Support for Meetings of the Science Technology Workgroup

Under FACA Subcommittee

FINAL

REVIEW OF SOURCE ATTRIBUTION

METHODOLOGIES

January 30, 1998

Submitted to:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

Submitted by:

Science Applications International Corporation

615 Oberlin Road, Suite 300

Raleigh, NC 27605

EPA Contract No. 68-D3-0030, WA No. IV-1

SAIC Project Number 01-1320-08-3501-007

TABLE OF CONTENTS

EXECUTIVE SUMMARY

1. INTRODUCTION	1 - 1

2. METHOD SUMMARIES	2 - 1

2.1	"An Easily Applied Observation-Driven Methodology for

Determining if the Formation of Secondary Aerosol is Limited by

Ammonia" by Charles Blanchard, Philip Roth, and Shelley

Tanenbaum	2 - 1

2.2	"The Method of Photochemical Indicators as a Basis for Analyzing

O3-NOx-ROG Sensitivity" by Sanford Sillman	2 - 1

2.3	"Ozone as a Function of Local Wind Direction and Wind Speed:

Evidence of Local and Regional Transport" by Rudolf Husar and

Wandrille Renard	2 - 2

2.4	"Estimating Emission Trading Weights for the Precursors of Ozone"

by Gary Dorris and Timothy Mount	2 - 2

2.5	"Source Identification Using Meteorological and Statistical Modeling"

by Stephen Masters	2 - 3

2.6	"Regional Simulation of Atmospheric Pollutants with the CAPITA

Monte Carlo Model" by Bret Schichtel and Rudolf Husar	2 - 3

3. ANSWERS TO THE EPA QUESTIONS	3 - 1

3.1	Method Review and Demonstration	3 - 1

3.2	Method Implementation Requirements	3 - 10

3.3	Potential Applications	3 - 13

4. REVIEWER RESPONSES TO THE INDIVIDUAL METHODS	4 - 1

4.1	"An Easily Applied Observation-Driven Methodology for Determining

if the Formation of Secondary Aerosol is Limited by Ammonia" by

Charles Blanchard, Philip Roth, and Shelley Tanenbaum	4 - 1

4.2	"The Method of Photochemical Indicators as a Basis for Analyzing

O3-NOx-ROG Sensitivity" by Sanford Sillman	4 - 6

4.3	"Ozone as a Function of Local Wind Direction and Wind Speed:

Evidence of Local and Regional Transport" by Rudolf Husar and

Wandrille Renard	4 - 15

4.4	"Estimating Emission Trading Weights for the Precursors of Ozone"

by Gary Dorris and Timothy Mount	4 - 26

4.5	"Source Identification Using Meteorological and Statistical Modeling"

by Stephen Masters	4 - 36

4.6	"Regional Simulation of Atmospheric Pollutants with the CAPITA

Monte Carlo Model" by Bret Schichtel and Rudolf Husar	4 - 46

ABBREVIATIONS AND ACRONYMS

EXECUTIVE SUMMARY

In July 1997 a three-day Source Attribution Workshop was jointly sponsored by the U.S. Environmental Protection Agency and the Science and Technical Support Work Group of the FACA Subcommittee for Ozone, Particulate Matter and Regional Haze Implementation Programs. The primary goal of the workshop was to examine various models for use in assessing the applicability of source attribution techniques for the non- linear pollutants ozone and fine particulate matter. During the workshop, ten source attribution methods were reviewed on their scientific basis and evaluated based on their potential uses under the new ozone and PM2.5 National Ambient Air Quality Standards.

Additional source attribution methods were proposed but could not be reviewed during the three-day workshop. From these techniques, EPA identified the following six additional source attribution methodologies which may be suitable for weight-of-evidence source attributions:

"An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia" by Charles Blanchard, Philip Roth, and Shelley Tanenbaum

"The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity" by Sanford Sillman

"Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport" by Rudolf Husar and Wandrille Renard

"Estimating Emission Trading Weights for the Precursors of Ozone" by Gary Dorris and Timothy Mount

"Source Identification Using Meteorological and Statistical Modeling" by Stephen Masters

"Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model" by Bret Schichtel and Rudolf Husar

Eighteen peer reviewers were identified and were each asked to review one methodology demonstration and assess the implementation requirements and potential applications. The reviewers were asked to respond to questions about the method's scientific basis, its strengths and weaknesses, past demonstrations, suggested additional tests and demonstrations, the required expertise for implementation, the feasibility of implementation in the next few years, and the method's potential uses in policy applications.

Brief summaries of the six methodologies appear in Chapter 2. Chapter 3 presents a brief synopsis of reviewer responses to each question in a tabular format. This tabular design allows readers to compare reviewer responses side-by-side and decide where general agreement was reached on each question. The design also allows readers to contrast responses to individual questions for the different methods. Chapter 4 presents the full method assessment by each peer reviewer. The full method reviews give insights on the current state of the science, suggest alternate methods, and point out minor deficiencies in the techniques.

1. INTRODUCTION

In July 1997 a three-day workshop was held to examine various models for use in assessing the applicability of source attribution techniques for non-linear pollutants (i.e., ozone and its precursors) and secondary particulate matter (and its precursors) for estimating initial Areas of Influence (AOIs), and for complementing uses of nested grid models to select and/or evaluate control strategies.

The workshop was designed to inform the Environmental Protection Agency (EPA) and the Science and Technical Support Work Group about "regions of influence" for control strategy development and about the degree to which the regions for the different pollutants overlap. It was also designed to inform the policy and scientific communities about the processes that must be represented in simulation models if they are to produce reliable estimates of transport and transformation (e.g., the need to include "wet" processes) and about the type and density of monitoring needed to infer transport. Furthermore, the workshop was designed to inform EPA and other participants about modeling methodologies for use as tools for integrating implementation strategies that comport with the new National Ambient Air Quality Standards (NAAQS) for ozone and particulate matter and the regional haze rule.

A peer review panel reviewed ten methods presented at the workshop based on the strengths and weaknesses of the respective tools/techniques on criteria such as estimating the temporal and spatial scales of "transport," determining the degree of atmospheric mixing, examining climatological and meteorological trends, and conceptualization of integrated implementation mechanisms for ozone, particulate matter, and regional haze.

EPA identified six additional source attribution methodologies which were submitted to the workshop and may be suitable for use in weight-of-evidence determinations. These methodologies are listed in Table 1-1 and were not reviewed during the workshop due to limited available resources.

A review committee consisting of eighteen knowledgeable experts were formed to review the six methodologies. Table 1-2 presents a list of the reviewers and their affiliations. Each reviewer received one method for evaluation. The reviewers were asked to respond to a series of seven questions designed to determine the extent to which the method has been adequately demonstrated, to assess the user needs for implementing the methodology, and to list potential applications of the method in terms of source attribution. The results of their reviews are presented in the following chapters.

TABLE 1-1. SOURCE ATTRIBUTION METHODOLOGIES

1.	"An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia" by Charles Blanchard, Philip Roth, and Shelley Tanenbaum

2.	"The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity" by Sanford Sillman

3.	"Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport" by Rudolf Husar and Wandrille Renard

4.	"Estimating Emission Trading Weights for the Precursors of Ozone" by Gary Dorris and Timothy Mount

5.	"Source Identification Using Meteorological and Statistical Modeling" by Stephen Masters

6.	"Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model" by Bret Schichtel and Rudolf Husar

TABLE 1-2. LIST OF REVIEWERS

Jeff Arnold	National Oceanic and Atmospheric Administation

Allen Basala	U.S. EPA Office of Air Quality Planning and Standards

Frank Binkowski	U.S. EPA National Exposure Research

Ellis Cowling	Southern Oxidants Study

Alice Gilliland	Science Applications International Corporation

D. Alan Hansen	Electric Power Research Institute

Dana Hartley	Georgia Institute of Technology

Jim Meagher and	Tennessee Valley Authority

Robert Imhoff

Brock Nicholson	North Carolina DEHNR

and Jay Lawrimore

S.T. Rao	New York Air Resources Board

Ted Russell	Georgia Institute of Technology

Perry Samson	University of Michigan

Mark Scruggs	U.S. Department of the Interior, National Park Service

Fred Vukovich	Science Applications International Corporation

Jeffrey West	GPU/GENCO

Steve Ziman	Chevron Corporation

2. METHOD SUMMARIES

2.1 "An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia" by Charles Blanchard, Philip Roth, and Shelley Tanenbaum

This observation-driven method qualitatively evaluates the responsiveness of aerosol nitrate particulate to nitrogen oxide (NOx) reductions. The method was derived based on simulations carried out using a thermodynamic equilibrium model and is consistent with the current understanding of equilibrium gas-aerosol relationships. Application of the method does not require the use of an emissions-driven model but instead requires measurements of ambient temperature, humidity, and gas- and aerosol-phase species concentrations. Example calculations are presented using data from three California field studies.

The method considers one of two sets of measurements to be indicators of the expected qualitative response of particulate nitrate to changes in either sulfate or total nitrate levels (total nitrate is the sum of gas-phase and aerosol nitrate, which are both products of the oxidation of nitrogen oxides). The first indicator includes the excess aerosol ammonium plus gas-phase ammonia divided by the sum of (gas-phase) nitric acid, particulate nitrate, and particulate sulfate. A system with an excess indicator is considered to be ammonia rich; a system with a deficit is ammonia limited. In an ammonia-limited system, particulate nitrate will not decrease in response to a reduction in total nitrate and will increase with a sulfate reduction. The second indicator is the ratio of particulate matter to total nitrate. When the ratio of particulate nitrate to total nitrate exceeds about 0.9, particulate nitrate decreases in response to a reduction in total nitrate and does not change with a sulfate reduction. When the ratio is less than a temperature-dependent value ranging from 0.6 to 0.9, a system is ammonia limited. In this case, particulate nitrate does not decrease in response to a reduction in total nitrate but does increase with a sulfate reduction.

2.2 "The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-
ROG Sensitivity" by Sanford Sillman

Currently the only way to determine whether the ozone levels in an area or region are more sensitive to changes in NOx or Reactive Organic Gases (ROG) is to run a model with the estimated emissions of these compounds and test what happens if NOx or ROG is reduced by a known amount. This procedure is time consuming and requires several iterations of a model. The method presented by Dr. Sillman suggests that a set of ambient measurements can be made to determine whether a region would be more responsive to NOx reductions or ROG reductions.

The method suggests that the ratio of measured ozone to total reactive nitrogen (O3/NOy) can be used as an indicator. The ratio is directly related to whether a region is more sensitive to NOx or ROG controls. However, to determine which values of the ratio correspond to NOx sensitivity and which to ROG sensitivity, a series of model runs must first be made.

Several other potential measurable ratios are also suggested in the method. These indicator ratios include O3/NOz, O3/HNO3, H2O2/HNO3, H2O2,/NOz, H2O2/NOy, peroxides/HNO3, peroxides/NOz and peroxides/NOy. However, many of these indicators are more sensitive to model assumptions than the O3/NOy ratio and more difficult to measure.

2.3 "Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport" by Rudolf Husar and Wandrille Renard

This method explores the connection between meteorological and ozone data sets collected from 1988 to 1995 in metropolitan areas within the OTAG region. The method's goal is to demonstrate if ozone observations should be attributed to local sources or regional ones transported from further away.

