1

PARTICULATE MATTER SOURCE APPORTIONMENT 

TECHNOLOGY (PSAT) IN THE CAMX 

PHOTOCHEMICAL GRID MODEL 

Greg Yarwood, Ralph E. Morris and Gary M. Wilson*

1. INTRODUCTION 

Airborne particulate matter (PM) is important because it causes health problems and 

environmental degradation and so many countries implement programs to control PM 

pollution (e.g., EPA, 1996). In recent years the emphasis on controlling PM pollution has 

shifted toward problems associated with fine PM (PM2.5 with particle diameter less than 

2.5 μm) because it is more strongly associated with serious health effects than coarse PM. 

Knowing what sources contribute to fine PM2.5 is essential for developing effective 

control strategies. Many components of PM2.5 are secondary pollutants and so 

photochemical models are important tools for PM air quality planning. The 

Comprehensive Air quality Model with extensions (CAMx; ENVIRON, 2004) is one of 

the photochemical grid models being used to understand PM pollution and visibility 

impairment in the US and Europe. The Particulate Matter Source Apportioning 

Technology (PSAT) has been developed for CAMx to provide geographic region and 

source category specific PM source apportionment. PM source apportionment 

information from PSAT is useful for: 

(1) Understanding model performance and thereby improving model 

inputs/formulation 

(2) Performing culpability assessments to identify sources that contribute 

significantly to PM pollution 

(3) Designing the most effective and cost-effective PM control strategies. 

 

*

 ENVIRON International Corporation, 101 Rowland Way, Suite 220, Novato, CA 

94945-5010. 

e-mail: gyarwood@environcorp.com 
2 Greg Yarwood ET AL.

Source apportionment for primary PM is relatively simple to obtain from any air 

pollution model because source-receptor relationships are essentially linear for primary 

pollutants. Gaussian steady-state models and Lagrangian puff models have been used 

extensively to model primary PM pollution from specific sources, which is source 

apportionment. The Gaussian and Lagrangian approaches work for primary PM because 

the models can assume that emissions from separate sources do not interact. This 

assumption breaks down for secondary PM pollutants (e.g., sulfate, nitrate, ammonium, 

secondary organic aerosol) and so puff models may dramatically simplify the chemistry 

(to eliminate interactions between sources) so that they can be applied to secondary PM. 

Eulerian photochemical grid models are better suited to modeling secondary pollutants 

because they account for chemical interactions between sources. Grid models do not 

naturally provide source apportionment because the impact of all sources has been 

combined in the total pollutant concentration. PSAT has been developed to retain the 

advantage of using a grid model to describe the chemistry of secondary PM formation 

and also provide source apportionment. 

This paper starts by reviewing several approaches to PM source attribution in grid 

models that were considered when PSAT was designed. Next, the PSAT algorithms are 

explained. Finally, example PSAT results are presented and compared to results from 

some other methods. 

2. APPROACHES TO PM SOURCE ATTRIBUTION IN GRID MODELS 

The need for PM source apportionment (PSAT) in CAMx was discussed in the 

introduction. Several potential approaches were considered at the PSAT design stage and 

these are discussed below. The approaches considered fall into two general categories, 

which we have called sensitivity analysis and reactive tracers. The reactive tracer 

approaches also could be called tagged species approaches. This section concludes with 

a discussion of an important fundamental difference between sensitivity analysis and 

source apportionment that explains why these two concepts should not be confused for 

secondary pollutants. 

2.1. Sensitivity Analysis Methods 

Sensitivity analysis methods measure the model output response to an input change, 

e.g., the change in sulfate concentration due to a change in SOx emissions. In general, 

sensitivity methods will not provide source apportionment if the relationship between 

model input and output is non-linear. For example, if sulfate formation is non-linearly 

relates to SOx emissions, the sum of sulfate (SO4) sensitivities over all SOx sources will 

not equal the model total sulfate concentration. This concept is discussed further in 

section 2.3. 

Brute Force or Direct Method. The brute force method estimates first-order 

sensitivity coefficients (e.g., dSO4/dSOx) by making a small input change (dSOx) and 

measuring the change in model output (dSO4). This method is simple and can be applied 

to any model, but is inefficient because a complete model run is required for each 

sensitivity 
Greg Yarwood ET AL. 3

coefficient to be determined. Accuracy also may be an issue for the brute force 

method because ideally the input change (dSOx) should be vanishingly small but for 

small input changes the output change may be contaminated by numerical precision or 

model “noise.” Higher-order sensitivity coefficients also can be estimated by the brute 

force method. 

