Proof-of-Concept Evaluation of Use of 

Photochemical Grid Model Source 

Apportionment Techniques for Prevention 

of Significant Deterioration of Air Quality

Analysis Requirements

Bret Anderson, Kirk Baker, Ralph 

Morris, Chris Emery, Andy 

Hawkins, Erik Snyder
Acknowledgements to 

Contributors

• Kirk Baker, USEPA OAQPS

• Ralph Morris and Chris Emery, ENVIRON

• Andy Hawkins, State of Kansas

• Erik Snyder, USEPA Region 6
Outline

• Introduction to the air quality analysis 

requirements under the federal major source 

permitting program

• Discuss current methods for conducting Class I 

air analyses

• Compare current methods to possible future 

methods (Dispersion models v. Photochemical 

models)

• Discuss computational and regulatory barriers to 

implementation
What is PSD?

• The Prevention of Significant Deterioration 

of Air Quality (PSD) program is the federal 

major source permitting program.

– National Ambient Air Quality Standards

– Maximum Allowable Increases (“Increments”)

– Air Quality Related Values (AQRV’s) in 

Federally Protected Class I ares
PSD & Class I Analyses

• PSD rules require that a new major source or 

significant modification to an existing major stationary 

source must undergo an analysis to determine if the 

source or modification would have an adverse 

impact(s) on air quality related values (AQRV’s) 

determined critical by the Federal Land Managers.

• The Federal Land Manager (Federal official charged 

with direct responsibility for management of such 

lands) has an affirmative responsibility to protect the 

AQRV’s (typically visibility and acid deposition) of such 

lands and to consider, in consultation with the EPA 

Administrator, whether a proposed source or 

modification will have an adverse impact on such 

values.
Federal Class I Areas
How Do We Conduct a Class I 

Air Quality Analysis

• Guidance for Class I analyses 

– Interagency Workgroup on Air Quality Modeling Phase 2 

Recommendations (1998)

– Federal Land Managers’ Air Quality Related Values Workgroup 

Phase 1 Report (2000/2010)

– 40 CFR Part 51, Appendix W (“Guideline on Air Quality Models”)

• CALPUFF modeling system used as primary modeling platform for 

visibility and acid deposition analyses required under PSD

• Diagnostic meteorological CALMET used to provide 3D 

meteorological fields to dispersion model

• HNO3/NO3 repartitioning using POSTUTIL

• Visibility post-processing using CALPOST
The World According to the 

Guideline on Air Quality Models

• PSD Regulations require adherence to the 

Guideline on Air Quality Models (“GAQM”)

• GAQM requires use of CALPUFF for air 

quality assessments for source-receptor 

distances > 50-km

• GAQM - 5 years of meteorological data for 

near-field (e.g. < 50-km) or 3 years of 

prognostic data for Class I impact analysis 

(typically distances > 50-km) 
Lessons Learned from the 

Regional Haze Process

• Over the past several years, numerous technical issues 

have been raised concerning the capability of the 

CALPUFF/CALMET modeling system to properly 

estimate Prevention of Significant Deterioration (PSD) 

increments. 

– Numerous issues with adequacy of CALMET meteorological 

fields have been documented by the EPA since 2007.

– Secondary particulate matter (PM; i.e., sulfate and nitrate) is the 

most important component of air quality related values (AQRVs), 

and CALPUFF uses a simplified chemical transformation 

algorithm that was developed over two decades ago. 

– Concern exists about the over prediction of impacts, which has 

led to proposed changes in visibility post-processing to 

compensate. 
Issues with CALMET 

Meteorology

Collapsing CBL’s Objective Analysis Errors
Issues with CALPUFF 

Chemistry
Need for a New Modeling 

Paradigm?

• The current modeling paradigm for both Class I increment analyses and 

AQRV analyses remains largely consistent with the original IWAQM Phase 

2 recommendations dating to the mid 1990’s. The Phase 2 

recommendations are summarized as follows:

– Use of the CALPUFF modeling system

– Use of fully developed time and space varying characterization of the meteorology using CALMET

– Placement of receptors within Class I areas of concern

– Background concentrations of ozone and ammonia are allowed to vary in time and space

– The concentration and AQRV impacts are computed to more directly correspond to the standards, 

increments, and thresholds of concern.