Under this method, the daily maximum ozone concentration (1-hour average) is placed in one of 32 meteorological bins (based on four wind speed ranges in eight compass directions). The average maximum ozone concentration is plotted versus the wind speed for each of the cities. A high negative slope in this plot is interpreted as indicating a strong contribution from local ozone production, and a low negative slope indicates a strong contribution from regional ozone sources. The differences between the slopes for different direction bins are also interpreted as suggesting the directionality of regional sources.

2.4 "Estimating Emission Trading Weights for the Precursors of Ozone" by Gary Dorris and Timothy Mount

This method constructs statistical representations of episodic photochemical models of ozone formation to estimate trading weights for NOx and volatile organic compounds (VOC). Different input configurations are chosen for the photochemical model (such as the Regional Oxidant Model or the Urban Airshed Model), and the associated output values are determined. Regression techniques are then used to fit response surfaces and generate statistical representations of the model's output.

The statistical representation is referred to as the multivariate ozone response surface (MORS). The MORS approach simplifies the relationships between input and output thereby making these relationships easier to understand. Emission trading weights are then determined for NOx and VOC reductions based on the expected decreases in ozone concentration associated with emission reductions in different regions.

2.5 "Source Identification Using Meteorological and Statistical Modeling" by Stephen Masters

This method proposes an approach to estimating the location, time, and size of a release through the combination of trajectory modeling (a meteorological technique) and regression analysis (a statistical technique). The method was developed to identify potential source regions for non-reactive pollutants.

The model performs a simulation on inert chemicals, where virtual sources are included to later help identify the correct source. The model uses prescribed meteorological data to estimate the transport and dispersion of the chemical. The statistical portion of the method then uses a linear regression to estimate the release rate from each virtual source, using the observed and modeled concentrations of the inert chemical at the sampling sites. A correlation coefficient is also calculated from the regression. Based on the release rate and the correlation coefficient, the method identifies an area which is the most likely source of the chemical.

In the paper, the method is demonstrated by using non-reactive tracer data available from the Across North America Tracer Experiment (ANATEX) and the trajectory model, the Global Atmospheric Multi-layer Transport Model (GAMUT). The trajectory model was run to simulate a release of a fixed amount of material over a specified time period for a set of virtual sources. Comparison of the predicted concentrations to the concentrations at the two actual release sites, using a subset of the ANATEX sampler concentration data, was carried out with the statistical technique. The resultant output allowed correlations to be developed between the multiple virtual sources, and the most likely release point was identified. Comparisons between the identified release points and the actual source locations gives an indication of how well the method works.

2.6 "Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model" by Bret Schichtel and Rudolf Husar

This method presents an extension of the Washington University CAPITA Monte Carlo model for the simulation of regional scale transport, transformation, and dry and wet removal. The method is based on following the trajectories of pollutant puffs whose paths are defined by a time-averaged velocity and a random component. The random component is developed based on an effective eddy diffusion coefficient. The method is designed in a modular framework which separates the emissions, transport, and kinetics calculations.

The transport module employs a quantized Monte Carlo technique for the simulation of atmospheric boundary-layer physics. Kinetic processes are simulated using rate equations where the rate coefficients are dependent upon meteorological variables, and consequently fluctuate in space and time. The rate coefficient equations are determined via a tuning process comparing simulated to observed measurements.

3. ANSWERS TO THE EPA QUESTIONS

For the review of the methodologies, the EPA formulated a set of seven multi-part questions to be answered by the reviewers. The answers to these questions have been divided into three sections dealing with the method review and demonstration, the implementation requirements, and the potential applications. Brief answers to each of the questions are given in this chapter,1 and each reviewer's detailed analysis is presented in Chapter 4.

3.1 Method Review and Demonstration

This section answers the questions about the scientific validity of the methods, their strengths and weaknesses, and which additional analyses should be done to validate the methods. The following four questions are answered:

1)	Does the method seem scientifically plausible?

2)	What are the method's key strengths and weaknesses?

3)	Has the method been adequately reviewed and demonstrated?

4)	What, if any, additional reviews/tests should be done?

Question 1: Does the method seem scientifically plausible?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Yes, and it has been peer- reviewed	Yes, but perhaps not for general applications
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	Yes, in terms of defining ranges and explaining how this relates to the chemistry	Yes, as long as meteorological conditions and chemical concentrations do not vary greatly with time	Yes, but not as a source attribution method
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	Not for source attribution	Not as it is proposed	Yes, but the method relies on some assumptions
Estimating Emission Trading Weights for the Precursors of Ozone	Yes, but the demonstration was weak	Yes, but the weaknesses must be recognized	Use of statistical methods appears plausible
Source Identification Using Meteorological and Statistical Modeling	Yes, but only for non-reactive pollutants	Yes, but only as a screening tool	Yes, but only for single source and no reactivity
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	Yes, as long as limitations are accepted	Weak scientific foundation

Question 2a: What are the method's key strengths?
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-
Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	The method's simplicity	A) Estimates atmospheric response to emissions changes

B) Objective, straightforward method based on ambient measurements
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	A) Represents an empirical check on modeled results

B) Could suggest NOx or VOC sensitivity	A) Avoids uncertainties in emissions inventories

B) Relies on actual measurements instead of modeled results	Could allow local agencies to determine the NOx and ROG sensitivity from measurements
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	Relies on data and not model results	A) Simple to apply with limited input data and computer resources

B) Limited training necessary	A) Simple to apply

B) Observation-based

C) Relatively easy to implement once data are screened
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	A) Selectively determines emission control effectiveness

B) Calculates individual control costs

C) Reduces necessary number of model runs

D) Weighs emission controls to high ozone/population areas

E) Accounts for the distance of sources	A) Identifies the bounds of the science, data, and applications

B) Captures heterogeneity of emissions/air quality relationships across pollutants, geographical areas, and release heights
Source Identification Using Meteorological and Statistical Modeling	Uses routinely available meteorological data	A) Simple model

B) Modest data needs	A) Observed concentrations produce more accurate source estimates

B) Grid resolution reasonable

C) Good start to be further developed for specific needs
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	Minimal data and computational requirements	A) Quick depiction of regional transport phenomena

B) Treats chemistry and transport separately

Question 2b: What are the method's key weaknesses?

1.	An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia

Reviewer 1:

A)	The method requires measurements of ammonia gas, nitric acid vapor, sulfate, nitrate, and ammonium particulate.

Reviewer 2:

A)	The technique has only been evaluated for Southern California.

B)	Only qualitative information is provided.

C)	The technique is not intended to address individual sources.

2.	The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity

Reviewer 1:

A)	There is a lot of scatter, even in the simulated data, which may be important in middle ranges.

B)	The method is most reliable under conditions where it is already known the conditions are NOx or VOC sensitive.

C)	The method requires model results to determine the appropriate ratios to evaluate and is therefore not completely empirical.

D)	This method places too much confidence in the photochemical grid model accuracy.

E)	It is very difficult to verify that VOC/NOx sensitivities inferred from simulated ratios actually correspond to the same ratios in measured values.

F)	The author states that peroxide-based indicators change with updated chemistry and are therefore not insensitive to model assumptions.

Reviewer 2:

A)	The method depends critically on the accuracy and precision of each measurement used as the basis for the indicator ratios used.

Reviewer 3:

A)	The sensitivity to NOx and ROG will be based on a measurement's calibration to a certain model.

Question 2b: What are the method's key weaknesses?

3.	Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport

Reviewer 1:

A)	Instantaneous wind directions and speeds were used in the analysis instead of spatially and temporally persistent wind directions and speeds.

B)	The effects of vertical mixing were either ignored or not discussed.

C)	The statistics are not rigorous.

D)	Surface winds were used instead of mixed layer winds.

Reviewer 2:

A)	The method does not account for the covariance of variables.

B)	Variance in average ozone concentrations may not relate to exceedances of an ozone standard concentration.

C)	The use of noon NWS 3m wind observations to characterize the transport speed and direction for 24 hours is not valid.

D)	Average ozone concentrations may not vary with wind direction at low wind speeds due to the high variability of winds.

E)	The reviewer questioned the processing of the ozone data with respect to the characterization of the sites and the quality control procedures.

F)	The fundamental basis for using the slope of the average ozone concentration versus wind speed to determine local versus regional ozone was not made clear in the document.

G)	The results disagree with more detailed, explicit methods.

H)	The definition of regional ozone as originating 50 to 100 kilometers away seems arbitrary.

I)	If a large degree of variability exists within a bin, the reliance on an average is misguided.

Reviewer 3:

A)	The method fails to acknowledge meteorological influences (e.g., temperature, cloud cover, and precipitation) other than wind direction.

4.	Estimating Emission Trading Weights for the Precursors of Ozone

Reviewer 1:

A)	The method is entirely dependent on photochemical models.

B)	The conclusions are often based on ozone changes of less than 5 ppb which the reviewer believes is less than the method's accuracy.

C)	The large cost savings proposed are highly questionable since controls on distant facilities take longer to achieve.

Reviewer 2:

A)	Since the method uses a priori information on control costs, some useful controls may not be considered.

B)	The method neglects controls outside of the policy space.

C)	The method only considers benefits in the receptor area.

Question 2b: What are the method's key weaknesses?

D)	The method relies on the statistical model to accurately represent the response of ozone within the photochemical model.

E)	The impact of the controls will be misrepresented if the response of ozone to control is not linear.

Reviewer 3:

A)	The method does not account for the relationship between stochastic emissions and air quality since emissions are a function of meteorological conditions.

B)	The research captures population residence density, but not the population exposure that it claims.

C)	The method does not differentiate tons by location, elevation, or species.

D)	The "reduced form" approach of the method masks factors which are perceived to be unimportant.

E)	The assumption of homogeneity in the transportation sector is erroneous.

F)	Some emissions inventory assumptions are erroneous.

G)	The transformation from emissions control costs to ozone reduction costs depends on the season, and the method needs to be further clarified.

H)	Ozone scavenging by NOx should be considered.

I)	Other methods simulate and then calibrate. This process removes the variability and precludes the probability statements regarding simulations.

5.	Source Identification Using Meteorological and Statistical Modeling

Reviewer 1:

A)	Problems with trajectory models include the lack of spatial and temporal resolutions and the number of simplifying assumptions.

B)	The model cannot be useful for tracing the path of air parcels under accelerating and decelerating flows associated with low pressure systems.

C)	The accuracy of a single trajectory or trajectories for a few days is very limited.

Reviewer 2:

A)	Tracer data may not be available to develop regression equations for ozone and PM in the area of concern.

B)	Without a dense network, it is not clear that the network adequately describes transport and not just plume centerlines.

C)	The ability of trajectory models to represent flow over long distances has been questioned, and this concern is not removed from the proposed methodology.

D)	Vertical mixing is predicated on twelve-hour mixing depths, and the winds have been generated on a minimal network.

Reviewer 3:

A)	Error analysis appears to be lacking in the regression.

B)	The method cannot be applied to multiple sources.

C)	This method cannot handle reactive pollutants.

