SOURCE APPORTIONMENT STUDY FOR STATE IMPLEMENTATION PLAN DEVELOPMENT IN THE COACHELLA VALLEY
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SOURCE APPORTIONMENT STUDY FOR STATE IMPLEMENTATION PLAN
DEVELOPMENT IN THE COACHELLA VALLEY
Bong Mann Kim, Melvin D. Zeldin, and Chung Liu
South Coast Air Quality Management District
Office of Planning and Rules
21865 E Copley Dr
P 0 Box 4939
Diamond Bar CA 91765-0939
ABSTRACT
A PMlo State Implementation Plan (SIP) has been developed for the Coachella Valley to
demonstrate attainment of the federal PMlo standards'. In 1987, the EPA recommended the use
of two receptor models for SIP developmentz. For the Coachella Valley SIP, both Principal
Component Analysis (PCA) and the Chemical Mass Balance (CMB) model were used to
apportion the PMlo concentrations measured at two sites (Palm Springs and Indio) to seven
emission source categories: geological, motor vehicle, secondary (ammonium nitrate and
ammonium sulfate), vegetative burning, limestone, marine, and residual oil sources. Geological
material is the major source, contributing 49 percent and 59 percent to the annual average PMlo
mass for Palm Springs and Indio, respectively.
For the CMB model application, a total of 44 soil profiles were obtained from the two sites
within the Coachella Valley and in other portions of the California desert to characterize regional
soil differences.
INTRODUCTION
The Coachella Valley, located in the Southern California desert east of the South Coast
Air Basin, has been designated as a nonattainrnent area for PMlo. Under the federal Clean Air
Act, a SIP must be prepared for the Coachella Valley to demonstrate attainment of the federal
PMlo standards.
The EPAz specifies three options for estimating the air quality impacts of PMlo emissions:
(1) use of receptor and dispersion models in combination;
(2) use of dispersion models alone; or
(3) use of two receptor models with control strategies developed using a proportional model.
The primary source of PMlo in the Coachella Valley is fugitive dust. Dispersion models
cannot adequately handle this type of source because of the non-persistent and spatially variable
nature of fugitive dust emissions. Therefore, under option (3), the District used chemical Mass
Balance (CMB) and Factor Analysis (FA) receptor modeling approaches to determine specific
sources.
This paper briefly reviews receptor modeling and its application to the Coachella Valley
PMlo source contribution estimates.
RECEPTOR MODELING
Receptor models can estimate the impacts from specific sources of emissions based on the
measurements made at specific monitoring (receptor) sites. The basic idea of receptor modeling is
1to account for all the chemical compounds deposited on air sampling filters by matching them
against known emission sources of the chemical compounds, referred to as "source fingerprints."
By accounting for the collected PMlo in this fashion, we are balancing the mass between
measurements and sources; hence the term "chemical mass balance" is commonly used. There are
two principal statistical approaches to solving the resulting mass balance equations: (1) least
squares fitting approach, and (2) multivariate statistical approaches. Receptor models and their
application have been reviewed by Cooper and Watson3, Gordon45, Henry et aI.6 and Hopke7. In
the following sections, the least squares fitting appproach of the CMB model is briefly explained
and this model is applied to the Coachella Valley PMlo data to estimate the source impacts.
The Chemical Mass Balance Model
The mathematical form, the CMB model can be expressed as:
where C is the ambient concentrations of chemical species, A is the source composition matrix, E
is the error in measurements, and S is the source contributions to be estimated. Usually, the
number of chemical species is larger than the number. of sources. Therefore, the system is
overdetermined and the least squares estimation method is applied. This method was first
proposed by Miller et a1.8 and developed further by Friedlanderg and Watsonlo. Details of the
CMB model can be found in the Receptor Model Technical Seriesll-16.
Principal Component Analysis
In most cases, the sources impacting a receptor site are qualitatively obvious, but
quantifying their contribution is usually a problem. If some sources are unknown, the statistical fit
may not be accurate. Hence, the EPAZ recommends the use of one other receptor modeling
approach such as PCA, as a corroborating analysis. In this study, therefore, PCA was applied to
the Coachella Valley PMlo data prior to the CMB analysis in order to identify the sources
contributing to the PMlo concentrations.
