Quantifying the Contribution of Important
Sources to PM Concentrations
• Overview
• What is a Source?
• Emissions
• Source Attribution Methods and Tools
• Discerning Among Source Categories
• Discerning Among Source Regions
• Discerning Among Specific Source Influences
• Uncertainties in Source-receptor Analyses
• Network Design Issues
• References/Appendix with PM2.5 AIRS codes
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PM Data Analysis Workbook: Source Attribution
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Overview
Why do we need to understand the sources of PM? When an area experiences elevated
concentrations of PM, particularly when the concentrations are in exceedance of the standard,
research and analysis is needed to investigate the possible sources of PM and PM precursors
leading to the high concentrations. The analysis and research required spans all aspects of the
regulatory community:
• Monitoring staff need to know whether or not their sampling and analysis set up is adequate
to identify the PM and precursor species that are critical for identifying potential sources in
their area.
• Analysts need to be able to identify potential sources and meteorological conditions to
assist policy makers and modelers in developing control strategies.
• Modelers need to know how well current emission inventories and dispersion models
represent the ambient conditions so that they can model future control scenarios and the
effect on PM concentrations.
• Policy makers need to know what sources are the principal contributors to PM so that
appropriate controls on PM and precursor emissions can be developed and implemented.
In a previous chapter of the workbook, data analyses exploring the spatial and temporal
characteristics of PM data were discussed. In this chapter, we first discuss “what is a source?”.
Important emissions sources are then described as well as source attribution methods and tools
and their uncertainties. Examples are then provided of how to discern among source categories,
source regions, and specific source influences.
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PM Data Analysis Workbook: Source Attribution
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What is a “Source”?: Primary versus Secondary
•
•
Primary PM are composed of material in the same chemical form as when
they were emitted to the atmosphere including windblown dust, sea salt, road
dust, mechanically generated particles and combustion-generated particles
such as fly ash and soot. This also includes particles formed from the
condensation of high temperature vapors formed during combustion (e.g., As,
Se, Zn). Concentrations of primary PM are a function of emission rate,
transport and dispersion, and removal rate.
Secondary particles are formed from condensable vapors generated by
chemical reactions of gas-phase precursors. Secondary processes can result in
either the formation of new particles or the addition of PM to preexisting
particles. For example, sulfate in PM is mostly formed by atmospheric
oxidation of SO2. Also, oxides of nitrogen react in the atmosphere to form
nitric acid vapor which in turn may react with NH3 to form particulate
ammonium nitrate. A portion of the organic aerosol is also due to secondary
processes. Secondary formation is a function of many factors including:
concentrations of precursors, concentrations of other gaseous reactive species
(e.g., ozone, hydroxyl radical), atmospheric conditions, and cloud or fog
droplet interactions.
It is considerably more difficult to relate ambient concentrations of secondary
species to sources of precursor emissions than it is to identify the sources of
primary particles.
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What is a “Source”?: Local vs. Transport
• A key question analysts face is “how do I tell the difference between
locally generated PM and PM transported into the area?” Policy
makers need to understand how much of the PM problem is under their
jurisdiction to control.
• Techniques for assessing the difference between local and transported
PM include:
– Spatial and temporal analyses (e.g., Are high concentrations
observed on a regional basis or only at a few “hot spots”?)
– Assessing the age of an air mass accompanied with trajectory
analysis
– The use of “tracers-of opportunity” and species ratios accompanied
with trajectory analysis (e.g., using potassium to identify forest fire
impact)
– The use of satellite information to corroborate transport (e.g.,
Saharan dust storm impact on United States sites)
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What is a “Source”?: Other Issues
• Regional issues. Analysts need to be able to assess how much an
urban area is contributing to a PM exceedance problem compared to
the regional background.
– As a first approximation of local versus regional contributions to
an urban area’s PM, the differences between the concentrations of
average urban and nearby rural monitoring data could be assessed.
This assumes that the PM at the rural sites are not “contaminated”
by the urban emissions and that the same regional sources have the
same impact on the rural monitors as the urban monitors (see
Schichtel, 1999).
– Another approach is to model the PM dependence on wind speed
and wind direction to classify a site as being dominated by local or
regional source contributions (Schichtel, 1999).
– Researchers are investigating the development of “regional
background” profiles to assist in apportioning PM. These profiles
could be used in an attempt to quantify the regional contribution to
PM concentrations.
– Studies have shown that samplers were strongly influenced by
sources less than 10 km away and that even minor sources close to
the sampler could overwhelm any regional component in a 24-hr
integrated sample (VanCuren, 1998). Others have shown that
individual emitters have a zone of influence less than 1 km (e.g.,
Chow et al., 1999).
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PM Emissions
• Knowledge of PM emissions is required for performing
source apportionment and assessing control measures.
• The majority of the PM2.5 mass over the US is of
secondary origin, formed within the atmosphere through
gas-particle conversion of precursor gases such as sulfur
oxides, nitrogen oxides, and organics.
• Precursor emissions that are well defined include sulfur
(SO2) and nitrogen (NOx) while the emissions of other
species such as organics, soil, and soot are poorly defined.
Key citation: Schichtel (1999)
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North American SO2 Emission Rates
The highest SO2 emission rates occur over the Ohio River Valley, eastern seaboard, and
urban locations, such as Atlanta and St. Louis. There are few major SO2 sources in the
west.
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North American NOx Emission Rates
•Area source NOx emissions are highest near cities.
•Point source emissions are highest over the Industrial Midwest.
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Major Sources of OC Emissions
•
•
•
•
•
•
•
•
•
Meat-cooking operations
Paved road dust
Fireplaces
Noncatalyst gasoline vehicles
Diesel vehicles
Surface Coating
Forest Fires
Cigarettes
Catalyst-equipped gasoline
vehicles
• Organic chemical processes
•
•
•
•
•
•
•
•
•
•
Brake lining
Roofing tar pots
Tire wear
Misc. industrial point sources
Natural gas combustion
Misc. petroleum industry
processes
Primary metallurgical processes
Railroad (diesel oil)
Residual oil stationary sources
Refinery gas combustion
Adapted from Cass, 1997
Sources listed most abundant to least abundant
for the Los Angeles urban area for 1982.
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Emissions Issues
• PM2.5 precursor emissions patterns vary across the US;
thus, PM2.5 speciation and concentrations also vary.
• Of importance to source apportionment is guidance on the
following:
– validating emission profiles and inventories
– improving emission profiles and inventories
– estimating emission profiles and inventories
– identifying unusual events
• Many of these topics are covered in the introduction and in
the emission inventory evaluation sections of the
workbook.
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Source Apportionment Overview (1 of 3)
•
•
•
Relating source emissions to their quantitative impact on ambient air pollution is referred to as source apportionment.
