Quantifying the Contribution of Important
Sources to PM Concentrations
•
•
•
•
Overview
What is a Source?
PM and Precursor Emissions
Source Apportionment
–
–
–
–
–
–
–
–
–
Overview
Spatial/Temporal Analyses
Cluster/Factor Analyses
Positive Matrix Factorization
UNMIX
Chemical Mass Balance Model
Handling Secondary PM
Trajectory Approaches
Method and Tool Availability
• Uncertainties in Sourcereceptor Analysis
October 1999
• Discerning Local Versus Nonlocal Influences
• Discerning Among Source
Categories
• Discerning Among Source
Regions
• Discerning Among Specific
Source Influences
• Network Design Issues
• References
• Appendix
– AIRS Codes for PM2.5
– Secondary Aerosol Source
Profiles
– PM2.5 Species Relationships
PM Data Analysis Workbook: Source Apportionment
1
Overview
Why do we need to understand the sources of PM? When an area experiences high
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 should 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 should be able to identify potential sources and meteorological conditions to assist
policy makers and modelers in developing control strategies.
• Modelers should 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 should 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
2
What is a “Source”?: Primary versus Secondary
•
•
Primary PM is composed of material in the same chemical form as when they
were emitted into the atmosphere including windblown dust, sea salt, road
dust, mechanically generated particles and combustion-generated particles
such as fly ash and soot. PM 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
3
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 U.S. sites).
– Model the dependence of PM on ozone to determine a component
of PM that is photochemically produced.
October 1999
PM Data Analysis Workbook: Source Apportionment
4
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, assess the differences between the concentrations of average
urban and nearby rural monitoring data. This assumes that the PM at rural
sites is not “contaminated” by urban emissions and that the same regional
sources have the same impact on rural monitors as on urban monitors (see
Schichtel, 1999a).
– Model the PM dependence on wind speed and wind direction to classify a
site as being dominated by local or regional source contributions
(Schichtel, 1999a).
– 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.
– Samplers were strongly influenced by sources less than 10 km away and
even minor sources close to the sampler could overwhelm any regional
component in a 24-hr integrated sample (VanCuren, 1998). However,
individual emitters can have a zone of influence less than 1 km (e.g.,
Chow et al., 1999).
October 1999
PM Data Analysis Workbook: Source Apportionment
5
PM and PM Precursor Emissions
• Knowledge of emissions is required for performing source
apportionment and assessing control measures.
• The majority of the PM2.5 mass over the United States 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
and nitrogen oxides (SO2, NOx) while the emissions of
other species such as organics, soil, and soot are poorly
defined.
Schichtel (1999b)
October 1999
PM Data Analysis Workbook: Source Apportionment
6
North American SO2 Emission Rates
SO2 Annual Emissions
Schichtel (1999b)
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.
October 1999
PM Data Analysis Workbook: Source Apportionment
7
Eastern U.S. NOx Emission Rates
•Area source NOx emissions are highest near cities.
Schichtel (1999b)
•Point source emissions are highest over the Industrial Midwest.
October 1999
PM Data Analysis Workbook: Source Apportionment
8
Major Sources of Organic Carbon 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 from most abundant to least abundant
for the Los Angeles urban area for 1982.
October 1999
PM Data Analysis Workbook: Source Apportionment
9
Emissions Issues
• PM2.5 precursor emissions patterns vary across the U.S.;
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.
October 1999
PM Data Analysis Workbook: Source Apportionment
10
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 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 rates and meteorological variables. 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 as 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 the
realization of the potential importance of receptor modeling as a complement to traditional dispersion modeling for source
apportionment.
October 1999
PM Data Analysis Workbook: Source Apportionment
11
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 (non-fossil-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 it 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 (PMF). 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 analysis-based model that does not employ additional constraints to limit the solution space.
October 1999
PM Data Analysis Workbook: Source Apportionment
12
Source Apportionment Overview (3 of 3)
•
•
One of the challenges that receptor modeling will have 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 PM10. 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 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.
Lewis, 1999
October 1999
PM Data Analysis Workbook: Source Apportionment
13
Source Apportionment Methods and Tools
• Source apportionment 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 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
– Positive matrix factorization (PMF)
– UNMIX
– Source-receptor models: chemical mass balance (CMB)
model
– Trajectory approaches
October 1999
PM Data Analysis Workbook: Source Apportionment
14
Assessing Spatial and Temporal Characteristics of PM
• Simple analyses of the spatial and temporal characteristics
of PM2.5 can be used to obtain information regarding the
data.
• These investigative analyses can include the use of time
series plots of PM mass and species concentrations, scatter
plots, individual sample “fingerprints”, box-whisker plots,
and summary statistics.
