511 0.000175

C Styrene 0.004370 0.000530 0.005900 0.000716

C o-Xylene 0.016620 0.000800 0.022437 0.001080

C n-Nonane 0.002140 0.000250 0.002889 0.000338

C Isopropylbenzene 0.001530 0.000150 0.002066 0.000203

C n-Propylbenzene 0.003530 0.000320 0.004766 0.000432

C m-Ethyltoluene 0.013810 0.000900 0.018644 0.001215

C p-Ethyltoluene 0.006330 0.000340 0.008546 0.000459

C 1,3,5-Trimethylbenzene 0.007490 0.000600 0.010112 0.000810

C o-Etyltoluene 0.004990 0.000450 0.006736 0.000608

C 1,2,4-Trimethylbenzene 0.021600 0.002200 0.029160 0.002970

C n-Decane 0.002550 0.000400 0.003443 0.000540

C 1,2,3-Trimethylbenzene 0.005320 0.000420 0.007182 0.000567

n-Butylcyclohehexane 0.002270 0.000330 0.003065 0.000446

C m-Diethylbenzene (1,3-Diethylbenzene) 0.001890 0.000110 0.002551 0.000149

C p-Diethylbenzene (1,4-Diethylbenzene) 0.007550 0.000650 0.010193 0.000878

1,3-Dimethyl-4-Ethylbenzene 0.002360 0.000220 0.003186 0.000297

C n-Undecane 0.002420 0.000240 0.003267 0.000324

1,2,4,5-Tetramethylbenzene 0.002720 0.000500 0.003672 0.000675

1,2,3,5-Tetramethylbenzene 0.003800 0.001000 0.005130 0.001350

m-Diisoproplylbenzene 0.001570 0.000930 0.002119 0.001256

1,2,3,4-Tetramethylbenzene 0.001720 0.000890 0.002322 0.001202

napthalene 0.008130 0.000810 0.010976 0.001094

n-Dodecane 0.002470 0.000280 0.003334 0.000378

n-Tridecane 0.000922 0.000098 0.001245 0.000132

n-Tetradecane 0.000420 0.000110 0.000567 0.000149

G: (0.833) (1.12)

Total NMOC 2335.66 1.000000 1.346

Mean summation top 85 VOC peaks:

Mean summation PAMS peaks: 1735.72

Difference, TNMOC normalization vs. PAMS normalization: 34.6%

1

 Data used with permission from Conner, T.L., W.A. Lonneman, and R.L Seila, 1995. Transportation-related volatile hydrocarbon source

profiles measured in Atlanta. JAWMA 45: 383-394.

2

 Statistical analysis of these uncertainties is presented and discussed in the text.

C - 1

APPENDIX C. INTERNET LINKS TO MODELING SOFTWARE AND DATA SETS

C.1 Receptor Models

! EPA-CMB8.2: www.epa.gov/scram001/

C.2 Source Profiles

! SPECIATE: www.epa.gov/ttn/chief/software/speciate

! Northern Front Range Air Quality Study Source Profiles (Particle Organics):

www.nfraqs.colostate.edu/nfraqs/index2.html

C.3 Ambient Measurements

! Center for Air Pollution Impact and Trend Analysis: capita.wustl.edu/

! IMPROVE Particle Measurements: www.epa.gov/ttn/amtic/visdata.html

C.4 Emissions Models and Inventories

! AP-42 Emissions Factors: www.epa.gov/ttn/chief/ap42/

! National Emissions Trends Report: www.epa.gov/ttn/chief/trends/trends98/

! National Emissions Inventory Data: www.epa.gov/ttn/chief/net/

C.5 Meteorological Models

! CALMET: www.epa.gov/scram001/

! MM5 (5th-Generation Penn State/NCAR Mesoscale Model):

www.mmm.ucar.edu/mm5/mm5v3/v3model.html

! WRF (Weather Research and Forecasting model): www.wrf-model.org

! National Centers for Environmental Prediction (NCEP) Products:

http://nomads.ncdc.noaa.gov/data-access.html

GFS (Global Forecast System)

HR GFS (Hign Resolution Global Forecast System)

Eta (regional mesoscale model)

NAM (North American Mesoscale)

NARR (North American Regional Reanalysis)

RUC (Rapid Update Cycle)
C - 2

C.6 Dispersion Models

! AERMOD: www.epa.gov/scram001

! CALPUFF: www.epa.gov/scram001/

! CMAQ (Community Multi-Scale Air Quality Model):

www.epa.gov/asmdnerl/models3/
D - 1

APPENDIX D. CMB MATHEMATICS

The source contribution (Sj) present at a receptor during a sampling period of length T

due to a source j with constant emission rate Ej is

(D-1)

where:

(D-2)

is a dispersion factor depending on wind velocity (u), atmospheric stability (F), and the location

of source j with respect to the receptor (xj

). All parameters in Equation A-2 vary with time, so

the instantaneous dispersion factor, Dj

, must be integrated over time period T (Watson, 1979).

Various forms for Dj

 have been proposed (Pasquill, 1974; Benarie, 1976; Seinfeld and

Pandis, 1998), some including provisions for chemical reactions, removal, and specialized

topography. None are completely adequate to describe the complicated, random nature of

dispersion in the atmosphere. The advantage of receptor models is that an exact knowledge of Dj

is unnecessary.

If a number of sources, J, exists and there is no interaction between their emissions to

cause mass removal, the total mass measured at the receptor, C, will be a linear sum of the

contributions from the individual sources.

(D-3)

Similarly, the concentration of elemental component i, Ci, will be

(D-4)

where: Fij = the fraction of source contribution Sj composed of element i. The number of

chemical species (I) must be greater than or equal to the number of sources (J) for a unique

solution to these equations.

Solutions to the CMB equations consist of: (1) a tracer solution; (2) a linear
D - 2

programming solution; (3) an ordinary weighted least squares solution with or without an

intercept; (4) a ridge regression weighted least squares solution with or without an intercept; and

(5) an effective variance least squares solution with or without an intercept. An estimate of the

uncertainty associated with the source contributions is an integral part of several of these

solution methods.

Weighted linear least squares solutions are preferable to the tracer and linear

programming solutions because: (1) theoretically they yield the most likely solution to the CMB

equations, providing model assumptions are met; (2) they can make use of all available chemical

measurements, not just the so-called tracer species; (3) they are capable of analytically

estimating the uncertainty of the source contributions; and (4) there is, in practice, no such thing

as a “tracer.” The effective variance solution developed and tested by Watson et al. (1984): (1)

provides realistic estimates of the uncertainties of the source contributions (owing to its

incorporation of both source profile and receptor data uncertainties); and (2) gives greater

influence to chemical species with lower values for uncertainty in both the source and receptor

measurements than to species with higher values for uncertainty. The effective variance solution

is derived by minimizing the weighted sums of the squares of the differences between the

measured and calculated values of Ci

 and Fij (Britt and Luecke, 1973; Watson et al., 1984). The

solution algorithm is an iterative procedure which calculates a new set of Sj based on the Sj

estimated from the previous iteration. It is carried out by the following steps expressed in matrix

notation. A superscript k is used to designate the value of a variable at the kth iteration.

1. Set initial estimate of the source contributions equal to zero.

(D-5)

2. Calculate the diagonal components of the effective variance matrix, Ve. All

off-diagonal components of this matrix are equal to zero.

(D-6)

3. Calculate the k+1 value of Sj.

(D-7)

4. Test the (k+1)th iteration of the Sj against the kth iteration. If any one differs by

more than 1%, then perform the next iteration. If all differ by less than 1%, then terminate the

algorithm.
D - 3

(D-8)

5. Assign the (k+1)th iteration to Sj and . All other calculations are performed

with these final values.

(D-9)

where: C = (C1 ... CI)

T

, a column vector with Ci as the ith component

S = (S1...SJ)

T

, a column vector with Sj as the jth component

F = an I x J matrix of Fij, the source composition matrix

= one standard deviation uncertainty of the Ci

 measurement

= one standard deviation uncertainty of the Fij measurement

Ve = diagonal matrix of effective variances 

The effective variance solution algorithm is very general, and it reduces to most of the

solutions cited above with the following modifications:

! When the are set equal to zero, the solution reduces to the ordinary weighted least

squares solution.