Zero-out modeling. The zero-out method differs from the brute force method in that 

a specific emissions input is set to zero and the change in output measured. Zero-out 

modeling can be used with any model but is inefficient because a complete model run is 

required for each source. Accuracy also may be an issue if the zero-out method is 

applied to a small emissions source. This method has been used extensively for source 

attribution because it seems intuitively obvious that removing a source should reveal the 

source’s impact. However, because zero-out modeling is a sensitivity method it does not 

provide source apportionment for non-linear systems because the sum of zero-out 

impacts over all sources will not equal the total concentration, as discussed further in 

section 2.3. 

Decoupled direct method (DDM). The DDM provides the same type of sensitivity 

information as the brute force method but using a computational method that is directly 

implemented in the host model (Dunker, 1981). The DDM has potential advantages of 

greater efficiency and accuracy relative to the brute force method (Dunker at al., 2002a) 

and the DDM implementation in CAMx (Dunker at al., 2002a,b) is currently being 

extended to PM species. Drawbacks of DDM are that the implementation is technically 

challenging, that using DDM for many sensitivities simultaneously requires large 

computer memory, and because DDM is a sensitivity method it does not provide source 

apportionment for non-linear systems (section 2.3.) 

Other sensitivity methods. There are other sensitivity methods that provide similar 

information to DDM such as adjoint methods (e.g., Menut at al., 2000; Elbern and 

Schmidt, 1999) and automatic differentiation in FORTRAN (ADIFOR; e.g., Sandhu, 

1997). They have similar advantages and disadvantages as DDM but may be less 

computationally efficient (Dunker et al., 2002a). 

2.2. Reactive Tracer Methods 

Reactive tracers (or tagged species) are extra species added to a grid model to track 

pollutants from specific sources. For example, a standard grid model calculates 

concentrations for a species X that has many sources and so the concentration of X is the 

total concentration due to all sources. A reactive tracer (xi) is assigned to for each source 

(i) with the intention that the sum of the reactive tracers will equal total concentration (X 

= ∑ xi). The challenge is to develop numerical algorithms for solving the reactive tracer 

concentrations that ensure that this equality is maintained. Depending upon the 

formulation of the tracer algorithms, it may be possible to model tracers for a single 

source of interest and omit tracers for all other sources, or it may be necessary to include 

tracers for all sources (as is the case for PSAT). Reactive tracers can potentially provide 

true source apportionment (X = ∑ xi), however the numerical value of the source 

apportionment will depend upon assumptions within the reactive tracer formulation. In 

particular, for any process that is non-linear in species concentrations (e.g., chemistry) 
4 Greg Yarwood ET AL.

there is no unique way to assign the total concentration change to the reactive tracers. 

This issue is discussed further in section 2.3. 

Source Oriented External Mixture (SOEM). Kleeman and Cass (1999) developed an 

approach called SOEM that tracks primary PM from different source categories/regions 

using that tagged species that are considered to represent seed particles. Reactive tracers 

are added to track secondary PM and related gases from different source 

categories/regions and source apportioned secondary PM condenses onto the seed 

particles. Chemical change for secondary PM and related gases is accounted for by 

expanding the chemical mechanism to treat different source regions/categories as 

separate precursor and product species. This requires thousands of chemical reactions 

and hundreds to thousands of chemical species, depending upon the number of source 

regions/categories. The main advantage of the SOME method is that it is potentially 

accurate, and the main disadvantage is computational demand. 

PSAT and TSSA. The PM Source Apportionment Technology (PSAT) uses reactive 

tracers to apportion primary PM, secondary PM and gaseous precursors to secondary PM 

among different source categories and source regions. The PSAT methodology is 

described in section 4. PSAT was developed from the related ozone source 

apportionment method (OSAT) already implemented in CAMx (Dunker at al., 2002b). 

Tonnesen and Wang (2004) are independently developing a method very similar to PSAT 

called Tagged Species Source Apportionment (TSSA) in a different photochemical grid 

model. Advantages of PSAT and TSSA are expected to be high efficiency and flexibility 

to study different source categories/regions. The accuracy of the PSAT (and TSSA) 

source apportionment results must be evaluated, as for any other method. 

