January 13, 2010 

The “Peer Review of Source Apportionment Tools in CAMx and CMAQ” contains the synthesis 

of a 5 panel review committee’s comments on the source apportionment implementations in 

CAMx and CMAQ. In addition, clarifications from the source apportionment developers are also 

included. The main document contains notations that refer to either the CAMx (cited as 

Response ENVIRON Page #) or CMAQ developer (cited as Response ICF Page #) response 

letter when a clarification or further discussion on a topic is available. 

The panel of reviewers consisted of 5 members: 

Dr. Harvey Jefferies, University of North Carolina 

Dr. Talat Odman, Georgia Institute of Technology 

Dr. Che-Jen (Jerry) Lin, Lamar University 

Dr. Zion Wang, University of California at Riverside 

Dr. Daewon Byun, University of Houston 

Comments from the developer for the CAMx implementation were submitted by Greg Yarwood 

of ENVIRON and by Sharon Douglas of ICF International for the CMAQ implementation. 

Kirk Baker 

United States Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 
Peer Review of Source Apportionment Tools in 

CAMx and CMAQ

Contract No: EP-D-07-102 

Assignment No: 2-06 

Version: 2 

Prepared by: Sarav Arunachalam, Ph.D., Institute for the Environment 

University of North Carolina at Chapel Hill 

Date due: August 31, 2009

Prepared for: Kirk Baker, Ph.D. 

U.S. EPA, OAQPS 

 C439-01 

 USEPA Mailroom 

Research Triangle Park, NC 27711 
UNC-CH: Peer Review of Source Apportionment Tools in CAMx and CMAQ Contract EP-D-07-102 

 Contents 

1. Introduction.................................................................................................................................... 1

1.2 Source apportionment (ozone, PM, Hg) implementation in CAMx ......................................3

1.1 Source apportionment (ozone, PM, Hg) implementation in CMAQ .....................................2

1.3 Outline of this report............................................................................................................................4

2. Source

2.1

Apportionment as Related to Common/Intended Applications .................... 5

Ozone: OSAT and OPTM implementation strengths and weaknesses ................................5

2.1.1 CAMx/OSAT ..................................................................................................................................................... 5

2.2 2

2.1.2 CMAQ/OPTM ................................................................................................................................................... 7

PM .5: PSAT and PPTM implementation strengths and weaknesses ................................ 10

2.2.1 CAMx/PSAT:...................................................................................................................................................10

2.2.2 CMAQ/PPTM..................................................................................................................................................11

3. Comparison to Other Methods Such as DDM, Zero­out, and TSSA .............................14

4. Documentation Strengths and Weaknesses ......................................................................17

5. Preprocessing and Postprocessing Strengths and Weaknesses.................................21

6. Supplemental Questions...........................................................................................................23

7. Suggestions for Improvements to or Additional Evaluation of Source

Apportionment ....................................................................................................................................24

References .............................................................................................................................................25

Appendix A: Peer Review Process Narrative.............................................................................27

Appendix B: Charge to Reviewers .................................................................................................29

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1. Introduction 

Grid-based air quality models (AQMs) are powerful tools used in air quality management. 

Models are usually used to predict ambient concentrations of pollutants, such as ozone and fine 

particulate matter (PM2.5), at multiple spatial and temporal scales. It is useful to understand 

specific sources or regions that contribute to the concentration levels estimated by AQMs. 

Understanding the contributions to specific geographic receptor locations from particular emission sources, specific model processes, or individual chemical pathways helps regulators develop 

effective emissions control strategies. Probing tools that help with this kind of assessment are 

broadly categorized into two types: mass balance analysis (MBA) techniques and sensitivity 

analysis (SA) techniques (Zhang et al., 2005). MBA provides quantitative information on the 

contribution of the various processes (e.g., transport and chemical reactions) to the modeled 

ambient concentrations, whereas SA provides quantitative information on the response of these 

concentrations to changes in the air pollution system. MBA techniques are appropriate for 

diagnostic evaluations of AQMs (i.e., to identify which chemical transformation pathways and 

which physical transport processes govern pollutant concentrations). MBA techniques are also 

useful for identifying which sources contribute to pollutant concentrations, while SA techniques 

provide information regarding how changes in the emissions affect the simulation results. This 

report focuses on source apportionment techniques for ozone, PM2.5, and mercury techniques 

that have been implemented in photochemical grid models. Source apportionment is an approach 

that is used to efficiently assess complete source contribution. The approach is evaluated for 

scientific credibility and briefly compared to other instrumented photochemical modeling 

approaches that also have the potential to estimate total source culpability: zero-out (brute force) 

and Decoupled Direct Method.

Source apportionment is an alternative approach to traditional brute-force (also called “zeroout”) modeling, and it has the advantage of being much more efficient with computational 

resources. For instance, to estimate the contribution from 20 source regions, a total of 20 individual zero-out simulations would be needed, compared with only one source apportionment simulation. The incremental run time associated with the additional source region tracking performed 

in one source apportionment simulation is far less than the time required to run numerous iterative brute-force simulations. There are multiple source apportionment methodologies that can be 

developed. For a particular application, developers can choose a method by deciding the most 

appropriate balance of computation speed, thoroughness in model formulation, and ease of use. 

Ozone source apportionment tracks the contribution to model ozone estimates from precursor 

emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs). Particulate source 

apportionment tracks contributions to particulate species from precursor emissions. Emissions of 

NOx are tracked through all intermediate nitrogen species to particulate nitrate ion. Sulfur 

dioxide and sulfuric acid emissions are tracked to particulate sulfate ion. Ammonia emissions are 

tracked to particulate ammonium ion. Even though ammonium nitrate is chemically coupled, the 

apportionment schemes typically do not attempt to determine which species is limiting the 

formation, and directly attributes precursor gases to specific particulate ions. Ozone, PM2.5, and 

mercury source apportionment are implemented in state-of-the-science photochemical grid 

models: the Community Multiscale Air Quality (CMAQ) model and the Comprehensive Air 

Quality Model with extensions (CAMx). These models are summarized in Sections 1.1 and 1.2. 

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Example uses of photochemical model source apportionment include: 

• Quantifying the contribution of precursor emissions from source sectors (e.g., points, onroad mobile, non-road, fires) or specific types of facilities (e.g., power plants, pulp and 

paper mills, chemical manufacturing facilities) to estimated concentrations at selected 

locations throughout the modeling domain. 

• Quantifying the contribution of emissions from selected geographical regions (e.g., one 

or more states) to regional ozone, mercury, or PM2.5 concentrations. 

• Quantifying the contribution from estimated boundary conditions to ozone, mercury, or 

PM2.5 concentrations at selected locations. 

• Tracking the fate of emissions from specific facilities to estimate their range of influence 

and contribution to ozone or PM2.5 concentrations in nonattainment areas throughout the 

modeling domain. 

• Quantifying the contribution of emissions from selected sources or source categories to 

visibility impairment in Class I areas (National Parks and Monuments). 

In this report, we summarize the comments provided by external peer reviewers of the two 

source apportionment techniques that are instrumented in CMAQ and CAMx. These comments 

were provided during a peer review process sponsored by the Air Quality Modeling Group of the 

U.S. Environmental Protection Agency’s (EPA) Office of Air Quality Planning and Standards. 

More information on the peer review process is given in Appendices A and B, while Appendix C 

provides the résumés of the peer reviewers. 

To summarize the four approaches that are the focus of this review, the features of 

CMAQ/PPTM, CMAQ/OPTM, CAMx/PSAT and CAMx/OSAT are quite similar, although 

CAMx/PSAT and CAMx/OSAT seem to be more scientifically sound for the following reasons: 

(a) careful consideration of mass conservation issues, 

(b) explicit treatment of dry deposition of the tagged (reactive tracers) species, and 

(c) separation of ozone production scenarios for NOx-limited and VOC-limited conditions. 

1.1 Source apportionment (ozone, PM, Hg) implementation in CMAQ 

The CMAQ model (Byun and Schere, 1999, 2006) numerically simulates the physical processes 

that determine the magnitude, temporal variation, and spatial distribution of the concentrations of 

ozone and particulate species in the atmosphere and the amount, timing, and distribution of their 

deposition to the earth’s surface. The simulation processes include advection, dispersion (or 

turbulent mixing), chemical transformation, cloud processes, and wet and dry deposition. 

ICF International has implemented the Ozone Precursor Tagging Methodology (OPTM) and the 

Particulate Precursor Tagging Methodology (PPTM) in CMAQ version 4.6. Within the model, 

tagging is accomplished by the addition of duplicate model species variables for each source, 

source category, or source region that is to be tagged. For OPTM, the duplicated modeled species 

are ozone, NOx, and VOCs. For PPTM, the duplicated species include all PM-related sulfur, 

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nitrogen, and secondary organic compounds, as well as primary organic carbon, elemental 

carbon, and other inorganic particulates. PPTM also tracks the contribution from all precursor 

gas and particulate phase mercury species to model estimates. The tagged species have the same 

properties and are subjected to the same processes (e.g., advection, chemical transformation, 

deposition) as the actual (or base) species. 

Because the tagged species are separate from the base species, tagging does not alter or affect the 

base simulation results. At each time step in the simulation, the effects of linear or speciesindependent processes, such as dry deposition and advection, are calculated directly for all 

tagged species. Potentially nonlinear processes, such as gas-phase chemistry, aqueous chemistry, 

and particle dynamics, are calculated for the overall (or base) species and apportioned to the 

tagged species. The results for the tagged species are not normalized to ensure that the sum of the 

tagged species equals the total that would be estimated by a regular model simulation. In fact, 

standard bulk species are not standard output from OPTM and PPTM; total bulk species such as 

ozone or PM2.5 sulfate ion are the sum of all tagged species. Thus, the difference between the 

sum of all tags and the overall concentration gives an estimate of the numerical uncertainty in the 

contribution estimates. The tagged species are included as additional species in the model output 

files. 

CMAQ OPTM requires an additional emissions processing step using a utility program called 

"ems ox". This program sums NO and NO2 emissions for each grid cell and each hour for each 

tagged group to form an additional emissions species called NOX. The program also estimates a 

new emissions species called VOC, which is the sum of the hydrocarbon species weighted by the 

number of carbons in the compound (ICF International, 2007). Technical details of the tagging 

methodologies are provided in Douglas et al. (2007). 

1.2 Source apportionment (ozone, PM, Hg) implementation in CAMx 

CAMx, like CMAQ, is an Eulerian photochemical dispersion model that allows for an integrated 

“one-atmosphere” treatment of gaseous and particulate air pollution, over many spatial scales 

from urban to continental. CAMx uses state of the science routines to model ozone and 

particulate matter formation and removal processes (ENVIRON, 2008). 

CAMx contains a variety of ozone source apportionment tools, including the original ozone 

source apportionment tool (OSAT) and the anthropogenic pre-cursor culpability assessment 

(APCA) tool (ENVIRON, 2008). Ozone source apportionment in CAMx tracks the contributions 

to each grid cell from emissions source groups, emissions source regions, initial conditions, and 

boundary conditions with reactive tracer species. Source apportionment tracers are treated using 

the standard model algorithms for vertical advection, vertical diffusion, and horizontal diffusion. 