• These investigations can help the analyst identify
important species, species relationships, time periods of
interest, and likely sources.
October 1999
PM Data Analysis Workbook: Source Apportionment
15
Examples Using Spatial and Temporal Data (1 of 3)
• Potassium nitrate (KNO3) is a
major component of all
fireworks.
• This figure shows all
available PM2.5 K+ data from
all North 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 effect on data.
October 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 Apportionment
16
•
•
•
•
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.
October 1999
Annual Average PM 2.5 (ug/m3)
Examples Using Spatial and Temporal Data (2 of 3)
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
Made using Excel; adapted from Cass, 1997.
Sites are arranged from west to east (the general
direction of transport in the Los Angeles basin.
PM Data Analysis Workbook: Source Apportionment
17
Examples Using Spatial and Temporal Data (3 of 3)
• 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.
October 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 Apportionment
18
Multivariate Analyses
• Multivariate analyses are statistical procedures used to infer
the mix of PM sources impacting a receptor location (see
the following tables for species/source links).
• 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
19
Key PM Species and Sources (1 of 3)
Species
Soluble Ions
Nitrate (NO3 )
P/S
S
=
Sulfate (SO4 )
S
+
Ammonium (NH4 )
+
Sodium (Na )
Chloride (Cl )
+
Potassium (K )
S
P
P
P
Major Anthropogenic Sources
NOx from fossil fuel combustion (energy,
mobile sources, biogenics, and industrial
processes)
SOx from fossil fuel combustion (energy
generation, industrial processes, mobile
sources)
NH3 from animal husbandry, fertilizer use,
sewage. Also mobile sources, combustion,
industrial processes
Sea water, open playas, de-icing
Sea water
Vegetative burning
Comments
Ammonium nitrate is a principal component
of secondary aerosol in the western U.S.
Natural sources: soil, forest fires, lightening
Ammonium sulfate is the primary component
of PM2.5 in the eastern U.S. Natural sources:
sea spray sulfate, volcano gaseous sulfur,
forest fires
Important compound in nitrate and sulfate
chemistry. Natural sources: undisturbed soil,
wild animals
Also vegetative burning
Prescribed burns, forest fires, residual wood
combustion, meat charbroiling
P = Primary
S = Secondary
Adapted from U.S. EPA (1998a, 1998b);
Watson and Chow (1998); Chow (1995)
October 1999
PM Data Analysis Workbook: Source Apportionment
20
Key PM Species and Sources (2 of 3)
Species
Metals
Nickel (Ni)
Calcium (Ca)
Iron (Fe)
Vanadium (V)
Aluminum (Al)
Silicon (Si)
Sulfur (S)
Phosphorus (P)
Lead (Pb)
Bromine (Br)
Manganese (Mn)
Chlorine (Cl)
Copper (Cu)
Titanium (Ti)
P/S
P
P
P
P
P
P
P/S
P
P
P
P
P
P
P
Major Anthropogenic Sources
Residual oil combustion
Crustal material
Crustal material
Residual oil combustion
Crustal material
Crustal material
Residual oil combustion
Fuel combustion
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
Adapted from U.S. EPA (1998a, 1998b);
Watson and Chow (1998); Chow (1995)
October 1999
PM Data Analysis Workbook: Source Apportionment
21
Key PM Species and Sources (3 of 3)
P/S
Species
Soluble Ions
Nitrate (NO3 )
S
=
S
Sulfate (SO4 )
+
Ammonium (NH4 )
+
Sodium (Na )
Chloride (Cl )
+
Potassium (K )
S
P
P
P
Comments
Major Anthropogenic Sources
NOx from fossil fuel combustion (energy,
mobile sources, biogenics, and industrial
processes)
SOx from fossil fuel combustion (energy
generation, industrial processes, mobile
sources)
NH3 from animal husbandry, fertilizer use,
sewage. Also mobile sources, combustion,
industrial processes
Sea water, open playas, de-icing
Sea water
Vegetative burning
Ammonium nitrate is a principal component
of secondary aerosol in the western U.S.
Natural sources: soil, forest fires, lightening
Ammonium sulfate is the primary component
of PM2.5 in the eastern U.S. Natural sources:
sea spray sulfate, volcano gaseous sulfur,
forest fires
Important compound in nitrate and sulfate
chemistry. Natural sources: undisturbed soil,
wild animals
Also vegetative burning
Prescribed burns, forest fires, residual wood
combustion, meat charbroiling
P = Primary
S = Secondary
Adapted from U.S. EPA (1998a, 1998b);
Watson and Chow (1998); Chow (1995)
October 1999
PM Data Analysis Workbook: Source Apportionment
22
Cluster and Factor Analyses
•
•
Cluster analysis is a multivariate procedure for grouping data by similarity
between observations (i.e., observations with similar chemical compound
concentrations are grouped).