The classical factor analysis model (Harman13 is expressed as
where C is the n species by m measurements data matrix, L is the n by p factors of factor loadings
matrix, F is the p by m factor scores matrix, and U is the n by m unique factor matrix. If the unique
factor term is not included in the analysis, and L is the eigenvector of C C and ~ F is the
eigenvector multiplied by the singular values of C ~ Cit, is called the principal component analysis
(PC-41,
C = LF. (3)
In PCA, L is called the component loadings matrix, and F is called the component scores
matrix. These two methods seem to be similar, but they are not. Detailed explanations of the
differences between these two methods are made by Jolliffelg, and Dillon and Goldsteinlg.
The basic idea of PCA is to reduce the dimensionality of a data set of interrelated variables
so that a minimum number of factors can explain the maximum variance of the interrelated data.
The principal components are extracted so that the first component accounts for the largest *amount of the total variation in the data, the second principal component accounts for the
maximum amount of the remaining total variation not already accounted for by the first principal
component, and so on. This is accomplished by orthogonally transforming the correlated data into
a new set of uncorrelated variables, called principal components. Mathematically, this procedure
is equivalent to an eigenanalysis, which produces eigenvalues and corresponding eigenvectors.
Eigenvectors form principal components and eigenvalues represent the relative importance of the
eigenvectors, i.e., eigenvalue represents the variance of the each principal component. The
principal component solution can be obtained from the singular value decomposition of the data
matrix (Jolliffel*).
For a valid PCA, the minimum number of measurements suggested by Henry et alP is,
where m is the number of measurements, n is the number of species, and df/n is the degrees of
freedom per species. In this study, the number of species is 21 and solving equation 4, a minimum
of 42 samples is required. Sixty measurements were made at Palm Springs and 50 at Indio.
Therefore, these measurements are sufficient for a valid PCA analysis.
APPLICATION OF RECEPTOR MODELING
The CMB model has been applied to special PMIOdata collected at the two Coachella
Valley sites: Palm Springs and Indio. In the following sections, sampling, analysis, and the CMB
model results are described. ,
Data Description
To obtain chemically-derived, or "speciated PMIOdata, the special 24-hour PMlO aerosol
sampling was conducted every six days from September 1988 to September 1989 at the two
sampling sites.
Special laboratory analysis were used to determine the chemical composition of the
collected air samples. Thirty-four trace elements (Al, Si, P, S, C1, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu,
Zn, Ga, As, Se, Br, Rb, Sr, Y, Zr, Mo, Pd, Ag, Cd, In, Sn, Sb, Ba, 4 Hg, and Pb) were
determined by X-ray fluorescence analysis. Water-soluble ionic species, such as Na, C1, K, NO3,
S04, and NHq were analyzed by ion chromatography. Organic and elemental carbon
concentrations were determined by thermal/optical methods. A more detailed description of the
monitoring site, sampling schedule, and sample analysis can be found in Cooperzo.
The CMB model requires a priori knowledge of source compositions for each emissions
source. However, these can vary with time and location. For the best result, source profiles should
be determined for the period and the location under study. A source testing program was
conducted2' to develop source profiles for the South Coast Air Basin (SCAB). Source composition
profiles for motor vehicle exhaust, soil and road dust, a fluid catalytic converter, construction and
demolition, a coke calciner, and a rock crusher sources were newly developed, and the remaining
source profiles were obtained from previously reported values. More than 150 source composition
profiles are now available for the SCAB. Because fugitive dust is such an important influence in
PMlo levels in the Coachella Valley, 23 additional soil and road dust profiles were developed.
Preliminary Analysis of the Ambient DataOf the entire set of chemically speciated data, only twenty-one species (Al, Si, C1, K, Ca, Ti,
V, Cr, Mn, Fe, Ni, Cu, Zn, Se, Br, Rb, Pb, OC, EC, N03, and S04) were selected for the CMB
analysis due to the uncertainty level in the ambient data.