Horizontal advection fluxes for each of the regular model species that make up NOX and VOC 

are combined and normalized by a concentration based weighted mean. The estimated 

normalized fluxes are used to advect the tracer species rather than solving them with the standard 

model formulation to improve consistency between tracer and regular model concentrations 

(ENVIRON, 2008). The deposition velocities for NOX tracers are the concentration weighted 

average of the deposition velocities for NO and NO2. The deposition velocities for VOC tracers 

are the concentration weighted average (done on a ppmC basis) of the deposition velocities for 

each of the CB05 species. Ozone tracers use the same deposition velocities as ozone 

(ENVIRON, 2008). 

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The APCA tool assesses regional and emission sector contribution to ozone formation and 

provides information that is most policy relevant. APCA is designed to provide more control 

strategy relevant information and recognizes that there are source categories such as biogenic 

emissions that can not be controlled so the model only attributes ozone to biogenic sources when 

it is due to the interaction of biogenic VOC + biogenic NOx. In the case where ozone formed due 

to biogenic VOC + anthropogenic NOx under VOC-limited conditions, OSAT attributes it to 

biogenic VOC, but APCA redirects the attribution to anthropogenic NOx. In NOx -limited 

conditions both OSAT and APCA attribute the ozone to anthropogenic NOx (ENVIRON, 2008). 

Similar to OSAT, the Particle Source Apportionment Technology (PSAT) has been instrumented 

in CAMx to provide PM source apportionment among specific geographic regions and source 

categories. Like the OSAT methodology, the PSAT technique uses reactive tracers, and can 

apportion both primary PM (elemental carbon, primary organic aerosol, crustal fine, other fine, 

crustal coarse, other coarse) and secondary PM (ammonium, sulfate, nitrate, secondary organic 

aerosol, and particulate mercury). In general, while a single tracer can track a primary PM 

species, tracking a secondary PM species requires several tracers to track the relationship 

between the gaseous precursors and the resultant secondary PM. If one wishes to apply CAMx 

with PSAT for all PM species, 32 tracers are needed for each source group. 

Besides ozone reaction tracers, CAMx also uses a separate family of “reaction timing tracers” to 

allow source-receptor transport times to be estimated. It does this by releasing unique timing 

tracers from each geographic area selected for source apportionment. This feature provides a way 

to investigate temporal features of the source-receptor relationships, and perhaps to develop 

temporally targeted emissions control strategies. 

Technical details of the formulation and implementation of the OSAT and PSAT algorithms are 

provided in the CAMx user’s guide (ENVIRON, 2008). 

1.3 Outline of this report 

The rest of this report focuses on the peer review comments provided by the external reviewers 

of the two source apportionment techniques, and contains the following sections: 

• Section 2: Summary of the strengths and weaknesses of CAMx/OSAT and 

CMAQ/OPTM, and then of CAMx/PSAT and CMAQ/PPTM 

• Section 3: Comparison of source apportionment to other methods such as DDM, 

zero-out, and the CMAQ Tagged Species Source Apportionment (CMAQ-TSSA) 

• Section 4: Strengths and weaknesses in the documentation for these techniques 

• Section 5: Strengths and weaknesses in preprocessing and postprocessing tools for these 

techniques 

• Section 6: Reviewer comments on possible future work needed for these techniques 

• Appendix A describes the peer review process that was adopted in the development of 

this report 

• Appendix B contains the charge questions provided to the reviewers by EPA 

• Appendix C contains the CVs of the peer reviewers 

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2. Source Apportionment as Related to Common/Intended 

Applications 

2.1 Ozone: OSAT and OPTM implementation strengths and 

weaknesses 

2.1.1 CAMx/OSAT 

All reviewers agreed that the Ozone Source Apportionment Tool (OSAT) in CAMx is a very 

mature and well-evaluated methodology. It is well documented, fully integrated into the 

operation of CAMx, and relatively easy to use. The methodology is designed so that all ozone 

and precursor concentrations are attributed among the selected source groupings at all times. 

Thus, for all receptor locations and times, the ozone (or ozone precursor concentrations) 

predicted by CAMx is attributed amongst the source groupings selected for OSAT. An important 

feature of the ozone reaction tracer approach used in OSAT is that the normal CAMx calculations are not perturbed; thus, OSAT estimates the same ozone and precursor concentrations as 

CAMx. 

The CAMx source apportionment methodology also permits “timing tracers” to be emitted from 

emission source regions. While several reviewers agreed that this is an interesting feature that 

does not exist in other methods, the degree of its reliability is questionable. Two tracers are 

emitted simultaneously for each tagged source: one that does not decay and one that does 

undergo decay at a fixed rate, thus allowing the two to be used to calculate the time of origin of 

the VOCs or NOx from that region. The process for instrumenting CAMx with these timing 

tracers is fully described in the manual, and the setup is fairly simple. 

One reviewer observed that once a tracer was emitted into the model, it never disappeared. 

Because at least two tracers are emitted each day for each source region specified, the file size 

keeps growing and quickly reaches an unusable large size. Further, the “restart” will remember 

timing tracers, but there is no way to run OSAT for just one day in the middle of a longer (e.g., 

12-day) simulation, without having run from the first day, even if the tracers from the previous 

days have decayed to meaningless concentrations. Thus, the reviewer felt that an extremely 

useful feature needs additional implementation improvements. 

Below are additional strengths and weaknesses of CAMx/OSAT that were identified by the 

reviewers. 

OSAT Strengths

(a) The explicit tracking of O3 production and O3 destruction separately, using integrated 

reaction rates, instead of as a single change in O3. 

(b) The use of Sillman’s indicator species to distinguish VOC- and NOx-limited regimes. 

(c) OSAT accounts for different VOC reactivities. 

(d) Mass is conserved by normalizing total advective fluxes by the “weighted mean” concentrations of the tracers. This is generally seen as a plus because advection is a linear process 

that becomes nonlinear in the model due to numerical issues. One reviewer believed that the 

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OSAT approach would restore the linearity by assuring that the sum of the components is 

equal to the bulk. 

(e) Use of multiple tracer species to account for ozone precursors from a given source region at a 

given receptor location. OSAT can also estimate the cumulative contribution to ozone 

production of these precursors while they were en route to the receptor. 

(f) Tracers can provide information on the relative importance of transport and chemistry. 

OSAT is also capable of ranking the relative importance of sources as well as ranking the 

contributors to ozone. 

(g) The methodology and model results have been peer-reviewed. 

(h) Ability to directly relate ozone concentration to the emission sources and background 

precursor concentrations (ICs/BCs), allowing policy makers to evaluate the model results 

easily. 

(i) The incorporation of the inert and decaying timing tracers to track the precursor transport 

pathways. 

(j) Masses of the reactive tracer species (NOx, VOCs, and O3) are kept consistent with the 

standard CAMx species. 

(k) Capable of tracking the range of precursor transport for both timing and ozone reaction 

tracers. 

(l) Deposition of the tracer species and ozone destruction reactions are treated more explicitly 

compared to CMAQ/OPTM. 

(m)Relatively computationally inexpensive compared to the traditional sensitivity techniques 

such as brute force (zero-out) methods. 

(n) CAMx/APCA is a nice added feature complementing CAMx/OSAT to provide “adjusted” 

apportionment results related to controllable (i.e., anthropogenic) emission sources. 

(o) Option to use a gridded “mask” to define source regions for tracking, which means the 

input files need no modification 

OSAT Weaknesses

(a) OSAT uses only the H2O2/HNO3 production ratio to determine VOC-limited or NOxlimited regime. Other indicators (HCHO/NOy, O3/NOz) are not used to assist in the 

determination. 

(b) Uses a single value (1/3) instead of a threshold range (e.g., 0.3-0.5) to determine whether 

a region is NOx or VOC limited. This may lead to errors in attribution in transitional 

ozone formation regimes. 

(c) Requires additional effort (compared to emissions processing for standard model input) in 

processing the emissions inventory of reactive tracer species when the emissions “mask” 

feature is not used. 

(d) May not represent the nonlinear response of atmospheric processes to emissions changes. 

Therefore, it can estimate the contribution of the tagged species only under the base-case 

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emission scenarios; it cannot estimate the effects of emission changes on ozone 

concentrations. 

(e) As shipped, CAMx outputs the tracer information only for layer 1. If the focus is to 

characterize sources of NOx or non-surface O3 this is problematic. 

(f) Although species naming is straightforward, it needs some metadata description in the 

file (thus, users should be encouraged to provide metadata information for each tag). 

2.1.2 CMAQ/OPTM 

The Ozone Precursor Tagging Methodology (OPTM) elicited a mixed response from the 

reviewers. One reviewer felt strongly that the OPTM cannot be fully evaluated in its current state 

because the documentation is insufficient to configure and run a simulation. The documentation 

clearly indicates that OPTM requires complex configuration even for a simple case. Further, the 

documentation’s technical formulation section raises many questions that were not answerable 

with the code supplied. Details on this reviewer’s comments are given in the paragraphs that 

follow. 

The “User Guide” (Douglas et al, 2007) and “Memo,” (Myers and Douglas, 2006) hereafter 

“documentation,” are inconsistent with the code provided. The documentation references utilities 

for tagged input creation (i.e., emis_optm, bcon_optm, icon_optm) that are either not supplied or 

are obscure. One reviewer felt there were inconsistencies between the documentation and the 

OPTM version of the CMAQ code given by the peer review panel team. The reviewer felt that 

without the entirety of tools and code, the documentation could not be evaluated as a guide for 

operation. 

The simplest source apportionment case—a spatial source apportionment—requires too many 

steps for preparation and configuration. Based on the instructions, any implementation requires 

editing INCLUDE files and adding tracer species to the model emissions, boundary conditions, 

and initial conditions input files. Other methods, specifically CAMx/OSAT, allow for the 

creation of a spatial mask to use existing emission inventories. The mask-style implementation 

would be particularly useful when only the CMAQ-ready inputs are available. 

The technical formulation raises several questions that need additional clarification: 

(a) Physical process magnitudes for tracers are inconsistent with corresponding model 

species. Other methodologies have solved this problem by utilizing process analysis data 

for component species and aggregating process magnitudes for the tagged tracer species. 

These data already exist in the models. [Response ICF Page 1] 

(b) Calculation of total oxidant (Ox) is incorrect. When two Ox species combine, as with NO3

(NO2+O) and N2O5 (NO2+NO3), the species should represent multiple units of Ox based 

on the number of “odd oxygen,” not the nitrogen. [Response ICF Page 1] 

(c) Based on the documentation, ratios of initial precursors are used to apportion net chemical change of tagged VOCs, NOx, and Ox. There are several false assumptions in the 

code that, if confirmed, should be addressed: [Response ICF Page 1] 

(1) Change in tagged species is solved algebraically, which inappropriately conserves 

initial tagged species. A differential solution would apply losses to the existing 

distribution of tags and to the distribution of chemically produced tags. 

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(2) Change in each region’s NOx and VOCs is proportional to the total local NOx and 

VOCs. This assumes that each region’s NOx and VOC reactivity (or deposition 

velocity) profile is equal to that of the local NOx and VOCs. In plumes or near 

sources, the locally contributed NOx and VOCs are likely to have a different 

reactivity profile. [Response ICF Page 2] 

(3) NOx production is proportional to existing NOx. It assumes that NOx precursors (i.e., 

HNO3, PAN, or NTR) are proportional to local NOx concentrations. These longerlived species may be transported and so not reflect local emissions of NOx. [Response 

ICF Page 2] 

(4) Total oxidant chemistry is apportioned based on precursor concentration distributions. 