In principle source apportionment can be performed in two complementary ways. The traditional approach is dispersion
modeling, in which a pollutant emission rate and meteorological information are input to a mathematical model that disperses
(and may also chemically transform) the emitted pollutant, generating a prediction of the resulting pollutant concentration at a
point in space and time. The inputs may be measured quantities but they need not be, in which case the modeling is a "what if"
exercise which explores the consequences of different emission rate and meteorological variable possibilities. The alternative is
receptor modeling, which may be defined as "a specified mathematical procedure for identifying and quantifying the sources
of ambient air contaminants at a receptor primarily on the basis of concentration measurements at that receptor." The
concentration measurements referred to are those of particular chemical or physical properties that are characteristic of
particular source emissions. In contrast to dispersion modeling, receptor modeling is diagnostic, not prognostic - it describes the
past rather than the future. In further contrast to dispersion modeling, receptor modeling has everything to do with
measurements and cannot be performed without them. While source apportionment in principle embraces both modeling
approaches, in common usage it is often taken as synonymous with receptor modeling. This overview is concerned only with
this restricted meaning of source apportionment.
Two milestones in the development of receptor modeling are worth noting. First, the Friedlander (1973) article is generally
recognized as the genesis of the chemical mass balance (CMB) receptor model, referred to at the time as chemical element
balance. CMB has a special status in the receptor modeling toolbox, being the only model up to the present that has been
officially approved (i.e., supported and distributed) by EPA. CMB is described elsewhere in this document.
A second milestone was the 1982 Mathematical and Empirical Receptor Models Workshop, now known as "Quail Roost II", and
the series of articles that resulted from it. The workshop was important for multiple reasons: (a) It introduced the concept of
sophisticated synthetic (simulated) data sets as test beds for comparing the performance of alternative receptor modeling
approaches, where the "truth" is known a priori by the constructors of the data sets; (b) it brought together many of the U.S.
receptor modeling practitioners in a "blind" intercomparison of their various methods when applied to common data sets,
including both synthetic and real sets; and (c) it resulted in a Glossary of receptor modeling terms that provided a common
language for this emerging field. The degree of success that was achieved in (b) was instrumental in bringing EPA to a
realization of the potential importance of receptor modeling as a complement to traditional dispersion modeling for source
apportionment.
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Source Apportionment Overview (2 of 3)
•
•
•
•
Receptor model types may be classified as single-sample or multivariate. In the first type the modeling analysis is performed
independently on each available sample. The simplest example of this is the 'tracer element' method, in which a particular property
(e.g., chemical specie) is known to be uniquely associated with a specific source, so that the total ambient mass impact of the
source may be estimated by dividing the measured ambient concentration of the property by the property's known abundance in the
source's emissions. The method is not often available because of the difficulties of finding unique tracers or knowing their
abundances. However even if the property is not uniquely associated with a source of interest, if its abundance in that source is
known, then the method can always be used to provide an upper limit for the source's impact. A novel example of this method is
the use of the radiocarbon (14C) content of an ambient sample to estimate the fraction of carbon in the sample that is biogenic (nonfossil-fuel related).
The best-known example of single-sample receptor modeling is of course chemical mass balance. CMB removes the need for
unique tracers of sources, but still requires the abundances of the chemical components of each source (source profiles) to be
known.
Multivariate receptor models require the input of data from multiple samples, and extract the source apportionment
information from all of the sample data simultaneously. The reward for the extra complexity of these models is that they
purport to estimate not only the source contributions but the source compositions (profiles) as well. The simplest example of a
multivariate method is 'tracer element/multiple linear regression'. This method requires tracers that are uniquely associated with the
sources of interest, but does not require their abundances to be known.
Additional multivariate receptor models include (a) absolute principal component analysis, (b) specific rotation factor analysis,
(c) target transformation factor analysis, (d) three-mode factor analysis, (e) source profiles by unique ratios - SPUR, (f) receptor
model applied to patterns in space - RMAPS, (g) UNMIX, and (h) positive matrix factorization. Most of these models are based
on factor analysis, or the closely related principal component analysis. In recent years the development and investigation of the last
three has been supported by EPA. In comparison with CMB far less is understood about the behavior and validity of these
multivariate models. Criticisms have been directed at specific models, in addition to the general criticism of any factor analysisbased model that does not employ additional constraints to limit the solution space.
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Source Apportionment Overview (3 of 3)
•
•
One of the challenges that receptor modeling will need to confront in the PM2.5 arena is the treatment of
secondary mass - products that result from atmospheric transformation processes between source and receptor, such
as sulfate and nitrate. While this has always been a problem for receptor modeling, it is more severe for PM2.5 than
for PM10 because the secondary contribution to PM2.5 is a larger fraction than for PM-10. CMB deals with this in a
limited way that isolates the total mass of a secondary component (e.g., sulfate) but cannot apportion it to individual
sources. Progress in this area will require a hybrid receptor model approach, i.e., the use of selected emissions rate,
meteorological, and chemical transformation information with an otherwise conventional receptor model. Receptor
modeling was invented for the very reason of avoiding the need for such frequently uncertain information, but it
seems inevitable that source apportionment of secondaries will require an extension of classical receptor modeling.
By combining elements of both receptor and dispersion models the intent is to minimize the weaknesses of the
separate approaches and maximize their combined strengths.
The development of receptor modeling over the past two decades has been strongly influenced by the extensive
use of inorganic species, particularly atomic elements measured by x-ray fluorescence. In the receptor modeling of
PM2.5 there is likely to be a new emphasis on organic species. This is because of the relatively greater contribution of
combustion sources and carbon to PM2.5 than to PM10. This will not be an easy transition, because of formidable
difficulties in organic aerosol sampling (both positive and negative artifacts can occur) and analysis (the presence of a
bewildering number of organic species, frequently at low concentrations). The Northern Front Range Air Quality
Study recently performed in the Denver area, and the continuing characterization of organic aerosol in Southern
California, provide some indications of the promise of this new direction.
Key citation: Lewis, 1999
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Source Attribution Methods and Tools
• Source attribution methods are used to resolve the
composition of PM into components related to
emission sources. Several methods are available.
• It is useful to apply more than one method (since data
requirements differ among them) and look for
consensus among results.
• Methods and tools discussed in this section include
the following:
– Spatial and temporal characteristics of data
– Cluster, factor, and other multivariate statistical
techniques
– PMF
– UNMIX
– Source-receptor models: CMB8
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Using Spatial and Temporal Data
• Potassium nitrate is a major
component of all fireworks.
• This figure shows all
available PM2.5 K data from
all N. American sites,
averaged to produce a
continental average for each
day during 1988-1997.
• Fourth of July celebration
fireworks are clearly
observed in the potassium
time series.
• Fireworks displays on local
holidays/events could have a
similar affect on data.