D)	The accuracy of the regression is sensitive to the sampling density, and the results could be given too much weight when there are too few samples.

Question 2b: What are the method's key weaknesses?

E)	The modeled results are not tested for validity.

F)	The meteorology is simplified.

Reviewer 1:

A)	The technique is only valid for linear models.

B)	The fluctuating velocity component should include some measure of uncertainty.

C)	The tuning of the rate coefficients introduces limitations, errors, and uncertainties to the method.

D)	Uncertainties and errors are introduced by the meteorological model used to develop the input wind fields.

Reviewer 2:

A)	This method specifies an artificial probability distribution for meteorological conditions which may not reflect the conditions of the region.

B)	The pollutant transport and emissions rates must be known with a high degree of certainty, and this may not be possible for NOx and VOCs.

C)	The relationship between transport and chemistry was presented for SO2, but the relationship for ozone, NOx, and VOCs may be different since the atmospheric lifetimes are different.

Question 3: Has the method been adequately reviewed and demonstrated?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Applications to California data are convincing	Not outside Southern California
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	No, because it has not been shown to be generally applicable to an airshed	Yes, but its effectiveness in real-world ozone management processes has not been demonstrated.	Not until several additional issues are addressed regarding measurements and placement
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	No	Not reviewed or demonstrated	No, and it makes questionable assumptions about transported pollutants
Estimating Emission Trading Weights for the Precursors of Ozone	No, since its conclusions are based on small changes in O3 concentration	No, and it should be reviewed by qualified statisticians.	No, but it should be given the same consideration as other models
Source Identification Using Meteorological and Statistical Modeling	No, and eight questions posed the need for further tests	No, further tests need to be done	No, it must first be modified to handle multiple sources and reactive pollutants
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	No, other domains and the effects of errors in the tuning should be investigated	No, the method and its review have not been adequately documented in the paper

Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Test in the Eastern U.S.	A) Test other areas of the country with high negative excess NH3

B) Examine sensitivity by running for other seasons

C) Test the equilibrium constraint
The Method of Photochemical Indicators as a Basis for Analyzing O3-
NOx-ROG Sensitivity	A) Compare with smog chamber tests

B) Set different reduction goals

C) Examine effect of nonuniform emission reductions	This method should be considered as EPA rethinks ozone management particles	A) Ensure that NOy is adequately measured

B) Assess model chemistry

C) Develop quantifiable error bars
Ozone as a Function of Local Wind Direction and Wind Speed:

Evidence of Local and Regional Transport	Use spatially and temporally persistent mixed layer winds	A) Look at covariance effect on the fundamental assumptions

B) Compare use of surface and mixed layer winds

C) Compare with field studies and photochemical grid models	A) Stratify the days by wind and temperature

B) Eliminate days with O3 concentrations not relevant to human health issues
Estimating Emission Trading Weights for the Precursors of Ozone	None suggested	Verify the statistical model against a matrix of photochemical runs	A) Validate data inputs and computer code

B) Analyze sensitivity at the method's "switch points"
Source Identification Using Meteorological and Statistical Modeling	A) Must be applied to all available ANATEX data to judge success

B) Currently only considers passive tracers	A) Compare trajectories with those of MM-5

B) Compare with other areas of the country

C) Paper provides a list of suggestions for future work	A) Modify to handle multiple sources and reactive pollutants

B) Test uniqueness and perform an error analysis

C) Determine minimum data needs

D) Compare with other scenarios
Regional Simulation of Atmospheric Pollutants with the CAPITA

Monte Carlo Model	A) Review the tuning method

B) Use an alternative set of meteorological fields

C) Thoroughly examine the uncertainties and limitations

D) Investigate other domains	A) Analyze method error

B) Sensitivity testing on NOx/VOC observations

3.2 Method Implementation Requirements

This section addresses questions about the feasibility of using the method under review. It addresses the timing and resource allocations necessary to introduce the method to States and EPA Regions. The following two questions are answered:

5)	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

6)	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Required ambient measurements are not currently routine	Required ambient measurements are not likely to be generally available
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	Measurements are expensive, require highly trained individuals, and are not routine. Peroxides are not measured routinely and reliably anywhere	The financial resources are not present in most states, although the personnel are	Most States do not currently have the expertise but could in the near future through cooperation with the universities
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	Data are available, and no special expertise is necessary	Method is so simple to use that it may lead to erroneous results	Implementation requires no particular expertise, but this method should only be included as one in a suite of tools
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	Personnel are required who are intimately familiar with experimental design and interpreting the accuracy of statistical models	Personnel need to be familiar with environmental statistics and modeling. Funding should be made available to implement the methodology
Source Identification Using Meteorological and Statistical Modeling	Simple method and available data, but not demonstrated for reactive pollutants	Not difficult to use but may require more PAMS data and sites than currently available	Observational data not available, and computer resources could be great for multi-source models
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	The method could be made routine very quickly	Insufficient details were given to determine staff or computational requirements. Required database manipulations may be cumbersome

Question 6: Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next four years)?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Could be in the next 4 years if the monitoring equipment becomes available	Cannot address with the information provided
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	Usable as soon as its practicality and robustness are demonstrated	In the longer term rather than the short term	Longer term is more likely, but States with NOy measurements could start soon
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	Not in the present form based on the scientific assumptions	No, and further development is not expected to yield different results	Yes, but only as one of a suite of tools and only when the assumptions are justified
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	Not at the state and local levels in the short term but maybe long term	This method could result in substantial cost savings now
Source Identification Using Meteorological and Statistical Modeling	The method's purpose for regulatory agencies was not identified	A number of years will be required to complete tracer studies	Definitely not short term and may not be useful long term based on portability
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	It could be made routine very quickly, but further tests are needed	The method insufficiently supports its science and has not been thoroughly evaluated

3.3 Potential Applications

The final multi-part question addressed the regulatory applications which the method might prove useful in evaluating. The reviewers were asked:

7)	For which of the following applications is the method appropriate (can be more than one)?

a)	For quality assurance of emission inventory estimates?

b)	For helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Work Group for the FACA?

c)	For help in selecting episodes to consider with nested regional air quality models?

d)	For prioritizing strategy selection for simulation with nested regional air quality models?

e)	For use in a weight-of-evidence determination as part of an attainment demonstration?

f)	For other purposes? Which ones?

Question 7a: Is the method appropriate for quality assurance of emission inventory estimates?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Not discussed	Not applicable
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	No	No	No
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	No	No	No
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	No	May be useful although not addressed in paper
Source Identification Using Meteorological and Statistical Modeling	Only for passive tracers if it can be shown to work for multiple sources and all meteorological conditions	No	Only for inert pollutants from a single source
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	Not discussed	No

Question 7b: Is the method appropriate for helping to identify Areas of Influence (AOI)?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Not discussed	No
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	No	Yes, with an adequate number of measurements	No, it does not address emissions
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	Possibly after reprocessing data	No, because the method is flawed	Yes, but only for preliminary AOIs
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	Seems like a logical application	With some adjustments
Source Identification Using Meteorological and Statistical Modeling	Not for reactive pollutants such as ozone and PM-fine	No	No
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	Yes, for primary aerosol and sulfate	No

Question 7c: Is the method appropriate for helping to select episodes to consider in nested regional air quality models?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Yes	Yes, for PM2.5 assessments
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	Perhaps, for selecting grid sizes	Not unless it would be possible to establish many sites in the grid	Yes, it could be useful in identifying the range of conditions
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	No	No, the method ignores episodes	No, but the information in the data sets could be useful for screening
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	No	Only indirectly by evaluating the robustness of current simulations
Source Identification Using Meteorological and Statistical Modeling	May be useful for identifying potential trajectory paths	No	Not discussed
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	Not discussed	Perhaps through Monte Carlo simulation of multiple episodes

Question 7d: Is the method appropriate for prioritizing strategy selection for simulation with nested regional air quality models?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Yes	Perhaps for PM2.5, when the issue is particulate nitrate
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	Perhaps for deciding whether to address NOx or VOC reductions	Not discussed	Yes, in terms of defining the sensitivity
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	No	No	No, but the information in the data sets could be useful for screening
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	Yes	Yes, especially if the cost algorithm is more transparent
Source Identification Using Meteorological and Statistical Modeling	Not until the method demonstrates an ability to track multiple sources	No	No
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	Not discussed	No

Question 7e: Is the method appropriate for use in a weight-of-evidence determination as part of an attainment demonstration?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Not discussed	Could provide qualitative, directional assessments for PM2.5
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	No, since models are necessary for attainment demonstration	Yes, since it accumulates evidence about the effectiveness of control strategies	No, although it could illustrate the sensitivity regime of an area
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	Perhaps, after data is reprocessed	No, because of flaws in the method	Yes, but only to the extent that it could quantify extreme weather events
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	Yes	Only indirectly as the statistical features may complement an attainment demonstration
Source Identification Using Meteorological and Statistical Modeling	Not until the method demonstrates its applicability to multiple sources	No	Not discussed
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	Not discussed	No

Question 7f: Is the method appropriate for other purposes? Which ones?
Response
Method	Reviewer #1	Reviewer #2	Reviewer #3
An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia	Not discussed	No
The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-ROG Sensitivity	Not discussed	Yes, as a research tool	Yes, identifying the sensitivity can help select control strategies
Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport	No	No	No
Estimating Emission Trading Weights for the Precursors of Ozone	Not discussed	No	Yes, to help develop the structure for refined trading programs
Source Identification Using Meteorological and Statistical Modeling	Not until further validation work is done	Yes, to help identify source receptor couples	Yes, in the emission estimations from single sources
Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model	Not discussed	No

4. REVIEWER RESPONSES TO THE INDIVIDUAL METHODS

4.1 "An Easily Applied Observation-Driven Methodology for Determining if the Formation of Secondary Aerosol is Limited by Ammonia" by Charles Blanchard, Philip Roth, and Shelley Tanenbaum

4.1a Reviewer 1

1.	Does the method seem scientifically plausible?

Yes, it is based upon a thermodynamical model reported in the peer reviewed literature. The authors have used this model correctly.

2.	What are the method's key strengths and weaknesses?

The strength is its simplicity. Its weakness is the requirement for five species: ammonia gas, nitric acid vapor, sulfate, nitrate, and ammonium particulate.

3.	Has the method been adequately reviewed and demonstrated?

The application to the California data is very convincing.

4.	What, if any, additional reviews/tests should be done?

Test in the eastern U.S.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

The method requires routine measurements of the five species listed above. These are not current routine measurements.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next four years)?

It is practical for the longer term of the next four years, pending acquisition of monitoring equipment in addition to the Federal Reference Method.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

Not discussed by the reviewer.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

Not discussed by the reviewer.

7c.	for help in selecting episodes to consider with nested regional air quality models?

Yes.

7d.	for prioritizing strategy selection for simulation with nested regional air quality	models?

Yes.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

Not discussed by the reviewer.

7f.	for other purposes? Which ones?

Not discussed by the reviewer.

Other Comments:

The title of this report is somewhat misleading in that it refers to secondary nitrate aerosol formation and illustrates its California origin by statements such as "secondary aerosol concentrations are lower in summer than in winter." This last statement is not true in the eastern part of the U.S. where sulfate and organic secondary aerosols have a summer maximum. Nitrate aerosols, if present in the eastern U.S. are more likely to be a colder season phenomenon.

