# Proof

**- of** 

**- Concept**  **Evaluation of Use**  **of**

**Photochemical Grid Model**  **Source Apportionment**  **Techniques for Prevention**  **of Significant Deterioration**  **of Air Quality** 

**Analy sis 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 ex isting 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 Analy**  **sis 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 w ith the original IWAQM Phase
2 recommendations dating to the mid 1990’s. The Phase 2
recommendations are summarized as follow s:

– 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. How ever, additional resources w ere not forthcoming
and the Phase 3 recommendations we re ultimately never pursued.

• IWAQM reactivated in 2009 to examine current modeling paradigm and determine if Phase 3 goals of use of PGM’s w ere achievable wi thin the PSD regulatory framew ork.

# 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 18191725334149576573818997105113121129137145153161169177185193201209217225233241249257265273281289297305313321329337345353361**L i** **g h** **t E** **x t** **i n** **c t** **i o** **n (** **M m** **- 1** **) Predicted CA**  **LPUFF Impact at Badlands (2002)** 

bFM

bCM

bEC

bOC

bNO3

bSO4

# CAMx Visibility Impacts

0 2 4 6 8 10 12 14 16 18191725334149576573818997105113121129137145153161169177185193201209217225233241249257265273281289297305313321329337345353361**L i** **g h** **t E** **x t** **i n** **c t** **i o** **n (** **M m** **- 1** **) Predicted CA**  **Mx Impact for Badlands (2002)** 

bFM

bCM

bEC

bOC

bNO3

bSO4

# CALPUFF v. CAMx

0 1 2 3 4 5 6 7 8110192837465564738291100109118127136145154163172181190199208217226235244253262271280289298307316325334343352361**V i** **s i** **b i** **l i** **t y** **[ d** **- d** **v ]**  **Predicted V isibility**  **Impairment in Badlands National Park**  **(2002)**

CALPUFF

CAMx

# Computational Comparison

(2007)

• Annual CALPUFF run wi th 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 w ith 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 w ould 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

– B ased 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 “zero

out” 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