August 1999
Poirot (1998)
Regional averaging and count of sample numbers were
conducted in Voyager, using variations of the Voyager script
on p. 6 of the Voyager Workbook Kvoy.wkb. Additional
averaging and plotting was conducted in Microsoft Excel.
PM Data Analysis Workbook: Source Attribution
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•
•
•
•
A simple material balance on
the annual average chemical
composition can be useful
(shown here: Los Angeles area
PM2.5).
EC concentrations were highest
in Central LA, consistent with
fresh motor vehicle emissions
and traffic density.
OC concentrations typically
accounted for the largest
portion of the PM2.5 at most
sites. More emphasis on OC
measurements may be
warranted.
Nitrate and ammonium
concentrations were highest at
the downwind site (Rubidoux)
consistent with NH3 emission
sources and secondary nitrate
formation.
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Annual Average PM 2.5 (ug/m3)
Spatial and Temporal Analyses
40
1993
35
30
25
20
15
10
5
0
San Nicolas Long Beach
Central LA
Azusa
Rubidoux
Organic Carbon
Elemental Carbon
Sulfate
Nitrate
Chloride
Ammonium
Sodium
Crustal
Trace
PM Data Analysis Workbook: Source Attribution
Made using Excel; adapted from Cass, 1997.
Sites are arranged from west to east (the general
direction of transport in the Los Angeles basin.
16
Spatial and Temporal Analyses
• Simple analyses of wind
direction and PM species
concentrations can be used to
begin an assessment of likely
sources. Temporal resolution
less than 24-hr of the PM data
may be necessary for this
analysis.
• PM10 zinc concentrations at
Crows Landing with respect to
wind direction are shown.
• High concentrations in the
northwest sector are consistent
with the refuse incinerator
located one km to the NNW of
the monitoring site.
August 1999
Zinc concentration distribution with
respect to wind direction (%)
N
0
30
315
20
45
10
W 270
0
225
90 E
135
180
S
Adapted from wind roses reported by Chow et
al., 1996. Radar plot prepared in Excel. Wind
direction is the direction from which the wind is
blowing. Data from Crows Landing, CA during
1990 summer intensive study.
PM Data Analysis Workbook: Source Attribution
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Multivariate Analyses
• Multivariate analyses are statistical procedures used to infer
the mix of hydrocarbon sources impacting a receptor
location.
• Procedures including cluster, factor/principal component,
regression, and other multivariate techniques are usually
available in statistical software packages.
• Literature review shows many refinements and options to
these analyses.
• A drawback to these analyses is that the analyst must infer
how certain statistical species groupings relate to emissions
sources.
• A nice feature of these analyses is the ability to summarize
a multivariate data set using a few components.
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PM Data Analysis Workbook: Source Attribution
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Key PM Species and Sources (1 of 3)
Species
Soluble Ions
Nitrate (NO3-)
P/S
Sulfate (SO4= )
S
Ammonium (NH4+ )
S
Sodium (Na+ )
Chloride (Cl-)
Potassium (K+ )
P
P
P
S
Major Anthropogenic Sources
Comments
NOx from fossil fuel combustion (energy,
mobile sources, biogenics, and industrial
processes).
SOx from fossil fuel combustion (energy
generation, industrial processes, mobile
sources).
Ammonium nitrate is a principal component
of secondary aerosol in the western US.
Natural sources: soil, forest fires, lightening.
Ammonium sulfate is the primary component
of PM2.5 in the eastern US. Natural sources:
sea spray sulfate, volcano gaseous sulfur,
forest fires.
Important compound in nitrate and sulfate
chemistry. Natural sources: undisturbed
soil, wild animals
NH3 from animal husbandry, fertilizer use,
sewage. Also mobile sources, combustion,
industrial processes
Sea water, open playas, de-icing
Sea water
Vegetative burning
Also vegetative burning
Prescribed burns, forest fires, residual wood
combustion, meat charbroiling
P = Primary
S = Secondary
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Key PM Species and Sources (2 of 3)
Species
Metals
Nickel (Ni)
Calcium (Ca)
Iron (Fe)
P/S
P
P
P
Residual oil combustion
Crustal material
Crustal material
Vanadium (V)
Aluminum (Al)
Silicon (Si)
Sulfur (S)
Phosphorus (P)
P
P
P
P/S
P
Residual oil combustion
Crustal material
Crustal material
Residual oil combustion
Fuel combustion
Lead (Pb)
Bromine (Br)
Manganese (Mn)
Chlorine (Cl)
Copper (Cu)
Titanium (Ti)
P = Primary
S = Secondary
August 1999
P
P
P
P
P
P
Major Anthropogenic Sources
Vehicle exhaust
Vehicle exhaust
Steel blast furnace, ferro manganese
Incinerator, marine
Smelter
Crustal material
Comments
Also smelters, incinerators
Also smelters, incinerators, steel blast
furnace
Also coal-fired boiler
Also smelters, antimony roaster
Not a significant contributor to vegetative
burning
Low concentrations in the US
Low concentrations in the US
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Key PM Species and Sources (3 of 3)
Species
Carbonaceous
Organic carbon (OC)
P/S
Major Anthropogenic Sources
Comments
P/S
Natural source = wild fires.
Elemental carbon (EC)
P
Polycyclic aromatic
hydrocarbons (PAHs)
Hopanes, sterenes
Guaiacols
Syringols
Lactons
Sterols
P = primary
S = secondary
P
Motor vehicle exhaust, vegetative and wood
burning, cooking.
Motor vehicle exhaust, wood burning,
cooking.
Motor vehicle, wood smoke
P
P
P
P
P
Motor vehicles
Wood smoke
Wood smoke (hardwood)
Meat cooking
Meat cooking, wood smoke
August 1999
Diesel exhaust primary contributor. Natural
source: wild fires.
PAHs can be emitted in gas phase, particle
phase, or a combination (gas-particle phase)
Primarily particle phase
Gas phase, particle phase, gas-particle phase
Particle phase, gas-particle phase
Gas phase, gas-particle phase
Particle phase
PM Data Analysis Workbook: Source Attribution
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Cluster and Factor Analyses
• Cluster analysis is a multivariate procedure for detecting natural
groupings in data.
– To produce clusters, you must be able to compute some measure of
dissimilarity between objects.
– Correlation measures are often used because they are not
influenced by differences in scale between objects. This is
important because PM species concentrations can vary over several
orders of magnitude.
• Factor analysis is a method of decomposing a correlation or
covariance matrix.
– Factors indicate the best associations among variables while
regression lines indicate the best predictions.
– The factor model expresses the variation within and the relations
among observed variables as partly common variation among
factors and partly specific variation among random errors.
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Cluster/Factor Analysis Example
Example PM2.5 cluster and factor analyses to be developed
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PMF Description
• Positive matrix factorization (PMF) can be used to
determine source profiles based on the ambient data.