– This is typically done using a Euclidean distance between each pair of
observations (squared differences between individual concentrations
summed across all species).
– 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 procedure for grouping data by similarity between
variables (i.e., variables that are highly correlated are grouped).
– This is typically done using the correlation between each pair of variables.
– 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
23
Cluster/Factor Analysis Example
Example PM2.5 cluster and factor analyses to be developed for
the workbook, see the following:
• Wongphatarakul et al., 1998 for example PM cluster
analysis
• Huang et al., 1999 for example conventional factor
analysis applied to PM data
October 1999
PM Data Analysis Workbook: Source Apportionment
24
Positive Matrix Factorization
Positive matrix factorization (PMF) was developed by Dr. P. Paatero
(Dept. of Physics, University of Helsinki). PMF can be used to
determine source profiles based on the ambient data. Features include
the following:
– PMF uses weighted least squares fits for data that are normally
distributed and maximum likelihood estimates for data that are
distributed long normally.
– PMF weights data points by their analytical uncertainties.
– PMF constrains factor loadings and factor scores to nonnegative
values and thereby minimizes the ambiguity caused by rotating
factors. This is one of the major differences between PMF and
principal component analysis (PCA).
– PMF expresses factor loadings in mass units which allows factors
to be used directly as source signatures.
– PMF provides uncertainties for factor loadings and factor scores
which makes the loadings and scores easier to use in quantitative
procedures such as chemical mass balance.
October 1999
PM Data Analysis Workbook: Source Apportionment
25
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 are 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
October 1999
PM Data Analysis Workbook: Source Apportionment
26
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
Sites
YUCH
WRST KATM
Polissar et al., 1998
Stacked bar plots prepared
using a spreadsheet program.
Figure 20
October 1999
PM Data Analysis Workbook: Source Apportionment
27
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.
October 1999
PM Data Analysis Workbook: Source Apportionment
28
UNMIX Analysis Example
•
•
•
•
UNMIX was applied to PM2.5 data
collected at Underhill, VT, during
1988-1995.
Six “sources” were identified using
mass (MF), particle absorption
(BABS), arsenic (As), calcium (Ca),
iron (Fe), nickel (Ni), selenium (Se),
silicon (Si), total sulfur (S), and nonsoil potassium (KNON).
The “sources” were further
investigated by performing back
trajectories and investigating time
series.
The smelter (“smelt”) source, oil
combustion, and winter coal
combustion source trajectories are
consistent with known emission
patterns.
Values represent the % of the element
accounted for by the source.
Poirot (1999)
October 1999
PM Data Analysis Workbook: Source Apportionment
29
Comparing UNMIX and PMF Results
• PM 2.5 data from Underhill, VT,
for 1988-1995 were analyzed
using both UNMIX and PMF.
• Results for arsenic sources (i.e.,
smelter), nickel sources (i.e., oil
combustion), and soil sources
compared well between the two
source apportionment methods.
• Consensus among results gives
the analyst more confidence
that the results are meaningful.
October 1999
PM Data Analysis Workbook: Source Apportionment
Poirot, 1999
30
Overview of the Chemical Mass Balance Model (1 of 3)
•
•
•
•
Miller et al. (1972) first proposed the mass balance model for apportioning ambient aerosol mass to its sources via its chemical
constituency. The basic concept of CMB is that composition patterns of emissions from various classes of sources are different
enough that their contributions can be identified by measuring concentrations of many species collected at the receptor site
(Gordon, 1988). Thus, CMB consists of an effective variance weighted, least squares solution to a set of linear equations which
expresses each receptor species concentration as a linear sum of the products of the source species profile and source
contributions. The source species profile (i.e., fractional amount of the chemical species in the emissions from each source type)
and the receptor concentrations are the basic input to the CMB model. The output consists of the amount contributed by each
source type to each chemical species and to the total receptor concentration. Input data uncertainties (analytical) are used both to
weight the importance of input data values in the solution and to calculate the uncertainties of the source contributions.
p
The basic formulation of the CMB may be expressed as:
ci = å aij s j , i = 1, n
i=1
where ci is the concentration of constituent or property i, aij is the fractional concentration of constituent or property i in the
emissions from source j as perceived at the receptor, sj is the total mass contribution of source j to the receptor, p is the total
number of sources contributing, and n is the total number of constituents or properties.
CMB makes several assumptions:
– Compositions of source emissions are constant over the period of ambient and source sampling.
– Chemical species do not react with each other (i.e., they add linearly). For many assessments, secondary formation of
particles is important. While CMB is not formulated to explicitly treat secondary transformation, a surrogate procedure is
available to give some information on at least the extent of secondary materials in the ambient data (discussed later in the
chapter).