2.3. Source Apportionment and Source Sensitivity 

Consider a chemical species X that has two sources A and B (so X = xA + xB) and 

which undergoes a second order self-reaction with rate constant k. The rate of chemical 

change is: 

dX/dt = −kX2 

 

dX/dt = −k(xA + xB)

2 

 

dX/dt = −kxA

2 − kxB2 − 2kxAxB

The homogeneous rate terms kxA

2

 and kxB

2

 clearly describe chemical change for 

pollutants from sources A and B (xA and xB), but the inhomogeneous rate term 2kxAxB is 

not uniquely associated with either source A or B. A reactive tracer (or tagged species) 

source apportionment method must deal with this inhomogeneous rate term either by 

developing rules to apportion the inhomogeneous term to sources A and B or modifying 

the chemistry to eliminate the inhomogeneous term. For a sensitivity method, the 

homogeneous quadratic rate terms generate second-order homogeneous sensitivity 

coefficients (sAA and sBB) and the inhomogeneous rate term generates a second-order 

inhomogeneous sensitivity coefficient (sAB). Consequently, the total concentration of X 
Greg Yarwood ET AL. 5

is incompletely described by the first-order sensitivity coefficients (sA and sB) that 

resemble source apportionments. 

The example presented above is a simple case of a non-linear chemical system that 

illustrates why source apportionment and source sensitivity are not the same thing for 

nonlinear systems. The implications are: 

(1) There is no unique source apportionment for chemical species that depend upon 

nonlinear reactions, such as secondary PM species and ozone. Nonetheless, 

reasonable source apportionment schemes can be developed and are useful tools 

for achieving the objectives listed in the introduction. 

(2) Sensitivity coefficients are not source apportionments for chemical species that 

depend upon nonlinear reactions, such as secondary PM species and ozone. 

Sensitivity coefficients are most applicable for predicting the model response to 

an input change (e.g., control strategy) and are very useful for this purpose 

(within the range of linear model response), but sensitivity coefficients should 

be used with care for source apportionment or culpability assessment. Likewise, 

source apportionments should be used with care for predicting model response 

to input changes. 

3. PSAT METHODOLOGY 

PSAT is designed to source apportion the following PM species modeled in CAMx: 

• Sulfate 

• Particulate nitrate 

• Ammonium 

• Particulate mercury 

• Secondary organic aerosol (SOA) 

• Six categories of primary PM 

The PSAT “reactive tracers” that are added for each source category/region (i) are 

described below. In general, a single tracer can track primary PM species whereas 

secondary PM species require several tracers to track the relationship between gaseous 

precursors and the resulting PM. Nitrate and secondary organic PM are the most 

complex aerosol species to apportion because the emitted gases (NO, VOCs) are several 

steps removed from the resulting PM (nitrate, SOA). The reactive tracers used by PSAT 

are listed below for each class of PM.

Sulfur

SO2i Primary SO2 emissions 

PS4i Particulate sulfate ion from primary emissions plus secondarily formed sulfate 

Nitrogen

RGNi Reactive gaseous nitrogen including primary NOx (NO + NO2) emissions plus 

nitrate radical (NO3), nitrous acid (HONO) and dinitrogen pentoxide (N2O5). 

TPNi Gaseous peroxyl acetyl nitrate (PAN) plus peroxy nitric acid (PNA) 

NTRi Organic nitrates (RNO3) 

HN3i Gaseous nitric acid (HNO3) 
6 Greg Yarwood ET AL.

PN3i Particulate nitrate ion from primary emissions plus secondarily formed nitrate 

Ammonia/Ammonium

NH3i Gaseous ammonia (NH3)

PN4i Particulate ammonium (NH4)

Secondary Organic

ALKi Alkane/Paraffin secondary organic aerosol precursors 

AROi Aromatic (toluene and xylene) secondary organic aerosol precursors

CREi Cresol Secondary secondary organic aerosol precursors

TRPi Biogenic olefin (terpene) secondary organic aerosol precursors

CG1i Condensable gases from toluene and xylene reactions (low volatility)

CG2i Condensable gases from toluene and xylene reactions (high volatility)

CG3i Condensable gases from alkane reactions

CG4i Condensable gases from terpene reactions

CG5i Condensable gases from cresol reactions

PS1i Secondary organic aerosol associated with CG1

PS2i Secondary organic aerosol associated with CG2

PS3i Secondary organic aerosol associated with CG3

PS4i Secondary organic aerosol associated with CG4

PS5i Secondary organic aerosol associated with CG5

Mercury

HG0i Elemental Mercury vapor 

HG2i Reactive gaseous Mercury vapor

HGPi Particulate Mercury 

Primary Particulate

PEC i Primary Elemental Carbon 

POA i Primary Organic Aerosol 

PFC i Fine Crustal PM 

PFN i Other Fine Particulate 

PCC i Coarse Crustal PM 

PCS i Other Coarse Particulate 

PSAT includes a total of 32 tracers for each source group (i) if source apportionment 

is applied to all types of PM. Since source apportionment may not always be needed for 

all species, the PSAT implementation is flexible and allows source apportionment of 

selected chemical classes in each CAMx simulation. For example, source apportionment 

for sulfate/nitrate/ammonium requires just 9 tracers per source group. 