• The major difference between principal component
analysis and PMF is that only positive factors can be
generated with PMF (no negative concentrations).
• Also PMF does not rely on information from the
correlation matrix but uses a point-by-point least-squares
minimization scheme. Profiles produced using PMF can
be directly compared to the input matrix since the profiles
are in the same units as the input data.
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PMF Analysis Example (1 of 2)
•
•
•
Polissar et al. (1998) used PMF to investigate the fine particle composition
data from seven National Park Service locations in Alaska for the period 19861995. The sites included the Northwest Alaska Areas National Park (NWAA),
Bering Land Bridge National Preserve (BELA), Gates of the Arctic National
Park (GAAR), Denali National Park (DENA), Yukon Charley National
Preserve (YUCH), Wrangell St. Elias National Park (WRST), and Katmai
National Park (KATM).
PMF uses the estimates of the error in the data to provide optimum data point
scaling and permits a better treatment of missing and below detection limit
values.
Up to eight source components were obtained for the data sets:
Factors
Cl, Na
Al, Si
Si
+
BC, H , K
Zn, Cu
Pb, Br
BC, Na, S
S
Sources
Sea salt
Soil dust I
Soil dust II, coal combustion
Forest fires, local combustion
Incinerators, smelter
Motor vehicles, smelter
Distant anthropogenic
Distant anthropogenic
BC = black carbon
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PMF Analysis Example (2 of 2)
All data
A
-3
Concentration (g m )
6
BC-H+-K - forest fires, local
BC-Na-S - anthropogenic
S - anthropogenic
5
4
3
2
1
0
NWAA
6
BELA
GAAR
BB
DENA
YUCH
WRST KATM
October - June (left)
July - September (right)
-3
Concentration (g m )
• The highest average PM2.5
concentration at the Bering
Land Bridge site (BELA) may
be due to the strong influence of
aerosol emissions from local
pollution sources in nearby
Nome plus PM transported into
the region.
• Note the large seasonal
difference in the forest fire
factor at Gates of the Arctic
(GAAR).
residual mass
Cl-Na - sea salt
Al-Si - soil
Si - soil, coal combustion
Zn-Cu - incineration
Pb-Br - motor vehicles
5
4
3
2
1
0
NWAA
BELA
GAAR
DENA
YUCH
WRST KATM
Sites
Figure 20
Polissan et al., 1998
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UNMIX
•
UNMIX is a multivariate receptor modeling package that inputs observations
of particulate composition and seeks to find the number, composition, and
contributions of the contributing sources or source types. UNMIX also
produces estimates of the uncertainties in the source compositions. UNMIX
uses a generalization of the self-modeling curve resolution method developed
in the chemometrics community (Henry, 1997).
•
Data Requirements: UNMIX inputs data in tabular format as flat ASCII files.
Each column represents one species and each row is one sample or
observation. It is very helpful to have a measure of total mass included in the
data. It is generally best to analyze data from one site at a time. Basically, the
more data the better, in terms of both species and observations. The upper limit
on the amount of data is determined by the size of the computer. Based on
experience, the practical lower limit on the number of observations is 50 to
100.
•
System Requirements: UNMIX is currently implemented as a MATLAB
program(see the website mathworks.com for more information). UNMIX has a
graphical user interface so the user need not be familiar with MATLAB itself.
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UNMIX Analysis Example
Example PM2.5 UNMIX analyses to be developed
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Chemical Mass Balance Modeling
•
•
•
•
The purpose of CMB receptor modeling is to apportion ambient PM to emission
sources. The source apportionment of ambient PM provides independent evaluation
of the relative contributions of sources to ambient levels of PM.
The CMB model uses an effective variance least squares solution to a set of linear
equations which express each measured chemical species concentration as a linear
sum of products of source profile species and source contributions.
Model input includes:
– Source profile species (fractional amount of species in the PM emissions from
each source type).
– Receptor (ambient) concentrations.
– Realistic uncertainties for source and receptor values. Input uncertainty is used
to weigh the relative importance of input data to model solutions and to estimate
uncertainty of the source contributions.
Model output includes:
– Contributions from each source type to the total ambient VOC and individual
hydrocarbon species and the uncertainty.
– Performance parameters.
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CMB Model Assumptions
CMB model assumptions include the following:
• Composition of source emissions are constant over the ambient and
source sampling period (can tolerate substantial variabilities).
• Chemical species do not react with each other (i.e., they add linearly)
(little known about this).
• All sources which may significantly contribute to the receptor have
been identified and their emissions characterized (minor contributors
may be omitted).
• Number of source categories £ number of chemical species (the larger
the difference, the better).
• Source profiles are linearly independent (degree of independence
depends on the variability of the source profile).
• Measurement errors are random, uncorrelated, and normally
distributed (effects unknown).
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CMB Application Protocol (1 of 2)
• Assess model applicability (e.g., data from well-characterized
methods, large number of species, major sources identified, source
profiles available, and reasonable uncertainties attached).
• Select source profiles for potential contributors (e.g., area, natural,
and point sources plus other sources identified in preliminary
analyses).
• Select sources for inclusion in the CMB solution (e.g., upwind point,
seasonal emitters, non-collinear profiles).
• Determine initial source contribution estimates (SCE) (e.g., use
variety of source profiles and fitting species combinations, determine
effects on results of alternate source profiles). May need to combine
similar source types due to collinearity.
• Examine model outputs and performance measures. Do spatial and
temporal results make sense considering meteorology and source
emission patterns?
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CMB Application Protocol (2 of 2)
• Check how the removal and addition of some species affects results.
The source profiles need to be the most precise for the most influential
species.
• Identify deviations from model assumptions (e.g., source
compositions constant, all sources included, source profiles
independent, etc.).
• Identify and correct model input errors (e.g., increase uncertainty of
profiles, provide different composites, identify and characterize
missing sources, stratify samples by meteorology).
• Verify consistency and stability of SCE (substitute different profiles
for same source type, add or drop species form fit, examine source
contributions to individual species).
• Evaluate results of CMB with respect to other source assessment
methods (e.g., compare SCEs among nearby sites, compare source
contribution variations over time with expected emissions and
meteorology, apply other receptor methods and compare results, apply
dispersion models and compare results).
August 1999
PM Data Analysis Workbook: Source Attribution
32
CMB Performance Goals (1 of 2)
•
•
Parameter
Target
R SQUARE
Standard Error (STDERR)
CHI SQUARE
PERCENT MASS
Degrees of freedom (DF)
T-Statistic (TSTAT)
U/S Clusters
RATIO (C/M)
Calculated/Measured
RATIO (R/U)
Residuals/Uncertainties
0.8 to 1.0
< SCE
< 4.0
80 to 120
>5
> 2.0
None
0.5 to 2.0
•
-2.0 to 2.0
R2 is used to measure the variance
in the ambient species
concentrations, which is explained
by the calculated species
concentrations via linear
regression. The closer the value is
to 1.0, the better the SCEs explain
the measured concentrations.