While Ox production is likely to be proportional to precursors (excluding situations 

noted in [1] and [3] above), Ox loss reactions should be apportioned based on current 

Ox distribution. [Response ICF Page 2] 

(d) Artificial “offset” to Ox potentially alters processes and requires further review of code. 

[Response ICF Page 3] 

The documentation raised questions that could not be answered. Simple case implementations 

should be simplified with internal emission apportionment routines. The reviewer suggested that 

the comments above be addressed first, and that OPTM then be re-reviewed with updated 

documentation and code. 

OPTM does not track ozone; instead, it tracks “the amount of oxidant defined as NO2 + NO3 + 

2*N2O5 + O3” from tagged sources. This is in an attempt to track simultaneously the titration by 

NO2 of ozone and its reformation from NO. The reviewer felt unsure whether this was sufficient 

to attribute the right fraction of ozone to a given NOx or VOC source. There are other species 

(e.g., odd hydrogen) involved in ozone formation, which can be important sources of 

nonlinearity. 

Other major strengths and weaknesses of OPTM identified by the group of reviewers are given 

below. 

OPTM Strengths

(a) OPTM accounts for the production of Ox (Ox = O3 + NO2 + NO3 + 2*N2O5) instead of 

ozone for each emissions component. 

(b) Use of NOx and VOC internal tagged species for tracking chemical production of Ox. It 

should be noted, however, that one reviewer felt that this could be considered a weakness, 

because these species could easily be estimated internally, saving users time and avoiding 

potential introduction of errors into the inputs. 

(c) Implementation utilizes basic CMAQ modeling methods and tools that CMAQ users are 

familiar with, such as the use of CMAQ standard I/O API protocols for the tagged 

species. 

(d) Straightforward implementation of forward computation for the “counter species,” which 

does not interfere with base CMAQ results. 

(e) An utility exists to edit the model code include (*.EXT) files to add new tags 

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(f) Ability to handle multiple ozone precursors in a single simulation. 

(g) Implemented into CMAQ version 4.6 (a fairly recent release) 

(h) Ability to directly relate ozone concentration to the emission sources and background 

precursor concentration (ICs/BCs), allowing policy makers to evaluate the model results 

easily. 

(i) Capable of tagging species from multiple source categories, in different geographical 

locations, and in initial and boundary conditions. 

(j) Capable of tracking the range of precursor transport. 

(k) Relatively computationally inexpensive compared to the traditional sensitivity techniques 

such as brute force (zero-out) methods. 

OPTM Weaknesses

(a) Mass conservation of the tagged species is not checked in the formulation. [Response ICF 

Page 3] 

(b) OPTM does not distinguish between NOx-limited and VOC-limited regimes. OPTM also 

assumes that all VOC species have the same reactivity. Therefore, ozone may be 

attributed to the wrong source. [Response ICF Page 3] 

(c) Assumption of the fractional change in the total oxidant (sum of NO2, NO3, N2O5, and 

O3) concentration due to dry deposition is questionable. [Response ICF Page 3] 

(d) The contribution to O3 by tagged NOx and VOCs is counted separately, which is 

somewhat misleading because O3 is formed through photochemical reactions of both NOx

and VOCs jointly. [Response ICF Page 3] 

(e) It appears that OPTM is developed only for CB05 chemistry. 

(f) Although species naming is straightforward, it needs some metadata description in the 

file (thus, users should be encouraged to provide metadata information for each tag) 

(g) Requires additional effort to prepare the emission inventory of the tagged species 

compared to emissions processing for standard emissions inputs. 

(h) OSAT may not represent the nonlinear response of atmospheric processes to emissions 

changes. Therefore, it can estimate the contribution of the tagged species only under the 

base-case emission scenarios; it cannot estimate the effects of emission changes on ozone 

concentrations. 

(i) Current OPTM setup requires the code to be recompiled for each new set of tags. This 

approach places an unnecessary burden on model users and increases the chances of 

making mistakes. [Response ICF Page 3] 

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2.2 PM2.5: PSAT and PPTM implementation strengths and 

weaknesses 

The reviewers identified that both CAMx Particle Source Apportionment Technology (PSAT) 

and CMAQ Particle Precursor Tagging Methodology (PPTM) assume that all secondary PM is 

linked to a specific precursor: sulfate to SOx, nitrate to NOx, ammonium to NH3, and secondary 

organic aerosol (SOA) to VOC emissions. For this reason, the reviewers agreed that a common 

weakness of both CAMx/PSAT and CMAQ/PPTM is that they cannot capture any indirect 

effects, such as an increase in nitrate concentrations resulting from a reduction in SO2 emissions. 

We present below additional comments on the specific strengths and weaknesses identified by 

the reviewers for each of CAMx/PSAT and CMAQ/PPTM. 

2.2.1 CAMx/PSAT: 

The other strengths and weaknesses of CAMx/PSAT identified by the reviewers are given below. 

PSAT Strengths

(a) PSAT accounts for different VOC reactivities (with OH). It also accounts for different 

aerosol yields in SOA production from condensable VOCs. 

(b) Can perform culpability assessment based on the generalized source apportionment 

scheme that can be applied to the nonlinear processes. 

(c) Allows source apportionment study for sulfate (SO4), particulate nitrate (NO3), 

ammonium (NH4), particulate mercury (Hg(p)), SOA, and six primary PM categories 

(EC, primary OC, crustal fine, other fine, crustal coarse, other coarse). 

(d) Single tracer can track the primary PM species, whereas secondary PM species require 

several tracers to track the relationship between gaseous precursors and the resulting PM 

concentrations. 

(e) PSAT uses tagged tracers to identify selected sources, source categories, and/or source 

regions contributing to simulated PM2.5 concentrations. Thus, it can assist regulators in 

designing the most effective and cost-effective PM control strategies. 

(f) PSAT source apportionment technique has been extensively tested and evaluated against 

other source apportionment techniques. 

(g) Ability to directly relate PM concentration to the emission sources and background PM 

concentration (ICs/BCs), allowing policy makers to evaluate the model results easily. 

(h) Capable of tracking species from multiple source categories, in different geographical 

locations, and in initial and boundary conditions. 

(i) Capable of tracking the range of PM transport 

(j) Can handle both primary and secondary particulate species in a single simulation 

(k) Relatively computationally inexpensive compared to the traditional sensitivity techniques 

such as brute force (zero-out) methods. 

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(l) Option to use a gridded “mask” to define source regions for tracking, which means the 

input files need no modification 

PSAT Weaknesses

(a) Mass conservation of the reactive species is not checked in the formulation. [Response 

ENVIRON page 1] 

(b) No negative production is allowed for each source category. Therefore, if adding a certain 

precursor emission results in negative production, the model would assume the 

contribution to be zero instead. This could result in inaccurate apportioning of source 

impacts on secondary aerosol production. [Response ENVIRON page 1] 

(c) PSAT is implemented for parallel processing using OpenMP but not with Message 

Passing Interface (MPI). [Response ENVIRON page 1] 

(d) Requires additional effort to prepare the emission inventory of reactive tracers when not 

using the emissions mask feature. [Response ENVIRON page 2] 

(e) May not represent the nonlinear response of atmospheric processes to emission changes 

(even though one of the design objectives of the technique is to account for nonlinearity 

in model response), particularly in the formation of secondary PM. Therefore, 

CAMx/PSAT can estimate the contribution of the tagged species only under the basecase emission scenarios; it cannot estimate the effects of emission changes on PM 

concentrations. 

2.2.2 CMAQ/PPTM 

The reviewers felt that the implementation of the CMAQ Particulate Precursor Tagging 

Methodology (PPTM) is mostly ready for use. The documentation provides usable instructions, 

although they need some improvement. However, the reviewers indicated that the current 

implementation, like the OPTM implementation, has too complex a configuration for even the 

simplest case (see Section 2.1.2). Model input file preparation is tedious, and the provided 

utilities require further development. 

The documentation for PPTM is comprehensible, but could benefit from more illustrative 

examples. The examples in the appendix would be more useful if provided in-line with the 

instructions and if better commented, especially with respect to numbered options. The 

documentation is also missing examples of editing the INCLUDE files (i.e., *.EXT). Editing 

INCLUDE files should be well documented because it is a particularly tedious process. 

Model code preparation utilities are not usable as provided. The “set_tag” utility gives a run-time 

error when editing AE_EMIS.EXT. Initial efforts to debug found that the AE_EMIS.EXT file’s 

species names could be modified to make the utility work. Following that modification, however, 

the utilities “nitrogen tag” section of code fails. Debugging found another error regarding string 

indexing. The commenting in the utility codes is lacking, which makes debugging difficult. The 

code modification is dispersed and tedious; therefore, preprocessing utilities are critical to 

making PPTM usable for community members. Nonreactive species implementation is ready, 

but gas-phase chemistry requires further review. 

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The PPTM documentation indicates a more detailed representation of gas-phase chemistry than 

in OPTM, although this difference must be confirmed. For example, the gas-phase nitrogen 

chemistry in PPTM uses different assumptions compared to OPTM’s—that is, in OPTM NOx is 

the tracer for ozone, while in PPTM the PM2.5 tracers are NO, NO2, HONO, HNO3, and PNO3. 

Unlike in OPTM, the PPTM documentation suggests that longer-lived species are tracked 

individually. This improvement over OPTM should be confirmed in the code, and if confirmed, 

ported to the OPTM implementation. The revised code should then be reviewed for consistency 

with comments (a) and (c)(1) through (c)(4) from Section 2.1.2 (the OPTM review). 

According to the PPTM documentation, “the results for the tagged species are not normalized to 

ensure that the sum of the tagged species equals the total.” This is of course not applicable for 

instances where the change in tagged species is apportioned from the change in the total (e.g., 

aqueous chemistry or cloud scavenging). Also, it is not true in all cases, as stated later in the 

documentation: “At the end of the gas phase chemistry step, tags are normalized such that they 

add up to the correct overall concentration.” When it is true (e.g., advection and diffusion), it 

can be a strength. “The difference between the sum of all tags and the overall concentration gives 

an estimate of the numerical uncertainty.” The difference may be due to hidden nonlinearities, 

such as those in advection. Although advection is a linear process in theory, its numerical 

solution necessitates nonlinear flux corrections. The difference after advection can be used as a 

measure of this nonlinearity. 

The apportionment is done at several points in the aerosol and cloud processes. This is much 

better than treating the entire module as a black box. It seems that this approach is similar to 

fitting several line segments between two points (input and output) instead of one long straight 

line. This way a curved line (nonlinearity) can be better approximated. Of course, this may not 

necessarily be what happens when different aerosol or cloud processes are apportioned 

separately. Some theoretical arguments or a numerical example would be useful to demonstrate 

that this is indeed a strength of the method, if it really is. The computational overhead seems to 

be high, especially for nitrogen tagging (50% increase for three nitrogen tags). 

Other major strengths and weaknesses of PPTM identified by the reviewers are given below. 

PPTM Strengths (related to code implementation)

(a) Distinction between species that go through linear processes and nonlinear processes, and 

appropriate “special treatment” based on the initial fractional distribution of species. 