One-year average ambient PMlo concentrations and their standard deviations of each
species for each site are given in Table I. This table also shows maximum and minimum
concentrations. For this period, the ,average concentration at Palm Springs is 35.1 ug/m3, while the
concentration at Indio is 58 ug/m3. Sulfate and nitrate are about the same for both sites; however,
organic carbon, elemental carbon, and crustal Al, Si, Ca, and Fe, concentrations are higher at
Indio than at Palm Springs. Palm Springs and Indio sites are located within 15 miles of each
other, however, their PMlo characteristics are quite different.
A mass balance for major source categories of the annual average PMlo concentrations has
been made for each site and the results are summarized in Table 11. Primary inorganics have been
assumed to be present as their major oxides, and the organic carbon cohcentration has been
transformed to the concentration of organic carbonaceous material by multiplying by 1.3 to
account for hydrogen and oxygen in the hydrocarbons. The total mass predicted by the mass
balance accounts for about 80 percent of the observed total mass, leaving 20 percent unexplained.
The carbonaceous material (1.30C+EC) accounts for 20.7 percent of the mass at Palm Springs
and 17.3 percent at Indio; secondary compounds (ammomium nitrate and ammonium sulfate)
account for 23.2 percent and 13.8 percent; crustal sources account for 32.8 percent and 42.8
percent; and other inorganics account for 2.2 percent and 2.6 percent for Palm Springs and Indio,
respectively. The other inorganics portion is mostly composed of material from motor vehicle and
marine sources. The material not accounted for in the unexplained category is probably aerosol
water and some minor sources. Concentrations of secondary compounds and other inorganics are
about the same for both sites; however, concentrations of crustal and carbonaceous material at
Indio are higher than at Palm Springs. This implies crustal and primary carbonaceous material
sources impact PMlo concentrations at Indio more than at Palm Springs.
Application of Principal component analysis
Broad source categories and their contributions have been estimated by mass balance in
the previous section. The next step is to identify the sources and their contributions to PMlo for
each site.
The PCA results are summarized in Tables III and IV for the Palm Springs and Indio sites,
respectively. These tables show the principal components and the eigenvalues of the correlation
matrix of the ambient data, the fraction of variance explained by each source, and the cumulative
sum of the fraction of variance explained by sources at each site. The number of contributing
sources to each site can be estimated by the importance of each component, which, in statistical
terms, is referred to as an "eigenvalue". Many analysts use a minimum eigenvalue of 1.0 as the
influential cutoff. A lower value of 0.5 is used here to be sure that all major influences are
considered. The number of sources identified is six for both sites.
At both sites, crustal, motor vehicle, secondary, marine, and other miscellaneous sources
have been identified. Each component attribute is determined by the significance, or "loadings" of
the chemical species. For example, component one has high loadings of Al, Si, K, Ca, Ti, Mn, and
Fe which are all associated with soils. Therefore, we can conclude that this represents an earth
crustal component. This first factor explains 59 percent of the total variance in the PMIOdata at
Palm Springs and 52 percent at Indio. The high loadings of Pb , Br, and EC for the second
component are clearly associated with tailpipe mobil emissions. This factor explains 13 and 17
percent of the total variance at Palm Springs and Indio, respectively.
4An important finding from the component one here is that Pb, Br, and OC are more
strongly correlated with the crustal component at Palm Springs than at Indio, meaning that car-
induced road dust is a significant contributor to PMlo at Palm Springs, but windblown dust is more
significant at Indio.
The third component has high loadings of sulfate and nitrate for both sites. This indicates a
secondary source and explains 7 and 9 percent of the total variance at Palm Springs and Indio,
respectively, presumably transported material from the SCAB, is evident in the data.
High loadings of C1 at both sites for the fourth component suggest a marine source,
probably from the Salton Sea during periods of southeasterly winds. This factor explains 6 percent
of the total variance in the PMlo data. The remaining two principal components explain minor
sources such as residual oil and explains 6 and 8 percent of the total variance in the PMlo data at
Palm Springs and Indio, respectively.
Crustal, motor vehicle, secondary, marine, and other minor sources have been identified as
the major sources contributing to the PMIO concentrations at Palm Springs and Indio. This
estimation can be confirmed by the correlation matrix (Tables V and VI) of the ambient data. For
example, at Palm Springs, A1 is highly correlated with Si (LOO), K(.96), Ca(.82), Ti(.96), Mn (.94),
and Fe(.96). High correlation of these species implies that they come from the same crustal
source.