! When the are set equal to the same constant value, the solution reduces to the

unweighted least squares solution.

! When a column is added to the Fij matrix with all values equal to 1, an intercept term is

computed for the variable corresponding to this column.

! When the number of source profiles equals the number of species (I = J), and if the

selected species are present only in a single, exclusive source profile, the solution reduces

to the tracer solution.

! When the expression (FT (Ve

k

)

-1 F) is rewritten as (FT (Ve

k

)

-1 F - NI), with N equal to

some non-zero number, known as the smoothing parameter, and I equal to the identity

matrix, the solution becomes the ridge regression solution (Williamson and Daboecia,

1983 and Henry et al., 1984).

Formulas for the performance measures are:

(D-10)
D - 4

(D-11)

(D-12)

(D-13)

The Singular Value Decomposition of the weighted F matrix is given by (Henry, 1992):

(D-14)

where U and V are I X I and J X J orthogonal matrices, respectively, and where D is a diagonal

matrix with J nonzero and positive elements called the singular values of the decomposition. 

The columns of V are called the eigenvectors of the composition and their components are

associated with the source types.
Appendix E. Summary of CMB PM10 Source Apportionment Studies

E - 1

Sampling Site Time Period

Primary

Geological

Primary

Construction

Primary

Motor

Vehicle

Exhaust

Primary

Vegetative

Burning

Secondary

Ammonium

Sulfate

Secondary

Ammonium

Nitrate

Misc.

Source

1

Mis

Sour

2

Central Phoenix, AZ (Chow et al., 1991) Winter 1989-90 33.0 0.0 25.0 2.3 0.2 2.8 0.0 0.0 

Corona de Tucson, AZ (Chow et al., 1992c) Winter 1989-90 17.0 0.0 1.6 0.0 1.9 0.0 0.0 0.0 

Craycroft, AZ (Chow et al., 1992c) Winter 1989-90 13.0 0.0 8.3 0.0 0.7 0.6 1.2a 0.0 

Downtown Tucson, AZ (Chow et al., 1992c) Winter 1989-90 26.0 5.1 14.0 0.0 1.0 0.2 1.3a 0.0 Hayden 1, AZ (Garfield) (Ryan et al., 1988) 1986 5.0 2.0b 0.0 0.0 4.0 0.0 74.0c 5.0d 

Hayden 2, AZ (Jail) (Ryan et al., 1988) 1986 21.0 4.0b 0.0 0.0 4.0 0.0 28.0c 0.0 Orange Grove, AZ (Chow et al., 1992a) Winter 1989-90 20.0 0.0 15.0 0.0 0.4 0.4 0.0 0.0 

Phoenix, AZ (Estrella Park) (Chow et al., 1991) Winter 1989-90 37.0 0.0 10.0 0.9 1.6 0.0 0.0 0.0 

Phoenix, AZ (Gunnery Rg.) (Chow et al., 1991) Winter 1989-90 20.0 0.0 5.5 0.0 1.0 0.0 0.0 0.0 

Phoenix, AX (Pinnacle Pk.) (Chow et al., 1991) Winter 1989-90 7.0 0.0 2.9 1.0 0.9 0.0 0.0 0.0 

Rillito, AZ (Thanukos et al., 1992) 1988 42.7 13.8b 1.2f 0.0 0.0 0.0 11.6g 0.0 

Scottsdale, AZ (Chow et al., 1991) Winter 1989-90 25.0 0.0 19.0 7.4 0.6 3.6 0.0 0.0 

West Phoenix, AZ (Chow et al., 1991) Winter 1989-90 30.0 0.0 25.0 10.0 0.4 3.1 0.0 0.0 

Anacapa Island, CA (Chow et al., 1996b) 2.2 0.0 4.9 0.0 3.4 1.0 9.6h 0.0 

Anaheim, CA (Gray et al., 1988) 1986 21.2 0.0 4.1i 0.0 7.0 9.8 0.4j 1.4h 

Anaheim, CA (Summer) (Watson et al., 1994a) Summer 1987 11.4 0.0 8.5 0.0 9.0 2.9 0.0j 6.5h 

Anaheim, CA (Fall) (Watson et al., 1994c) Fall 1987 13.2 0.0 37.2 0.0 3.7 38.5 0.0j 3.1h Azusa, CA (Summer) (Watson et al., 1994c) Summer 1987 34.9 0.0 15.9 0.0 11.4 6.1 0.0j 5.7h Bakersfield, CA (Magliano, 1988) 1986 27.4 3.0 5.5 9.6l 5.6 0.0 0.5j 0.0 

Bakerfield, CA (Chow et al., 1992a) 1988-89 42.9 1.6 7.7 6.5 5.5 12.7 1.0m 1.5n 

Burbank, CA (Gray et al., 1988) 1986 21.3 0.0 6.1i 0.0 7.2 10.2 0.1j 0.9h Burbank, CA (Summer) (Watson et al., 1994c) Summer 1987 14.0 0.0 17.0 0.0 12.4 6.5 0.0j 5.7h 

Burbank, CA (Fall) (Watson et al., 1994c) Fall 1987 11.0 0.0 39.1 0.0 3.1 25.1 0.0j 1.9h Chula Vista 1, CA (Bayside) (Cooper et al., 1988) 1986 6.7 0.0 0.8 0.0 7.5 0.0 0.4j 2.7h 

Chula Vista 2, CA (Del Ray) (Cooper et al., 1988) 1986 8.2 0.3 1.5 0.0 8.9 0.0 0.6j 1.8h 

Chula Vista 3, CA (Cooper et al., 1988) 1986 9.7 0.3 1.4 0.0 8.2 0.0 0.6j 1.7h Claremont, CA (Summer) (Watson et al., 1994c) Summer 1987 19.4 0.0 14.4 0.0 9.5 6.3 0.0j 4.7h Crows Landing, CA (Chow et al., 1992a) 1988-89 32.2 0.0 2.2 3.4 2.8 6.5 0.5m 1.5n 

Downtown Los Angeles, CA (Gray et al., 1988) 1986 23.8 0.0 6.4i 0.0 7.6 11.2 0.0 1.3h Downtown Los Angeles, CA (Summer) (Watson et al., 1994c) Summer 1987 12.7 0.0 16.2 0.0 13.0 4.4 0.0j 6.5h 

Downtown Los Angeles, CA (Fall) (Watson et al., 1994c) Fall 1987 9.4 0.0 41.1 0.0 3.9 27.5 0.0j 1.8h Fellows, CA (Chow et al., 1992a) 1988-89 29.0 1.4 2.1 3.4 5.1 7.5 7.0m 1.4n 

Fresno, CA (Magliano, 1988) 1986 17.1 0.7 4.0 9.2l 1.8 0.0 0.1j 0.0 

Fresno, CA (Chow et al., 1992a) 1988-89 31.8 0.0 6.8 5.1 3.6 10.4 0.3m 1.0n 

Hawthorne, CA (Summer) (Watson et al., 1994c) Summer 1987 7.5 0.0 5.6 0.0 15.0 0.6 0.0j 7.0h 

Hawthorne, CA (Fall) (Watson et al., 1994c) Fall 1987 8.9 0.0 35.1 0.0 5.1 20.4 0.0j 3.7h Indio, CA (Kim et al., 1992) 33.0 3.0 4.4 7.1 3.6 4.1 0.2j 1.0h Kern Wildlife Refuge, CA (Chow et al., 1992a) 1988-89 15.1 2.0 2.2 4.0 3.3 1.5 0.5m 1.5n 

Lennox, CA (Gray et al., 1988) 1986 16.0 0.1 4.6i 0.0 7.6 7.9 0.2j 3.1h Long Beach, CA (Gray et al., 1988) 1986 20.7 0.0 5.1i 0.0 8.0 9.2 0.1j 2.0h 
Appendix E. Summary of CMB PM10 Source Apportionment Studies

Sampling Site Time Period

Primary

Geological

Primary

Construction

Primary

Motor

Vehicle

Exhaust

Primary

Vegetative

Burning

Secondary

Ammonium

Sulfate

Secondary

Ammonium

Nitrate

Misc.