(b) Distinct identification of contributions of ICs/BCs. 

(c) Use of CMAQ standard I/O API protocols for the tagged species. 

(d) Use of averaged concentrations (between layers BLEV and ELEV) for the output. 

(e) Sulfur tagging in aerosol phase utilizing intermediate process checkpoint concentrations. 

(f) SOA tagging, including tracking of toluene and cresol for each tag; distinction of 

anthropogenic and biogenic contributions. 

(g) CMAQ/PPTM can assist regulators in designing effective PM control strategies. 

(h) Implemented into CMAQ version 4.6 (a fairly recent release). 

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(i) Can handle both primary and secondary particulate species in a single simulation. 

(j) Ability to directly relate PM concentration to the emission sources and background PM 

concentration (ICs/BCs), allowing policy makers to evaluate the model results easily. 

(k) Capable of tagging species from multiple source categories, in different geographical 

locations, and in initial and boundary conditions. 

(l) Capable of tracking the range of PM transport. 

(m)Relatively computationally inexpensive compared to the traditional sensitivity techniques 

such as brute force (zero-out) methods. 

PPTM Weaknesses

(a) Mass conservation of the tagged species is not checked in the formulation. [Response ICF 

page 4] 

(b) PPTM assumes that all secondary PM is linked to a specific precursor: for example, 

sulfate to SOx, nitrate to NOx, ammonium to NH3, and SOA to VOC emissions. For this 

reason, PPTM cannot capture any indirect effects, such as an increase in nitrate 

concentrations due to a reduction in SO2 emissions. [Note: also a weakness of 

CAMx/PSAT] [ Response ICF page 4] 

(c) Sulfur tagging in aerosol lacks methods to account for individual chemical production 

pathways that are the main causes of nonlinearity in the sulfate production. [Note: also a 

weakness of CAMx/PSAT] [Response ICF page 4] 

(d) Although species naming is straightforward, it needs some metadata description in the 

file (thus, users should be encouraged to provide metadata information for each tag). 

(e) The code design is intrusive. It is not apparent in the user’s guide why the developers 

chose the current design of CMAQ/PPTM, as opposed to the approach used by 

CAMx/PSAT and CMAQ/TSSA. 

(f) Current CMAQ/PPTM design requires the CMAQ source code to be modified and 

recompiled for each set of PPTM simulations. Even though the “set_tag” utility program 

is provided to create the *.EXT files from templates, the PPTM approach not only 

increases the complexity of the source code (multiple versions of executables), it also 

increases the chances of making mistakes. [Response ICF page 4] 

(g) Current PPTM approach also requires emissions tracers to be added to CMAQ emissions 

inputs. Again, this intrusive approach to emissions processing requires extra work and is 

prone to processing/QA errors. [Response ICF page 4] 

(h) PPTM outputs are written into standard CMAQ output files. This would lead to large 

output CMAQ files, increasing the potential for file corruption and access problems. A 

better approach is to create additional output files specifically for CMAQ/PPTM results. 

[Response ICF page 4] 

(i) May not truly represent the nonlinear response of atmospheric processes to emission 

changes (even though one of the design objectives of the technique is to account for 

nonlinearity in model response), particularly in the formation of secondary PM. 

Therefore, PPTM can estimate the contribution of the tagged species only under the baseUNC-EMAQ (2-06)-013 13 August 20, 2009 
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case emission scenarios; it cannot estimate the effects of emission changes on PM 

concentrations. 

3. Comparison to Other Methods Such as DDM, Zero-out, 

and TSSA 

In this section, source apportionment techniques are compared with alternative approaches to 

estimating source contribution, such as the CMAQ Tagged Species Source Apportionment 

(CMAQ-TSSA) (Tonnessen et al, 2007, Wang et al, 2009) the Decoupled Direct Method (DDM) 

(Dunker et al, 2002a) and the traditional brute-force (“zero-out”) methods for source 

sensitivities. 

DDM is conceptually different from source apportionment because this technique estimates 

source sensitivity rather than total contribution. Sensitivity is something that can be translated 

into a response to an emission control strategy. The first-order sensitivity is defined as the 

change in concentration resulting from an infinitesimal emission perturbation (or first derivative 

of concentration with respect to emissions). The second-order sensitivities are the derivatives of 

the first-order sensitivity with respect to the same or other emissions (mixed derivatives). Thirdorder sensitivities are the derivatives of the second-order sensitivities, and so on. The response to 

an emission change can be approximated using Taylor series expansion. Typically, only firstand second-order sensitivities are calculated by DDM (second-order sensitivities are not yet 

developed for aerosol or cloud processes). The first-order sensitivities are sufficient to represent 

a linear response. Second- and higher-order sensitivities are needed to capture the nonlinearities 

of the response. When only (first- and) second-order sensitivities are used, an error is introduced 

due to the absence of higher-order terms. This error becomes larger for larger emission changes. 

Hence the (approximate) response is relatively more accurate for small changes in emissions. As 

an example, the response calculated from DDM sensitivities would be much more accurate for a 

30% reduction of the emissions than for a 70% reduction of the emissions. 

Source apportionment, on the other hand, is a concept designed for efficiently estimating the 

total contribution of emissions from a certain source. It is most suited for judging which source is 

the cause of conditions present in the model. Theoretically, the contribution of a source is 

equivalent to the response that should be observed when that source is taken out completely.1

Zeroing out a source is the maximum possible perturbation of the base case, which includes 

100% of that source’s emissions. DDM sensitivities are the least reliable for 100% emission 

reductions. So, if the emission control decision to be made involves shutting down or completely 

turning off a source, then source apportionment may be the more appropriate application. 

The tools reviewed here (OPTM, OSAT, PPTM, PSAT) utilize the same counter-species forward 

integration method, in which a set of counter species are individually (if possible) or as a group 

(if the responses are expected to be nonlinear) computed utilizing the identical algorithms of the 

base model to provide the source-receptor relationships of the inputs (ICs, BCs, and emissions of 

precursor species). For the species that can be linearly superimposed, the method is quite useful. 

On the other hand, for the species or inputs that go through nonlinear processes or depend on 

other species, the total mass budget of each processing step is used to prorate the incremental 

 

1

 In practice, when the same source is zeroed out, a different response can be obtained from the models, as is pointed 

out both in OPTM/PPTM and OSAT/PSAT documents. 

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contributions. Because of the positive-definiteness requirements of the concentrations in AQMs, 

the source apportionment results for the nonlinear processes or that involve multiple species 

(e.g., species interactions such as NOx reductions changing sulfate or SOA) may not correctly 

apportion individual contributions of the tagged inputs. Because the tagging process does not 

interfere with the base model simulation, the source apportionment results are expected to be 

closer to those from the DDM- and Higher Order DDM (HDDM)-based tools (Hakami et al, 

2003, 2004) if the “zero-out” of the inputs is accounted for with the sensitivity (gradient) 

information. 

The brute-force “zero-out” simulation changes the base state when multiple “zero-outs” of inputs 

are realized. It may be closer to the way the real atmosphere would respond if sufficient inputs 

are changed, but computing the response to each of the inputs could be very tedious. On the 

other hand, if the delta (control) is large, we may not obtain a proper understanding of the shape 

of the response surface subject to the various magnitudes of input controls. Often entire states, 

emissions sectors, or metropolitan areas are zeroed out, which may lead to a drastic change in 

regional chemistry that is unrealistic. Therefore, a large number of simulations with incremental 

changes would be required to cover the whole range of control possibilities. Accomplishing this 

across multiple changes could be very costly and time consuming in terms of both computer and 

human time. 

For DDM and HDDM, the base state does not change when estimating the gradient surface 

(response surface). Then the effects of control, including the zero-out, could be estimated using 

the gradient during the postprocessing. The magnitude of delta changes that can be applied 

depends on the nonlinearity of the system to the input changes. Usually, the higher-order DDM 

scheme can extend the range of control deltas, although it will still be difficult to justify the total 

zero-out of certain inputs. 

For the counter-species-based tools reviewed here, as for DDM or HDDM, the base state is not 

altered during the computation. The effects of each tag can be assessed only at the 100% control 

level (i.e., zero-out). However, note that “zero-out” postprocessing of DDM and HDDM are 

actually not recommended (i.e., the computed sensitivity may not be as valid). In this regard, it 

would not be reasonable to select the tagged species to represent only small perturbations in the 

inputs. The counter-species tools reviewed here are more comparable to results from brute-force 

zero-out simulations. 

CMAQ-TSSA is a particulate source apportionment approach implemented in an earlier version 

of the CMAQ model. It has not been implemented in recent versions of CMAQ. The CMAQTSSA approach makes simplifying assumptions, just as PPTM (or OPTM) and PSAT (or 

OSAT). The reviewers felt that TSSA may be more accurate than other methods because it 

employs the integrated reaction rates for the tagged species in gas-phase chemistry. However, for 

aerosols, it splits the change in the bulk species proportionally to the tagged species. Similar to 

PSAT, only one version/copy of CMAQ/TSSA executable is needed to perform multiple sets of 

source apportionment runs that vary in the species/source categories. CMAQ/TSSA creates new 

source apportionment output files and does not “perturb” the standard CMAQ outputs, which 

again is an approach more comparable to PSAT. It is important to note that TSSA was not 

specifically reviewed as part of this peer review process so a comprehensive list of strengths and 

weaknesses are not presented. 

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The most accurate approach would be a method that splits each pollutant into different sourcespecific species and tracks these source-specific species separately through the model, and makes 

no assumptions of linearity (except perhaps that all PM species can be traced back to a specific 

precursor gas-phase species or primary PM component; note that this would still be a significant 

weakness of source apportionment). An example of this approach can be found in Kleeman and 

Cass (2001). Such a method would be extremely computationally demanding, but it would be the 

most accurate. If implemented in CMAQ and CAMx, it could be used as a benchmark for the 

more computationally efficient methods discussed here. 

To summarize: 

(a) A zero-out run is a sensitivity simulation, and sensitivity experiments are affected by 

nonlinearity in chemical reactions, so they are not definitive in characterizing source 

contributions. Because PSAT and PPTM track mass contributions to PM formation, they 

identify source contributions in a way that accounts for chemical nonlinearity. 

(b) DDM in an air quality model is an efficient and accurate way of performing sensitivity 

analysis to model inputs. However, while DDM gives information on the atmospheric 

response to emissions changes, and finds the uncertainty in the response, it does not attribute 

concentration among all sources regions and categories. DDM also may or may not be 

applicable for large perturbations. 

(c) Compared with other source apportionment methods, TSSA is similar to CAMx/PSAT and 

CAMx/OSAT, but slightly different in the treatment of chemistry and dry deposition. 

The source apportionment approaches are fundamentally different from the DDM and zeroout approaches. DDM calculates first- and higher-order sensitivity coefficients with a greater 

computational efficiency, although the implementation of DDM may be somewhat 

technically challenging, and it may not effectively captures the nonlinear response of models. 

Zero-out (or certain percentage of emission reduction) simulations might be computationally 

expensive, but the method is direct, intuitively easy to understand, and does not require 

additional modeling skills as compared to standard simulations. 