Standard error is the variance of
the SCE.
August 1999
•
•
•
Chi square (2) is used to consider the
uncertainty of the calculated species
concentrations (weighted sum of squares of
the differences between calculated and
measured fitting species concentrations).
Values < 1.0 indicate a very good fit.
The percent mass is the percent ratio of the
sum of model-calculated SCEs to the
measured mass concentration. This is used to
track the percent explained mass; a value
near 100 percent can be misleading because
poor fits can force a high percent mass.
The t-statistic is the ratio of the SCE to its
standard error. The standard error of the SCE
is an indicator of the precision in the model
estimates. Values < 2.0 identify model
estimates that are not significantly different
from 0.
Degrees of freedom is the number of species
in the fit minus the number of sources in the
fit.
PM Data Analysis Workbook: Source Attribution
33
CMB Performance Goals (2 of 2)
•
•
•
•
The ratio of the calculated species mass (CALC) to measured species mass
(MEAS) is used to identify species that are over- or under-accounted for by the
model. A ratio >1.0 means that more mass for a given species was accounted
for by the model than was measured in the ambient sample.
The ratio of the residuals to the uncertainty is the signed difference between
CALC and MEAS divided by the uncertainty of the difference. It is used to
identify species that are over- and under-accounted for by the model.
The normalized modified pseudo-inverse matrix (MPIN), a diagnostic output
of CMB7, indicates the degree of influence each species concentration has on
the contribution and standard error of the corresponding source category.
MPIN is normalized such that it takes on values from -1.0 to 1.0. Species with
MPIN absolute values of 1.0 to 0.5 are associated with influential species.
Maximum source uncertainty and minimum source projection (Henry, 1992)
are used to assess clusters of sources which the model cannot easily distinguish
between and that are likely to be interfering with the model's ability to provide
a good set of SCEs.
August 1999
PM Data Analysis Workbook: Source Attribution
34
Source Profiles
• Use profiles that are representative of the study area during
the period when ambient data were collected.
• Include ubiquitous sources such as gasoline and diesel
exhaust, secondary components (sulfate, nitrate,
ammonium), sea salt (if coastal site), vegetative burning
(e.g., forest fires, residential fireplaces), and crustal
material.
• Include point sources identified in the emission inventory.
• Try available source profiles in sensitivity tests to
determine the best ones for use (minimize collinearity).
August 1999
PM Data Analysis Workbook: Source Attribution
35
How to Obtain Source Profiles
Source profiles may be obtained from the following sources:
• Source measurements made in your region during the period
for which ambient data are available. See
http://charon.cira.colostate.edu/DRIFinal/ for Northern Front Range air
quality study source profiles.
• Literature review. Key references are included in the reference
section.
• EPA SPECIATE (http://www.epa.gov/ttn/chief/software.html#speciate)
• Analysis of ambient data using tools such as PMF and
UNMIX.
August 1999
PM Data Analysis Workbook: Source Attribution
36
CMB Example Analyses (1 of 2)
Summer 1996 - Beacon Hill
Burning
12%
Industry
8%
Soil
4%
Nitrate
13%
Sea Salt
1%
Sulfate
28%
August 1999
Mobile
34%
S o urce Ap p o rtio nm ent M o nthly Averag e P ercentag es
Seattle area, Washington
M obile
Feb-97
Dec-96
S ulfate
N itrate
Oct-96
Aug-96
B urning
Jun-96
50
40
30
20
10
0
Apr-96
• Note seasonal differences in
monthly average PM2.5 source
contribution percentages of
burning (fall/winter) and
secondary sulfate (summer).
• Differences in winter and
summer burning contributions
are also noticeable using pie
charts for the data set.
Indus try
Winter 1996-97 - Beacon Hill
Industry
2% Soil Nitrate
3%
5%
Burning
32%
Sea salt
4% Sulfate
13%
PM Data Analysis Workbook: Source Attribution
Mobile
41%
From Maykut et al.
Prepared with Excel
37
CMB Example Analyses (2 of 2)
August 1999
40
Annual Average PM 2.5 (ug/m3)
• When additional
organic carbon species
and source profiles are
available, CMB can be
used to provide
substantial breakdown
of the organic carbon
component.
• The dominant sources
of PM2.5 organic
aerosol were diesel
exhaust, gasoline
exhaust, meat cooking,
and woodsmoke.
1982
35
30
25
20
15
10
5
0
West LA
Downtown LA
Pasadena
Rubidoux
Diesel Exhaust
Gasoline Exhaust
Woodsmoke
Meat Cooking
Other Organics
Sulfate
Nitrate
Ammonium
PM Data Analysis Workbook: Source Attribution
Made using Excel; adapted from Cass, 1997
Other organics include paved road dust, cigarette
smoke, vegetative detritus, tire wear debris, and
secondary organics.
38
Checking Source Apportionment Results
Kraft Paper Mill - Beacon Hill 1996-97
• Three Kraft paper mills in the
Washington state study area are
located to the south, north, and
northwest of the monitoring
site.
• Agreement between the wind
directions associated with
specific profiles and the actual
locations of the sources adds
credibility to the source
apportionment results.
August 1999
N
6000
NW
NE
4000
2000
W
0
E
SW
PM Data Analysis Workbook: Source Attribution
SE
S
From Maykut et al.
Plot: nanograms of PM attributed
to the source by wind direction.
39
Method and Tool Availability
• Species relationships and the development of reasonable
constraints on the data can be investigated using scatter
plots.
• Cluster, factor, and principal component analyses and
linear regression are available in most standard statistical
packages.
• A beta version of PMF is currently being tested by EPA.
• A beta version of UNMIX is being tested by EPA and
others. Contact Ron Henry for a copy (rhenry@usc.edu)
• CMB8 modifications are planned. The current version is
available from ftp://eafs.sage.dri.edu/cmb80/model/
August 1999
PM Data Analysis Workbook: Source Attribution
40
Uncertainties in Source-Receptor Analyses
• Many emitters have similar species composition profiles. The
practical implication of this limitation is that one may not be able to
discern between the dust emitted by agricultural practices and dust
emitted by mobile sources on unpaved roads.
• Species composition profiles change between source and receptor.
Most source-receptor models cannot currently account for changes due
to photochemistry. Since nitrates, sulfates, and some organic carbon
compounds are primarily of secondary origin, current methods cannot
tie these compounds to their primary emission sources.
• Receptor models cannot predict the consequences of emissions
reductions. One cannot estimate source profiles resulting from
changes in emissions and predict ambient concentrations using
receptor models.