Source

1

Mis

Sour

2

E - 2

Long Beach, CA (Summer) (Watson et al., 1994c) Summer 1987 11.1 0.0 6.3 0.0 10.9 0.8 0.1j 2.2h 

Long Beach, CA (Fall) (Watson et al., 1994c) Fall 1987 11.3 0.0 42.8 0.0 3.8 23.2 0.0j 2.7h Magnolia, CA (Chow et al., 1992b) 1988 31.7 0.0 11.2 0.0 4.9 19.7 0.3j 1.2h 

Palm Springs, CA (Kim et al., 1992) 16.4 1.4 2.3 5.1 3.7 4.2 0.1j 0.5h 

Riverside, CA (Chow et al., 1992b) 1988 32.6 0.0 7.0 0.0 4.8 21.4 0.3j 1.3h 

Rubidoux, CA (Gray et al., 1988) 1986 43.1 4.0j 5.6i 0.0 6.4 21.3 0.3j 1.0h Rubidoux, CA (Summer) (Watson et al., 1994b) Summer 1987 34.9 4.5 17.3 0.0 9.5 27.4 0.0j 5.1h 

Rubidoux, CA (Fall) (Watson et al., 1994b) Fall 1987 19.2 16.1 30.3 0.0 2.1 31.6 0.0j 1.1h Rubidoux, CA (Chow et al., 1992b) 1988 48.0 0.0 10.2 0.0 5.3 21.7 0.4j 1.5h 

San Jose, CA (4th St.) (Chow et al., 1995b) 13.1 0.0 9.2 31.3 2.3 13.3 0.9h 0.0 

San Jose, CA (San Carlos St.) (Chow et al., 1995b) 11.8 0.0 8.9 31.3 2.1 12.8 0.7h 0.0 San Nicolas Island, CA (Summer) (Watson et al., 1994c) Summer 1987 1.6 0.0 0.9 0.0 3.7 0.5 0.0j 4.3h 

Santa Barbara, CA (Chow et al., 1996b) 9.5 0.0 14.7 0.0 3.2 1.0 6.4h 0.0 

Santa Barbara, CA (GTC) (Chow et al., 1996b) 3.2 0.0 5.1 0.0 2.8 0.5 6.3h 0.0 Santa Maria, CA (Chow et al., 1996b) 7.4 0.0 7.6 0.0 3.1 1.4 5.7h 0.0 Santa Ynez, CA (Chow et al., 1996b) 4.6 0.0 6.8 0.0 2.2 0.6 4.0h 0.0 Stockton, CA (Chow et al., 1992a) 1989 34.4 0.5 5.2 4.8 3.1 7.0 0.7m 1.8n 

Upland, CA (Gray et al., 1988) 1986 25.4 0.4j 4.1i 0.0 6.4 14.5 0.6j 0.6h Vandenberg AFB, CA (Watt Road) (Chow et al., 1996b) 4.5 0.0 3.2 0.0 1.9 1.0 9.3h 0.0 

Telluride 1, CO (Central) (Dresser and Baird, 1988) Winter 1986 32.0 0.0 0.0 98.7 0.0 0.0 61.3p 0.0 Telluride 2, CO (Society Turn) (Dresser amd Baird, 1988) Winter 1986 12.1 0.0 0.0 7.3 0.0 0.0 7.3p 0.0 Pocatello, ID (Houck et al., 1992) 1990 8.3 7.5q 0.1 0.0 0.0 0.0 0.0 0.0 

S. Chicago, IL (Hope et al., 1988) 1986 27.2 2.4 2.8 0.0 15.4s 0.0 15.1t 2.2u 

S.E. Chicago, IL (Vermette et al., 1992) 1988 14.7v 0.0 0.9f 0.0 7.7 0.0 0.8t 0.3h 

Reno, NV (Non-sweeping) (Chow et al., 1990) Winter 1987 9.7 0.0 8.7 0.1 0.6 0.2 0.0 0.0 

Reno, NV (Sweeping) (Chow et al., 1990) Winter 1987 11.8 0.0 11.0 1.2 0.8 0.2 0.0 0.0 

Reno, NV (Chow et al., 1988) 1986-87 14.9 0.0 10.0 1.9 1.3 0.6 0.0 0.0 

Sparks, NV (Chow et al., 1988) 1986-87 15.1 0.0 11.6 13.4 2.7 0.9 0.0 0.0 

Verdi, NV (Chow et al., 1988) 1986-87 7.8 0.0 4.0 1.1 0.9 0.1 0.0 0.0 

Follansbee, OH (Skidmore et al., 1992) 1991 10.0 0.0 35.0 0.0 16.0 0.0 9.3t 0.0 

Mingo, OH (Skidmore et al., 1992) 1991 12.0 0.0 14.0 4.1 15.0 0.0 3.4t 11.0x 

Sewage Plant, OH (Skidmore et al., 1992) 1991 22.0 0.0 12.0 0.0 13.0 0.0 6.6t 8.7x Steubenville, OH (Skidmore et al., 1992) 1991 8.3 0.0 14.0 0.8 14.0 0.0 3.8t 5.0x WTOV Tower, OH (Skidmore et al., 1992) 1991 7.4 0.0 16.0 0.2 15.0 0.0 3.4t 7.9x Wuhan, China (Zelenka et al., 1992) 55.0 21.4 1.2 49.2l 28.1 17.0 49.5y 13.6z 
Appendix E. Summary of CMB PM10 Source Apportionment Studies

E - 3

a

 Smelter background aerosol.

b

 Cement plant sources, including kiln stacks, gypsum pile, and kiln area.

c

 Copper ore.

d

 Copper tailings.

e

 Copper smelter building. 

f

 Heavy-duty diesel exhaust emission.

g

 Background aerosol.

h

 Marine aerosol, road salt, and sea salt plus sodium nitrate.

i

 Motor vehicle exhaust from diesel and leaded gasoline.

j

 Residual oil combustion.

k

 Secondary organic carbon.

l

 Biomass burning.

m Primary crude oil.

n

 NaCl + NaNO3.

o

 Lime.

p

 Road sanding material.

q

 Asphalt industry.

r

 Phosphorus/phosphate industry.

s

 Regional sulfate.

t

 Steel mills.

u

 Refuse incinerator.

v

 Local road dust, coal yard road dust, steel haul road dust.

w Incineration.

x

 Unexplained mass.

y

 Residential coal burning.

z

 Aluminum processing.

aa Primary lead smelter.

Appendix F. Summary of CMB VOC Source Apportionment Studies

F - 1

Study, Location, and PeriodMeasurements Source Apportionment Method FindingsLower Fraser Valley, BritishColumbia, Canada (7/89 to8/91) (Jiang et al., 1997). 

24-hour canister samples everysixth day at 8 sites in LowerFraser Valley for non-methaneorganic compounds (NMOC)a

with 558 species.

Emissions profiles are grouped basedon 74 SARVAPb or AIRSc and

converted to molar emissions ofemitted NMOC species forhydrocarbons (e.g., aromatics, alkanes[parafins], alkenes [olefins], alkynes,oxygenated compounds [esters,carboxylic acids, ketones, aldehydes,alcohols, ethers], others [Si-, S-, N-,or halogen-containing], andunidentified mixtures [C1 to C12

mixtures and >C12 mixtures]). 

Descriptive data analyses were madeto compile emissions and ambientmeasurements.

Overall high degree of similarity was found between emissambient NMOC measurements. Discrepancies between emissions inventory and ambient Nbiogenic compounds (isoprene, -pinene, -pinene) and some light-duty vehicle exhaust. Emission profile is used to calculate rate constants and proin photochemical modeling. Light-duty gasoline vehicles account for ~80% of NMOC Speciated emission profiles for light-duty gasoline exhaust periodically in the emissions inventory.