• IWAQM (1992) envisioned a third phase to add more advanced modeling 

techniques representing the likely emergence of photochemical grid models 

(PGM’s) for routine. However, additional resources were not forthcoming 

and the Phase 3 recommendations were ultimately never pursued. 

• IWAQM reactivated in 2009 to examine current modeling paradigm and 

determine if Phase 3 goals of use of PGM’s were achievable within the PSD 

regulatory framework. 
Common Concerns Regarding Use of 

PGM’s for Single Source Analyses

• PGM’s can only resolve the dynamics and chemistry of a 

point source plume to the grid resolution specified and 

use of a high resolution grid (e.g., 100 m to 1 km) to 

resolve a plume would require extensive model inputs 

and model run times.

• To assess the impacts of a source two runs have to be 

performed, a base case with the source and a zeroout

case where the emissions of the source are eliminated. 

– As PGM runs are already more costly than a source-oriented 

plume model like CALPUFF, the need to do multiple zero-out 

runs to assess the individual impacts of multiple sources is quite 

costly.
Advances in Source 

Apportionment

• PM Source Apportionment Technology (PSAT) that 

allows the separate tracking of individual source PM 

impacts so that the individual impacts from many 

different sources can be obtained cost-effectively in one 

run; ozone can also be tracked for single sources by 

dumping reactive tracers for ozone.

• Threading of the PSAT PM source apportionment 

through the full-chemistry PiG module so that the early 

plume chemistry and plume dynamics can be tracked by 

the subgrid-scale PiG module until the plume size is 

commensurate with the grid resolution when the plume 

can be adequately simulated by the grid model.
• In FY 2008, the USEPA initiated a program to reevaluate 

CALPUFF due to the technical concerns that had come to 

light during the BART process.

• In FY 2009, the USEPA/ENVIRON developed software 

platform to couple CALPUFF directly to MM5/WRF, 

eliminating need for CALMET.

• In FY 2010, the USEPA initiated a second program with the 

UNC/ENVIRON team to demonstrate the use of a PGM with 

source apportionment techniques for estimating the ozone 

and AQRV impacts due to a single source or group of sources 

and compare the results with CALPUFF for existing and 

hypothetical test sources using procedures like those that 

would be used in a far-field analysis for PSD.

EPA/FLM Long Range Transport 

Modeling Project
Example Visibility Analysis

• CALPUFF Version 5.754

– MESOPUFF II Chemistry 

• CALMET Version 5.53a 

– CALMET configured to 

best preserve original 

prognostic met 

• Initialized with CENRAP 2002 

36-km MM5

• Prognostic upper air 

(NOOBS=1)

• Prognostic precipitation 

(NPSTA=-1)

• CAMx Version 4.41

– CENRAP Base02f 

Inventory

– CENRAP 36-km MM5 

Meteorology

– Probing Tool: Particulate 

Source Apportionment 

(PSAT)

– Plume-in-Grid: GREASD 

PiG

– Flexinest: Optional 12 km 

imposed on modeling grid 

to limit initial dispersion of 

PiG puffs
Modeling Domains

CALPUFF Domain: 

243 x 195 x 10 (6-km)

CAMx Domain: 

148 x 112 x 19 (36-km)
CALPUFF Visibility Impacts

0

2

4

6

8

10

12

14

16

18

19

172

5

3

3

414

9

5

7

6

5

7

3

818

9

9

7

10

5

11312112

9

13

7

14

5

15

3

16116

9

17

7

18

5

19

3

2

01

2

0

9

2172

2

5

2

3

3

2

41

2

4

9

2

5

7

2

6

5

2

7

3

2

81

2

8

9

2

9

7

3

0

5

3133

21

3

2

9

3

3

7

3

4

5

3

5

3

3

61

Lig

ht E

xtin

ctio

n (M

m-1)

Predicted CALPUFF Impact at Badlands (2002)

bFM

bCM

bEC

bOC

bNO3

bSO4
CAMx Visibility Impacts 

0

2

4

6

8

10

12

14

16

18

19

172

5

3

3

414

9

5

7

6

5

7

3

818

9

9

7

10

5

11312112

9

13

7

14

5

15

3

16116

9

17

7

18

5

19

3

2

01

2

0

9

2172

2

5

2

3

3

2

41

2

4

9

2

5

7

2

6

5

2

7

3

2

81

2

8

9

2

9

7

3

0

5

3133

21

3

2

9

3

3

7

3

4

5

3

5

3

3

61

Lig

ht E

xtin

ctio

n (M

m-1)