– All sources with a potential for significantly contributing to the receptor have been identified and have had their emissions
characterized.
– The source compositions are linearly independent of each other.
– The number of sources or source categories is less than the number of chemical species.
– Measurement uncertainties are random, uncorrelated, and normally distributed.
October 1999
PM Data Analysis Workbook: Source Apportionment
31
Overview of the Chemical Mass Balance Model (2 of 3)
•
•
•
•
In fact, these assumptions pose a limitation of the model because source compositions are not constant (they vary with changes
in process inputs, loads and cycles); components do react with each other and systems are not linear; one rarely knows exactly
how many sources are contributing to a receptor; there are many more sources than components which can be practically
measured; many sources have very similar compositions; measurement errors are not necessarily random, uncorrelated, or
normally distributed; and very few sources have their own unique tracer components (Watson, 1984). While the implicit
assumptions are fairly restrictive and will never be totally obeyed in actual practice, CMB can tolerate deviations from these
assumptions with some penalty in uncertainty. Several studies have been published that document CMB's tolerance to such
deviations.
The limitations of receptor models may be offset by their advantages. They are relatively simple compared to sourceoriented models of comparable accuracy and precision. And because an analytical method of determining the effects of
systematic errors on the mass balance equations has been developed, the precisions required of measurements to provide a target
precision for the model output can be estimated.
CMB has been used in a great number of actual air pollution studies, some of which are described here. Size-fractionated
samples were analyzed during the summer of 1982 in Philadelphia (Dzubay, 1988). With the promulgation in 1987 of National
Ambient Air Quality Standard for suspended particles nominally  10 m in diameter (PM10), receptor modeling (notably CMB)
was employed in support of state implementation plan (SIP) development. CMB was applied to the apportionment of PM10 from
the West Orem Steel Plant during episodes in the winter of 1978/88 in Utah Valley (Cooper et al., 1989). CMB was also applied
to the apportionment of fine and coarse particles in Windsor, Ontario from January through November 1991 (Conner et al.,
1993). CMB was applied to the chemically speciated diurnal particulate matter samples acquired in California's South Coast Air
Basin during the summer and fall of 1987 as part of the Southern California Air Quality Study (Watson et al., 1994).
CMB7 (for DOS) and its User's Manual were developed under contract with Desert Research Institute (DRI) and released in
1990. These products have been uploaded to EPA's modeling website (www.epa.gov/scram001). As explained in a README
file, a source profile library (SPECIATE) is accessible elsewhere (www.epa.gov/ttn/chief) and is applicable (with some
reformatting described in the README) for input to CMB7.
October 1999
PM Data Analysis Workbook: Source Apportionment
32
Overview of the Chemical Mass Balance Model (3 of 3)
•
•
DRI began development on CMB8 (for WINDOWS) and its
documentation in late 1995. This documentation included an
updated User's Manual and Protocol for Applying and
Validating the CMB Model. Key features of CMB8 include:
correcting a bug associated with using the AUTOFIT feature;
enabling the Britt and Luecke exact least squares solution;
providing additional source profile and fitting species
combinations (arrays); optimizing fitting (individual and
AUTOFIT) so that sources with contribution estimates that are
either negative or lower than their standard errors while
appearing in an uncertainty/similarity cluster are eliminated;
automatically eliminating species with missing values; and
expanding options in the configuration and Output files. DRI
released a preliminary version in late 1998 (model and
documentation available via anonymous ftp: eafs.sage.dri.edu)
and tested in early 1999. EPA discovered problems with
CMB8's execution and errors in its documentation. In an effort
to solve these problems, and also to enhance the model to make
it more robust and user friendly, EPA is letting another contract
to complete the work. A final product is anticipated for spring
2000.
An example of how CMB8 may be applied to PM2.5 is
included in Section 5 of the (draft) CMB8 Applications and
Validation Protocol for PM2.5 and VOC for the Northern Front
Range Air Quality Study (Watson et al., 1998). Other
examples will be cited as more experience is gained and as
new results become available in the literature.
October 1999
PM Data Analysis Workbook: Source Apportionment
Coulter, 1999
33
Chemical Mass Balance Modeling
•
•
•
•
The purpose of CMB receptor modeling is to apportion ambient PM (or any
categorical pollutant) 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 expresses each measured chemical species concentration as a linear
sum of products of source profile species and source contributions, and then solves a
set of linear equations.
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 PM and individual
species and the uncertainty.
October 1999
PM Data Analysis Workbook: Source Apportionment
34
CMB Model Assumptions
• Composition of source emissions do not change during travel from the
point of emission (where the source profile is defined) to the point of
receptor site measurements (minor contributors are frequently omitted).