Western Washington Study,Seattle, WA (6/95 to 9/95, and7/96 to 8/96) (Fujita et al.,

1997b).3-hour C2 - C10 canister and

carbonyl DNPHd samples

beginning at 0600 and 1200PDT twice per week (Tuesdaysand Thursdays) at 7 sites duringmornings and at 6 sites duringafternoons during summer1995. Hourly C2 - C10 canister

and carbonyl DNPH samplesbeginning at 0900, 1200, and/or1500 PDT at 8 sites duringozone episodes in summer1996. Total NMHCj = sum of 25

abundant hydrocarbons.

Effective variance weighted leastsquares CMBe with composite motor

vehicle exhaust (including tunnel,garage, roadside, and dynamometerfor light-duty gasoline and dieselexhaust), gasoline evaporation, liquidgasoline, gasoline vapor, CNGf,

GNGg, LPGh, architectural coatings,

industrial solvents and coatings, andbiogenic profiles. 14 to 27 stable species used in CMBcalculation with 56 to 69 other speciesin profiles and ambient data forvalidation. 

Average source contributions to total measured NMHC in 

Summer 1995 Summer 1996

Vehicle exhaust 39% to 57% 40% to 104%

(including 0% (including 0.8%to 38% diesel to 66% dieselexhaust and exhaust and19% to 54% 16% to 65% light-duty light-dutyvehicle exhaust) vehicle exhaust)

Liquid gasoline 2% to 23% 0% to 37%Gasoline vapor 7% to 15% 7% to 36%Gas (CNG) 0% to 7.7% 0% to 5.7%Gas (GNG) 0.6% to 12% 0% to 7.1%Gas (LPG) 0% to 3.1% 0% to 2.4%Biogenics 0.2% to 2.5% 1.2% to 7.8%Unexplained 0% to 28% –10% to –50%Larger errors in source contribution estimates were reporteuncertainties in quantifying unidentified sources and fewer 
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 2

San Joaquin Valley and SanFrancisco Bay Area, CA (7/90to 8/90) (Fujita et al., 1995b). 

2-hour C2 - C12 canister and

carbonyl DNPH samples at0800, 1000,1200, and 1400PDTat 34 sites in centralCalifornia representing urban,nonurban, oilfield background,and forested areas. NMOG =mass equivalent sum of all GCpeaks from canister sample pluscarbonyls from DNPH. 

Effective variance weighted leastsquares CMB with motor vehicleexhaust, gasoline evaporation, liquidgasoline, CNG, GNG, LPG, oilproduction, architectural coatings,industrial solvents and coatings,biogenics, and acetone profiles fromother studies. 26 stable species used in CMBcalculation with 57 other species inprofiles and ambient data forvalidation.

Typical average contributions to NMOG:Vehicle exhaust 35% to 70% Gasoline evaporation 10% to 40%Coatings and solvents 1% to 2% Oil production 30% to 50% (southern 

sites near oilfields), 5% to 15% (other sites)

Biogenic: 10% to 15% (Yosemite and Sequo

measured isoprene only)

Others 2% to 20% (morning), 

20% to 60% (afternoon)

Most NMOG compounds were explained well by the profisamples. Reactive precursors and end-products diverged fafternoon samples, resulting in large contributions from "Ovehicle contributions were ~twice proportions from inventsquares.

Los Angeles, CA (southernCalifornia and southeast desertareas) (6/74 to 9/74) (Mayrsohnand Crabtree, 1976; Mayrsohn

et al., 1977).

3-hour C1 - C10 samples were

acquired starting 0200, 0600,1100, 1200, and 1400 PDT at 6south coast air basin sites(around Los Angeles, LongBeach, and El Monte) and at 2southeast desert air basin sites(Banning and Palm Springs). NMHC = is the sum of 39species plus C11 - C18

compounds.

Multi-regression analysis and ordinaryleast square equations were used withvehicle exhaust, liquid gasoline,gasoline vapor, CNG, GNG, and LPGprofiles derived in 1973.

Average contributions to NMHC (C1 to C10):

CMBcalculated Regression

Vehicle exhaust 53% 47%Liquid gasoline 12% 31%Gasoline vapor 10% ––CNG 5% 8%GNG 19% 14%LPG 1% ––
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 3

Los Angeles, CA (8/86) (Harleyet al., 1992).

Hourly, 4-, and 8-hour canistersamples collected at 0400,0600, 0800, and 1100 PDT at 9sites between 8/10/86 and8/21/86.

Effective variance least squares CMBwith revised and respeciated organicgas emissions profiles for gasolineengine exhaust (non-catalyst,catalyst), unburned gasoline (wholeliquid gasoline, headspace gasolinevapor), commercial jet exhaust,architectural coatings (solvent-borne,water-borne, thinning solvents),industrial coatings, and industrialadhesives.

Average contributions to NMOC:Gasoline engine exhaust 31% to 37%Whole liquid gasoline 32% to 38%Headspace gasoline vapor 5% to 13%Waste and natural gas 10% to 15%Dry cleaning 0% to 4%Degreasing solvents 5% to 12%Respeciation of organic gas emissions result in large changemission estimates for 1,3-butadiene, ethylene glycol, metReactivity for surface coatings and thinning solvents are reKey discrepancies between CMB and the emissions inventunburned gasoline. Excess unburned gasoline is suspected emissions from tailpipe, hot-soak evaporative, and fuel spi

Southern California Air QualityStudy (SCAQS), Los Angeles,CA (7/87 to 9/97) (Fujita et al.,

1994)Hourly C2 - C12 canister samples

at 0700, 1200, and 1600 PDT(PST during fall) at 8 sitesduring summer and at 6 sitesduring fall. Additional samplesat 0300, 0900, and 1400 at twosites during the summer and fallin Southern California. Total NMHC = all GC/FIDpeaks except for oxygenatedcompounds.

Effective variance weighted leastsquares CMB with motor vehicleexhaust, gasoline evaporation, liquidgasoline, gasoline vapor, CNG, GNG,LPG, architectural coatings, industrialsolvents and coatings, and biogenicprofiles from CARB’s modelingemissions data system and auto/oilprogram. 27 stable species used in CMBcalculation with 34 other species inprofiles and ambient data forvalidation.

Average CMB-calculated source contributions to total NM

Summer Fall

Vehicle exhaust 49% to 54% 54% to 68%Liquid gasoline 11% to 17% 14% to 15%Gasoline vapor 10% to 11% 7% to 11%Other 21% to 30% 11% to 21%CMB estimates for 0700 to 0800 PDT sampling periods arthan the emissions inventory, with even larger discrepanciNon-motor-vehicle hydrocarbon emissions are overestimatwhile on-road motor vehicle emissions are underestimatedPhotochemical modeling with adjusted (increased) on-road model performance.
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 4

Los Angeles, CA (7/95 to10/95) (Fujita et al., 1997b)

3-hour C2 - C12 canister samples

starting at 0700 and 1400 PDTfor six 7-day periods at 3 ARBsites and for 3-day periods at 8CRCk sites. 

NMHC = sum of 25 speciesplus MTBEl.

Effective variance weighted leastsquares CMB with composite motorvehicle exhaust (including tunnel,garage, roadside, and dynamometerfor light-duty gasoline and dieselexhaust), gasoline evaporation, liquidgasoline, gasoline vapor, CNG, GNG,LPG, architectural coatings, industrialsolvents and coatings, and biogenicprofiles. 27 stable species used in CMBcalculation with 56 other species inprofiles and ambient data forvalidation.