August 1999
PM Data Analysis Workbook: Source Attribution
41
Discerning Local vs. Non-local Influences
• Background and Rationale
• Influence of wind speed
on local concentrations
• Mid-Atlantic Rural Sites
PM2.5 vs. Surface Wind
Speed
• Mid-Atlantic Urban Sites
PM2.5 vs. Surface Wind
Speed
• Seasonal Urban-Rural
Difference
• Conclusions
August 1999
PM Data Analysis Workbook: Source Attribution
Key citation: Schichtel (1999)
42
Background and Rationale
• At any given location, the PM concentration is the
combination of the PM mass originating from non-local
sources transported in and the PM mass originating from
local sources.
• The local/non-local contributions depend on emissions,
transport and aerosol formation and removal processes, so
there is no “typical” non-local concentration.
• Quantifying the local contributions identifies the part of
the PM problem that can be controlled locally.
August 1999
PM Data Analysis Workbook: Source Attribution
43
Influence of Wind Speed on Local Concentrations
• The concentration C is equal to the sum of the background (Co) and
local contribution (QL/UH)
• Assuming a fixed source region length (L), emission rate (Q) and
mixing height (H), as the wind speed (U) increases the concentration
from the local sources decreases asymptotically approaching the
background concentration.
August 1999
PM Data Analysis Workbook: Source Attribution
44
PM2.5 vs. Surface Wind Speed at Urban and Rural Sites
The PM2.5 at these urban sites during the cold season (Nov - March) decline
sharply (60%) with increasing wind speeds compared to the rural sites
(30%). This implies the urban PM2.5 is dominated by local sources.
August 1999
PM Data Analysis Workbook: Source Attribution
45
Local Emission Influenced PM2.5
• Local PM emissions cause
concentrations at sites 9, 10, & 11 to
have a different temporal pattern with
higher concentrations than the other,
more regional sites
• Concentrations at sites 9, 10, & 11 are
above 35 μg/m3 on September 15,
1994 but concentrations decrease to
less than 30 μg/m3 within three
kilometers.
August 1999
PM Data Analysis Workbook: Source Attribution
46
Seasonal Urban-Rural Difference
• The excess PM2.5 at Washington DC ranges from >10 g/m3 in the
winter to 1-2 g/m3 in the summer.
• The Phoenix excess PM2.5 ranges from 11 - 16 g/m3 in the winter to
3 g/m3 in the summer.
August 1999
PM Data Analysis Workbook: Source Attribution
47
Local vs. Non-local Sources: Conclusions
• In the examples shown here using IMPROVE data, MidAtlantic and Southwest urban center PM2.5 concentrations
are dominated by local sources during the winter.
Concentrations and wind speed were used to assess this.
• At the Mid-Atlantic urban sites, the summer local sources
contribute about 10% of the PM mass compared to up to
70% in the winter.
• At the urban Southwest, the local sources contribute about
30% of the summertime PM mass compared to about 80%
in the winter.
• Careful comparison of urban and rural site data in a region,
with the use of meteorological data, can be used to assess
local vs. non-local contributions to PM.
August 1999
PM Data Analysis Workbook: Source Attribution
48
Discerning Among Source Categories
• Natural vs. anthropogenic sources
• Anthropogenic source categories
– Wood smoke
– Utilities
– Motor vehicles
• Refinement of anthropogenic source categories
– Dust from anthropogenic activities: soil types and
sources
– Proscribed burn vs. wood stoves
– Oil vs. coal utilities
– Motor vehicle exhaust vs. evaporative vs. diesel, etc.
August 1999
PM Data Analysis Workbook: Source Attribution
49
Using Spatial and Temporal Analyses
• Fe and Al concentrations
strongly correlate, suggesting a
common source influence.
Ratios are consistent with soil.
• Fe and K concentrations do not
correlate as well. The lower
K:Fe ratio of 0.6 is indicative of
soil. Higher ratios are
consistent with woodsmoke.
• Data corresponding to the July
4th weekend are highlighted.
Concentrations of PM2.5 iron with silicon, aluminum, and
potassium at Chiricahua National Park in Arizona.
Poirot, (1998)
Microsoft Excel used to prepare scatter plot and calculate
regression coefficients.
August 1999
PM Data Analysis Workbook: Source Attribution
50
PM2.5 Species Relationships
Relationships applied to estimate mass composition from species data
or investigate a source
Species/Source
Sulfate
Soil K
OC concentrations
Presence of
secondary organic
aerosol
Motor vehicle vs.
forest fire
OC abundance
Burning vs. soil
Si, Ca, Fe in coalfired station
emissions
Se
Relationship
3 x Sulfur (S)
0.6 x Fe
+
Total K  15 to 30 times soluble K (K )
1.4 x OC carbon measured
OC/EC > 2.0
Comments
Assumes all sulfur is sulfate
Assumes all Fe is from soil and
ratio is same for all soils
Accounts for H, O, S, and N
For summertime sampling at
downwind receptors
OC/(OC+EC) ~ 0.6 (MV)
OC/(OC+EC) ~ 0.9 (forest fire)
Based on several studies.
Residential combustion OC falls
between MV and forest fires.
~50% residential wood combustion PM2.5
~70% forest fires
+
K / K  0.8 to 0.9 (burning)
Very low for soil
30 to 50% of corresponding levels in
geological material
Present in coal-fired power station
emissions with no scrubber or wet
scrubber, but not in emissions from a unit
with a dry limestone scrubber
This table is a “work-in-progress” intended to provide tips and relationships that can assist data analyst in assessing their data.
Additional relationships will be added as more literature is reviewed and more analyses become available. Current listings are from
the IMPROVE program and from EPA, 1998.
August 1999
PM Data Analysis Workbook: Source Attribution
51
Discerning Among Source Regions
•
•
•
•
By direction from receptors
By distance from receptors
By location (using both direction and distance)
By regional fingerprint
Example PM2.5 analyses to be developed
August 1999
PM Data Analysis Workbook: Source Attribution
52
Discerning Among Specific Source Influences
• Within local jurisdiction
• Beyond local jurisdiction
• Changes over time, space, and species
Example PM2.5 analyses to be developed
August 1999
PM Data Analysis Workbook: Source Attribution
53
Network Design Issues
• Identify and reduce uncertainties in source apportionment
• Provide routine and enhanced measurements for source
apportionment
• Improve emissions profiles and inventories
• Identify inefficient/redundant measurements
Example PM2.5 analyses to be developed
August 1999
PM Data Analysis Workbook: Source Attribution
54
Decision Matrix for Source Apportionment
SCIENCE/TECHNICAL QUESTIONS/OBJECTIVES
Discerning Local vs. Non-local influences
Discerning among source categories
Discerning among source regions
Discerning among specific source influences
Uncertainties in source-receptor analyses
Source attribution methods and tools
APPLICABLE DATA
PM Mass
FRM Mass
Non-FRM Mass
Continuous Mass
PM Speciation
NAMS Trends
State
Supersites
IMPROVE
Special Studies
Meteorology
Surface
Upper-Air
Other Air Quality Data
Secondary (e.g., Ozone)
Primary (e.g., SO2, NOx, CO)
VOCs
August 1999
ü
ü
ü
ü
ü
ü
Contributions of vehicle emissions to ambient carbon PM
Trajectory clusters analysis
Literature review of source attribution methods
Tracers of opportunity: K
Decision matrix to be used to select example projects that
will illustrate how others have explored the
characterization of PM. To use the matrix, find your
technical topic area at the left. Follow this line across to
see which example projects illustrate analyses pertaining
to the topic area. For each of these projects, follow down
the column to see which data were used in the example.