The PSAT approach to source apportionment is described below. Consider two 

model species A and B that are apportioned by reactive tracers ai and bi, respectively. 

Reactive tracers must be included for all sources of A and B including emissions, initial 

conditions and boundary conditions so that complete source apportionment is obtained, 

i.e., A = Σai and B = Σbi. 

The general approach to modeling change over a model time step ∆t is illustrated for 

a chemical reaction: 
Greg Yarwood ET AL. 7

A → B 

The general equation for species destruction is: 

 ai(t+∆t) = ai(t) + ∆A x [ai / Σai ] 

Here the relative apportionment of A is preserved as the total amount changes. 

This equation applies to chemical removal of A and also physical removal by processes 

such as deposition. 

The general equation for species production (e.g, chemical production) is: 

 bi(t+∆t) = bi(t) + ∆B x [ai / Σai ] 

Here the product B inherits the apportionment of the precursor A. 

In some cases, source category specific weighting factors (wi) must be added to the 

equation for species destruction: 

 ai(t+∆t) = ai(t) + ∆A x [wiai / Σwiai ] 

An example is chemical decay of the aromatic VOC tracers (ARO), which must be 

weighted by the average OH rate constant of each AROi. ARO tracers for different 

source groups have different average VOC reactivities because the relative amounts of 

toluenes and xylenes differ between source categories. 

In some cases, source category specific weighting factors (wi) must be added to the 

equation for species production: 

 bi(t+∆t) = bi(t) + ∆B x [wiai / Σwiai ] 

An example is chemical production of condensable gases (CG1 or CG2) from 

aromatic VOC tracers, which must be weighted by aerosol yield weighting factors. The 

aerosol yield weighting factors depend upon the relative amounts of toluenes and xylenes 

in each source group. 

Several aerosol reactions are treated as equilibria: 

A ↔ B 

If A and B reach equilibrium at each time step, it follows that their source 

apportionments also reach equilibrium: 

 ai(t+∆t) = [ai(t) + bi(t)] x [A / (A+B) ] 

 bi(t+∆t) = [ai(t) + bi(t)] x [B / (A+B) ] 

Examples are the equilibrium between gas phase nitric acid and aerosol nitrate, gas 

phase ammonium and aerosol ammonium, and condensable organic gases (CG) and 

secondary organic aerosols (SOA). 

4. TESTING PSAT 

The initial PSAT testing was for primary PM species (e.g., fine crustal material) 

because these species are the most straightforward to implement and test (because there is 
8 Greg Yarwood ET AL.

no chemistry). The results for primary PM species are not shown here but the tests were 

successful in that sum of PSAT tracers over all source groups remained identical to the 

total model concentration (A = Σai) for all grid cells and hours. The primary PM tests 

confirmed the implementation of PSAT for all CAMx algorithms except chemistry. 

The next stage of PSAT testing included the chemistry algorithms and results are 

shown here for two different types of testing for sulfate and nitrate. Sulfate was tested by 

comparing PSAT to zero out results in full 3-D CAMx simulations. As discussed above, 

zero out results are not expected to agree perfectly with PSAT because zero out does not 

give true source apportionment in non-linear systems. However, non-linearity in sulfate 

formation chemistry is expected to be less important than for other secondary PM species 

making sulfate the best candidate for zero out testing. 

The most complex PSAT chemistry algorithm is for nitrate. Zero out tests were not 

used for nitrate because the relationship between NO emissions and nitric acid may be 

highly non-linear. Therefore, conduct 1-D (box model) tests were used to evaluate PSAT 

results for nitrate by comparing against the method used by Kleeman and Cass (2001) to 

track nitrate apportionment in the Source-Oriented External Mixture (SOME) method, 

discussed above. 