Average contributions to sum of NMHC and MTBE with vehicle profiles are:

3 ARB sites 8 CRC sites

Vehicle exhaust 54% to 64% 56% to 68%

(including 11% (including 10%to 15% diesel to 15% dieselexhaust and exhaust and38% to 50% 42% to 54% light-duty light-dutyvehicle exhaust) vehicle exhaust)

Liquid gasoline 0.6% to 11% 1% to 14%Gasoline vapor 15% to 29% 10% to 20%Gas (CNG) 3.1% to 3.7% 2.2% to 2.7%Gas (GNG) 5.2% to 8.7% 6.6% to 8.6%Gas (LPG) 2.6% to 3.7% 1.9% to 3.0%Biogenics 0.2% to 0.3% 0.1% to 0.2%Coatings (architectural) 3.2% to 5% 0.3% to 1.1%Coatings (industrial) 1.7% to 9.3% 4.1% to 6.9%Coatings (other) 1.7% to 10% 1.1% to 8.9%Unexplained –11% to –3.5% –0.9% to 7.8%Ratios of tailpipe to evaporative emissions are 2.4 in the mafternoon. Morning samples attribute 48% to vehicle exhaevaporative emissions. Emission rates of heavy-duty diesel hydrocarbons are twicgasoline on a -per-mile basis. About 50% of diesel emissi

Motor-vehicle-related emissions and sources of ethane and for >90% of ambient NMHC. Evidence of emissions from found, but not as significant as the 30% to 40% identified 

Boulder, CO (2/91 to 1/91)(Goldan et al., 1995).

Hourly C3 - C10 NMHC with

Auto-GCm, NO, NOx, NOy, CO,

and SO2 at one

traffic-dominated site betweenBoulder and Denver, CO. 

Calculated correlations and ratiosamong measured components,especially with respect to NOy. These

were compared with speciatedestimates from the 1985 NationalAcid Precipitation AssessmentProgram Emissions Inventory.

Did not explicitly calculate source contributions to NMHC
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 5

1996 Phoenix Ozone Study,Phoenix, AZ (summer, 1996)(Fujita and Lu, 1997)Four-hour canister samplesbeginning at 0700 and 1130 attwo sites and at 0700 at onesite.

Effective variance weighted leastsquares CMB with composite motorvehicle exhaust (including tunnel,garage, roadside, and dynamometerfor light-duty gasoline and dieselexhaust), gasoline evaporation, liquidgasoline, gasoline vapor, CNG, GNG,LPG, architectural coatings, industrialsolvents and coatings, and biogenicprofiles. 27 stable species used in CMBcalculation with 56 other species inprofiles and ambient data forvalidation.

Average contributions to sum of NMHC at 3 sites are:Vehicle exhaust 5.4% to 11.7% Gas (LPG) Gasoline exhaust 51.5% to 59.0% Biogenics Liquid gasoline 6.6% to 8.9% Coatings (architeGasoline vapor 2.8% to 7.3% Coatings (industrGas (CNG) 1.2% to 3.2% Unexplained Gas (GNG) 5.6% to 8.0%Because of the reactivity of isoprene, biogenic contributionActual contributions may be 5 to 10 times higher than CM

Coastal Oxidant Assessment forSoutheast Texas (COAST)Study, Houston, TX (7/93 to8/93) (Fujita et al., 1995a; Lu,

1996).Hourly canister (C2 - C10 HC)

and DNPH cartridge (C1-C7

carbonyl compounds) from 6surface sites, 6 times per day,during summer 1993. Measurements aloft (aircraft)starting ~0600 and ~1200 CDTeach day at 6 locations. Hourlyauto-GC measurements at 2sites. Total NMHC = sum of 25abundant hydrocarbons.

Effective variance weighted leastsquares CMB with motor vehicleexhaust, gasoline evaporation, liquidgasoline, gasoline vapor, CNG, GNG,LPG, architectural coatings, industrialsolvents and coatings, and biogenicprofiles from CARB’s modelingemissions data system and auto/oilprogram. 27 stable species used in CMBcalculation with 34 other species inprofiles and ambient data forvalidation.

Average CMB-calculated source contribution to total NMH

2 auto- 6 surface 6 alofGC sites sites sites

Vehicle exhaust 19% to 27% 19% to 36% 17% tLiquid gasoline 12% to 15% 7% to 15% 1% to Gasoline vapor 13% to 14% 7% to 20% 0% to CNG 11% to 12% 3.5% to 6.4% 8% to Industrial 9% to 18% 22% to 50% 19% tBiogenic 0.4% to 1.6% 0.5% to 1.8% 0.1% Unexplained 22% to 27% –2% to 16% –1% tProfiles need to be developed in Houston area to separate gcontribution. Vehicle exhaust and industrial sources (e.g., NMHC contributors. Source contributions aloft were 20% surface. Emissions inventory overestimates biogenic emisfor the sum of liquid gasoline, gasoline vapor, industrial, a
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 6

1996 Paso del Norte OzoneStudy, El Paso, TX (8/96 to9/96) (Fujita et al., 1998)

Hourly auto-GC measurementsat one site and two-hourcanister samples five times aday at four sites. Additionalsurvey canister samples at fivesites.

Effective variance weighted leastsquares CMB with diesel exhaust,gasoline vehicle exhaust, propanebuses, gasoline evaporation, liquidgasoline, gasoline vapor, CNG, LPG,industrial, solvent-based primers andenamels and isoprene.27 stable species used in CMBcalculation with 34 other species inprofiles and ambient data forvalidation.

Average CMB-calculated source contribution to total NMH

El Paso El Paso JuarezGC sites sites sites

Diesel exhaust 1% to 6% < 2% 2% to Gasoline exhaust 26% to 44% 50% to 65% 50% tPropane bus 0.3% to 2.8% < 2% 2% to Liquid gasoline 26% to 33% negligible 2% to Gasoline vapor 0.3% to 3.7% includLPG 0.5% to 3.4% 0 to 3% 2% to CNG 1.7% to 5.8%Industrial 7% to 21% 10% to 20% 10% tSurface coating 1.8% to 5.8% 1% to 1.5% 2% to Biogenic 0% to 0.1% negligible negligUnexplained 0% to 1.3%Source contribution estimates for auto-GC samples are aveweek and by wind direction.

Chicago, IL (9/85 to 10/85)(O’Shea and Scheff, 1988).45-minute Teflon bag samplesat 1200 and 1300 LDT. NMHC = sum of <C4,isobutane, n-butane, isopentane,2,3-dimethylbutane,2-methylpentane,3-methylpentane, hexane, andbenzene analyzed by GC/FID.

Ordinary weighted least squares CMBwith vehicle exhaust, petroleumrefining, and gasoline vapor profiles.

Average contributions to NMHC:Vehicle exhaust 47% to 62%Gasoline vapor 7% to 13%Industry (refining) 9% to 13%Others 5% to 15%CMB calculations were compared with dispersion model remissions inventory and to develop pollution control strate
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 7

Chicago, IL (11/86 to 2/87)(Aronian et al., 1989).

4-hour C2 - C7 and Tenax trap

(aromatic and chlorinated)samples beginning 0800 and1200 LDT at 3 downtownChicago sites. 20-hour samplesstarting 1200 LDT at onecentral city site. NMOC = sum of 23 organiccompounds (including C2 - C7,

alkanes, and aromatic andchlorinated compounds)analyzed by GC/FID andGC/MS.

Ordinary least squares CMB with 8profiles (vehicle exhaust, gasolinevapor, architectural coatings, graphicarts, vapor degreasing, dry cleaning,wastewater treatment, and petroleumrefining). 23 stable species used in CMBcalculation.

Average contributions to NMOC:

Emission

NMOC Inventory

Vehicle exhaust 35% 34%Gasoline vapor 8.4% 7.6%Solvent (architectural coating) 1.5% 5.5%Solvent (graphic arts) 1.3% 9.8%Vapor degreasing 2.4% 3.1%Dry cleaning 0.5% 0.1%Industry (refining) 18% 1.3%Others 33% 39%Largest discrepancies were found for petroleum refining cothe CMB-calculated and emissions inventory estimates.

Chicago, IL (7/87 to 9/87)(Scheff and Wadden, 1993).4-hour C2 - C7 and Tenax trap

(aromatic and chlorinated)samples beginning 0800 and1200 LDT at 3 downtownChicago sites. 20-hour samplesstarting 1200 LDT at onecentral city site. NMOC = sum of 23 organiccompounds (including C2 - C7,

alkanes, and aromatic andchlorinated compounds)analyzed by GC/FID andGC/MS.

Ordinary least squares CMB with 8profiles (vehicle exhaust, gasolinevapor, architectural coatings, graphicarts, vapor degreasing, dry cleaning,wastewater treatment, and petroleumrefining). 23 stable species used in CMBcalculation.