Application of the CMB model
Results and Discussion. The CMB model (Version 7.0, which has been prepared by EPA
and the Desert Research Institute), was applied to the Coachella Valley PMlo data following the
application and validation protocols of Pace and Watsonzz.
Annual average source contributions at the two sites in the Coachella Valley were
calculated from the individual CMB results and are shown in Table W. Seven different source
categories contribute to PMlo concentrations at Palm Springs and Indio: geological(road dust, soil
dust), motor vehicle, secondary (ammonium nitrate and ammonium sulfate), vegetative burning,
limestone, marine, and residual oil sources. The geological source is the major source contributing
to the PMlo mass at both sites. This source contributes 49 percent and 59 percent to the total PMlo
mass for Palm Springs and Indio, respectively, averaged over one year.
Secondary source contributions (ammonium nitrate and ammonium sulfate) to the total
PMlo mass are about the same at both sites, which are 7.9 ug/m3 for Palm Springs, and 7.7 ug/m3
for Indio. However, their percent contribution is 23 and 14 for Palm Springs and Indio,
respectively.
Motor vehicle source contributions at Palm Springs (2.3 ug/m3) are less than Indio (4.4
ug/m3). This higher motor vehicle source contributions at Indio than Palm Springs can be
explained by two reasons. The annual average concentration for Br(.0094 ug/m3) and Pb(.0251
ug/m3) is higher at Indio than Br(.0069 ug/m3) and Pb(.0148 ug/m3) at Palm Springs. Also,
crustal sources are mostly soil dust at Indio and road dust at Palm Springs. In other words, when
the road dust source is used in the CMB analysis, road dust takes some of the elemental
concentrations for motor vehicle source such as Pb, Br, OC, and EC in the CMB fit because road
dust has a greater fraction of OC, EC, Pb, and Br than soil.
PMlo calcium concentrations at Palm Springs and Indio are not well explained by road dust
or soil dust. Some of calcium is unexplained by the geological source only. This discrepancy is not
large, however, a limestone source was added to the CMB analysis to account for the excess
calcium. The limestone source contribution to the PMlo mass is 1.4 ug/m3, or 4 percent, for Palm
Springs, and 3.0 ug/m3, or 5 percent, for Indio.
5There were also unexplained amounts of PMlo potassium concentrations at both sites, so a
vegetative burning source was added to the CMB analysis to account for the excessive potassium.
Vegetative burning, which includes both agricultural burning and wood combustion sources,
accounts for 5 ug/m3 at Palm Springs, and 7 ug/m3 at Indio. This higher contribution at Indio was
predicted in the preliminary analysis of the PMlO data because there were more primary carbon
sources at Indio.
Source contributions of PMlo concentrations for three unusual days, May 10, 1989, July 9,
1989, and July 27, 1989, at the two sites are shown in Table VIII. Speciated data are not available
at Indio for July 27, 1989 due to sampler shut-down after 95 minutes. For these unusual days, the
geological source was the major source contribution to the PMlo concentration. This substantiates
the meteorological analyses which indicated that large amounts of wind-driven blowing dust were
transported into the Coachella Valley.
The 24-hour design value of 198 ug/m3 was selected for Indio. This value was measured on
August 14, 1989; however, speciated data for this day are not available. The sampler
malfunctioned after 90 minutes and flow rates were not measured. Therefore, source contributions
to the 198 ug/m3 are estimated from the regression analysis of three days' CMB results. PMlo
concentrations for the three days are 576, 112, and 94 ug/m3, respectively and their source
contributions estimated from the CMB analysis are shown in Table IX.
Table X shows the estimated source contributions for 198 ug/m3 measured on August 14,
1989. This table shows that 76 percent of the PMlo concentration is from the geological source, 11
percent from the secondary source, 8 percent from the vegetative burning source, and 3 percent
from the motor vehicle source. If the motor vehicle source and secondary source contributions are
assumed to be transported from the SCAB, then these two sources are not locally controllable.
Control should be focused on the geological source.