The method uses an equilibrium model to examine when one can expect particulate nitrate to be responsive to either NOx reductions or ammonia reductions. The method also shows how particulate nitrate might increase with a decrease in sulfate aerosol. If one's interest is in nitrate aerosol response to reductions in NOx or ammonia, the method should work. The main difficulty is in the type of observations required. The optimal measurements are nitric acid vapor, ammonia gas, particulate sulfate, nitrate and ammonium. This is for the excess ammonia method. Further, to show equilibrium, PM10 and PM2.5 measurements are necessary. In the California area, the requirement for a PM10 measurement may not be necessary as described in the report.

In summary, the method described in the report requires five species be measured very carefully. When temperatures are cooler that 290 K, the method will give a useful indication of whether NOx or ammonia control will reduce secondary nitrate aerosol. In practical terms, the strategies designed for reduction of photochemical oxidants recommend some NOx reductions so the issue of ammonia reductions remains to be addressed. The method would be useful there also. The Federal Reference Method for PM will not provide monitoring data useful for this method.

4.1b Reviewer 2

1.	Does the method seem scientifically plausible?

In principle, yes. However, the reviewer had some reservations concerning its utility for general application, which is discussed further in this review.

2.	What are the method's key strengths and weaknesses?

The method has the following strengths:

The technique provides a relatively straightforward, objective method based on ambient measurements which can be used to design an effective strategy for the reduction of particulate nitrate. The technique provides a means of estimating the atmospheric response (particulate nitrate concentrations) to changes in total nitrate and sulfur dioxide emissions based on the ambient total nitrate/particulate nitrate ratio and/or excess ammonia determined from measurements.

The method has the following weaknesses:

The technique has only been evaluated for Southern California. As the authors point out, the technique only provides qualitative information. The technique is not intended to address individual sources, so source attribution in the traditional source-receptor sense is not possible.

3.	Has the method been adequately reviewed and demonstrated?

Not discussed by reviewer.

4.	What, if any, additional reviews/tests should be done?

Evaluations in other areas of the country would be useful, particularly if data sets are available from areas with high negative excess ammonia. Perhaps, to expand the knowledge of the sensitivity of the technique, the effects of the atmospheric conditions during other times of the year should be investigated as well as the constraint of thermodynamic equilibrium.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

The implementation of this technique requires ambient measurements that the reviewer does not feel will be generally available (i.e., total and particulate nitrate or, alternatively, ammonia, ammonium, sulfate, nitrate, and nitric acid). The reviewer could not speak to the availability of the technique (proprietary?) or the ease of use.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

The reviewer could not address this question with the information provided.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

This question is not applicable to this technique.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

Not terribly useful.

7c.	for help in selecting episodes to consider with nested regional air quality models?

Yes, for PM2.5 assessments.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

This technique may provide, at least, some information for forming strategies for PM2.5 when particulate nitrate is an issue.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

The technique could provide some qualitative, directional assessments (again for PM2.5).

7f.	for other purposes? Which ones?

None.

4.2 "The Method of Photochemical Indicators as a Basis for Analyzing O3-NOx-
ROG Sensitivity" by Sanford Sillman

4.2a Reviewer 1

1.	Does the method seem scientifically plausible?

Dr. Sillman has demonstrated persuasively that certain value ranges of his various photochemical indicators derived from photochemical grid model simulations correspond to conditions where ozone formation is sensitive to either VOC (ROG) emissions reductions or to NOx emissions reductions (or to transition conditions where neither one is particularly favored over the other). He has also rationalized these tendencies in terms of the chemistry of the system. In doing so, he has done a nice job of clarifying the free radical environment prevailing under the "NOx- or VOC-limited" conditions.

2.	What are the method's key strengths and weaknesses?

The key strengths are that it represents an empirical check on modeled results and in principle could be used in the absence of modeling (other than to initially define the critical indicator values) to suggest NOx or VOC sensitivity.

The method has the following weaknesses:

A) There is a lot of scatter even in the simulated data. This is not so important near the extrema (very high and low values), but may be important in the mid range where the relationship between the ratio of species and ozone production sensitivity to NOx or VOC reductions becomes fuzzy. This would be of particular concern if ambient indicator ratios are being compared with model-derived transition values.

B) It is most reliable under conditions where you already know whether you are in a NOx or VOC sensitive situation (i.e., where VOC/NOx ratios are relatively high or low). So the question arises, "What more am I learning by applying these indicator ratios than I already knew from examining VOC/NOx ratios?"

C) The method depends on models to establish what the relevant indicator ratio ranges or thresholds (transition values) should be for use in inferring NOx/VOC sensitivity. Thus, it is not wholly empirical and uses model-derived transition values to infer (from ambient measurements) NOx/VOC sensitivity conditions that are then used to corroborate model derived values.

D) Applying the ratios derived from modeling results to ambient data implicitly assumes that the modeled ratios are accurate. Because it is known that the condensed mechanisms in these models simulate some of the indicator species neither explicitly nor well (meteorological and emissions errors aside), the reviewer believes that this method places too much confidence in the photochemical grid model accuracy.

E) It is very difficult to verify that NOx/VOC sensitivities inferred from simulated ratios actually correspond to sensitivities applicable at the same ratios derived from measured values.

F) Although Sillman claims "...because it suggests that the NOx-ROG-indicator correlation in models is largely independent of model assumptions," he later apparently contradicts this with a technical note about "transition values" for peroxide-based indicators changing with updated chemistry and dry deposition rates, which the reviewer reads as saying that the indicators are sensitive to model assumptions.

3.	Has the method been adequately reviewed and demonstrated?

It is now being reviewed as part of the NARSTO assessment and by the Journal of Geophysical Review (JGR) reviewers, since it was submitted to JGR for publication. The reviewer could not evaluate the adequacy of that review. In the reviewer's perspective, the method makes chemical sense but has not been adequately evaluated. The question remains, "How generally applicable to an airshed are the transition values derived from a single or just a few simulations?"

4.	What, if any, additional reviews/tests should be done?

Even though there may be limitations or concerns regarding conclusions drawn from analysis of smog chamber data and their applicability to low-NOx ambient conditions, it would be worthwhile to test the indicators versus such data. At least these data are generated under controlled conditions and do not have the complicating factor of ill defined meteorological and emissions variability that thwart definitive testing of the concepts against ambient data.

Since all plots are based on 35% VOC and NOx reductions, it would be interesting to see what "transition values" result from 25%, 50%, and 75% emissions reductions. How would transition values change if emission reductions were made in a nonuniform, spatially heterogeneous way across the domain?

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

Many (most) of the species (NOy, NOz, HNO3, H2O2 and other peroxides) from which the indicator ratios are derived are not routinely measured. Work at the Southern Oxidants Study SCION sites, operated by ARA, Inc., has demonstrated that NOy, NO, NO2 (and, thus, NOz), and HNO3 can be reliably measured routinely but at a fairly substantial cost and by highly trained individuals. To the reviewer's knowledge, H2O2 (and other peroxides) is not reliably monitored routinely anywhere, although it could be, given sufficient resources dedicated to the acquisition of specialized equipment, extraordinary maintenance procedures, and the training of personnel.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e, next 18 months)? Longer term (within the next 4 years)?

It should be able to be used as soon as its practicality and robustness have been demonstrated.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

No. It gives no direct information on emission inventory accuracy.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

No. It gives no direct information on where airborne materials came from.

7c.	for help in selecting episodes to consider with nested regional air quality models?

Perhaps. If measured indicator ratios vary considerably spatially, this would indicate the advisability of using higher model resolution in the region of large gradients. However, this does not really address episode selection.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

Perhaps. As an example, if measured indicator ratios indicate NOx limitation, then more emphasis could be placed on exploring optimal NOx reduction strategies, and it would be the converse for indicated VOC- limited conditions.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

No. Because an "attainment demonstration" necessarily relies on photochemical modeling to translate emissions reductions to ambient concentrations, the reviewer can see little value added in then looking at indicator ratios from the modeled results when one already knows how the model predicted the ozone would change at that location.

7f.	for other purposes? Which ones?

Not discussed by the reviewer.

4.2b Reviewer 2

1.	Does the method seem scientifically plausible?

Yes, as long as external factors such as those listed below do not change greatly from time to time and place to place within the nonattainment area of concern:

A) the type of meteorological regime in which the control of ozone is desired,

B) the vertical and horizontal directions and distances of transport of ozone precursors in the air mass of concern,

C) the extent of the aging of the air mass of concern, and

D) the chemical species mix of VOC precursor compounds actually present in the air mass.

In addition, for the method to be scientifically plausible, the changes in emissions of NOx, methane, carbon dioxide, and other species of oxygenated and non-
oxygenated VOCs cannot change greatly either in geographical distribution or in either mass or molar ratios with respect to each other.

2.	What are the method's key strengths and weaknesses?

The method has the following strengths:

A) It avoids uncertainties in emissions inventories.

B) It deals exclusively with direct, simultaneous ground-based and upper-air-based concentration measurements of ozone, various ozone precursor chemicals, and reaction products, rather than concentration estimates from mathematical models of emissions and/or atmospheric chemical processes.

The method's key weakness is that it depends critically on the accuracy and precision of each measurement, which is used as the basis for the indicator ratios.

3.	Has the method been adequately reviewed and demonstrated?

The method has been adequately reviewed. However, demonstration of its effectiveness in real-world ozone management processes is still in a trial-and-error mode of consideration.

4.	What, if any, additional reviews/tests should be done?

Many of the assumptions that formed the foundation for EPA-454/B and earlier EPA guidance documents for States need to be reexamined while giving further consideration to the strengths and limitations of this method.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

Widespread implementation of this method would require additional field chemical measurement capabilities that exceed those of many State and local air quality management agencies. The U.S. EPA Office of Air Quality Planning and Standards and many of the EPA Regional Offices have the personnel resources but may lack the financial resources necessary to guide the State and local air quality offices in augmenting the field chemical measurement capabilities that would be required.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

The method would more likely be adopted successfully in the longer term (within the next 4 years) than in the short term (next 18 months).

7.	For which of the following applications do you believe the method is appropriate (can be more than one). Please indicate briefly why you are of this opinion.

7a.	for quality assurance of emission inventory estimates?

This method does lend it itself well to verification of estimates for specific point, area, mobile, biogenic, or other sources of ozone precursor emissions. This is true because the field chemical measurements needed to use the method are not the same as those that would permit direct verification of emissions estimates.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

If an adequate number of well-dispersed ground-level and upper-air monitoring measurements were made, this method would lend itself very well to determining areas of influence (AOI).

7c.	for help in selecting episodes to consider with nested regional air quality models?

The reviewer did not see how this method could be of much help in selecting episodes to consider with nested grid air quality models unless it was possible to establish a measurement site in locations where the finest- scale portions of the nested grid would be used.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

Not discussed by the reviewer.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

The reviewer thought that the weight of evidence approach was a constructive step in the right direction since many past efforts to use deterministic approaches have largely failed. There are simply too many uncertainties in emissions estimates and too much variability in the direction, speed, and variability of transport winds, scope and direction of large eddies, height of boundary layer, speed of breakup of atmospheric inversions, and other factors to use deterministic approaches. This method provides a valuable additional set of tools by which to accumulate evidence about the effectiveness of emission control efforts in decreasing ozone concentrations.