4.1. PSAT Sulfate Testing 

The PSAT performance for sulfate was tested using a CAMx database developed by 

the Midwest RPO (MPRO) for PM and visibility modeling of the Eastern US. The 

modeling was from June 18 to July 21, 2001 and used a 36-km modeling grid with 

meteorology developed using the mesoscale model version 5 (MM5). The modeling 

domain was sub-divided to geographic areas according to regional planning organizations 

(RPOs) responsible for developing regional visibility and PM control strategies in the 

U.S. The sub division of the modeling domain to RPOs is shown in Figure 1 and the 

RPOs are labeled by their respective acronyms (MRPO, MANE-VU, VISTAS, CERAP 

and WRAP). The state of Illinois (IL) was split out from the Midwest RPO (MPRO) to 

test the ability of PSAT to apportion the contribution from a single state. Four 

hypothetical point sources were added near the middle of the MRPO, MANE-VU, 

VISTAS and CENRAP areas (shown by the + symbols in Figure 1) to test the ability of 

PSAT to track contributions from singe sources. The hypothetical point sources were 

chosen to be generally representative of a large coal-fired utility source but do not 

represent actual sources at these locations. In total, sulfate was apportioned to 11 source 

groups including a remainder area for Canada, Mexico and over water. 
Greg Yarwood ET AL. 9

Figure 1. The CAMx modeling domain for PSAT testing showing subdivision to geographic areas and locations of four hypothetical point 

sources (+ symbols). 

The sulfate impacts from the hypothetical MRPO point source are compared in 

Figure 2 at a single hour (hour 15) on 28 June 2001. The spatial distribution of sulfate 

impacts is very similar in the PSAT and zero out results as shown by the edge of impacts 

plume (0.1 μg/m3

 level). There are differences in the areas of larger impacts (e.g. the 1 

and 2 μg/m3 levels) and these are due to the effects of non-linear chemistry in the zero out 

test. As discussed above, sensitivity methods such as zero out do not provide accurate 

source apportionments for non-linear processes. Sulfate formation can be limited by the 

availability of oxidants, especially hydrogen peroxide, which will tend to depress the 

maximum impact levels in zero out runs as well as shift impacts further downwind (to 

where oxidant availability is no longer limiting). The oxidant limiting effect on zero out 

sulfate impacts is most easily seen from the 2 μg/m3

 level extending further downwind 

over Lake Michigan in the zero out result than the PSAT result. 

(a) PSAT (b) Zero Out 
10 Greg Yarwood ET AL.

Figure 2. Comparison of sulfate impacts (μg/m3) from the hypothetical MRPO point 

source on 28 June 2001 at hour 15: (a) PSAT result; (b) Zero out result. 

The episode average sulfate impacts from the hypothetical MRPO point source are 

compared in Figure 3 for the entire 28 June to July 21, 2001 modeling period. The 

spatial distribution of sulfate impacts is very similar in the PSAT and zero out results. 

The maximum impact occurs very close to the source and is higher in the PSAT result 

(2.2 μg/m3

) than the zero out result (1.8 μg/m3) due to the effect of oxidant limitation on 

sulfate impacts determined by the zero out method. 

 

(a) PSAT (b) Zero Out 

Figure 3. Comparison of episode average (June 18 to July 21, 2001) sulfate impacts 

(μg/m3) from the hypothetical MRPO point source: (a) PSAT result; (b) Zero out result. 
Greg Yarwood ET AL. 11

The PSAT sulfate tests provided a comparison of the efficiency of the PSAT method 

compared to zero out modeling. Zero out modeling requires a new model run for each 

source contribution determined, so the incremental time for each “apportionment” is the 

same as for the model base case. In contrast, the marginal cost for each PSAT source 

apportionment was about 2% of the time required for the base case. 

4.2. PSAT Nitrate Testing 

The PSAT nitrate algorithms were tested using CAMx for a 1-D (box model) 

problem in order to focus upon the ability of the PSAT chemical algorithms to track 

nitrate apportionment. The box model problem was for summer conditions and PSAT 

was used to apportion nitrated between 20 ppb of initial NOx and 100 ppb of NOx 

emission injected continuously through the 24 hour run. The PSAT results were 

compared to the SOME algorithm of Kleeman and Cass (2001) where the reactive 

nitrogen (NOy) reactions in the chemical mechanism are duplicated to provide two 

different types of NOy (think of them as “red” NOy and “blue” NOy). There was no 

ammonia in the box model so that nitric acid remained in the gas phase rather than 

forming PM nitrate. 