SUMMARY OF RECEPTOR MODEL APPLICATIONS
The CMB receptor model has been applied to Coachella Valley PMlo concentrations
measured at Palm Springs and Indio. The two sampling sites are located within 15 miles, however,
PMIOconcentrations and source contributions to PMlo mass are quite different. Indio has higher
PMlo concentrations than Palm Springs and 59 percent of the annual average PMlo concentration
at Indio is from the geological source while 49 percent is from the geological source at Palm
Springs.
A 24-hour design value of 198 ug/m3 was determined for Indio. This value was measured
on August 14, 1989 and the source contributions are estimated for this day. Seventy-six percent of
the 198 ug/m3 is from the geological source, 11percent from the secondary source, 8 percent from
the vegetative burning source, and 3 percent from the motor vehicle source. If the secondary and
motor vehicle contributions are primarily from the transport of PMIO from the SCAB, then
geological sources have to be controlled to attain the federal standard.
REFERENCES
1. M.D. Zeldin, S. Liu, A. Taber, S. Cohanim, B.M. Kim, and K. Nolan, State Implementation
J,&~lan for PMlO in the Coachella Valley, South Coast Air Quality Management District, Diamond
d,,wib Bar, 1990.
W
2. PMlO SIP Development Guideline, EPA-45012-86-001, U.S. EPA, Research Triangle Park,
NC. (1987).3. J.A. Cooper and J.G. Watson, "Receptor oriented methods of air particulate source apportionment," J. Air. Poll. Control. Assoc. 30:1116 (1980). 4. G.E. Gordon, "Receptor models," Environ. Sci. Technol. 14:792 (1980). 5. G.E. Gordon, "Receptor models," Environ. Sci. Technol. 221132 (1988). 6. R.C. Henry, C.W. Lewis, P.K. Hopke, and H.J. Williamson, "Review of receptor model fundamentals,"Atmos. Environ. 18:1507 (1984). 7. P.K. Hopke, Receptor Modeline in Environmental Chemistry. John Wiley & Sons, New York, (1985). 8. M.S. Miller, S.K. Friedlander,and G.M. Hidy, "A Chemical Element Balence for the Pasadena Aerosol," J. Colloid Interface Sci., 39:165 (1972). 9. S.K. Friedlander, "Chemical Element Balances and Identification of Air Pollution Sources," Environ. Sci. Tech., 7:235 (1973). 10. J.G. Watson Jr., Chemical Elemental Balance Receptor Model methodolow for assessing the sources of fine and total suspended uarticulate matter in Portland. Oregon. Ph.D., dissertation, Oregon Graduate Center, Beaverton, Oregon (1979). 11. Receptor Model Technical Series, Volume I: Overview of receptor model application to particulate source apportionment, EPA-45014-81-016a, PB82-139429, U.S. EPA, Research Triangle Park, NC, 1981. 12. Receptor Model Technical Series, Volume II: Chemical Mass Balance, EPA-450181-016b, PB82-187345, U.S. EPA, Research Triangle Park, NC, 1981. 13. Receptor Model Technical Series, Volume I11 (Revised): CMB User's manual (Version 7.0), U.S.EPA, Research Triangle Park, NC, In Preparation. I 14. Receptor Model Technical Series, Volume IV: Technical considerations in source 1 apportionment by particle identification, EPA 45014-83-018, PB84-103340, U.S.EPA, Research Triangle Park, NC, 1983. 15. Receptor Model Technical Series, Volume V: Source apportionment techniques and considerations in combining their use, EPA 45014-84-020, PB85-111524, U.S. EPA, Research Triangle Park, NC, 1984. 16. Receptor Model technical Series, Volume VI: A wide to the use of Factor Analvsis and Multiple Regression (FA/MR) technifaues in source auuortionment, EPA 45014-85-007, PB86- 107638, U.S. EPA, Research Triangle Park, NC, 1985. 17. H.H. Harmon, Modem factor analvsis, University of Chicago Press, Chicago (1976). 18. I.T. Jolliffe, Principal comuonent analvsis, Springer- Verlag, New York (1986). 19. W.R. Dillon and M. Goldstein, Multivariate analvsis: Methods and applications, John Wiley & Sons, New York (1984). 20. J.A. Cooper, Ambient PMlO concentrations in the Coachella Valley, report to the South Coast Air Quality Management District, El Monte CA (1990). 21. NEA, PMlO source composition libray for the South Coast Air Basin, final report to the South Coast Air Quality Management District, El Monte, CA (1987). 22. T.G. Pace and J.G. Watson, Protocol for applying and validatine the CMB model, EPA-45014- 87-010, Research Triangle Park, NC (1987).