7f.	for other purposes? Which ones?

The Method of Photochemical Indicators has been used as a research tool in a wide variety of regional field studies of ozone accumulation in various parts of the United States. It certainly will continue to serve a very valuable function in future studies of this kind.

4.2c Reviewer 3

1.	Does the method seem scientifically plausible?

The method is not a source attribution method. However, for the purpose it is meant to serve, it does have some plausibility.

2.	What are the method's key strengths and weaknesses?

The method's key strengths are that it could allow local agencies to determine the NOx and ROG sensitivity for local ozone directly from measurements. The weakness is that the sensitivity determined will be based on a calibration by a certain model. Thus, model dependencies are not removed. However, model runs are greatly diminished and an agency can more quickly determine what the NOx and/or ROG sensitivity is without having to do model runs for each day of interest.

3.	Has the method been adequately reviewed and demonstrated?

The method has been tested and written up in several reviewed journal articles. However, it is clear that there are still several issues to be addressed. The first major issue is how well the required photochemical indicators can be measured. The second is how reliable the model calibrations are. The concern is that once a calibration is set, changes will not be made in the interpretation of measurements until well after model errors are uncovered. Other issues include concern over the location of the observation site in relation to the emitters, the fact that these indicators are only good during the day, and that dry deposition errors can be critical.

4.	What, if any, additional reviews/tests should be done?

It seems the first question is to ensure that NOy can be adequately measured in order to directly determine O3/NOy from observations. Second, the model chemistry needs to be assessed. There still is not a quantifiable set of error bars for the determined ratios that could help to determine whether the NOx and ROG sensitivity determined through this method are reliable.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

Because of the nature of the measurements, most State and local agencies may not have the expertise currently. However, one fairly straightforward way to establish this expertise is to initiate cooperative agreements with State universities. At least in several States (e.g., Georgia, California, Michigan, Colorado, etc.) there is the scientific expertise to make these measurements. This cooperation would make this method quite feasible in the near future.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

Because of the series of tests needed, this technique is more likely to get going in the longer term (within next 4 years). However, those States that do measure NOy could begin to look at using this method in the interim.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

This will not help at all. This method in no way addresses the emissions inventory.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

It is unlikely that this method will help identify AOIs. This method is simply not meant to identify spatial patterns or emissions, rather it addresses the nature of the chemistry forming ozone.

7c.	for help in selecting episodes to consider with nested regional air quality models?

It may be able to help with selecting episodes. If these observations are available, then it becomes easy to categorize the chemical sensitivity of different episodes and help select a range for air quality models.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

Likewise, as in 7c. above, this method could help with selecting strategies. By helping to define a range of chemical sensitivities, it would also help to ensure that a strategy has been properly tested.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

It is possible that by using this method to illustrate the sensitivity regime of an area to NOx and/or ROG it can be more precisely illustrated how a strategy would influence future events. However, this method is not likely to be an important source of information for such a purpose.

7f.	for other purposes? Which ones?

The method is really meant to help determine the NOx and/or ROG sensitivity of the ozone chemistry. This could be most helpful for determining useful control strategies.

4.3 "Ozone as a Function of Local Wind Direction and Wind Speed: Evidence of Local and Regional Transport" by Rudolf Husar and Wandrille Renard

4.3a Reviewer 1

1.	Does the method seem scientifically plausible?

This study used the daily maximum ozone concentration (DMOC) from various databases and wind data (specifically the 12 noon observation) from the National Weather Service over the region used for the OTAG modeling domain to analyze the relationship between wind speed and direction and the DMOC in various subregions and urban areas in that domain. Previous studies in the eastern United States have found that high ozone is normally found on the back side of a high pressure system (HPS) where the winds are generally weak and from the south; low ozone is found post frontal passage when the winds are strong and from the north, and air is flowing to the region from rather pristine ozone source regions (Decker et al., 1976; Wolff et al., 1977; Vukovich et al., 1977, King and Vukovich, 1982; Ludwig et al., 1977; Husar et al., 1979; Vukovich, 1979; Wolff and Loiy, 1980). Therefore, it is not surprising that the results of this study in many ways reflect those results. However, application of the methodology and results of this study toward source attribution is stretching the information beyond the bounds for which it may be useful for the following reasons:

A) The 12 noon instantaneous wind at each station was used in the analysis. As a result, one cannot determine the spatial and temporal persistence of the wind speed and direction on a given day. The wind may be from the south at noon, but at 11:00 AM, it may have been from the east, at 11:50 AM, from the northeast, at 12:10 PM from the southwest, etc. Marked temporal variations of the wind speed may have also occurred. Furthermore, the wind direction at 12 noon 25 km upstream from the site may be from the southeast or southwest. If the center of a HPS passed over or near the site, though the wind direction may indicate a wind from the south, the air mass affecting the location may have originated north of the site because of the looping done by air parcels near the center of a HPS. Without some notion of spatial and temporal persistence of the wind, it is impossible to predict with any reasonable accuracy the direction from which the source of the air mass originated or that a wind from the south is truly associated with flow from the south. Under strong wind speeds which are related to a strong pressure gradient, temporal persistence may be a given, but this may not be the case for spatial persistence.

B) Transport in the boundary layer may best be described by a mixed layer wind rather than the surface wind. The authors point out Blumenthal's 1997 results that "while the surface winds may tend to be from the southwest, the mixed layer wind may be from the west."

C) In Figure 5, 8 am relationships between the DMOC and wind speed for a given directional increment are presented based on average values. The reviewer thinks that it is important to see a plot of the data that were used to create these figures. This way, one can see the spread in the data for a given wind speed and wind direction increment. This will provide a notion of the representativeness of the mean. Past studies of the relationship between ozone and the wind has shown little correlation (i.e., magnitude-wise) between the two (Bach, 1975; Vukovich, 1995). This observation suggests that there may be a large spread in the ozone data for a given wind speed, which would imply that the mean is not a very representative quantity. It is similar to the relationship between maximum temperature and the DMOC. There is a general trend for the DMOC to increase with temperature, but at any given temperature, the spread has been as large as 100 ppb, leaving considerable doubt as to the relationship between the DMOC and temperature at any instant.

D) When the wind speed was about 7 m/s, the authors assumed that local contributions to the observed ozone was minimized. Using this assumption, they estimated the local contribution as the difference between the DMOC at 1 m/s and that at 7 m/s. This assumption had its basis from a model they developed in which the local contributions were directly proportional to the emission density (Q) and a characteristic length (L) over which local emissions affected the site and were inversely proportional to the wind speed (U) and mixing height (H). The assumption is that at U = 7 m/s, the local contribution is negligible. If H = 1 km when U = 7 m/s, and assuming L=10 km, then the local term is approximately 1.4 Q when U = 7 m/s. At U =1 m/s, H will increase markedly due to convective mixing, which is severely reduced at high wind speeds, so that for low wind speeds, H is around 3 km in the eastern U.S.. Therefore, the local term is approximately 3.3 Q when U = 1 m/s. When U =7 m/s, the local contribution is a little more than 40 percent than that at 1 m/s, which suggests that there is still a sizeable local effect. Therefore, differences between the mean DMOC at 1 m/s and that at 7 m/s probably do not reflect the contributions from the urban region.

2.	What are the method's key strengths and weaknesses?

The strength of this method is that it relies on data and not model results. The weaknesses are as follows:

A) Instantaneous wind directions and speeds were used in the analysis and not spatially and temporally persistent wind directions and speeds.

B) The influence of vertical mixing was neglected and not discussed.

C) The statistics are not rigorous.

D) Surface winds were used instead of mixed layer winds.

3.	Has the method been adequately reviewed and demonstrated?

No, the method has not been adequately reviewed and demonstrated. The reviewer thinks that a better approach would be to create these analyses using spatially and temporally persistent mixed layer winds. One set of statistics should be based on the spatially and temporally persistent mixed layer wind data set and another on those mixed layer winds that are not spatially and temporally persistent. In the latter case, a temporally averaged wind statistic might be more representative than an instantaneous wind.

4.	What, if any, additional reviews/tests should be done?

See the answer to question 3.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

As implemented, the methodology requires AIRS air quality data and weather data from the National Weather Service. These data are readily available. No special expertise is necessary, beyond familiarity with the data and the computer.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

Without some improvement in the way the wind data are processed and a realistic statistical analysis of the data, the reviewer does not think the methodology is useful in its present form. It remains to be seen if it will be useful with additional data processing. (See the response to question 1)

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

No.

7b.	for helping to identify areas of influence (AOI), as defined in the	2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

Possibly could be an aid in this area, but only after reprocessing the data (See the response to question 1).

7c.	for help in selecting episodes to consider with nested regional air quality models?

No.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

No.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

It possibly could be an aid in this area, but only after reprocessing the data (See response to question 1).

7f.	for other purposes? Which ones?

Not discussed by the reviewer.

4.3b Reviewer 2

1.	Does the method seem scientifically plausible?

The method as proposed is not scientifically plausible.

2.	What are the method's key strengths and weaknesses?

The method has the following strengths:

The method is simple to apply with very limited input data and computer resource requirements. The analyses can be performed by personnel with only limited training.

The method has the following weaknesses:

A) The method has a complete lack of accounting for the covariance of variables. For example, wind speed may be inversely correlated with mixing height, temperature, and emissions. The equation given by the authors for estimating local concentrations of a nonreactive species is C = C0 + QL/UH, where Q is the source strength, L is the length scale of the emitting region, U is the wind speed, and H is the mixing height. By ascribing all the variance in ozone concentrations to wind speed, serious errors in attribution may result. Likewise, the directional attribution in the ozone roses might simply reflect that lower wind speeds, or another variable associated with higher ozone, is simply more common for a given noontime wind direction at that particular location. Evidence that covariance in a parameter which varies by latitude may account for some of the variability for the different locations with wind speed can be found by examining Figure 9. The data from Figure 9 for the percent change in the relative DMOC from the lowest to the highest wind speed bin were plotted versus the latitude for each of the cities.

Latitude	% Change
Boston	42.34	0
New York	40.67	10
Chicago	41.84	15
Atlanta	33.76	28
Houston	29.77	60
A linear regression of these data (shown above) yields an R2 of 0.89 and a slope of -3.9. This agreement indicates that the slope which the authors ascribe solely to local versus regional effects is highly correlated with latitude. This implies that other variables may be important.

B) Variance in average ozone concentrations may not relate to statistically extreme events such as exceedances of an ozone standard concentration.

C) The use of noon NWS 3 meter wind observations to characterize the transport speed and direction for 24 hours is not a valid methodology. Measurements at this elevation are not representative of the flow that occurs in the bulk of the mixed layer. The authors themselves point out that the use of surface winds and the location differences of the meteorological sites and the ozone sites is problematic. Statements such as "The wind directional sectors that show high values of the ozone rose provide 'fingers' that point toward the source areas of high ozone concentrations" are unfounded.