• 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 sources or source categories is less than the 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). It may be necessary to
combine chemically similar source categories or add additional fitting
species to the model.
• Measurement uncertainties are random, uncorrelated, and normally
distributed (effects unknown).
October 1999
PM Data Analysis Workbook: Source Apportionment
35
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?
October 1999
PM Data Analysis Workbook: Source Apportionment
36
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 from 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).
October 1999
PM Data Analysis Workbook: Source Apportionment
37
Example 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.
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.
Note that these performance goals are subjective and based on early
experience with TSP and PM10 models. Goals may change as more PM2.5
CMB applications are performed.
October 1999
PM Data Analysis Workbook: Source Apportionment
38
Example CMB Performance Goals (2 of 2)
•
•
•
•
•
Degrees of freedom (df) is the number of species in the fit minus the number of
sources in the fit. Some researchers recommend df >> 5.
The ratio of the calculated species mass (C) to measured species mass (M) 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 C
and M 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 0.5 to 1.0 are associated with influential species.
U/S clusters: 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. Results should not contain uncertainty clusters.
October 1999
PM Data Analysis Workbook: Source Apportionment
39
Source Profiles
• For source-receptor modeling, 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).
Accurate source profiles are the key to successful modeling.
October 1999
PM Data Analysis Workbook: Source Apportionment
40
CMB Example Analyses (1 of 2)
Burning
12%
Industry
8%
Soil
4%
Nitrate
13%
Sea Salt
1%
Sulfate
28%
October 1999
Mobile
34%
Seattle area, Washington
M obile
B urning
Feb-97
Dec-96
S ulfate
N itrate
Oct-96
50
40
30
20
10
0
Aug-96
•
S o urce Ap p o rtio nm ent M o nthly Averag e P ercentag es
Jun-96
•
Source apportionment example for the
Seattle, Washington area for 19961997 using PM2.5 data.
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.
Summer 1996 - Beacon Hill
Apr-96
•
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 Apportionment
Mobile
41%
From Maykut et al. (1998)
Figures made using Excel.
41
CMB Example Analyses (2 of 2)
October 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, wood smoke,
and meat cooking.
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
Chart generated in Excel; adapted from Cass, 1997.
Other organics include paved road dust, cigarette
smoke, vegetative detritus, tire wear debris, and
secondary organics. See also Scheff et al., 1984.
PM Data Analysis Workbook: Source Apportionment
42
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.
October 1999
N
6000
NW
NE
4000
2000
W
0
E
SW
PM Data Analysis Workbook: Source Apportionment
SE
S
From Maykut et al. (1998)
Radar Plot: nanograms of
PM attributed to the source
by wind direction.
43
Uncertainties and Limitations 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. One approach here is to add additional species
to reduce collinearity. For example, specific VOC species could be used
with PM profiles to differentiate between natural soil dust from motor
vehicle related dust.
• 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. This is discussed
further in the following pages.
• 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.
However, source-receptor models can check if control plans achieve their
desired reductions.
October 1999
PM Data Analysis Workbook: Source Apportionment
44
Handling the Apportionment of Secondary PM (1 of 2)
•
•
•
•
For many assessments, secondary formation of particles is important. While CMB is not formulated to explicitly treat
secondary transformation, a surrogate procedure is available to give some information on at least the extent of secondary
materials in the ambient data. The surrogate procedure was developed for CMB7 for treating secondary PM10 particles.
One of the key assumptions made by CMB7 is that chemical species do not react with each other, i.e., 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 (e.g., U.S. EPA, 1998b). Thus, CMB7 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 (e.g., many designated “Serious PM10 Areas” by EPA), 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 (OC). If sources of such materials are not explicitly treated, CMB7 will tend to underaccount
for total particle mass (% MASS value). As stated in the CMB 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). With this technique, the secondary species are “apportioned to chemical compounds rather than
directly to sources.”
Setting up secondary “source” profiles. A table in the appendix to this section 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%.
October 1999
PM Data Analysis Workbook: Source Apportionment
45
Handling the Apportionment of Secondary PM (2 of 2)
•
•
•
Secondary “source” profiles (continued). 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 CMB7 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.
These examples are described as profiles for secondary species. However, the secondary profile may not represent
secondary aerosol exclusively. For example, Watson et al. (1994) indicate that the OC profile in the appendix table
may account for contributions from fugitive sources not included in the CMB7 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.
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 CMB7 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 CMB7 calculations
because of the likelihood of some amount of water associated with hygroscopic (or deliquescent) sulfates. The
amount of mass due to this water depends on the form of the sulfate and relative humidity factors.