Average contributions to NMOC:

Emission

NMOC Inventory

Vehicle exhaust 21% 39%Gasoline vapor 7.1% 7.6%Solvent (architectural coating) 3.1% 5.5%Solvent (graphic arts) 1.0% 9.8%Vapor degreasing 3.4% 3.1%Dry cleaning 0.3% 0.1%Industry (refining) 7.4% 1.3%Largest discrepancies were found for petroleum refining cothe CMB-calculated and emissions inventory estimates.
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 8

Detroit, MI (7/88 to 8/88)(Chung et al., 1996).

1-hour canister samples at 0600,0800, 1200, 1400, and 2200LDT at 18 sites. NMHC = sum of 24 abundanthydrocarbons.

Ordinary weighted least squares CMBwith vehicle exhaust, gasoline vapor,petrochemical, architectural coatings,graphic arts, polyethylene, refinery,and coke oven profiles from otherstudies. Sensitivity tests with 18 stable and 24reactive species yielded similar sourcecontribution estimates. 

Average contributions to NMHC:

CMB- Emissioncalculated Inventory

Vehicle exhaust 28% 33%Gasoline vapor 9% 7%Solvent (architectural coating) 2.5% 3.8%Solvent (graphic arts) 4.7% 0.7%Industry (refinery) 17% 0.7%Industry (coke oven) 3.7% 2.0%Other 35% 39%CMB model calculations can be used to verify the emissioprior to ozone modeling to derive more reliable source/rec

Southeast Michigan OzoneStudy (SEMOS), Detroit, MI(7/93 to 8/93) (Scheff et al.,

1996).2-hour canister data at 0600,1000, and 1400 LDT at 4 sitesfor 8 days during summer 1993. NMOC = sum ofchromatographic peaks.

Ordinary weighted least squares CMBwith vehicle exhaust, gasoline vapor,petrochemical, architectural coatings,graphic arts, polyethylene, refinery,and coke oven profiles from otherstudies. Sensitivity tests with 18 stable and 24reactive species yielded similar sourcecontribution estimates.

Average CMB-calculated contributions to NMOC:Vehicle exhaust: 37% to 40%Liquid gasoline 2% to 9%Gasoline vapor 1% to 3%Solvent (architectural coating) 2% to 5%Solvent (graphic arts) 1% to 4%Industry (refinery) 3% to 10%Industry (coke ovens) 1% to 4%Percent of total NMOC explained by CMB ranged from 5469% during early evening (i.e., 1800 LDT). Good agreememissions inventory for sum of vehicle and gasoline, archicoke ovens. CMB reports higher contributions from refineindustry than does inventory. Study demonstrated the effedevelopment of emission control strategy.
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 9

Beaumont, TX, Detroit, MI,Chicago, IL, Washington, DC,and Atlanta, GA (summers of1984 to 1988) (Kenski et al.,

1995).1-hour and 3-hour canistersamples at 0600 and 0900 LDT,except at Detroit with 1-hoursamples at 0600, 0900, 1200,1400, and 2200 LDT. NMHC = sum of 24 abundanthydrocarbons.

Ordinary weighted least squares CMBwith vehicle exhaust, gasoline vapor,petrochemical, architectural coatings,graphic arts, polyethylene, refinery,and coke oven profiles from otherstudies. Sensitivity tests with 18 stable and 24reactive species yielded similar sourcecontribution estimates. 

Average CMB-calculated contributions to NMHC:Vehicle exhaust: 28% to 55% Gasoline vapor 9% to 20% Solvent (architectural coating) 2% to 6% Solvent (graphic arts) 5% to 12% Industry (refinery) 9% to 17% (in cities with Industry (coke ovens) 4% (in Detroit and WashiPolyethylene 7% (in Beaumont only) Motor vehicle fractions agreed with inventories, but refineproportions were substantially different.

Boston, MA (8/95 to 9/95)(Fujita et al., 1997b).

Hourly C2 - C12 canister samples

at 0700 and 1300 EST for 14days at 3 main sites and 3supplemental sites. Total NMHC = 25 mostabundant NMHC species plusMTBE.

Effective variance weighted leastsquares CMB with composite motorvehicle exhaust (including tunnel,garage, roadside, and dynamometerfor light-duty gasoline and dieselexhaust), gasoline evaporation, liquidgasoline, gasoline vapor, CNG, GNG,LPG, architectural coatings, industrialsolvents and coatings, and biogenicprofiles. 27 stable species used in CMBcalculation with 56 other species inprofiles and ambient data forvalidation.

Average source contributions to sum of NMHC and MTBEVehicle exhaust: 50% to 69% (including 17

exhaust and 27% to 48% lexhaust)

Liquid gasoline 2% to 19%Gasoline vapor 3% to 10%Gas (CNG) 3.1% to 3.5%Gas (GNG) 2.0% to 4.4%Gas (LPG) 0% to 0.3%Gas (biogenic) 1.4% to 1.5%Solvent (architectural coating) 0.4% to 0.6%Solvent (industrial coating) 1.5% to 3.0%Unidentified 6% to 11%Unexplained 2% to 5%Ratios of tailpipe to evaporative emissions with and witho4.0 and 7.9 in the morning, and 3.9 and 7.4 in the afternooratios are 2 to 4 times those found in Southern California. attribute 50.4% and 50.1% to vehicle exhaust and 12.5% aemissions with and without the East Boston sites, respectivMotor-vehicle-related emissions and sources of ethane and for >90% of ambient NMHC. Evidence of emissions from found, but not as significant as the 30% to 40% identified 
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 10

1995 NARSTO-NortheastOzone Study (6/95 to 8/95)(Fujita and Lu, 1998)Four, 3-hr canister samples atfive sites and three, 3-hrsamples at three sites. Also 13,hourly auto-GC and 7 canisterbased PAMS sites operated bystate and local air pollutionagencies in the northeasternstates.

Effective variance weighted leastsquares CMB with composite motorvehicle exhaust (including tunnel,garage, roadside, and dynamometerfor light-duty gasoline and dieselexhaust), gasoline evaporation, liquidgasoline, gasoline vapor, CNG, LPG, and biogenic profiles. 27 stable species used in CMBcalculation with 56 other species inprofiles and ambient data forvalidation.

Average CMB-calculated source contribution to total NMH

10 surface 6 surface 7 alofPAMS sites sites sites

Diesel exhaust 3% to 44% 6% to 8% 6% to Gasoline exhaust 17% to 62% 7% to 23% 9% to Liquid gasoline 2% to 19% 1% to 7% 1.5% Gasoline vapor 12% to 31% 2% to 6% 0.8% CNG 7% to 12% 3% to 34% 7% to LPG 1% to 30% 0.6% to 3.2% 0.7% Biogenic 1% to 12% 2% to 10% 0% to Unidentified 46% to 72% 59% tUnexplained -8% to 17% –4% to -8% –6% tUnidentified compounds are the largest fraction of total NMNortheast supplemental hydrocarbon measurement. These in area that reflect more regional air quality, rather than PAmore urban/suburban area. Because of the reactivity of isocontributions are lower limits. Actual contributions may bthan CMB estimates (Fujita, 1997). 

Newark, NJ (7/80 to 8/90)(Scheff and Klevs, 1987).1-hour canister samples at 0600and 0800 EDT at 2 sites: hightraffic and industrial. NMHC = sum of 24 abundanthydrocarbons.

Ordinary weighted least squares CMBwith vehicle exhaust, gasoline vapor,petrochemical, paint, and refineryprofiles from other studies. Sensitivity tests with 18 stable and 24stable and reactive species yieldedsimilar source contribution estimates.

For average NMHC:Vehicle Exhaust 15% to 21%Gasoline vapor 18% to 33%Petrochemical 3% to 6%Paint 1% to 7%Refinery 24% to 27%Unexplained 15% to 28%Industrial site showed three times the petrochemical and rethan the nearby urban site.
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 11

Kanawha Valley, WV(Charleston, WV) (4/87 to 3/88)(Cohen et al., 1991a, 1991b).