D) The lack of variability of average ozone concentrations with wind direction at the lowest wind speed range considered (< 3 m/s) could well be due to the high degree of variability of the winds with time during low wind speed conditions.

E) The ozone data used have some deficiencies: (I) Lack of characterization of the sites of the ozone data used is a serious weakness. Are the sites urban, rural, regional, or a mix of all of the above? (II) Rather than QC the data for each site, eliminating the questionable observations, the authors attempted simply to eliminate sites that did not seem to fit in with their neighbors. At that, they only eliminated 6 of 709 monitoring sites, which is an amazingly low number. (III) The criteria were not stated for assigning a given site to a specific urban area.

F) The fundamental basis for ascribing the slope of average DMOC versus wind speed to regional versus local ozone is not made clear in the document. Is it assumed that regional concentrations are invariant with wind speed?

G) The method gives results that disagree with more detailed, explicit methods. For example, data collected during the 1995 SOS Nashville/Middle Tennessee Ozone study showed that the high ozone concentrations observed for Nashville were very localized and due primarily to local emissions (Valente et al., Ozone Production During an Urban Air Stagnation Episode Over Nashville, Tennessee, JGR-Atmospheres, Submitted for publication). Concentrations in the urban area were as high as 140 ppb, with the surrounding regional concentrations about 60 ppb. The method reviewed states that "Evidently, the Nashville metropolitan area contributes less local ozone than the other major southern metropolitan areas."

H) The definition of regional ozone as that which originates at distances greater than 50-100 km away seems arbitrary.

I) No information was presented on the variability of the data within each bin. Only the average was presented. If a large degree of variability exists within a bin, the reliance on the average is misguided.

3.	Has the method been adequately reviewed and demonstrated?

This is a new methodology, and, as such, has not been reviewed. The weaknesses in the method and the fundamental basis in the exposition reviewed mean that the method has not been demonstrated to be effective.

4.	What, if any, additional reviews/tests should be done?

With so many deficiencies in the method, further development would be a daunting task. The place to start would be to look at the effect of covariance on the fundamental assumptions. The use of surface winds to represent pollutant transport can be evaluated by comparison with air mass trajectories for the mixed layer. This approach should be evaluated through comparison with episodic analysis using photochemical grid models and field studies.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

The method is simple to apply. Too simple. The use of the results could lead an agency to determine source attribution which is erroneous. See the response to question 2 for two examples of this.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

No. The method is untenable as proposed, and it is not expected that further development would yield results.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

No. Emissions are not considered.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

No. For the reasons given above, the method is flawed.

7c.	for help in selecting episodes to consider with nested regional air quality models?

No. The method ignores episodes entirely.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

No. If it could identify areas of influence, then it could help in strategy selection. Please see comments in the response to question 7b.

7e	for use in a weight of evidence determination as part of an attainment demonstration?

No. The method is flawed.

7f.	for other purposes? Which ones?

No.

4.3c Reviewer 3

1.	Does the method seem scientifically plausible?

The authors suggest that their technique can be used for three purposes:

A) Source identification,

B) Imported vs. "homegrown" ozone apportionment, and

C) Transported ozone estimation.

The analysis methods employed rely on a couple of assumptions. First, that segregation by surface wind pattern will illuminate source regions for observed ozone through triangulation. Second, that under high wind speed conditions the observed ozone will necessarily be transported. The techniques they employ are all scientifically plausible: ozone concentrations at a given location are most certainly influenced by transport, and stratification of ozone concentrations by meteorological factors should help ascertain the amount of transport versus local production and the consistency of elevated ozone downwind of urban areas.

2.	What are the method's key strengths and weaknesses?

The method has the following strengths:

This technique is simple, observation-based, and relatively easy to implement once the data have been properly screened.

The method has the following weaknesses:

The method fails to acknowledge the myriad of other meteorological influences which will alter ozone concentrations. Ozone concentrations are known to be positively correlated with temperature, and this relationship is arguably stronger than any relationship with wind direction. It is also reasonable to expect ozone to be influenced by cloud cover (as a surrogate for available sunlight) and precipitation, yet the data used in the method apparently ignore these influences.

3.	Has the method been adequately reviewed and demonstrated?

The reviewer did not think that the method had been adequately reviewed. For source identification the conclusions one can draw from triangulation are qualitative at best and ignore the other important influences of temperature, mixing, cloud cover, and precipitation which may all be directionally biased.

For imported versus local concentration differentiation this technique is a good first step. The use of surface winds versus the winds at the transporting level above the surface is debatable. In the afternoon (the authors use local noon) it is reasonable to expect a correlation with winds aloft as afternoon mixing redistributes the momentum of the mixed layer to the surface, but this would not always be true. But even here it requires careful analysis as the "imported" ozone can in some cities, notably in the south, be recycled from the urban area's previous day emissions. Moreover, it is important to recognize that the "imported" concentration itself is influenced by meteorology and will vary from day-to-day.

The contention that one can estimate "transported" ozone best under high wind speed conditions is also debatable. Again, the amount of ozone transported will vary from day to day. The reviewer suspects that Boston, for example, might see relatively high "imported" ozone under high wind conditions if those conditions were associated with transport from the Midwest, while Atlanta would get lower "imported" values at high winds. This method discounts the possibility that ozone cannot be imported to an urban area by a slow moving air mass.

4.	What, if any, additional reviews/tests should be done?

This tool is one of many which would need to be employed to evaluate regional backgrounds for a specific urban area. By itself, it may not give a complete understanding of the local ozone climatology. This method would need to be used in the context of a more thorough analysis of case studies to better understand the regionality of ozone concentrations. To begin, the reviewer recommends that the days be stratified by winds and temperature to understand how the "imported" ozone is influenced by temperature. The reviewer might also eliminate days with concentrations below 80 ppb since they are less relevant to human health issues.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

The implementation of this technique does not require any particular expertise, save the work of a database person working with an air pollution specialist and meteorologist to insure data quality. The reviewer argues that the technique is not sufficiently stand-alone such that this level of analysis would be sufficient to help decision-makers. This tool would need to be included in a suite of analysis tools performed by air quality specialists in order to provide useful guidance on how much ozone is transported into an urban area on a given day and how to use this information ultimately in deterministic control strategy photochemical modeling.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

With the caveat stated above, the method as it stands can be used immediately.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

No.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

Yes, for preliminary AOI, but it is not quantitative enough to merit use for basing regulatory control. While the 2/13/97 document was not sufficient for the reviewer to review the criteria for AOI designation, the reviewer presumed that the "Final AOI" would need to be based on a deterministic model demonstration that the AOI emissions have a significant impact on ozone in the Area of Violation. As a screening tool, this method could be used to identify the AOI in the near-field (i.e., a nearby city) but will provide less guidance about the influence of cities further upwind. The reviewer does not see how this method could, for example, quantify the influence of the Philadelphia urban area on the New York urban area.

7c.	for help in selecting episodes to consider with nested regional air quality models?

Perhaps the method demonstrated would not be as helpful as the data sets which the authors have compiled. Given these data sets, the modeling community could define the situation they are interested in exploring (multi- site exceedances, low-wind speed situations, multi-day events, etc.) and query this data set to identify candidate days.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

The reviewer referred to the response given for question 7c.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

Only to the extent that the data set could be used to quantify how extreme weather events (for example, extended heat wave) influence ozone exceedances. Attainment for nominal meteorological conditions could be designed with some built-in risk that the extreme meteorological event will only happen once in a blue moon. This data set could help define the frequency of the 'blue moon' weather event.

7f.	for other purposes? Which ones?

Not Applicable.

4.4 "Estimating Emission Trading Weights for the Precursors of Ozone" by Gary Dorris and Timothy Mount

4.4a Reviewer 1

The Dorris method for establishing emission weights is interesting but entirely dependent on photochemical models. Its conclusions are very often based on ozone changes of less than 5 ppb which the reviewer believes is far less than the model's accuracy. It is entirely possible that different episodes could give entirely different answers, particularly for the more distant sources and for cost effectiveness.

However, the conclusions presented seem to be consistent with scientific knowledge in that the most effective controls will be within a short distance of the receptor sites, if the goal is to reduce peak ozone. Low cost distant controls will reduce ozone but it is really the intraurban transport of ozone that is the primary contributor to regional and peak ozone in the Northeast.

The large cost savings proposed are also highly questionable since shifting emission reductions around (i.e., controlling the less costly and distant facilities first will only prolong the nonattainment and human exposure unless equal or greater percent reductions are achieved near the receptors). In conclusion, the reviewer thinks the concepts presented by Dorris deserve considerable merit but the primary goal of reducing peak ozone and human exposure seems to be lost in the process.

4.4b Reviewer 2

1.	Does the method seem scientifically plausible?

The method seems scientifically plausible as long as the weaknesses under Question 2 are recognized.

2.	What are the method's key strengths and weaknesses?

The method has the following strengths:

A) It estimates the effectiveness of emission controls based on the type of emissions and the location of the sources.

B) The method determines the cost of reducing ozone in the problem area from each type of emissions control in each source area.

C) It reduces the number of model runs needed to assess control effectiveness within policy space.

D) It takes advantage of the linear response of ozone within the narrow band of controls inside policy space.

E) More weight is put on emission controls that will reduce ozone in high population areas and areas with the highest ozone concentrations.

F) The distance of sources from ozone problem areas is accounted for.

The method has the following weaknesses:

A) It uses a priori information on control costs and regional and species contribution to ozone formation to narrow the range of emission controls considered. This can be a problem if prior knowledge is deficient because some useful controls may not be properly evaluated.

B) Controls beyond policy space (nonlinear portion of the ozone response) are avoided because they may create biases within the statistical model. This creates the potential to omit controls that may be needed to meet ozone standards.

C) The method does not account for the cumulative impact of emissions control. This technique only considers ozone reductions in the receptor area. It does not consider that control strategies may produce significant benefits in other areas when calculating costs versus benefits.

D) It relies on the statistical model to accurately represent the response of ozone within the photochemical model.

E) If the response of ozone to the controls is not linear at all places within policy space, then the impact of the controls will be misrepresented.

3.	Has the method been adequately reviewed and demonstrated?

The reviewer did not feel that it was possible to answer this question. Because the accuracy of this method relies on a statistical model, it should also be thoroughly reviewed by qualified statisticians.

4.	What, if any, additional reviews/tests should be done?

As a further demonstration of the capability of this method, the accuracy of the statistical model could be verified against a matrix of photochemical model runs. After the MORS is created, additional photochemical model runs could be performed to determine if the impact of the emission controls in the photochemical modeling matches the impact predicted by the MORS.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

Perhaps. This method requires personnel intimately familiar with experimental design and personnel who can adequately interpret the accuracy of statistical models.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

The technical difficulties involved in implementing this method may not make it practical for use by many State or local agencies in the short term. Wider use may be practical in the long term.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

Not discussed by reviewer.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

Identification of areas of influence (AOI) would seem to be a logical use of this method.

7c.	for help in selecting episodes to consider with nested regional air quality models?

Not discussed by reviewer.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

It could be useful for prioritizing strategy selections for simulation in regional or sub-regional air quality modeling.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

An important use of this method would be in a "weight of evidence determination" in attainment demonstrations.

7f.	for other purposes? Which ones?