Table I. Annual average concentration (ug/m3) of each species for each site.
Palm S ~ r i n a -
Indio
Mean Std. Max. Min. Mean Std. Max. Min.
Total
Mass
Al
Si
n
K
Ca
'n
v
Cr
Mn
Fe
Ni
Cu
Zn
se
Br
Rb
Pb
OC
EC
So4
NO3
Table 11. Major source categories of annual average PMlO concentrations for each site,
based on the mass balance.
Source Category Palm Springs India
Carbonaceous
Material
Secondary
Compounds
Other
Inorganics
Unexplained 7.40 (21.08) 13.65(23.6)
* ug/m3
percentage to the total massTable 111. Principal components and eigenvalues of the correlation matrix of the ambient
data at Palm Springs.
Component number 1 2 3 4 5 6
Component ID Crvstal Auto Second. Marine Other Other
Eigenvalues 12.34 2.75 157 1.3 .68 55
Variance explained 59 .I3 - .a7 .M .03 .03
Cumulative variance 59 .?2 .79 .86 .89 91
Table IV. Principal components and eigenvalues of the correlation matrix of the ambient
data at Indio.
Component number 1 2 3 4 5 6
Component ID Crustal Auto Second. Marine Other Other
Eigenvalues 10.84 3.47 1.81 1.33 1.06 .61
Variance explained 52 .17 .w .M .05 .03
Cumulative variance 52 .68 .n .83 .88 .91
.94 -24 -.lo -.05
.94 720 -.I3 -.01
.'j .15 .02 2
.
5
.91 -.I7 -.W .W 1
.93 -.21 -.21 .M
.PL -.32 -.I6 .W
.62 -.23 .27 -.I4
.48 -.29 .27 .M
.93 -.W -.I3 .07
.94 -.24 -.20 .02
.94 -.m .08 -.w
.26 .69 -.39 -.05
.76 .38 -3 .14
.67 .a .25 -.I8
.m 59 .34 -.I1
.92 -.I4 -.25 -.W
.'A .81 7% -.03
58 53 .33 .19
.16 .84 -.24 .14
.62 .19 52 -.43
35 .13 .71 34Table VII. Annual average source contributions for the Coachella Valley
Source Type Palm Springs - Indio
Ammonium Sulfate
Ammonium Nitrate
Motor Vehicle
Geological
Limestone
Vegetative Burning"
~ a h e 0.5x 0.1 (1.41' 1.0x0.2 11.71'
Residual Oil 0 . 1 0.0
~ 0.4 0 . 2 3 0.0 0.3
Total Mass predicted 33.83 1.6 56.2~
2.4
Total Mass observed 35.1 3 2.3 58.0~
3.3
* Mean* standard error (% of total mass predicted)
Table VIII. Source contribution estimates of PMlO concentrations during three 'unusual event'
days
Mav 10,1989 July 9,1989 Juh 27.1989
Source Type P.S. Indio P.S. lndio P.S. lndio
Ammonium Sulfate
Ammonium Nitrate
Motor Vehicle
Geological
Limestone
Marine
Vegetative burning
Percentage of total mass predictedTable IX. Source contribution estimates of PMlO concentrations for three days at Indio
Nov. 11,1988 May 10,1989 Aug. 2,1989
Source Tvpe
Ammonium Sulfate
Ammonium Nitrate
Motor Vehicle
Geological
Vegetative Burning
5.33 6.40 *
6.71 8.06
2.93 13.54
56.22 (67.57)
8.50 (10.21)
I:;]
5.95 1.09)
520.10 95.33)
8.65 1.59)
t:::
4.02 4.31
70.23 75.20)
8.36 (8.95)
* Percentage of total mass predicted
Table X. Estimated source contributions for August 14,1989 from Table IX at Indio.
Total Mass
Ammonium Sulfate
Ammonium Nitrate
Motor Vehicle
Geological
Vegetative Burning
Other
* Percentage of total massYou can also read