We further provide a comparison to other source sensitivity/source attribution methods: 

(a) Data analysis methods, such as chemical mass balance (CMB) and positive matrix 

factorization (PMF), are limited by the availability of ambient monitoring data, and can 

be used only to evaluate historical episodes. 

(b) Back-trajectory analyses cannot adequately treat the complex chemistry of secondary PM 

formation. 

(c) The source-oriented external mixture (SOEM) method is potentially the most accurate 

approach for tracking source contributions. However, this method is computationally 

demanding, as it solves an increasing number of stiff, coupled differential equations for 

each time step in the gas and aerosol chemistry modules as well as increasing the number 

of species in the aerosol calculation. 

(d) Carbon tracking was implemented into CMAQ to track primary organic and elemental 

carbon contributions. However, this approach does not address a number of other gases 

and aerosol species in CMAQ. 

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4. Documentation Strengths and Weaknesses 

In this section, we provide a composite summary of the reviewers’ responses to a series of 

charge questions relating to documentation. 

a. Is the current documentation for CMAQ/PPTM and CMAQ/OPTM adequate? 

The reviewers felt that the documentation was barely adequate, and suggested that it could be 

more detailed for a typical modeler who wants to use the tools for routine model applications. 

The documentation provides very nice descriptions on how the technologies are implemented. 

However, the operational aspects to implement the technology, including tagged emission 

preparation, building the tools with the CMAQ modeling system, modifying the extension files, 

and execution of the tools, could be more explicit and organized. The documentation’s indication 

of the estimated increase in the computational cost is helpful, giving users an idea of the elapsed 

time when using the tools. 

Ideally, there are two types of documentation needed for this type of application. The first type is 

a user’s guide. The second type consists of the online comments inserted into the CMAQ source 

code. The shortcomings of each of these are discussed below. 

The shortcomings in the CMAQ/PPTM and CMAQ/OPTM user’s guide identified by the 

reviewers include the following: 

(a) Does not provide adequate discussions comparing PPTM/OPTM to other sensitivity 

approaches (zero-out run, DDM, etc.) available to model users. 

(b) Some parts of the user’s guide need more work. One example is the aerosol dynamics 

section for secondary organic aerosols. The technical discussions in this subsection seem 

to suggest that PPTM uses the kinetic approach, but it is not clear from the 

documentation. 

(c) Does not explain what modules OPTM/PPTM were implemented for. For example, for 

which CMAQ chemistry solver(s) has OPTM/PPTM been implemented? 

(d) Does not cover whether the OPTM/PPTM is incorporated into the CMAQ CVS 

repository. 

(e) Does not adequately address how the “tags” are assigned. 

(f) Does not include sample CMAQ run scripts with the OPTM/PPTM option. 

(g) Does not cover the performance of OPTM/PPTM in terms of efficiency (speed) and 

accuracy (error) as compared to the traditional zero-out method. 

(h) Lacking in formal statistical comparison. It seems that results presented were simply 

summaries from OPTM/PPTM runs and lack rigorous evaluation of these results 

compared with those from other methods (e.g., zero-out comparison). 

(i) Details for species-specific approach (1) for each reactive species (sulfate, nitrogen, and 

secondary organics) and (2) for nonreactive species (primary organic carbon, elemental 

carbon, and other inorganic particles) are missing. 

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(j) Does not provide insights as to why model users should choose CMAQ/PPTM and 

CMAQ/OPTM over other source apportionment methods such as CAMx/PSAT, 

CAMx/OSAT, and CMAQ/TSSA. 

(k) On page 6 (last paragraph) of the technical description and user’s guide, the discussion 

regarding the offset in the oxidant tracers for ozone titration by NO seems a bit arbitrary 

and should be clarified. 

(l) In the documentation of CMAQ/PPTM, the discussion on how the aerosol dynamics and 

cloud process calculation is formulated could be further elaborated. How does the tool 

assign I and J modes for each tagged PM species? 

(m)The rationale behind the nitrate apportionment should be explained in the CMAQ/PPTM 

documentation. Currently, formulae under nitrogen tagging are difficult to follow. 

The shortcomings for online documentation in the CMAQ/PPTM and CMAQ/OPTM source 

code include the following: 

(a) As EPA developed CMAQ, extra efforts were expended to make the code “readable.” 

However, the CMAQ/PPTM and CMAQ/OPTM implementation in CMAQ does not 

fully follow the “rules of coding.” 

(b) Although the CMAQ/PPTM code starts with the line “cicfpptm”, the comments do not 

explain where the new code ends and how it differs from the standard CMAQ release. 

(c) The comments do not always provide sufficient information describing the purpose of the 

new code in each OPTM/PPTM section. 

(d) The OPTM/PPTM code fails to explain what each variable means at the beginning of 

each subroutine. The code also does not document the unit used for each variable. 

b. Is the current documentation for CAMx/PSAT and CAMx/OSAT adequate? 

The reviewers felt that the documentation for PSAT and OSAT is well written, but could also be 

improved. Operationally, it could be more detailed for a typical modeler who wants to use the 

tools for routine model applications. The documentation provides excellent technical discussion 

on the methodology and model formulation of OSAT and PSAT. The CAMx user’s guide also 

provides sufficient references on other source apportionment methodologies. The paper by 

Dunker et al. (2002b) complements the OSAT documentation. The online documentation and 

comments in the CAMx code are also better than those in CMAQ/OPTM and CMAQ/PPTM. 

The operational aspects regarding how to implement the technology, including reactive tracers 

emission preparation, how to build the source apportionment tools, and the step-by-step 

procedure to run CAMx/PSAT and CAMx/OSAT, could be more detailed. A few other shortcomings in the CAMx OSAT and PSAT documentation identified by the reviewers include the 

following: 

(a) In the documentation of CAMx/PSAT, only the concentration change of the reactive 

tracers is discussed. Some description on how the aerosol dynamics and cloud processes 

are treated in the formulation would be helpful for readers. 

(b) On page 6-8 of the OSAT documentation, it states that IC/BC ozone is split equally 

between the VOC- and NOx-limited categories. This seems to contradict Figure 6-2. 

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(c) On page 6-9, advective flux normalization could be better explained by using equations. 

There is probably a difference between the weighting of tracers N and O3N because the 

first one combines NO and NO2 but the second one splits O3. Some equations (e.g., on 

pages 6-9 and 6-10) are written like FORTRAN code. The same quantities appear on both 

sides of the equation without distinction of their position in time. 

Two typographical errors are pointed out: 

(a) On page 6-13 “based on response the of the peak ozone” 

(b) On page 6-34, “you will may be asked,” 

c. Are the above model formulations sufficiently documented for technical critique and 

understanding? 

The reviewers generally felt more satisfied with the documentation for CAMx/PSAT and 

CAMx/OSAT than with the documentation for CMAQ/OPTM and CMAQ/PPTM. The 

documentation of CAMx/PSAT and CAMx/OSAT is particularly well written. It provides 

detailed scientific assessments of the technologies and their relationship to other source 

apportionment technologies. The discussion on the rationales and design goals of the source 

apportionment tools is also very well described in the documentation. However, for a few 

nonlinear processes, detailed presentations of intermediate tagging species would be useful. The 

CMAQ OPTM/PPTM documentation could use a better overview of the source apportionment 

methodology. One reviewer felt that only after reading documentations for both CAMx and 

CMAQ together does one get a complete overview of the source apportionment procedures. 

d. Is the discussion and presentation of the above models and their features clear? Please 

note any specific sections of the documentation that were unclear or confusing. 

The responses to this question are combined in the responses to questions 4a and 4b above. 

e. Is the documentation for each of the above models sufficient to guide a typical user in the 

use of the model and its preprocessors? 

This is the area in which the reviewers felt that the documentation could be more explicit and 

detailed. Considering the additional efforts required to implement the technologies in modeling 

exercises, step-by-step instructions (especially in the preparation of tagged emissions), information on modifications needed for the model runs, and explanations of potential problems that 

may be encountered in the application of the technology should be included that are aimed at a 

typical user who is implementing the technologies. Again, the reviewers felt that this portion of 

the documentation was satisfactory for CAMx/OSAT and CAMx/PSAT, but not for 

CMAQ/OPTM and CMAQ/PPTM. 

Because the reviewers were not asked to go through a complete modeling exercise using these 

tools, they felt that additional deficiencies might be uncovered as one goes through the entire 

process, from preprocessing through postprocessing. 

f. What improvements are desirable for the documentation of each of the above models? 

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As noted in the responses to question 4e above, the reviewers felt that additional details on the 

operational aspects of the models would be very helpful. The application of CMAQ/OPTM and 

CMAQ/PPTM is easier to follow, although the execution of CAMx source apportionment tools 

seems to be more straightforward. A step-by-step procedure for running CAMx/PSAT and 

CAMx/OSAT would be very helpful. 

In PPTM/OPTM documentation, a more detailed explanation of the Emissions and IC/BC Utility 

programs is recommended. The description of the examples (Douglas et al, 2007 Section 2.2 and 

3.2) for OPTM and PPTM application procedures could be expanded. The PSAT documentation 

includes a comparison of source apportionment with source sensitivity. One reviewer urged that 

the documentation authors add a discussion of the practical value of source apportionment and 

source sensitivity in a regulatory context, placing that new text before the somewhat theoretical 

comparison already in the document. In other words, in a real-world context, why do we need 

source apportionment? Including examples of how source apportionment is (or could be) used 

by regulators would be extremely useful. Moreover, the reviewer suggested that a similar 

discussion is needed in the documentation for all of the tools. 

g. What changes in organization of the documentation of each of the above models are 

desirable? 

The organization of the PPTM/OPTM documentation could be revised such that the processes 

common to sulfur, nitrogen, and organics tagging are covered in a general tagging section. This 

way some of the repetition could be avoided. Further, for a few nonlinear processes, detailed 

presentations of intermediate tagging species would be useful. 

h. Does the documentation contain useful and appropriate evaluations of source 

apportionment for ozone and PM2.5 species compared to other approaches? 

The PPTM/OPTM documentation does not contain any comparative evaluation. There are no 

comparisons or other types of evaluations for OSAT either. The PSAT documentation contains a 

comparison to a sulfate zero-out run, which is quite useful. There is also a comparison of reactive 

nitrogen (NOy) apportionment by PSAT to the SOEM algorithm of Kleeman and Cass (2001). It 

is not clear, however, whether this one-dimensional (box-model) problem is relevant or robust 

enough to evaluate PSAT. There is a more realistic comparison of PSAT to a method similar to 

SOEM in Wagstrom et al. (2008). 

The reviewers indicated that there are some data and visualizations showing that all the source 

apportionment tools agree reasonably well with the model results based on zero-out methods. 

However, they would have liked to see information on how much the source apportionment 

results would differ if the same emission tagging were applied for different grid resolutions and 

for emissions with very different reactivity. Some specific additional comments are given below. 

CAMx-OSAT/PSAT: 

(a) The CAMx user’s guide contains more useful and appropriate evaluations than does the 

CMAQ/PPTM user’s guide. 

(b) The CAMx user’s guide has a good comparison of the OSAT/PSAT methodologies with 

other approaches. 

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(c) The CAMx user’s guide also provides some comparison of OSAT/PSAT results with 

results from other approaches (such as from zero-out and SOEM runs). 

(d) The CAMx user’s guide’s references section is much better produced than that of the 

CMAQ-OPTM/PPTM user’s guide. 