The PSAT apportionment of NOy to initial conditions (ICs) and emissions during the 

24 hour box model simulation is shown in Figure 4. The total NOy apportioned to ICs 

remains constant at 20 ppb throughout the simulation but the apportionment changes over 

time from NOx (RGN-IC) at the start to PAN (TPN-IC), organic nitrates (NTR-IC) and 

nitric acid (HN3-IC). The NOy apportioned to emissions increased linearly throughout 

the simulation and the apportionment also evolved as the NOx emissions (RGN-E) 

reacted. At the start of the simulation RGN (NOx) is dominated by ICs whereas late in 

the day (hour 18) RGN is dominated by emissions. The source apportionments shown in 

Figure 4 are reasonable and show necessary attributes (such as conserving the total 20 

ppb of ICs). 

Figure 4. PSAT apportionment of reactive nitrogen species to initial 

conditions and emissions during a 24 hour box model simulation. 
12 Greg Yarwood ET AL.

The SOEM apportionment of NOy to ICs and emissions during the 24-hour box 

model simulation is shown in Figure 5. The time evolution of the source apportionments 

for the ICs and emissions is very similar for SOME (Figure 5) and PSAT (Figure 4). It 

is not clear that either method is more “correct,” or indeed that a correct source 

apportionment result exists for this test, but the consistency between the PSAT and 

SOME results is encouraging. 

Figure 5. Source-Oriented External Mixture (SOME) apportionment of 

reactive nitrogen species to initial conditions and emissions during a 24-

hour box model simulation. 

5. SUMMARY 

Several approaches to PM source apportionment were considered and a reactive 

tracer (or tagged species) method has been developed for the PM Source Apportionment 

Technology (PSAT) implemented in CAMx. Initial tests show that PSAT results are 

reasonable and that PSAT is much more efficient than sensitivity methods such as zero 

out modeling. Theoretical considerations suggest that PSAT may be a better approach to 

source apportionment than sensitivity methods because it is better able to deal with nonlinear chemistry. Further testing is needed in real world applications. The PSAt 

algorithms will become publicly available in a future CAMx release. 

6. ACKNOWLEDGMENT 

The Midwest Regional Planning Organization (MRPO) and the Lake Michigan Air 

Directors Consortium (LADCO) sponsored the PSAT development. The authors 

acknowledge assistance from and helpful discussions with Mike Koerber and Kirk Baker 

of LADCO. 
Greg Yarwood ET AL. 13

7. REFERENCES 

Dunker A.M., 1981. Atmos. Environ. 1981, 15, 1155-1161. 

Dunker A.M., G. Yarwood, J.P. Ortmann, G.M. Wilson, 2002a, The decoupled direct 

method for sensitivity analysis in a three-dimensional air quality model – 

implementation, accuracy and efficiency, Environ. Sci. Technol., 36:2965-2976. 

Dunker A.M., G. Yarwood, J.P. Ortmann, G.M. Wilson, 2002b, Comparison of source 

apportionment and source sensitivity of ozone in a three-dimensional air quality 

model, Environ. Sci. Technol., 36:2953-2964. 

Elbern, H.; Schmidt, H. J. Geophys.Res. 1999, 104, 18583-18598.1999 

ENVIRON, 2003, User’s guide to the Comprehensive Air Quality model with extensions 

(CAMx) version 4.00 (January, 2003); http://www.camx.com 

EPA, 1996, Air quality criteria for particulate matter, Environmental Protection Agency 

report EPA/600/P-95/001aF-cF.3v. Research Triangle Park, NC 

EPA, 2003, Air quality criteria for particulate matter, Environmental Protection Agency 

draft report EPA/600/P-99/002aB. Research Triangle Park, NC 

Kleeman M.J., G.R. Cass, 2001, A 3D Eulerian source-oriented model for an externally 

mixed aerosol, Environmental Science and Technology, 35: 4834-4848. 

Menut L., R.Vautard, M.Beekmann, C.Honoré, 2000, Sensitivity of Photochemical 

Pollution using the Adjoint of a Simplified Chemistry-Transport Model, Journal of 

Geophysical Research – Atmospheres, 105, D12: 15,379-15,402, 

Sandhu A. 1997 

Tonnesen G., B. Wang, 2004, 

CMAQ Tagged Species Source Apportionment, July 22, 2004; 

http://www.wrapair.org/forums/aoh/meetings/040722/UCR_tssa_tracer_v2.ppt