12-hour VOC (Tenax-GCdownstream of glass-fiber filter)and particles.Samples starting 0600 and 1800EST for 15 days per month for4 months at 3 sites. 19 VOCs were measured.A mobile van also collectedNO2, NO3, O3, light scattering,

and meteorologicalmeasurements alone with VOCand particles at one in-valleysite.

Univariate and multivariatecorrelation, analysis of variance(ANOVA), and factor analysismethods were used to resolve sourcecontributions.

Factor analysis with % variance explained:Vehicle-related exhaust 12% to 48% (e.g., high(including tailpipe emissions, correlations for decane,fuel evaporation, and road dust) m-xylene, and benzene)

General VOCs 15% to 17% (e.g., high c

methylchloroform, toluen

Chlorinated VOCs 5% to 6% (e.g., high corr

carbon tetrachloride, met

Forest fire and combustion 11% to 38% (e.g., high related emissions correlations for 

benzene, decane, particle organic and elemental carbon)

Acid particles 5% to 13% (e.g., high

high correlations for H2SO4, S)

VOC is a good indicator for motor vehicle exhaust if the awith alkylated-aromated compounds.Local and regional sources contributed to pollution levels WV, using a combination of gas and particle data.

1990 Atlanta Ozone PrecursorStudy, Atlanta, GA (7/90 to8/90) (Henry et al., 1994).

Hourly C2 - C10 canister samples

with auto-GC at 6 sites.Total NMOC = 54 hydrocarbonspecies with ethane excluded.

GRACEn and SAFERo statistical

methods used to derive vehicle-relatedsource compositions for vehicles inmotion (tailpipe plus running losses),evaporative gasoline, and headspacegasoline vapor. Statistically derivedcompositions were compared withmeasured source compositions. 37species were used to derive sourceprofiles.

Unburned gasoline comprised ~50% of tailpipe emissions. unaccounted sources for whole gasoline.Most studies include the whole gasoline contribution with vapor component.1990 Atlanta data shows that the whole gasoline componethan the headspace gasoline vapor component. Relative so62% roadway vehicle emissions, 15% whole gasoline, and GRACE/SAFER provide cost-effective alternatives to estibut require several hundred observations.
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 12

1990 Atlanta Ozone PrecursorStudy, Atlanta, GA (7/90 to8/90) (Lin and Milford, 1994).Hourly C2 - C10 canister samples

with auto-GC at 2 sites.Total NMOC = 54 hydrocarbonspecies with ethane excluded.

Source profiles were decay-adjustedto account for different reaction ratesof VOCs for roadway vehicle andgasoline vapor emissions. PCAp,

CMB, and decay-adjusted CMB wereapplied to test synthetic and ambientdata. 13 stable species used in CMBcalculation with 36 other species inprofiles and ambient data forvalidation.

Decay factors assumed for propylene were 0.78 to 0.92 (mto 0.60 (afternoon samples), and 0.59 to 0.76 (average). Adjusted CMB source contributions to NMHC in % NMH

CMB- Lewis etcalculated al. (1993)

Roadway vehicle emissions 61% to 65% 44%Whole gasoline not calculated 15%Headspace gasoline vapor 4% to 8.2% 4%Total vehicle-related sources in summer 1990 66% to 73% 62%CMB adjusted for decay factor gives small improvements estimates.

1990 Atlanta Ozone PrecursorStudy, Atlanta, GA (7/90 to8/90) (Conner et al., 1994).

Hourly C2 - C10 canister samples

with auto-GC at 6 sites.Total NMOC = 54 hydrocarbonspecies with ethane excluded.

Source profiles for roadway vehicleemissions, whole gasoline, andheadspace gasoline vapor weredeveloped. Profiles for pure propane,natural gas, and industrial coatings(auto painting) were used. 18 stablespecies used in CMB calculation with35 other species in profiles andambient data for validation.

Average contribution to total NMOC in % of total NMOC Roadway vehicle emissions 69% to 79%Whole gasoline 6% to 16%Headspace gasoline vapor 4% to 15%Propane and natural gas 2% to 5%Industrial coatings (auto painting) 0% to 5%Emissions inventory (based on MOBILE model for mobile underestimate actual motor vehicle source contributions ba10% to 30%, assuming 2% to 47% biogenic emissions.Emissions inventory generally overestimate point and area 20%.
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 13

1990 Atlanta Ozone PrecursorStudy, Atlanta, GA (7/90 to8/90) (Lewis et al., 1996).

Hourly C2 - C10 canister samples

with auto-GC at 1 site. Total NMOC = 54 hydrocarbonspecies with ethane excluded,using 5 weekday samples.

Source profiles for roadway vehicleemissions, whole gasoline, andheadspace gasoline vapor weredeveloped. Profiles for pure propane,natural gas, and industrial coatings(auto painting) were used. 16 fittingspecies used in CMB calculation with31 remaining species in profiles andambient data for validation.

Average contribution to total NMOC in % of total NMOC Roadway vehicle emissions 49%Whole gasoline 10%Headspace gasoline vapor 3.7%Propane 4.9%Natural gas 2.9%Isoprene 2.2%Industrial solvent 2.6%Unexplained 24.4%Uncertainties in source contribution estimates are 9% to 14natural gas, propane-rich, and isoprene-rich emissions; 30% evaporative gasoline emissions; and 50% for industrial solthe VOC 14C abundanceq attributed 9% to 17% VOC as bio

mid-morning and late evening samples, respectively. Addprogress using linear programming-factor analysis (e.g., Grefine source profiles.

Tokyo, Japan (7/81) (Wadden et

al., 1986).

1- to 1.5-hour aloft Pyrex glassvessel (aircraft, 300 to 1,500m)samples from 2 days (7/16/81and 7/18/81). NMHC = 18 compounds fromC2 to C6.

Ordinary least squares CMB with 8profiles (vehicle exhaust, gasolinevapor, petrochemical plants, paintsolvents, degreasing, dry cleaning,petroleum refinery, and rubberproduction).

Average contributions to NMHC:Vehicle exhaust 7%Gasoline vapor 11%Paint solvents 27%Petroleum refinery 27%Unexplained 29%Short-term (~1 hour) samples are useful in providing diurninformation to delineate source/receptor relationships.
Appendix F. Summary of CMB VOC Source Apportionment Studies

Study, Location, and PeriodMeasurements Source Apportionment Method Findings

F - 14

Sydney, Australia (9/79 to 6/80)Nelson and Quigley, 1982,Nelson et al., 1983)

Hourly C2 - C10 samples

acquired on 400 ml glass gaspipettes btween 0600 and 1200on non-windy days at 3 sites.NMHC = sum of 69 compoundsfrom C2 to C10.

Comparison between ambientmeasurements, source measurements(i.e. vehicle exhaust, evaporativegasoline, industrial solvents), andemissions inventory were made.

Average contributions to NMHC:Vehicle exhaust 36 ± 4%Evaporative gasoline 32 ± 4%Evaporative solvents 23 ± 4%Gas leakage 3.5 ± 5%Industrial Processes 5 ± 1%Source contribution estimates vary significantly from the c(high concentration of vehicle exhaust) to the sites near the concentrations of evaporative and industrial process emissSensitivity of source contribution estimates in source comp4.0%.Study results are in reasonable agreement with hydrocarboThe study results indicate a somewhat greater contribution gasoline relative to vehicle exhaust and solvent evaporatio

The Netherlands (1974 to 1994)(cities of Delft, 1974 and 1977;Hague, 1974 and 1977;Kollumerwaard, winter andsummer 1994; and Brabart,summer 1994) (Guicherit,1997).Hourly C2 - C5 samples

acquired with cold trap(stainless steel loop packed withglass beads) submerged intoliquid nitrogen. Hourly C3 - C9 andpreconcentrated C6 - C16

samples acquired with carbontrap (carbosieve, carbotrap,carbotrap C) at 3 sites.

Comparisons between ambientmeasurements, source measurements(tunnel and dynamometer), andemissions inventory were made fordata collected over the last 20 years.