CMAQ-OPTM/PPTM: 

(a) The CMAQ-OPTM/PPTM user’s guide has very little information on the comparison of 

OPTM/PPTM to other approaches. A well thought-out methodology should be able to 

withstand a rigorous comparison with other approaches. However, this discussion is 

notably lacking in the CMAQ-OPTM/PPTM user’s guide. 

(b) There is no information on the performance of CMAQ-OPTM/PPTM versus zero-out 

runs or other approaches (DDM, SOEM, etc.). 

(c) Other than the developers’ work (i.e., references to CMAQ and CMAQ’s tools (I/O API 

and SMOKE), the user’s guide does not refer to or quote any other source apportionment 

methodologies. 

5. Preprocessing and Postprocessing Strengths and 

Weaknesses 

In this section, we provide a composite summary of the reviewers’ responses to a series of 

charge questions relating to preprocessing and postprocessing tasks/tools. 

a. In general, how manageable do you consider the task of preprocessing/model setup to 

run source apportionment for CMAQ/PPTM, CMAQ/OPTM, CAMx/OSAT, and 

CAMx/PSAT? 

The reviewers agreed that the task of preprocessing/model setup to run CMAQ/PPTM, 

CMAQ/OPTM, CAMx/OSAT, and CAMx/PSAT is manageable for experienced modelers but 

may not be as easily adapted for routine operation. The preparation required for the additional 

tagged or reactive tracers species can be massive, especially when many emission 

sources/locations are tagged (which is the primary strength of the source apportionment tools). 

Regarding the model setup to run source apportionment tools, more detailed and explicit 

descriptions seem necessary. It would be very helpful if detailed operational procedures for 

building and running the tools could be included in the documentation. Some specific comments 

for each of the two sets of tools are given below. 

CMAQ/PPTM and CMAQ/OPTM

It seems generally manageable to apply these, especially utilizing the general tools such as 

“set_tag” for updating the tagged species list in the model, and scripts based on m3xtract for the 

preparation of emissions file. However, the following features need to be addressed: 

(a) A method to check the completeness of code modifications (i.e., whether the 

modifications of the INCLUDE files are correct). 

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(b) The number of “tagged” species allowed in one I/O API file is generally the default, so 

the I/O API libraries will usually need to be recompiled by the user to include many more 

species. 

(c) Setting up CMAQ-OPTM/PPTM requires more work than a regular CMAQ run. The 

biggest problem is having to maintain the different copies of CMAQ-OPTM/PPTM 

executables and input files. If a proper naming convention were not agreed upon at the 

initiation of a project, the housekeeping for these would become a nightmare. This 

problem would be exacerbated if model/data transfer occurs among different 

agencies/groups. 

CAMx/OSAT and CAMx/PSAT

Considering that several different groups have applied CAMx/OSAT and CAMx/PSAT for 

source apportionment study over many different geographical areas, the preprocessing and 

postprocessing of these tools seem manageable. 

(a) Setting up a CAMx-OSAT/PSAT source apportionment run is quite manageable for a 

modeler with previous experience running CAMx. 

(b) The file structure of CAMx is not as portable as netCDF and requires additional modeling 

skills to deal with the model datasets. 

b. Are postprocessing tools easily available and easy to use for CMAQ/PPTM, 

CMAQ/OPTM, CAMx/OSAT, and CAMx/PSAT? 

There is no postprocessing tool provided for CMAQ-PPTM/OPTM. However, since the output 

of CMAQ-PPTM/OPTM is directly written as additional species in the standard CMAQ output 

files, the users can simply follow the general routines provided by I/O API utility tools to extract 

the needed output for postprocessing. On the other hand, if postprocessing run scripts or utility 

programs were to be provided, that would certainly be helpful for the users, who could then 

obtain the source apportionment results more directly. 

The reliance of PPTM and OPTM on standard CMAQ postprocessors is an advantage, but at the 

same time it also limits the analyses to only those that can be performed with CMAQ postprocessors. The analyses desirable for source apportionment results may be somewhat different 

than the analyses typically conducted with standard CMAQ results. For example, pie charts at 

certain receptor locations on a map showing the fractions of contributions may be more desirable 

than several maps of individual contributions. It is not clear how the various contribution charts 

in the documentation were prepared. It would be helpful if there were a postprocessor to generate 

these types of charts from standard model outputs. For example, formulae needed to calculate the 

amount of ozone attributed to each tag could be provided as a PAVE script. Scripts to interface 

with other charting options (e.g., MS Excel) could also be provided to the user. 

For CAMx/OSAT, additional output files for instantaneous concentration, surface average 

concentration, and reactive tracer concentration at a given receptor location are written in either 

FORTRAN binary or ASCII format. This is a plus in terms of simplifying the postprocessing 

effort, since it is not necessary to deal with the large output files of the standard simulation. The 

reactive tracer concentration at a user-specified location output to an ASCII file format can be 

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visualized easily using a rudimentary knowledge of Excel. For CAMx/PSAT, it is not clear from 

the documentation whether any postprocessing tools are provided. 

6. Supplemental Questions 

(c) What do you consider the general advantages and disadvantages of zero-out modeling 

when a 100% emissions perturbation is necessary for an application such as source 

culpability? 

Zero-out modeling implies 100% reduction of emissions in the model and source culpability 

implies that the contribution of a source that is emitting at full capacity is being sought. If these 

are the intended implications, then one of the reviewers opined that zero-out modeling would 

yield the wrong contribution of a source to a pollutant that is a non-linear function of precursor 

emissions (e.g., ozone, secondary organic aerosol). Source apportionment methods such as those 

reviewed here (not considering their weaknesses in tracking non-linear relationships) are better 

suited for this purpose. However, if the goal is to see what would happen if a source were 

eliminated, then zero-out modeling is the better approach. 

In addition to the above, the reviewers identified the following advantages and disadvantages: 

Advantages:

• Zeroing-out directly changes the chemical composition, which should reflect the 

chemistry better than any of the source apportionments techniques. 

• Intuitively easy to understand and to interpret the model results. 

Disadvantages: 

• Multiple runs are required if there are multiple sources that need to be evaluated 

• Computationally expensive 

• Although the zero-out modeling is useful in assessing the source culpability, if the area 

and amount of subtraction are substantial, then the base state may be changed and thus 

the response to the perturbation may be different from the base state. Further, sum of 

culpability may not add up to be balanced to the large changes in the state because the 

system would become more nonlinear. 

(d) Do you feel that the implementation of mercury tagging in CMAQ/PPTM and 

CAMx/PSAT has any important weaknesses that may need to be addressed or strengths 

as a tool for culpability assessment? 

One reviewer felt that the mercury tagging in CMAQ (and CAMx/PSAT, too, if the 

implementation schemes are similar) is a good tool for culpability assessment in terms of 

concentration and dry deposition of mercury because the inconsistent chemistry between the 

tracer and base mercury species is much less of an issue due to two reasons: 

(1) Mercury emission is many orders of magnitude smaller compared to VOC and NOx 

emission. As a result, the presence of mercury in the air composition does not affect the 

photochemistry of oxidant formation, which is an important driver for mercury 

deposition. 

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(2) Air concentration of mercury is very small and existing model data indicate that the 

response of mercury dry deposition to air concentration is very linear. This agreed with 

the linear assumption in the source apportionment tool much better compared to the case 

of O3. The response of mercury wet deposition to concentration is not as linear because of 

wet scavenging and aqueous chemistry, though. 

Other reviewers however felt that the strengths and weaknesses that were listed for these models 

were general so they would also apply to mercury tagging. However, they could not answer the 

question of whether there are weaknesses (or strengths) particular to the mercury chemistry 

without further review of the models. Specifically, if the aqueous-phase and thus the response of 

the system to the input changes were significantly nonlinear, then similar difficulties would exist 

as the culpability assessment of the SOA, and hence further review was warranted. 

7. Suggestions for Improvements to or Additional Evaluation 

of Source Apportionment 

It is not clear whether either CMAQ-PPTM/OPTM or CAMx-PSAT/OSAT can perform source 

apportionment simulations for ozone and particulate matter simultaneously. In light of the oneatmosphere modeling in the CMAQ framework and CAMx modeling system, the capability of 

simultaneous source apportionment calculation for O3 and PM should be considered [Response 

ENVIRON page 1]. Because there are complex interactions among the ozone precursors, the 

radical species, and various particulate species (including SO4, NO3, and SOA), how the tagged 

species (or the reactive tracers in CAMx-PSAT/OSAT) interact with each other, and how 

consistent these interactions are compared to those in the standard simulation, need to be 

carefully examined. 

To encourage the application of these very useful tools, the reviewers recommended that 

additional emission processing tools and utility programs that facilitate the use of the source 

apportionment tools be developed and provided. The implementation of source apportionment 

tools into a modeling exercise requires extensive preprocessing work in the preparation of tagged 

emissions data (including IC and BC species) the addition of new model parameters in CMAQ or 

CAMx, modification of multiple run scripts, and dealing with the increased number of input and 

output parameters. These tasks can be cumbersome, so it would be very useful if additional 

utility programs were available to reduce the level of effort required. 

It would be beneficial if the source apportionment tools could be more modular and portable. 

The codes seem to be closely tied to each science processor in their respective AQM. With the 

current pace of model advancement and mechanism updates, the source apportionment tools may 

not immediately work with the newer releases of the models and so may require additional code 

modifications by the users or by the developers. Such portability and code maintenance issues 

may present a challenge to a typical modeler. 

The way that the four tools (CMAQ/PPTM, CMAQ/OPTM, CAMx/PSAT, and CAMX/OSAT) 

calculate the concentration change of the tagged species (or reactive tracers) is similar: the 

concentration change of a tagged species at each time step is calculated according to the concentration change of the corresponding species in the standard model weighted over its fraction in 

the total tagged species. This brings about some potential concerns that require further 

examination. 

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• First, the reactivity of the gas mixture in the standard model may not represent the 

reactivity of a tagged species, but the concentration change of the tagged species is 

calculated based on the concentration change of the gas mixture in the standard model. 

This may overestimate the concentration change for a tagged species having low 

reactivity and underestimate the concentration change for a tagged species having high 

reactivity. For example, for two emission sources (A and B) located in the same grid cell 

of the model domain, if Source A emits only the low-reactivity VOCs (VOCA) and 

Source B emits only high-reactivity VOCs (VOCB), the VOC mixture in that grid cell 

should have a reactivity somewhere between VOCA and VOCB. In CMAQ/OPTM, 

because ozone attributed to a tagged source is directly proportional to the oxidant production contributed by the tagged species, this can produce a bias in the source apportionment. In CAMx/OSAT and CAMx/PSAT, the concentration change of the reactive VOC 

tracers is also weighted according to their respective OH reactivity, which should 

produce a smaller bias. However, this needs to be verified with additional model data. 

[Response ENVIRON page 1] 

• Similarly, the tools may produce different source apportionment results at different grid 

resolutions because of the different degrees of instantaneous dilution upon the emission 

of the tagged and standard species and/or the inclusion of a different number of emission 

sources at different grid sizes. Testing the tools at two different grid resolutions would be 

helpful to assess whether the apportionment results are sensitive to grid resolution. 