Integration of costs into the evaluation of control strategies is desperately needed. This method can contribute much needed information. It would best be used by a select number of qualified individuals or groups to evaluate the cost effectiveness of a range of possible emission control strategies.

4.4c Reviewer 3

1.	Does the method seem scientifically plausible?

The reviewer responds, "According to which science: statistics, economics, atmospheric chemistry and physics?" The method does not attempt to expand the science of atmospheric chemistry and physics nor economics. However, by the creative use and transfer of statistics to concepts and data of other disciplines, the method has created opportunities to minimize the regrets and lower the cost of control strategy implementation. The use and description of statistical methods appear scientifically plausible.

2.	What are the method's key strengths and weaknesses?

The method has the following strengths:

The research puts forth the concept of "policy space." Specifically, the research seeks to identify the bounds of the science, data, and applications. How far can scientists, analysts, and policy makers push the clean air production function and still be on ground that is somewhat firm empirically, scientifically, and analytically? The "ridge lines" of the old EKMA diagram seemed to have gotten at this in a less transparent way. However, by integrating a "proxy" for "stack height" considerations with better information of NOx/VOC and ozone, the approach captures some of the heterogeneity in emissions to air quality relationships across pollutants, geographic areas, and "release heights."

The method has the following weaknesses:

The research lacks perspective for what is put forth. Specifically, there is a stochastic emissions to air quality relationship. The authors capture some of this. However, whether or not it is the major source of variability is unknown. For example, the meteorological conditions of wind velocity and direction, as well as precipitation and temperature, vary dramatically for ozone episodes within and across ozone seasons. These stochastic factors also impact biogenic emissions. Hence, the authors may have the "perfect" source receptor matrix for economic and regulatory conditions of the year 2005 and meteorological conditions of 1988. However, if these conditions only manifest themselves 5% of the time, how much regret has this new application helped the policy makers avoid? This is not to discount the contributions of the authors. However, putting their contributions in perspective would enhance appreciation for what has been produced and the work that remains to be done.

There are other examples in the perspectives area. Calling this a technique which incorporates population exposure is misleading. The GNYMA has variability in terms of the vertical and horizontal ozone gradient which is not addressed in this analysis. Furthermore, human activity patterns are not included as autonomous or endogenous factors in the structure of the model. Neither are population characteristics included. Consequently, it would be more accurate to present that the research captures the population residence density and not population exposure.

Lack of transparency is another weakness in the method

Additional specific comments follow:

Page 3. Failure to differentiate tons by location, elevation, and species does not necessarily lead to a one-size-fits-all similar removal cost across a broad region. It may.

Page 6. Footnote 8 should appear on this page. Also, the MORS enhances the transparency of some aspects of a full-scaled photochemical model.

Page 6. The transparency of the full-scale photochemical model does not necessarily increase with the introduction of a "reduced form" approach. In fact, the reduced form approach masks what are judged to be insignificant factors in these simulations. The "reduced form" approach is a good communication tool to describe what may or may not matter within the structure and data inputs of the model.

Page 9. The authors assert that homogeneity in the transportation sector leads to similar behavioral responses. This assertion without supporting documentation is nothing more than conjecture. One has only to compare the horse and buggies of Lancaster County, Pennsylvania with Manhattan Island, New York to get this point.

Page 10. The authors fail to say anything about biogenic emissions of NOx. They also fail to give adequate support for classifying VOC emissions as ground level (which contradicts the fact that some trees are quite tall and that tall stacks emitting NOx also emit VOC).

Page 11. The example for the MORS estimation could prove more relevant if presented in terms of the form of the 1-hour or 8-hour NAAQS.

Page 12. The spatial and temporal dimensions of the unit of ozone removed could be clarified.

Page 13. The transformation from emissions costs to ozone reduction costs can be simplified. However, this is done legitimately only when one modifies the cost effectiveness figures to account for the variation in control costs throughout the year. Furthermore, it is unclear whether this transformation is for the entire ozone season which may encompass parts of the spring and fall or whether this is for the episode period or the maximum expected concentration day. This point should be clarified.

Pages 31 and 32. At the end of page 31 and beginning of page 32, the reader is left with the impression that there is nothing but good that comes from more NOx control. However, that is simply not the case especially, in certain urban areas were NOx helps in ozone scavenging. This point should be clarified.

Page 32. The first footnote describes the "adjustment" procedure so that everything fits. This is the type of thing that needs to be highlighted and contrasted with the approach. The methodology uses the data to generate the relationships of emissions to air quality concentrations. Statistical methods and structure plausibility tests are performed to help in model selection and results interpretation. However, other practitioners simulate and then calibrate. This process removes the variability and precludes probability statements regarding the simulation results.

3.	Has the method been adequately reviewed and demonstrated?

This is an appropriate question. However, to bring more meaning to the responses and to help the reviewers, there are other questions which should first be asked and answered. This information will provide sorely needed context for the reviewers and those making the ultimate decisions regarding the utility and validity of this research. For example, some reviewers may argue that until they see the computer code and input data, they will not deem the method to be adequately reviewed and demonstrated. However, if this research meets the criteria applied to "black boxes" such as UAM-V and ICF Kaiser's utility model, some reviewers may find it acceptably demonstrated.

The questions are as follows. What are the criteria for adequate review and demonstration? What were the findings of the peer review and demonstration for the base year emissions inventory, projection year emissions inventory, Mobile 5.1, allocation of regional and national emissions to the 5 kilometer grids, and allocation of annual emissions to seasonal and episode levels? What are the criteria for adequate review and demonstration of the photochemical models that relate to this work such as UAM-V, ROM, etc? To what extent have verification, validation, and transparency been part of the review and demonstration process for the emissions inventory and air quality components, which serve as the foundations for the present work?

Moreover, the answer to the question of method adequacy is not only dependent on the criteria and results for the foundation of the modeling but also the intended use of the results. The opportunity costs of waiting for perfection before releasing this tool could be significant. Specifically, the States and U.S. EPA are presently crafting regulations which will cost billions of dollars to implement. Furthermore, to some observers, the environmental regulatory community has been using sledge hammers to carve out control strategies. Now that someone has come up a pneumatic drill, there are a whole series of questions asked of the pneumatic drill designers and operators that should also be asked of the sledgehammer designers and operators.

Given the potential for cost savings, which has been ascribed to this method (about a factor of 6) and EPA's commitment to finding cost-effective solutions for environmental problems, this research cannot be ignored. An opportunity for timely, concurrent review of method, application, data inputs, and results for plausibility tests in critical regulatory, policy, and guidance analyses should be afforded for these researchers.

The reviewer is not asserting that the simulation results are sufficient in their present form to establish the AOI/AOV for the GNYMA. However, the reviewer is not sure that present available methods used to make such determinations would yield superior results from a technological perspective.

4.	What, if any, additional reviews/tests should be done?

This is a function of the intended use of the results. If used as an input to the regulatory, policy, and guidance development process, the reviewer believes that verification of data inputs and validation of computer code, while maintaining the property privileges of the authors, would be in order. Plausibility checks should be part of this effort. Plausibility checks should also be part of the present air quality modeling process.

Page 25. The reviewer believes sensitivity analyses should be conducted departing from what appears to be "1/100" and "positive 30" switch points used in the analysis. For example, 1/75 and 1/125 and 20 and 40 should be tried. In this way, the apparently arbitrary switch points may or may not be better supported.

Page 32. Noting the significance level of the F-test results for the R-squared would help make the authors' point about too few data points and connecting the dots.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

If these data and expertise do not exist, they should. Environmental statistics and modeling were almost prerequisites for EPA employees hired in the 1970's and 1990's. Furthermore, EPA still hosts an environmental statistics and modeling conference each year as an opportunity to get up to speed and keep pace with the latest developments in the field. In addition, EPA has made promises to the public and Congress about cost-effective solutions to environmental problems. Moreover, the President made his feeling on this clear with Executive Order 12866 and the Presidential Directive on Implementing the New Ozone and Particulate Matter NAAQS. If the EPA, State, and local agencies do not have these skills, the academic and consulting communities do.

In terms of funding, the Clean Air Investment Fund (mentioned in the Presidential Directive on NAAQS implementation) could augment present resources if agencies collecting these fees sought to use them to develop better ozone NAAQS attainment strategies as opposed to finding technologies aimed at what might prove to be an inappropriate ozone precursor emission reduction target.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

Given the dollars which are at stake in EPA's recently proposed regulatory action and the potential savings offered by this method, the practical time period is now.

That does not mean that in the longer term the Agency should not seek to refine its data, tools, and human capital. It should.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

Why should any one develop quality assured emissions inventories? Well, the result may be less costly control strategies and control strategies which when implemented result in regulatory success, not failure. The objective of the reviewed research paper was not to foster quality assurance of emissions inventories. However, it may do just that. Furthermore, the incentive would be for more resolution as well as quality assured and validated data on stack heights, VOC chemical constituencies, area and mobile source emissions as well as biogenics.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

With some adjustments (e.g., peer review for specific application [see earlier comments] specific recognition of the stochastic elements in the emissions, meteorology, and air quality foundations of this model), this certainly offers some positive possibilities.

7c.	for help in selecting episodes to consider with nested regional air quality models?

The model may be of use in an indirect sense. Specifically, knowing how robust the current simulation results and others (e.g., different stack height) are in terms of the variety of episode types, one would find between and within ozone seasons would be valuable information in emissions trading or least-cost command and control strategy development.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

Yes, it would especially be if the cost algorithm and who initially pays how much were made more transparent.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

Perhaps, but, only indirectly. There are statistical features which enhance the attractiveness of this model compared to those which are purely deterministic. However, without a built-in comfort level for the underlying emissions inventory, episode selection, and air quality models, it is difficult to see a buy-in to this model's results vis a vis attainment demonstration. However, the statistical features may be used to complement the foundation components in an attainment demonstration.

7f.	for other purposes? Which ones?

Yes, for helping to develop the structure for refined trading programs.

4.5 "Source Identification Using Meteorological and Statistical Modeling" by Stephen Masters

4.5a Reviewer 1

1.	Does the method seem scientifically plausible?

Atmospheric transport and dispersion models have been applied in the past by numerous investigators to study the long-range transport of passive pollutants. The approach used here is applicable to non-reactive pollutants only. Since ozone and fine particles are formed from a variety of reactions involving SOx, NOx, and VOCs, the proposed methodology cannot be readily applied.

2.	What are the method's key strengths and weaknesses?

The method can use routinely available meteorological data. There are a number of papers in the literature documenting significant uncertainties associated with the use of trajectory models (e.g., see Stunder, in Journal of Applied Meteorology, 1996, pp. 1319-1331). The problems with these types of models include spatial and temporal resolutions of routinely available meteorological observations and the many simplifying assumptions in the trajectory model. The models cannot be useful in tracing the path of air parcels under accelerating and decelerating flows which are commonly associated with low pressure systems. The accuracy of a single trajectory or trajectories for a few days is very limited.