Coulter, 1999
October 1999
PM Data Analysis Workbook: Source Apportionment
46
Handling the Apportionment of Secondary PM
• CMB does not explicitly treat secondary transformation and, thus, will
tend to underaccount for total particle mass.
• A surrogate procedure, using single constituent source profiles, can be
used to give information on secondary materials in the ambient data.
• Secondary species are “apportioned to chemical compounds rather than
directly to sources.” Secondary source profiles consisting of “pure”
organic carbon and ammonium sulfate, bisulfate, and nitrate can be used
to apportion the remaining NH4+, SO4=, NO3-, and OC that would not be
apportioned to the primary particle profiles.
• The secondary profile may not represent secondary aerosol exclusively.
Contributions from primary fugitive sources (e.g., OC from cooking,
plant parts, or tire wear) not included in the CMB calculation may be
included in the results. In such a case, the technique provides an upper
estimate of the amount of aerosol attributable to secondary formation.
• This single constituent source profile technique cannot yield any
information on the specific source types contributing to the species in the
single constituent profiles.
October 1999
PM Data Analysis Workbook: Source Apportionment
47
Trajectory Approaches (1 of 2)
• Detailed air mass history calculations using the CAPITA Monte
Carlo model are being employed to investigate long-term,
synoptic-scale meteorological conditions associated with ambient
air quality and deposition measurements at various locations.
• By combining multi-year sets of regional-scale meteorological
data and local ambient pollution monitoring data, a long-term or
“climatological” description of an airshed can be presented in
probabilistic terms.
• One result of these analyses is an estimate of predominant source
regions for periods of high concentrations (or deposition) of
specific air pollutants at a site. For example, the analyses address
the question: During episodes with high concentrations of
sulfate, which areas were likely upwind?
Poirot et al., 1998
October 1999
PM Data Analysis Workbook: Source Apportionment
48
Trajectory Approaches (2 of 2)
• Trajectory cluster analysis is a method to categorize a large
set of trajectories into groups of similar trajectories.
• Goals of these analyses are to minimize differences among
trajectories in a cluster and maximize differences among
clusters.
• The analyses result in distinct clusters representing different
synoptic regimes.
• The analyses are useful for estimating pollutant source regions,
interpreting forecast trajectory errors, identify similar
meteorological scenarios for case studies, and compare data on
a cluster-by-cluster basis (example written for precipitation
data, but could be applied to other pollutant data.)
Rolph et al., 1999
October 1999
PM Data Analysis Workbook: Source Apportionment
49
Example Air Mass History Analysis
Upwind probabilities for high aerosol arsenic at three Champlain Basin sites
Poirot et al. (1998)
Shaded areas show 20%, 40%, and 60% of upwind probability on highest concentration day
• Upwind probability plots for high arsenic concentrations have a strong
NW orientation at all three sites, pointing directly toward a smelter region.
• The location of several large smelters are also identified in the plots, with
the smelter identified as a green dot appearing to be the most likely
contributor (the yellow dot is the receptor location).
• High arsenic levels paper to be excellent tracers for influence in the Lake
Champlain Basin from the smelter region.
October 1999
PM Data Analysis Workbook: Source Apportionment
50
Example Trajectory Cluster Analysis
• As an example, a set of 25 backtrajectories is investigated. The
trajectories correspond to a subset of
precipitation samples taken in 1993 at
State College, PA (AIRMoN site PA15).
The trajectories originate at 09 UTC at
an altitude of 2000 m agl and have a
duration of 36 hours.
• Trajectory cluster analysis identified five
clusters, depicted in the figures by the
mean trajectory for each of the clusters.
Each cluster represents a unique
transport path and associated travel time.
In this case, the clusters are all composed
of five trajectories. The cluster number is
arbitrary.
Rolph et al., 1999
October 1999
PM Data Analysis Workbook: Source Apportionment
51
Method and Tool Availability (1 of 2)
• 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. An on-line
description of these techniques is available at
http://www.sbg.ac.at/geo/idrisi/geostat/tutorial/multivariate_statistics/gg616.html
• A version of PMF is currently being tested by EPA. The software
consists of stand-alone executables (run from the DOS prompt), hence
this version runs only on a PC. The order form for PMF is available at
ftp://rock.helsinki.fi/pub/misc/pmf/pmforder.pdf. "PMF" refers to both PMF2 (a
2-way deconvolution) and PMF3 (a 3-way deconvolution). Dr.
Paatero, the developer of PMF, is currently developing a more flexible
version of the deconvolving concept and that software is called ME
(multi-linear engine). Information regarding the ME is also available
from the website. There is a fee to purchase these tools ($400, $600,
and $750 for 1, 2 or 3 of the tools).