[Response ENVIRON page 2] 

• For all these methods, it is necessary to track the effects of nonlinearities in all processes, 

but most urgently in chemistry. The treatment of chemistry in TSSA could be used as an 

example. Basically, the production and loss of each tracer (or tagged component) should 

be tracked independently. Processes that are treated as black boxes, where the change in 

the overall mass (or flux) is split proportionally to the components (tags), are essentially 

assumed to be linear. It is desirable to have more explicit tracking of the tags through 

these processes. [Response ENVIRON pages 2-3] 

In the CAMx user’s guide, there is no detailed information on the treatment of the nonlinear 

processes for CAMx/PSAT and CAMx/OSAT, other than the general approach of treating the 

nonlinear chemical processes based on the mass budget redistribution according to the initial 

concentrations of each step. In the CMAQ documents, at least there are statements that internal 

checkpointing is utilized to assure mass balancing. However, in one reviewer’s opinion, if at all 

possible the direct computation of counter species would be preferable, to identify the degree of 

nonlinearity of each processing step, then reconcile these with the results of the base simulation. 

References 

Byun, D.W. and J.K.S. Ching, ed., Science Algorithms of the EPA Models-3 Community 

Multiscale Air Quality (CMAQ) Modeling System, 1999, EPA/600/R-99/030(peer 

reviewed). [Available from National Exposure Research Laboratory, RTP, NC] 

Byun, D.W., and Schere, K.L., 2006. Review of the Governing Equations, Computational 

Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality 

(CMAQ) Modeling System. J. Applied Mechanics Reviews, 59(51), 2006. 

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Douglas, S., T. Myers and Y. Wei (2006). Implementation of Mercury Tagging Methodologies 

in the Community Multiscale Air Quality (CMAQ) Model, Technical Description and User’s 

Guide, Prepared for the U.S. EPA OAQPS, July 2006. 

Douglas, S., T. Myers and Y. Wei (2007). Implementation of Ozone and Particle Precursor 

Tagging Methodologies in the Community Multiscale Air Quality (CMAQ) Model, 

Technical Description and User’s Guide, Prepared for the U.S. EPA OAQPS, November 

2007. 

Dunker, A., Yarwood, G., Ortmann, J., & Wilson, G. (2002a). The decoupled direct method for 

sensitivity analysis in a three-dimensional air quality model implementation, accuracy, and 

efficiency. Environ. Sci. Technol., 36(13), 2965-2976. 

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

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

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

ENVIRON, Inc. (2008). Comprehensive Air Quality Model with extensions, Version 4.5, User’s 

Guide, May 2008. 

Hakami, A., M. T. Odman, and A. G. Russell (2003), High-order, direct sensitivity analysis of 

multidimensional air quality models, Environ. Sci. Technol., 37, 2442. 

Hakami, A., M. T. Odman, and A. G. Russell (2004), Nonlinearity in atmospheric response: A 

direct sensitivity analysis approach, J. Geophys. Res., 109, D15303, 

doi:10.1029/2003JD004502. 

Kleeman, M.J., and G.R. Cass (2001). A 3D Eulerian Source-Oriented Model for an Externally 

Mixed Aerosol. Environ. Sci. Technol 35, 4834-4848. 

Myers, T., and S. Douglas (2007). Implementation of Ozone and Particle Precursor Tagging 

Methodologies in the Community Multiscale Air Quality (CMAQ) Model, Technical 

Memorandum, Prepared for the U.S. EPA OAQPS, March 2007. 

Tonnessen, G. and Z. Wang (2007). Development of a Tagged Species Source Apportionment 

Algorithm (TSSA) to Characterize 3-Dimensional Transport and Transformation of 

Precursors and Secondary Pollutants, Final Report, University of California, Riverside, May 

2007. 

Wagstrom, K.M., S.N Pandis, G. Yarwood, et al. (2008). Development and application of a 

computationally efficient particulate matter apportionment algorithm in a three-dimensional 

chemical transport model. Atmos. Environ. 42, 5650-5659. 

Wang, Z. S., C.-J. Chien, and G. S. Tonnesen (2009), Development of a Tagged Species Source 

Apportionment Algorithm (TSSA) to Characterize 3-Dimensional Transport and 

Transformation of Precursors and Secondary Pollutants, J. Geophys. Res.,

doi:10.1029/2008JD010846, in press (accepted 23 July 2009) 

Zhang, Y., K. Vijayaraghavan, and C. Seigneur (2005), Evaluation of three probing techniques in 

a three-dimensional air quality model, J. Geophys. Res., 110, D02305, 

doi:10.1029/2004JD005248. 

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Appendix A: Peer Review Process Narrative 

In an effort to provide for independent, credible peer reviews of source apportionment techniques 

that are available in air quality models, the Air Quality Modeling Group of the U.S. Environmental Protection Agency’s Office of Air Quality Planning and Standards issued a work 

assignment (WA) under a general contract (EP-D-07-102) with the University of North Carolina 

at Chapel Hill’s Institute for the Environment to manage and coordinate the peer review process. 

Once the work assignment was issued (October 2008), UNC prepared a detailed work plan for 

the approval of the EPA Work Assignment Manager (WAM), Dr. Kirk Baker. The work plan 

provided a description of the tasks to be completed, the estimated time frame, and the estimated 

person-hours and cost requirements. The description below describes the process by which the 

peer review for the source apportionment tools in CAMx and CMAQ was conducted. 

The information provided to UNC under the WA Statement of Work included: 

• a “Charge to Reviewers,” formulated by EPA, that outlines the specific direction and 

technical scope of the task for the peer review team; 

• a list of qualified candidates known to EPA from which to choose the peer reviewers 

(UNC then added a few more candidates to the list); and 

• a list of materials to be provided to the peer reviewers by UNC and EPA 

UNC contacted people on the qualified candidates list, described the project to them, and 

requested their participation based on their interest and availability. Five candidates were 

retained: Dr. Daewon Byun (University of Houston), Dr. Harvey Jeffries (University of North 

Carolina at Chapel Hill), Dr. Jerry Lin (Lamar University), Dr. Talat Odman (Georgia Institute 

of Technology), and Dr. Gail Tonnesen (University of California, Riverside). However, Dr. 

Tonnesen was subsequently unable to complete the peer review due to schedule conflicts, so we 

identified one of her colleagues, Dr. Zion Wang, to replace her. The chosen peer review team 

was approved by the WAM. Dr. Sarav Arunachalam was designated the chairperson, in that he 

would compile a report that would summarize all of the peer reviewers’ comments and opinions 

into one concise document. 

UNC provided a subcontract mechanism through which the peer reviewers could be compensated for their time. UNC then reproduced and distributed the review materials to all reviewers. 

These materials were provided to UNC by EPA, and consisted of the following: 

(a) Source code for CAMx with OSAT and PSAT. 

(b) Source code for CMAQ with OPTM and PPTM. 

(c) Inputs and outputs for test case CAMx application with OSAT and PSAT. 

(d) Inputs and outputs for test case CMAQ application with OPTM and PPTM. 

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(e) Douglas, S., T. Myers, and Y. Wei (2006). Implementation of Mercury Tagging 

Methodologies in the Community Multiscale Air Quality (CMAQ) Model, Technical 

Description and User’s Guide, prepared for the U.S. EPA OAQPS, July 2006. 

(f) Myers, T., and S. Douglas (2007). Implementation of Ozone and Particle Precursor 

Tagging Methodologies in the Community Multiscale Air Quality (CMAQ) Model, 

Technical Memorandum, prepared for the U.S. EPA OAQPS, March 2007. 

(g) Douglas, S., T. Myers, and Y. Wei (2007). Implementation of Ozone and Particle 

Precursor Tagging Methodologies in the Community Multiscale Air Quality (CMAQ) 

Model, Technical Description and User’s Guide, prepared for the U.S. EPA OAQPS, 

November 2007. 

(h) ENVIRON, Inc. (2008). Comprehensive Air Quality Model with Extensions Version 4.5, 

User’s Guide, May 2008. 

(i) Tonnessen, G., and Z. Wang (2007). Development of a Tagged Species Source Apportionment Algorithm (TSSA) to Characterize 3-Dimensional Transport and Transformation of Precursors and Secondary Pollutants, Final Report, University of California, 

Riverside, May 2007. 

Once these materials were distributed, UNC scheduled a teleconference call that involved the 

peer reviewers, Sarav Arunachalam of UNC, and Kirk Baker of EPA. The conference call was 

conducted on 9/30/2008 to discuss the charge to the reviewers and establish commonality in the 

peer reviewers’ efforts, and to initiate the peer review. As part of this discussion, the peer 

reviewers identified additional pieces of documentation that might be helpful or relevant for the 

review. Dr. Arunachalam distributed any additional information among the peer review team. 

The peer reviewers were instructed to perform their review according to instructions in the 

“Charge to Reviewers” (Appendix B in this report). It was agreed that contact among them was 

permissible and encouraged. 

After the reviewers provided their individual comments, UNC and EPA reviewed the comments, 

and generated a few additional questions for the reviewers. The reviewers were asked to respond 

to these questions, which were mostly requests for clarification of their original comments. UNC 

then compiled all the comments into a draft report and distributed this via e-mail on August 21, 

2009 to all parties for their review. 

The final version of the report was compiled, reproduced, and submitted to EPA by UNC. This 

compilation included the addition of other information, such as this overview of the process, and 

the résumés of the reviewers (Appendix C). 

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Appendix B: Charge to Reviewers 

1. Model formulation

(a) What are the strengths and weaknesses of CMAQ/PPTM? 

(b) What are the strengths and weaknesses of CAMx/PSAT? 

(c) What are the strengths and weaknesses of CMAQ/OPTM? 

(d) What are the strengths and weaknesses of CAMx/OSAT? (OSAT includes both the 

OSAT and APCA ozone source apportionment approaches) 

(e) Are there any modules or features of CMAQ/PPTM, CMAQ/OPTM, CAMx/PSAT or 

CAMX/OSAT in which an improved formulation or treatment is necessary? If so, please 

discuss what is needed. 

(f) In general, how do the above source apportionment approaches compare with other 

source apportionment methods like TSSA and source sensitivity approaches like DDM 

and zero-out simulations? 

2. Documentation

(a) Is the current documentation for CMAQ/PPTM and CMAQ/OPTM adequate? 

(b) Is the current documentation for CAMx/PSAT and CAMx/OSAT adequate? 

(c) Are the above model formulations sufficiently documented for technical critique and 

understanding? 

(d) Is the discussion and presentation of the above models and their features clear? Please 

note any specific sections of the documentation that were unclear or confusing. 

(e) Is the documentation for each of the above models sufficient to guide a typical user in the 

use of the model and its preprocessors? 

(f) What improvements are desirable for the documentation of each of the above models? 

(g) What changes in organization of the documentation of each of the above models are 

desirable? 

(h) Does the documentation contain useful and appropriate evaluations of source apportionment for ozone and PM2.5 species compared to other approaches? 

3. Evaluation 

(a) In general, how manageable do you consider the task of preprocessing/model setup to run 

source apportionment for CMAQ/PPTM, CMAQ/OPTM, CAMx/OSAT, CAMx/PSAT? 

(b) Are postprocessing tools easily available and easy to use for CMAQ/PPTM, CMAQ/OPTM, 

CAMx/OSAT, CAMx/PSAT? 