Go to the next page to see which data analysis tools were
used.
Example Projects
PM2.5 as f(WS, WD): local/regional source attribution
Source Attribution
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
PM Data Analysis Workbook: Source Attribution
ü
ü
ü
ü
55
Decision Matrix for Source Apportionment
TOOLS DEMONSTRATED
AMDAS
Other Statistical Methods
Voyager
Trajectory Methods
Factor, cluster analyses
PMF
UNMIX
CMB8
SPECIATE
August 1999
Contributions of vehicle emissions to ambient carbon PM
Trajectory clusters analysis
Literature review of source attribution methods
For each of the projects that are of interest to you,
follow down the column to see which data analysis
tools were used.
Tracers of opportunity: K
Example Projects
PM2.5 as f(WS, WD): local/regional source attribution
Source Attribution
ü
ü
ü
ü
ü
PM Data Analysis Workbook: Source Attribution
ü
56
References
Key Citations
Cass, G.R. (1997) Contribution of vehicle emissions to ambient carbonaceous particulate matter - a review and synthesis of
the available data in the South Coast Air Basin. Final report prepared for Coordinating research council, CRC
contract number A-18-1. February.
Chow J.C., Watson J.G., Lu Z., Lowenthal D.H., Frazier C.A., Solomon P.A., Thuillier R.H., Magliano K. (1996)
Descriptive analysis of PM2.5 and PM10 at regionally representative locations during SJVAQS/AUSPEX. Atmos.
Environ., 30, No. 12, pp. 2079-2112.
Fujita E.M., Watson J.G., Chow J.C., and Lu Z. (1994) Validation of the chemical mass balance receptor model applied to
hydrocarbon source apportionment in the Southern California Air Quality Study. Environ. Sci. Technol. 28, 16331649.
Lewis, C.L. (1999) Personal communication.
Main H.H., Chinkin L.R., and Roberts P.T. (1998) PAMS data analysis workshops: illustrating the use of PAMS data to
support ozone control programs. Web page prepared for the U.S. Environmental Protection Agency, Research
Triangle Park, NC by Sonoma Technology, Inc., Petaluma, CA, <http://www.epa.gov/oar/oaqps/pams/analysis> STI997280-1824, June.
Pace T.G. and Watson J.G. (1987) Protocol for applying and validating the CMB model. Report prepared by Office of Air
Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA-450/4-87010, May.
Schichtel B.A. (1999) Local and regional contributions of fine particulate mass to urban areas in the mid-Atlantic and
southwestern US. Paper prepared by Center for Air Pollution impact and Trend Analysis, Washington University, St.
Louis, MO. Contract no. 7D-0869-NAEX. Available at
http://capita.wustl.edu/capita/capitareports/PMFineAn/PM_vs_Tran/finalreport/BltPhx_PMvsWnd_FinalReport.html
U.S. EPA (1998) National air quality and emissions trends report, 1997.
U.S. Environmental Protection Agency (1998) CMB8 application and validation protocol for PM2.5 and VOC. Report
prepared by U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA 454/R-98-xxx, October.
U.S. Environmental Protection Agency (1987) Procedures for reconciling differences in receptor and dispersion models.
Report prepared by U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA 450/4-87-008, May.
August 1999
PM Data Analysis Workbook: Source Attribution
57
References
Other References
Bruns M.A. , Graham K.J., Scow K.M., VanCuren T. (1998) Biological markers to characterize potential sources of soilderived particulate matter. Paper 98-TP43.02 (A994) presented at the A&WMA 91st annual meeting, June 14-18, San
Diego, California.
Chow J.C., Watson J.G., Lowenthal D.H., Egami R.T., Frazier C.A., Pritchett L.C., Neuroth G.A., Guyton J. (1991) Source
apportionment of PM2.5 in Phoenix, Arizona. Paper 91-52.1 presented at the 84th annual A&WMA meeting,
Vancouver, B.C., June.
Chow J.C., Watson J.G., Green M.C., Lowenthal D.H., DuBois D.W., Kohl S.D., Egami R.T., Gillies J., Rogers C.F.,
Frazier C.A., Cates W. (1999) Middle- and neighborhood-scale variations of PM10 source contributions in Las Vegas,
Nevada. J. Air Waste Manage. Assoc. 49: 641-654.
Chow J.C., Watson J.G., Lowenthal D.H., Egami R.T., Solomon P.A., Thuillier R.H., Magliano K., Ranzieri A. (1998)
Spatial and temporal variations of particulate precursor gases and photochemical reaction products during
SJVAQS/AUSPEX ozone episodes. Atmos. Environ. Vol. 32, No. 16., pp. 2835-2844.
Chan Y.C., Simpson R.W., Mctainsh G.H., Vowles P.D., Cohen D.D., Bailey G.M. (1999) Source apportionment of PM2.5
and PM10 aerosols in Brisbane (Australia) by receptor modeling. Atmos. Environ. 33, pp. 3251-3268.
Cheng M., Gao N., Hopke P.K. (1996) Source apportionment study of nitrogen species measured in southern California in
1987. J. Environ. Engineering Vol. 122, No. 3, pp. 183-190, March.
EC/R Inc. (1999) Evaluation of source apportionment methods: source apportionment study literature review. Prepared for
U.S. EPA, contract no. 68-D-98-006, work assignment no. 2-4, EC/R project no. EAM-204. March
Eldred, R.A. (1997) Regional patterns of fine carbonaceous particle concentrations at remote sites throughout the United
States, AWMA-AGU Spec. Conf. on Visual Air Quality: Aerosols and Global Radiation Balance, A&WMA.
Fraser M.P., Cass G.R., Simoneit B.R.T. (1999) Particulate organic compounds emitted from motor vehicle exhaust and in
the urban atmosphere. Atmos. Environ. 33, pp. 2715-2724.
Friedlander S.K. (1973) Chemical element balances and identification of air pollution sources. Environ. Sci. Technol., 7,
pp. 235-240.
August 1999
PM Data Analysis Workbook: Source Attribution
58
References
Fujita E.M., Watson J.G., Chow J.C., and Magliano K.L. (1995) Receptor model and emissions inventory source
apportionments of nonmethane organic gases in California's San Joaquin Valley and San Francisco Bay Area. Atmos.