October 1999
PM Data Analysis Workbook: Source Apportionment
52
Method and Tool Availability (2 of 2)
• The current version of UNMIX is being tested by EPA and others.
Contact Ron Henry for a copy (rhenry@usc.edu). UNMIX is currently
implemented as a MATLAB program (see the website mathworks.com
for more information); therefore, the user must have a current version of
MATLAB in order to run UNMIX.
• CMB8 modifications are planned. The current version, CMB8.0, is
available from ftp://eafs.sage.dri.edu/cmb80/model/
• The CAPITA Monte Carlo model was developed in the 1980's to provide
quantification of regional atmospheric transport, transformation, and
removal processes governing the source receptor relationship.
Information regarding the model is available at
http://capita.wustl.edu/CAPITA/CapitaReports/MonteCarloDescr/mc_pcim0.html#monte
• Information regarding the use of airmass history models and techniques
for source attribution is available at
http://capita.wustl.edu/capita/capitareports/airmasshist/EPASrcAtt_jul17/index.htm
• Information regarding the NOAA trajectory cluster model is available at
http://www.arl.noaa.gov/slides/ready/index.html
October 1999
PM Data Analysis Workbook: Source Apportionment
53
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. For example, Northern
Front Range air quality study source profiles are available at
http://charon.cira.colostate.edu/DRIFinal/
• 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.
• Local, state, and federal agencies. For example, California has
information available at http://arbis.arb.ca.gov/emisinv/emsmain/emsmain.htm
October 1999
PM Data Analysis Workbook: Source Apportionment
54
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
October 1999
PM Data Analysis Workbook: Source Apportionment
Schichtel (1999b)
55
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.
October 1999
PM Data Analysis Workbook: Source Apportionment
56
Influence of Wind Speed on Local Concentrations
Schichtel (1999b)
• 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
57
PM2.5 vs. Surface Wind Speed at Urban and Rural Sites
Schichtel (1999b)
The PM2.5 at these urban sites during the cold season (November-March)
declines sharply (60%) with increasing wind speeds compared to the rural
sites (30%). This implies the urban PM2.5 is dominated by local sources.
October 1999
PM Data Analysis Workbook: Source Apportionment
58
Local Emission Influenced PM2.5
Monitoring Sites
• 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.
Schichtel (1999b)
October 1999
PM Data Analysis Workbook: Source Apportionment
59
Seasonal Urban-Rural Difference
Schichtel (1999b)
• 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
60
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 site, 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
61
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.
October 1999
PM Data Analysis Workbook: Source Apportionment
62
Example Discerning Natural vs. Anthropogenic
Sources 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
63
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
October 1999
PM Data Analysis Workbook: Source Apportionment
64
Example Discerning Among Source Regions
by Location
Upwind probabilities for high aerosol selenium at three Champlain Basin sites.
Shaded areas show 20%, 40%, and 60% of upwind probability on highest concentration day.
Poirot et al., 1998
• Upwind probability plots for high selenium concentrations have a
strong SW orientation at all three sites, pointing directly toward
Midwestern (coal-burning) sources along the Ohio River Valley.
• Selenium appears to be an excellent tracer for influence from coal
combustion at these receptor sites.
• This analysis was performed using air mass history calculations.
October 1999
PM Data Analysis Workbook: Source Apportionment
65
Discerning Among Specific Source Influences
• Within local jurisdiction
• Beyond local jurisdiction
• Changes over time, space, and species
Example PM2.5 analyses to be developed
October 1999
PM Data Analysis Workbook: Source Apportionment
66
Network Design Issues
• Identify and reduce uncertainties in source apportionment
• Provide routine and enhanced measurements for source
apportionment
• Improve emission profiles and inventories
• Identify inefficient/redundant measurements
Example PM2.5 analyses to be developed
October 1999
PM Data Analysis Workbook: Source Apportionment
67
Decision Matrix for Source Apportionment
SCIENCE/TECHNICAL QUESTIONS/OBJECTIVES
Discerning Local vs. Non-local influences
By species
Temporal
Spatial
Discerning among source categories
Natural vs. anthropogenic
Anthropogenic
Refinement of anthropogenic source categories
Discerning among source regions
by direction from receptor
By distance from receptor
By location
Regional fingerprints
Discerning among specific source influences
within local jurisdiction
Beyond local jurisdiction
Trends in specific sources
Uncertainties in source-receptor analyses
Source attribution methods and tools
Network Design Issues
October 1999
PM Data Analysis Workbook: Source Apportionment
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
Comparing PM2.5 among cities Wongphatarakul et al., 1998
Regional apportionment White and Macias, 1991
NFRAQS USEPA, 1998b
Local vs. nonlocal contrib. Schichtel, 1999b
Trajectory clusters Rolph et al., 1999
Air mass history Poirot, 1998
Factor Analysis aerosols Polissar et al., 1998
NFRAQS Watson et al., 1998
Factor analyses of aerosol Huang et al., 1999
ü
Vehicle emissions contrib. to PM carbon Cass, 1997
ü
Trajectory analysis Poirot et al., 1998
Tracers of opportunity: K Poirot, 1998
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. Go to the next page to see the data sets
and data analysis tools that were used.