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4. Supplemental Questions 

(a) What do you consider the general advantages and disadvantages of zero-out modeling when 

a 100% emissions perturbation is necessary for an application such as source culpability? 

(b) Do you feel that the implementation of mercury tagging in CMAQ/PPTM and CAMx/PSAT 

has any important weaknesses that may need to be addressed or strengths as a tool for 

culpability assessment? 

UNC-EMAQ (2-06)-013 30 August 20, 2009 
“Response to Comments on OPTM and PPTM in Peer Review of Source Apportionment Tools in 

CAMx and CMAQ” 

Received from Sharon Douglas on October 30, 2009 

Contact Information: 

Sharon Douglas 

ICF International 

101 Lucas Valley Road 

Suite 260 

San Rafael, CA 94903 

sdouglas@icfi.com
Response to Comments on OPTM and PPTM in “Peer Review of Source 

Apportionment Tools in CAMx and CMAQ”

The document “Peer Review of Source Apportionment Tools in CAMx and CMAQ” 

critiques the methodologies used in the CAMx model and the CMAQ model for 

assessing contributions to pollutant concentrations. ICF would like to take this 

opportunity to respond to some of the comments on the PPTM and OPTM methods 

implemented in CMAQ. 

The review seems predisposed to favor the CAMx methods over the OPTM/PPTM 

methods in CMAQ, which is perhaps understandable since the former method has been 

available for some time and is more familiar to the modeling community. This is 

especially apparent in the lists of strengths and weakness of the two methods. For 

instance, on p. 9 the reviewers state that “a common weakness of both CAMx/PSAT and 

CMAQ/PPTM is that they cannot capture any indirect effects, such as an increase in 

nitrate concentrations resulting from a reduction in SO2 emissions.” The reviewers 

therefore view this as a common weakness of both methodologies. In the summary of 

weaknesses, this is listed in item “b” on p. 13 as a specific weakness of the PPTM 

implementation. Although common to both methodologies, this is not listed as a specific 

weakness of PSAT. 

Below, we provide responses to some of the criticisms of OPTM/PPTM in CMAQ. 

Reviewer comments are in italics, and our response is in standard type face. 

Responses to Selected Comments on CMAQ/OPTM 

p. 7: (a) Physical process magnitudes for tracers are inconsistent with corresponding 

model species. Other methodologies have solved this problem by utilizing process 

analysis data for component species and aggregating process magnitudes for the 

tagged tracer species. These data already exist in the models.

The approach appears to have been misunderstood. The OPTM methodology is very 

much consistent with the CMAQ processes. While using process analysis may be one 

approach to getting some of the information that is required to track the species through 

the model processes, OPTM does this explicitly for each process. The OPTM approach 

maintains consistency between the overall modeled NOx, VOC, and oxidant with the 

total tagged NOx, VOC and oxidant at each process step. 

p. 7: (b) Calculation of total oxidant (Ox) is incorrect. When two Ox species combine, as 

with NO3 (NO2+O) and N2O5 (NO2+NO3), the species should represent multiple units 

of Ox based on the number of “odd oxygen,” not the nitrogen.

We agree that the reviewer was correct. This issue has subsequently been fixed and 

our tests showed that it had minimal effects on the results. 

p. 7 – 8: (c) Based on the documentation, ratios of initial precursors are used to 

apportion net chemical change of tagged VOCs, NOx, and Ox. There are several false 

assumptions in the code that, if confirmed, should be addressed: 

(1) Change in tagged species is solved algebraically, which inappropriately conserves 

initial tagged species. A differential solution would apply losses to the existing 

1
distribution of tags and to the distribution of chemically produced tags. 

The reviewer is incorrect in his or her assumption of how the tagging is implemented. 

Losses of NOx and VOC are attributed to the precursor tags in each process. That 

includes the gas phase chemistry. 

(2) Change in each region’s NOx and VOCs is proportional to the total local NOx and 

VOCs. This assumes that each region’s NOx and VOC reactivity (or deposition 

velocity) profile is equal to that of the local NOx and VOCs. In plumes or near 

sources, the locally contributed NOx and VOCs are likely to have a different 

reactivity profile. 

Although the OPTM implementation does not make any explicit differentiation between 

VOCs of differing reactivity, the calculation of chemical changes made by the chemical 

mechanism would still respond to any overall changes in VOC reactivity. In addition, the 

assumption that the various tagged sources or categories have roughly similar reactivity 

is reasonably accurate, for typical situations. The current implementation of OPTM 

allows it to handle many tagged sources (either regions or categories of sources) 

simultaneously. The situations where the assumption of similar reactivity could result in 

inaccuracies in the OPTM attribution are unusual, such as tagging of a single source that 

emitted highly reactive compounds. Addition of more detailed tracers to the OPTM 

implementation could improve the accuracy of the representation of these unusual 

cases. The cost would be a dramatic increase in the number of tag species required in 

the model. 

The reasoning of the above response is based consideration of differences in VOC 

reactivity. The question, however, brings up an example of a plume which is more a 

case of low hydrocarbon to NOx ratio than a VOC reactivity issue. This is more the 

province of a plume-in-grid treatment, and, at the resolution of modeling grids currently 

in use, neither the model nor OPTM can represent the very low hydrocarbon to NOx 

ratio found in plumes. 

(3) NOx production is proportional to existing NOx. It assumes that NOx precursors (i.e., 

HNO3, PAN, or NTR) are proportional to local NOx concentrations. These longerlived 

species may be transported and so not reflect local emissions of NOx.

This is similar to the comment regarding reactivity. Additional tags could keep track of 

other species besides NOx and VOC. We’re assuming, however, that for the most part 

NOx and VOC gradually decay via chemistry and deposition, for example. There can be 

some short term production of NOx from species such as PAN, but to keep number of 

tags to a manageable level, we’ve assumed that this production will be small compared 

to fresh emissions. This could be tracked using additional tags, but it would also require 

additions to the code to track the changes during chemistry and additional complexity. 

(4) Total oxidant chemistry is apportioned based on precursor concentration 

distributions. While Ox production is likely to be proportional to precursors (excluding 

situationsnoted in [1] and [3] above), Ox loss reactions should be apportioned based on 

current Ox distribution. 

Actually OPTM does apportion Ox loss based on current Ox distribution. So again this 

appears to be a misunderstanding. Although the changes are not tracked separately for 

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production and destruction terms through the chemistry (again, additional tags and 

complexity could do that), when the net change in oxidant is negative, the change is 

apportioned to the tags according to the proportions of oxidant tags, not according to the 

precursor tags. 

p. 8: (d) Artificial “offset” to Ox potentially alters processes and requires further review of 

code. 

The offset is only included directly in the oxidant tracers for the advection step – and 

really only to overcome a limitation of the advection scheme. Other processes that 

depend directly on the magnitude of the species, such as deposition, are apportioned to 

the tags based on the change in the model species. The same change is therefore 

attributed to the tag regardless of whether there is an offset or not. 

p. 9: (a) Mass conservation of the tagged species is not checked in the formulation. 

For CMAQ/OPTM, mass is allocated to the oxidant, NOx, and VOC tracers at each time 

step and the sums of all the oxidant, NOx, and VOC tracers are required to be the same 

as the overall oxidant, NOx, and VOC concentrations. Therefore, mass consistency and 

consistency of the tracer species and the standard species is ensured. 

p. 9: (b) OPTM does not distinguish between NOx-limited and VOC-limited regimes. 

OPTM also assumes that all VOC species have the same reactivity. Therefore, ozone 

may be attributed to the wrong source. 

See our response above. 

p. 9: (c) Assumption of the fractional change in the total oxidant (sum of NO2, NO3, 

N2O5, and O3) concentration due to dry deposition is questionable. 

Dry deposition is a linear process and the changes in separate species add 

algebraically. 

p. 9: (d) The contribution to O3 by tagged NOx and VOCs is counted separately, which 

is somewhat misleading because O3 is formed through photochemical reactions of both 

NOx and VOCs jointly. 

The fact that ozone is the result of the interaction of VOC and NOx is why OPTM 

attributes ozone formed to both VOC and NOx, not to one or the other as some other 

methods do. 

 p.9: (i) Current OPTM setup requires the code to be recompiled for each new set of 

tags. This approach places an unnecessary burden on model users and increases the 

chances of making mistakes. 

The set_tag program makes the changes automatically and it has been fixed to avoid 

the problems encountered by reviewers during testing. OPTM/PPTM implementation in 

CMAQ followed the existing structure of the code which requires modifying include files 

to add species (even inert tracer species). Although many of these include files are 

actually unnecessary in CMAQ (since the information could be generated within the 

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code), we did not wish to restructure the code which would make it more difficult to move 

the OPTM/PPTM implementation to later versions of CMAQ. 

Responses to Selected Comments on CMAQ/PPTM 

p. 13: (a) Mass conservation of the tagged species is not checked in the formulation. 

In the implementation of CMAQ/PPTM, the overall species are not simulated 

concurrently with the tagged species. The mass consistency of the implementation is 

therefore dependent on the mass consistency of the CMAQ algorithms that handle the 

various processes. A separate CMAQ run without tagging can be compared to a CMAQ 

PPTM run to assess the uncertainties in the simulations due to strictly numerical effects. 

(b) PPTM assumes that all secondary PM is linked to a specific precursor: for example, 

sulfate to SOx, nitrate to NOx, ammonium to NH3, and SOA to VOC emissions. For this 

reason, PPTM cannot capture any indirect effects, such as an increase in nitrate 

concentrations due to a reduction in SO2 emissions. 

This is an issue comment to both methodologies, but is only listed as a weakness for 

PPTM. 

p. 13: (c) Sulfur tagging in aerosol lacks methods to account for individual chemical 

production pathways that are the main causes of nonlinearity in the sulfate production. 

PPTM accounts for the net change in aerosol sulfur due to the several separate 

processes in CMAQ. It assumes, of course, that each of these processes acts on 

aerosol sulfate in proportion to the separate tag concentrations. 

Why is this issue not also listed for PSAT, which takes the overall change in sulfate due 

to each major process and does some apportionment? 

p. 13: (f) Current CMAQ/PPTM design requires the CMAQ source code to be modified 

and recompiled for each set of PPTM simulations. Even though the “set_tag” utility 

program is provided to create the *.EXT files from templates, the PPTM approach not 

only increases the complexity of the source code (multiple versions of executables), it 

also increases the chances of making mistakes. 

See response to OPTM comment “j” above. 

p. 13: (g) Current PPTM approach also requires emissions tracers to be added to CMAQ 

emissions inputs. Again, this intrusive approach to emissions processing requires extra 

work and is prone to processing/QA errors. 

In order to correctly identify emissions by source category or by political boundary, it is 

necessary to prepare tags in the emissions step. A simple grid cell masking technique 

can also be used with PPTM, but that cannot differentiate source categories, nor can it 

correctly identify emissions from different states or counties since grid cells can overlap 

several of these entities. 

(h) PPTM outputs are written into standard CMAQ output files. This would lead to large 

output CMAQ files, increasing the potential for file corruption and access problems. A 

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better approach is to create additional output files specifically for CMAQ/PPTM results. 

Files are large, but, in all but the most extreme cases, no special handling is required 

except the use of an IOAPI version compiled for a larger number of variables. 

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