Environ. 29, 3019-3035.
Fujita E.M. (1998) Hydrocarbon source apportionment for the 1996 Paso Del Norte ozone study. EPA contract 68-D30030, work assignment III-130. Prepared by Energy and Environment Engineering Center, Desert Research Inst., for
U.S. EPA, Dallas, Texas.
Gofa F., Gertler A.W., Jennison B., Goodrich A. (1998) Truckee Meadows PM and VOC apportionment study: Winter 1997.
Paper 98-RA89.02 presented at the A&WMA 91st annual meeting, June 14-18, San Diego, California.
Henry R.C. (unknown) Apportionment of Project MOHAVE fine particle sulfur form the summer intensive. Prepared for
the national park service air quality research branch.
Henry, R. C. (1997) History and Fundamentals of Multivariate Air Quality Receptor Models, Chemometrics and Intelligent
Laboratory Systems. 37:525-530.
Henry R.C. (1992) Dealing with near collinearity in chemical mass balance receptor models. Atmos. Environ. 26, 933-938.
Huang S., Rahn K.A., Arimoto R. (1999) Testing and optimizing two factor-analysis techniques on aerosol at Narragansett,
Rhode Island. Atmos. Environ., 33, pp. 2169-2185.
Killus, J.P. and Moore G.E. (1991) Factor analysis of hydrocarbon species in the south-central coast air basin. Bull. Am.
Meteorol. Soc., 733-743.
Lee E., Chan C.K., Paatero P. (1999) Application of positive matrix factorization in source apportionment of particulate
pollutants in Hong Kong. Atmos. Environ. 33, pp. 3201-3212.
Maykut N., Knowle K., Larson T.V. (?) Seattle PM2.5 characterization studies.
North American Front Range Air Quality Study reports and data available at http://charon.cira.colostate.edu/
Paterson K.G., Sagady J.L., Hooper D.L., Bertman S.B., Carroll M.A., Shepson P.B. (1999) Analysis of air quality data
using positive matrix factorization. Environ. Sci. Technol., 33, 635-641.
PES (1994) Technical support for enhanced air quality modeling analysis for the purpose of the development of the 1994
ozone state implementation plan guidance. Report prepared by Pacific Environmental Services, Inc., January.
August 1999
PM Data Analysis Workbook: Source Attribution
59
References
Polissar A.V., Hopke P.K., Paatero P., Malm W.C., Sisler J.F. (1998) Atmospheric aerosol over Alaska 2. Elemental
composition and sources. J. Geophysical Research, Vol. 103, No. D15, pp. 19045-19057.
Poissant, L., J.W. Bottemheim, P. Roussel, N.W. Reid, H. Niki (1996) Multivariate analysis of 1992 SONTOS data subset.
Atmos. Environ., Vol. 30, No. 12, pp. 2133-2144.
Poirot R. (1998) Tracers of opportunity: Potassium. Paper available at
http://capita.wustl.edu/PMFine/Workgroup/SourceAttribution/Reports/In-progress/Potass/ktext.html
Schauer J.J., Rogge W.F., Hildemann L.M., Mazurek M.A., Cass G.R., Simoneit B.R.T. (1996) Source apportionment of
airborne particulate matter using organic compounds as tracers. Atmos. Environ., Vol. 30, No. 22, pp. 3837-3855.
Sisler, J.F., Huffman D., Latimer D.A. (1993) Spatial and temporal patterns and the composition of the haze in the Untied
States: an analysis of data from the IMPROVE network, 1988-1991, ISSN: 0737-5352-26, CIRA, Colorado State
University
Sisler, J.F., Malm W.C, Gebhart K.A. (1996) Spatial and seasonal patterns and long term variability of the composition of
haze in the United States, an analysis of data form the IMPROVE network, ISSN: 0737-5352-32, CIRA, Colorado
State University
VanCuren T. (1998) Spatial factors influencing winter particle sample collection and interpretation. Proceedings of the
PM2.5: A Fine Particle Standard Specialty Conference, edited by J. Chow and P. Koutrakis. Volume 1, pp. 78-107.
Watson J.G., Chow J.C., Lowenthal D.H., Pritchett L.C., Frazier C.A., Neuroth G.R., Robbins R. (1994) Differences in the
carbon composition of source profiles for diesel- and gasoline-powered vehicles. Atmos. Environ., Vol. 28, No. 15,
pp. 2493-2505.
Wonphatarakul V., Friedlander S.K., Pinto J.P. (1998) A comparative study of PM2.5 ambient aerosol chemical databases.
Environ. Sci. Technol., Vol. 32, No. 24., pp. 3926-3934.
August 1999
PM Data Analysis Workbook: Source Attribution
60
Appendix: AIRS Codes for PM2.5
88102
88103
88104
88107
88109
88110
88111
88112
88113
88114
88115
88117
88118
88121
88124
88126
88127
88128
88131
88132
88133
88134
88136
August 1999
Antimony PM2.5
Arsenic PM2.5
Aluminum PM2.5
Barium PM2.5
Bromine PM2.5
Cadmium PM2.5
Calcium PM2.5
Chromium PM2.5
Cobalt PM2.5
Copper PM2.5
Chlorine PM2.5
Cerium PM2.5
Cesium PM2.5
Europium PM2.5
Gallium PM2.5
Iron PM2.5
Hafnium PM2.5
Lead PM2.5
Indium PM2.5
Manganese PM2.5
Iridium PM2.5
Molybdenum PM2.5
Nickel PM2.5
88140
88142
88143
88146
88147
88152
88154
88160
88161
88162
88163
88164
88165
88166
88167
88168
88169
88170
88172
88176
88180
88183
88184
Magnesium PM2.5
Mercury PM2.5
Gold PM2.5
Lanthanum PM2.5
Niobium PM2.5
Phosphorous PM2.5
Selenium PM2.5
Tin PM2.5
Titanium PM2.5
Samarium PM2.5
Scandium PM2.5
Vanadium PM2.5
Silicon PM2.5
Silver PM2.5
Zinc PM2.5
Strontium PM2.5
Sulfur PM2.5
Tantalum PM2.5
Terbium PM2.5
Rubidium PM2.5
Potassium PM2.5
Yttrium PM2.5
Sodium PM2.5
PM Data Analysis Workbook: Source Attribution
88185
88186
88301
88302
88303
88305
88306
88307
88308
88403
Zirconium PM2.5
Wolfram PM2.5
Ammonium Ion PM2.5
Sodium Ion PM2.5
Potassium Ion PM2.5
Organic Carbon PM2.5
Nitrate PM2.5
Elemental Carbon PM2.5
Carbonate Carbon PM2.5
Sulfate PM2.5
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