PM2.5 as f(WS, WD) Schichtel, 1999a
Example Projects
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
68
Decision Matrix for Source Apportionment
TOOLS DEMONSTRATED
AMDAS
Other Statistical Methods
Voyager
Trajectory Methods
Factor, cluster analyses
PMF
UNMIX
CMB8
SPECIATE
October 1999
PM Data Analysis Workbook: Source Apportionment
ü
ü
ü
ü
ü
ü
Comparing PM2.5 among cities Wongphatarakul et al., 1998
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
Regional apportionment White and Macias, 1991
ü
ü
ü
NFRAQS USEPA, 1998b
ü
ü
ü
ü
Local vs. nonlocal contrib. Schichtel, 1999b
ü
ü
ü
Trajectory clusters Rolph et al., 1999
ü
ü
Air mass history Poirot, 1998
ü
Factor Analysis aerosols Polissar et al., 1998
ü
NFRAQS Watson et al., 1998
Vehicle emissions contrib. to PM carbon Cass, 1997
ü
Factor analyses of aerosol Huang et al., 1999
Trajectory analysis Poirot et al., 1998
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
Tracers of opportunity: K Poirot, 1998
For each of the projects that are of interest to you,
follow down the column to see the data sets and data
analysis tools that were used.
PM2.5 as f(WS, WD) Schichtel, 1999a
Example Projects
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
ü
69
References
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PM Data Analysis Workbook: Source Apportionment
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Fraser M.P., Cass G.R., Simoneit B.R.T. (1999) Particulate organic compounds emitted from motor vehicle exhaust and in the
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of nonmethane organic gases in California's San Joaquin Valley and San Francisco Bay Area. Atmos. Environ. 29, 30193035.
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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.
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PM Data Analysis Workbook: Source Apportionment
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References
Henry R.C. (unknown) Apportionment of Project MOHAVE fine particle sulfur from the summer intensive. Prepared for
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Huang S., Rahn K.A., Arimoto R. (1999) Testing and optimizing two factor-analysis techniques on aerosol at Narragansett,
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Maykut N., Knowle K., Larson T.V. (1998) Seattle PM2.5 characterization studies. Draft report prepared by Puget Sound
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PM Data Analysis Workbook: Source Apportionment
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References
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October 1999
PM Data Analysis Workbook: Source Apportionment
73
References
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PM Data Analysis Workbook: Source Apportionment
74
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October 1999
PM Data Analysis Workbook: Source Apportionment
75
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
October 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 Apportionment
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
76
Secondary Aerosol Source Profiles (Wt% of Mass)
Table A-1.
Secondary Aerosol Source Profiles (Weight % of Mass)
Species
b
a
AMSUL
AMBSUL
AMNIT
OC
PM-2.5 & Coarsec
PM-2.5 & Coarsec
PM-2.5 & Coarsec
PM-2.5 & Coarsec
Conc. ± Unc.d
Conc. ± Unc.d
Conc. ± Unc.d
Conc. ± Unc.d
Cl-
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
NO3-
0.000
±
0.000
0.000
±
0.000
77.5
±
7.8
0.000
±
0.000
SO4=
72.7
±
7.3
83.5
±
8.3
0.000
±
0.000
0.000
±
0.000
NH4+
27.3
±
2.7
15.7
±
1.6
22.6
±
2.3
0.000
±
0.000
Na+
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
TC
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
100
±
10
OC
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
100
±
10
EC
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Na
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Al
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Si
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
P
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
S
24.3
±
2.4
27.9
±
2.8
0.000
±
0.000
0.000
±
0.000
Cl
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
K
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Ca
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Ti
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
V
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Cr
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Mn
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Fe
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Ni
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Cu
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Zn
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
As
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Se
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Br
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Sr
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Mo
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Cd
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Sn
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Sb
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
Ba
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
0.000
±
0.000
a
October 1999
Reproduced with permission from Watson et al. (1994).
PM Data Analysis Workbook: Source Apportionment
b
c
TC (Total Carbon) = OC + EC; Sum does not include Na+ , Cl-, S, or TC.
PM-10 - PM-2.5
Watson et al., 1994
77
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 analysts 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.
October 1999
PM Data Analysis Workbook: Source Apportionment
78