Predicted CAMx Impact for Badlands (2002)

bFM

bCM

bEC

bOC

bNO3

bSO4
CALPUFF v. CAMx

0

1

2

3

4

5

6

7

8

1

10192

8

3

7

4

6

5

5

6

4

7

3

8

2

91

10

0

10

9

11812

7

13

6

14

5

15

4

16

3

17

2

18119

0

19

9

2

0

8

2172

2

6

2

3

5

2

4

4

2

5

3

2

6

2

2

71

2

8

0

2

8

9

2

9

8

3

0

7

3163

2

5

3

3

4

3

4

3

3

5

2

3

61

Visibility [d-d

v]

Predicted Visibility Impairment in Badlands National Park 

(2002)

CALPUFF

CAMx
Computational Comparison 

(2007)

• Annual CALPUFF run with no 

spinup

• Each annual run on single 

compute node

– 2 x 2.8 GHz XEON CPUs, 2 GB RAM

• Single source, entire annual 

simulation can be completed in 

less than 6-8 hours

• Annual CAMx run with 15 days of 

spinup per calendar quarter.

• Each calendar quarter run on a 

separate compute node (105 

days).

– 2 x 2.8 GHz XEON CPUs, 2 GB RAM

• With 7 source regions (single 

sources) and 2 source groups 

(point and area), average of 3 

hours per simulation day or 8 

simulation days per CPU day.

• 105/8 = 13.125 days for calendar 

quarter to complete.

• Run sequentially, it would require 

47.5 days to complete.
Barriers to Implementation -

Computational

• The modeling platforms for the permit modeling 

community are largely Microsoft Windows based 

and are engineered for serial applications of 

models. 

• The meteorological and photochemical modeling 

community is largely Unix/Linux based.

• Time necessary for annual PGM simulations 

compared to simplified CALPUFF chemistry.
Computational Considerations

• Adaptation of the PGM platforms to operate in a 

Windows based environment?

– The permit modeling community typically does not have the 

same level of fluency in either the Unix/Linux operating system 

or Fortran based programming skills that are essential skill 

requisites

– IT authorities within State and Local permitting agencies often 

lack the familiarity and/or resources to dedicate to systems 

administration and security for Unix/Linux based systems, and 

thus actively prevent the acquisition of such equipment or, if 

such hardware is acquired, prevent the presence of such 

equipment on the State’s internal network. 
Barriers to Implementation -

Regulatory

• The operational construct for the permit modeling 

community is highly rigid

– Based upon a series of regulations and guidelines which 

restrict operational flexibility in order to promote more 

general consistency in the application of models. 

• The operational construct of the meteorological and 

photochemical modeling communities is vastly 

different, 

– Based upon a more loosely binding set of EPA 

recommendations which typically encourage adapting both 

science and modeling techniques to produce the most 

scientifically feasible answer given the constraints of the 

state-of-the-science.
Regulatory Considerations

• The differences in the operational paradigms between the two 

communities will require both the EPA and the FLM’s to 

develop a more rigid set of operational procedures similar to 

the current permit modeling paradigm in order to insure both a 

scientifically sound and consistent set of procedures to 

prevent an ‘anything goes’ process as would likely develop 

without such procedures.

• Length of meteorological record for PGM’s will likely have to 

be expanded to be consistent with requirements of GAQM 

(e.g. 3 years of prognostic data).

• Development of significance thresholds for single source 

(cause or contribute test) required for NAAQS 

demonstrations.
Conclusion

• PGM’s capable of assessing single source impacts for 

both AQRV and ozone requirements under PSD.

• Source apportionment eliminates need for multiple “zeroout” runs

• Significant barriers remain to implementation of PGM’s 

– Increased computational requirements

– Increased training requirements for permit modeling 

staff 

– Creation of a hybrid regulatory and guidance 

framework for implementation of PGM’s within a 

regulatory permit modeling paradigm which is highly 

rigid and prescriptive