No quantitative calculations were provided.Using ambient measurements to validate emissions estimathat major hydrocarbon emissions are parafins (46%), aromolefins (15%), with the remainder consisting of acetylene aToluene concentrations were consistently >20%, which cloemissions derived from tunnel and dynamometer measuremDiesel exhaust accounted for 14% to 21% of NMHC with Elevated ambient temperature during summer resulted in hemissions and increased photochemical degradation.
Appendix F. Summary of CMB VOC Source Apportionment Studies

F - 15

a NMOC = non-methane organic compounds. b SAROAD = U.S. EPA’s Storage and Retrieval of Aerometric Data system. c AIRS = U.S. EPA’s Aerometric Information Retrieval System. d DNPH = C18 cartridge impregnated with 2,4-dinitrophenylhydrazine. e CMB = Chemical Mass Balance f CNG = compressed natural gas. g GNG = geogenic natural gas. h LPG = liquefied petroleum gas. i California Air Resources Board, Sacramento, CA.

j NMHC = non-methane hydrocarbon. k CRC = Coordinating Research Council, Atlanta, GA. l MTBE = methyl-t-butyl ether, a major component in reformulated gasoline. m Auto-GC = automated gas chromatography with flame ionization detector (FID). n GRACE = Graphical Ratio Analysis for Composition Estimates, used to generate constraints (e.g., ratios) for SAFER model

input.

o SAFER = Source Apportionment by Factors with Explicit Restriction. p PCA = Principle Component Analysis. q Klouda et al. (1996)

G - 1

APPENDIX G. PROCEDURES FOR TREATING SECONDARY PARTICLES

One of the key assumptions made by EPA-CMB8.2 is that chemical species do not react

with each other; Section 1.2 of the EPA-CMB8.2 Users Manual (EPA, 2004) and Section 4.1 of

this Protocol. This means that compositions for the source categories are obtainable which

represent the source profile as it is perceived at the receptor for the chemical species of interest

(Section 4.3.1 of this Protocol). Thus, EPA-CMB8.2 assumes no changes to the aerosol during

transport and ideally apportions the primary material that has not changed between source and

receptor. However, certain species, e.g., sulfur (S), that dominate polluted airsheds have both

primary and secondary sources.

In such airsheds, secondary aerosols may contribute significantly to the ambient loading

seen at receptors. These secondary materials are often in the form of reactive species such as

NH4

+

, SO4

=

, NO3

-

, and organic carbon. If sources of such materials are not explicitly treated,

EPA-CMB8.2 will tend to underaccount for total particle mass (% MASS value in the Main

Report (Table 4.2-1 of this Protocol). If a compound which is secondarily formed or is normally

associated with regional scale pollution (such as sulfate) is included as a fitting species, a single

constituent source type must also be included in the fit. Use of the single constituent source

profile for secondary particles was initially suggested by Watson (1979) and is described briefly

in the Section 4.3.2 of this Protocol. With this technique, the secondary species are apportioned

to chemical compounds rather than directly to sources (Section 4.1 of this Protocol).

Table G-1 illustrates an example of the way the technique was used in an actual application

for California's South Coast Air Basin (Watson et al., 1994). Secondary source profiles

consisting of "pure" ammonium sulfate (AMSUL), ammonium bisulfate (AMBSUL), ammonium

nitrate (AMNIT), and organic carbon (OC) were used to apportion the remaining NH4

+

, SO4

=

,

NO3

-

, and OC that would not be apportioned to the primary particle profiles. For some

secondary species thought to be significant (e.g., note the OC column), a source profile was

created which includes only that component, in which the percentage composition in the profile

is set to 100%. For other secondary species, only some chemical components may have been

measured. For instance, elemental S and/or sulfate ion (SO4

=

) may be measured rather than

ammonium sulfate, (NH4)2SO4. In such a case, the respective species abundances in the

(NH4)2SO4 would equal the mass % of each species in (NH4)2SO4. Thus, in the AMSUL profile

the abundance of S in pure (NH4)2SO4 is listed as 24.3% and the abundance of SO4

=

 is listed as

72.7%. Examples are also given for other secondary species and their chemical components. In

all cases, the uncertainty was arbitrarily set to 10%. In the EPA-CMB8.2 calculations, the

portion of a measured secondary species not accounted for by other source types becomes

assigned to its corresponding single constituent source type, as represented by profiles such as

those described here.

The examples given above are described as profiles for secondary species. However, the

secondary profile may not represent secondary aerosol exclusively. For example, Watson et al.

(1994) indicated that the OC profile in Table G-1 may account for contributions from fugitive

sources not included in the EPA-CMB8.2 calculation (e.g., cooking, plant parts, or tire wear) in

addition to secondary sources. In such a case, the technique may be considered as a means to get

an upper estimate of the amount of aerosol attributable to secondary formation.
G - 2

One of the advantages of using the single constituent source profile technique is that it can

account for that part of the ambient mass that is not accounted for by the primary sources

included in the EPA-CMB8.2 calculations. However, this technique cannot yield any

information on the specific source types contributing to the species in the single constituent

profiles. Furthermore, the ambient mass may still be underestimated in some cases. For

example, Conner et al. (1993) reported that fine particle mass may have been underaccounted for

in their CMB calculations because of the likelihood for some amount of water associated with

hygroscopic (or deliquescent) sulfates. The amount of mass due to this water depends of the

form of the sulfate and relative humidity factors.
1

Reproduced with permission from Watson et al. (1994)

2

TC (Total Carbon) = OC + EC; sum does not include Na+, Cl-, S, or TC.

3

PM10 - PM2.5

4

Conc. is the average abundance (% of total mass) for several samples of emissions from the same source type. Unc. is the

standard deviation of the abundances for these samples.

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Table G-1. Secondary Aerosol Source Profiles (abundances are % of total mass)1

Species2 AMSUL AMBSUL AMNIT OC

PM2.5 & Coarse3 PM2.5 & Coarse3 PM2.5 & Coarse3 PM2.5 & Coarse3

Conc. ± Unc.4 Conc. ± Unc.4 Conc. ± Unc.4 Conc. ± Unc.4

Cl- 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

NO3

- 0.0 ± 0.0 0.0 ± 0.0 77.5 ± 7.8 0.0 ± 0.0

SO4

= 72.7 ± 7.3 83.5 ± 8.3 0.0 ± 0.0 0.0 ± 0.0

NH4

+ 27.3 ± 2.7 15.7 ± 1.6 22.6 ± 2.3 0.0 ± 0.0

Na+ 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

TC 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 100 ± 10

OC 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 100 ± 10

EC 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Na 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Al 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Si 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

P 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

S 24.3 ± 2.4 27.9 ± 2.8 0.0 ± 0.0 0.0 ± 0.0

Cl 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

K 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Ca 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Ti 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

V 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Cr 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Mn 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Fe 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Ni 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Cu 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Zn 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

As 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Se 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Br 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Sr 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0
G - 4

Mo 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Cd 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Sn 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Sb 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Ba 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Hg 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Pb 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Sum 100 ± 8 99.1 ± 8.5 100 ± 8 100 ± 10
G - 5

EPA, 2004. EPA-CMB8.2 Users Manual. Report No. EPA-452/R-04-011. December

2004. U.S. Environmental Protection Agency, Research Triangle Park, NC.

Watson, J. G., 1979. Chemical Element Balance Receptor Model Methodology for

Assessing the Sources of Fine and Total Suspended Particulate Matter in Portland,

Oregon. Ph.D. Dissertation, Oregon Graduate Center, Beaverton, OR; pp. 412-413.

Watson, J.G., J.C. Chow, Z. Lu, E.M. Fujita, D.H. Lowenthal, D.R. Lawson, and L.L.

Ashbaugh, 1994. Chemical Mass Balance Source Apportionment of PM10 during the

Southern California Air Quality Study. Aerosol Sci. Technol., 21(1): 1-36.

Conner, T.L., J.L. Miller, R.D. Willis, R.B. Kellogg, and T.F. Dann, 1993. Source

Apportionment of Fine and Coarse Particles in Southern Ontario, Canada. Presented at

the 86th Annual Meeting and Exhibition, Air & Waste Management Association, Denver,

CO; 13 - 18 June 1993.

REFERENCES