3.	Has the method been adequately reviewed and demonstrated?

No. The rationale for selecting data from only four periods and five samplers from all available observations is not discussed. Are these the meteorological situations when the model is expected to perform well? What criteria are used to identify concentrations associated with previous or subsequent releases before setting them to zero? Would this approach work under summertime meteorological conditions which are important for ozone, PM2.5, and regional haze? The method for mixing heights is not discussed. How reliable are the estimated mixing heights? What is the impact of uncertainties in the estimated mixing heights on the predicted concentrations? How is the statistical significance of the correlations evaluated? Although the author states that multiple regression techniques can be used if there is more than a single source, it is not clear how this method can be used to handle reactive pollutants such as ozone and secondary particulates that have many ground-level and elevated sources for its precursor emissions.

4.	What, if any, additional reviews/tests should be done?

The model must be applied to all available ANATEX data to assess the success and failure of the model to accurately predict the concentrations at the receptors and identify the source locations. Even then, the methodology may be useful to study only passive tracers.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

Since the method is simple and data are available from the NWS, it can be used by regulatory agencies. However, its use for reactive pollutants has not been demonstrated.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short-term (i.e., next 18 months) Longer term (within the next 4 years)?

Given the current air pollution problems confronting the regulatory agencies, the author does not identify where and for what purpose this method can be used. Even if one is interested in applying this method for non-reactive pollutants, the robustness of the proposed methodology has not been demonstrated by the author.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

If the robustness of this methodology can be demonstrated for all meteorological conditions and for multiple sources, it may be useful for quality-assuring emissions of passive tracer emissions.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

The approach as presented in the manuscript is not applicable to reactive pollutants such as ozone and fine particles. These pollutant problems are complex in nature because of the chemical reactions and spatially inhomogeneous emission sources.

7c.	for help in selecting episodes to consider with nested air quality models?

May be useful for identifying potential trajectory paths under certain episodic conditions.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

The method's ability to track multiple elevated and ground level emission sources has not been demonstrated. Therefore, its use is limited.

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

As stated by the author, the method needs further testing and assessment for quantifying its applicability to multiple sources (both elevated and ground- level) under all possible meteorological conditions before it can be used even in a weight of evidence determination.

7f.	For other purposes? Which ones?

As noted above, further validation work is necessary to demonstrate the method's usefulness to any regulatory applications.

4.5b Reviewer 2

1.	Does the method seem scientifically plausible?

The answer to this question depends on how the method is used. If it is used as a mechanism to do an initial analysis to identify source and receptor areas, it may provide a screening technique for a more rigorous set of analyses that can be done afterwards.

2.	What are the method's key strengths and weaknesses?

The strengths of the proposed methodology are the simplicity of the modeling and the data needs to drive the model. The weaknesses are the availability of tracer data (which, for use in looking at source receptor relationships for ozone or PM will be tracers of opportunity contained in ambient air samples) by which to develop a regression equation that can relate the model concentrations to the receptor area. In addition, there are some real concerns with the use of "tracer" data. Unless the detection network is dense enough, it is not clear that the network can adequately describe transport. Concentrations detected may not describe the "plume centerline," and may only represent a small portion of the released mass, with much of the mass being unaccounted for. A second potential weakness is the ability of the model to treat the distribution of the released material adequately. Trajectory models, in general, have been questioned on their ability to represent flow over long distances, as noted in the July Source Attribution Workshop. This problem is not removed from the proposed methodology. Redistribution among multiple layers representing vertical mixing is predicated on 12 hour mixing depths, and winds are also generated on a minimal network.

3.	Has the method been adequately reviewed and demonstrated?

See the response to the next question.

4.	What if any additional reviews/tests should be done?

One would ideally want to compare the GAMUT trajectories to those which would be generated with a more sophisticated approach, such as trajectories generated using MM-5 model as the meteorological driver. These comparisons should be done for specific periods of time to yield comparisons in time/space. This test would be an indication of how good the GAMUT assumptions are. A second set of tests would look at other areas of the country in which there have been long distance tracer releases, and or samples containing tracers of opportunity such as chlorinated hydrocarbons, and also Eulerian modeling to see how this method compares to more sophisticated modeling. It should be noted that the paper itself provides a summary which identifies potential problems and suggestions for further work.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

The method in and of itself is not inherently difficult to use, but it may, if shown to be feasible for one or more uses under question 7, require more PAMS or PM PAMS sites than now available.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

The answer to this question depends on whether this method proves to be useful for any of the topics under question 7. If it is, it will require a number of years to acquire the needed monitoring system and determine which tracers of opportunity might be useful (i.e., those which do not undergo chemical transformation but act like inert gases with low rates of deposition).

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

If this method has any real utility, it is most likely one of a suite of analyses that can help identify potential source receptor couples. However, it appears that, due to uncertainties inherent in the methodology, it may be limited to consideration as one of a number of screening tools for potential couples. This identification would trigger more rigorous analysis.

7a.	for quality assurance of emission inventory estimates?

Not discussed by the reviewer.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

Not discussed by the reviewer.

7c.	for help in selecting episodes to consider with nested regional air quality models?

Not discussed by the reviewer.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

Not discussed by the reviewer.

7e.	for use in a weight of evidence determination as part of an attainment

demonstration ?

Not discussed by the reviewer.

7f.	for other purposes? Which ones?

Not discussed by the reviewer.

4.5c Reviewer 3

1.	Does the method seem scientifically plausible?

If the scenario remained simple (i.e., one source and no pollutant reactivity), then the method could be plausible. With some improvements, it could be an innovative way to combine observational and modeling results to approximate a single source for an inert chemical.

One major scientific problem is that once multiple sources are introduced from different geographical areas, many difficulties could arise. First, it may not be possible to obtain a unique result, where different scenarios involving the sources could produce similar pollutant distributions. Secondly, it does not appear that the error estimates (e in equation 1?) from the regression are considered. If a multiple linear regression was needed, the error estimates could become so large that the source estimates would not be scientifically sound.

The method suggested here has a similarity to inverse modeling where observations and model predictions are compared to estimate source emissions. In the Kalman filter inverse method, the emissions from separate sources are commonly designated as separate "tracers" in the model. If the problem is not under determined, the Kalman filter can be shown to give unique results. In order to estimate multiple sources with reasonable accuracy, a similar approach may be needed. Furthermore, the Kalman filter method also includes the influence of error from the model and observations when making the estimation. Scientifically, a method which includes the influence of errors would be more plausible when identifying single or multiple sources.

Another major scientific issue is that this method would need major modifications if any chemical or deposition processes were involved. First, a model capable of simulating these processes would be needed. As for secondary pollutants, problems may arise when applying a linear regression approach. Many complications could make this statistical technique inappropriate, when introducing reactive pollutants.

2.	What are the method's key strengths and weaknesses?

The method has the following strengths:

A) Including observed concentrations can help produce more accurate source estimates.

B) The GAMUT grid resolution can isolate virtual sources to approximately a 4- county area.

C) The method is a good initial idea, which may be useful if modified/developed for more specific needs.

The method has the following weaknesses:

A) Error analysis appears to be lacking in the regression.

B) The method cannot be applied to multiple sources.

C) This method is not able to handle reactive pollutants.

D) The accuracy of the regression is sensitive to the sampling density, and the results	could be given too much weight when there are too few samples.

E) The modeled results are not tested for validity.

F) The meteorology is simplified, and the resulting consequences are not considered.

3.	Has the method been adequately reviewed and demonstrated?

No, the method has not been reviewed or demonstrated thoroughly. The method has not been modified and tested for multiple source identification, and the method has not been developed to handle reactive pollutants.

Also, some major questions stand out:

A) What if the method is applied under the incorrect assumption that there is one source, when there are in fact multiple sources?

B) Can we be sure that the regression will give unique results?

C) How much do the model and observational errors affect the results for single and multiple source scenarios?

D) Do we know all the limitations of this method? Tools can be misused, when the limits are not clarified.

E) As argued in the discussion, will we always be able to know if release rates are too high, so that they can be flagged as unrealistic?

4.	What, if any, additional reviews/tests should be done?

A) Modify the method for multiple source identification.

B) Test the method for uniqueness.

C) Perform an error analysis. This could provide useful information about how the method could be improved. It will also determine if error propagation is a problem, particularly for multiple sources.

D) Modify the model so that it can handle specific reactive pollutants.

E) Test the limits to the method, for single and multiple sources, to see what conditions should be specified for application. (For example, the method should not be used if there are less than x sampling sites.)

F) Compare the results with more transport scenarios.

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

This method's results are sensitive to the sampling density. Therefore, the methodology may require data currently unavailable, depending on the application. The results shown in this paper suggest that the method can be useful with as little as five sampling sites, assuming an inert pollutant and one source. In that case, the resources could be made available. However, this may not be the case for multiple source identification or for reactive pollutants. Until the method is further developed for reactive pollutants and multiple sources, it should not be assumed that the necessary data can be developed.

If the method were successfully modified for these changes, it may become very computationally demanding. If so, the method may require computational resources not readily available.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

Because too many changes and tests are needed, the method would only be useful for longer term application. However, this method will need to be redeveloped and retested for each different pollutant, so portability could be impractical.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

At this time, this method would not be useful unless it were applied to inert pollutants from single sources. If the method were further developed to handle the reactivity of the pollutant and multiple sources, then it could be useful for testing emission inventory estimates.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

No, at this point, this method cannot handle reactive pollutants. The method must also be able to handle multiple sources, including those that overlap, before it can be useful for establishing AOIs. Therefore, ozone and PM2.5 AOIs could not be estimated from this method.

7c.	for help in selecting episodes to consider with nested regional air quality models?

Not discussed by the reviewer.

7d.	for prioritizing strategy selection for simulation with nested regional air quality	models?

No. If it could handle reactive pollutants and multiple sources, the method might be helpful in locating major sources.

7e.	for use in a weight of evidence determination as part of an attainment
demonstration?

Not discussed by the reviewer.

7f.	for other purposes? Which ones?

This model could currently be useful in the emission estimation of inert chemicals from single sources.

4.6 "Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model" by Bret Schichtel and Rudolf Husar

4.6a Reviewer 1

The technique being described and applied is based on following the trajectories of pollutant puffs (or particles, packets or quanta) whose path is defined by a time averaged velocity and a random component. The random component can be developed a number of ways, and the method uses one of the more common approaches of using the effective eddy diffusion coefficient. This method requires using a large number of puffs, so that a statistically valid representation of the resulting field can be developed. The wind fields can be developed by a number of approaches as well, and here the technique uses both the NGM and RAMS. The method could just as well use MM-5, diagnostic approaches, etc. The basis behind the technique is solid, as long as one fully accepts and understands the limitations and uncertainties, as discussed below.

The basis behind the technique is that the relevant physical and chemical processes are applied independently to each quanta, and it is assumed that this introduces little error when discretized over each time step (the technique used two-hour time steps which may introduce some error in terms of capturing the correct emissions in each packet, when looking at distributed area sources since they are added together within each grid). For example, over any one time step, the technique determines how much the pollutant mass of the quanta has changed due to chemistry and deposition, as well as the change in location. The effect of vertical diffusion is captured by a random velocity fluctuation in that direction. At night, above the mixed layer, there is no fluctuating component. Ignoring nighttime mixing can add a small error.

The supplied article only follows sulfur emissions, the transformation to sulfate, and the deposition of both. The loss and transformation of both are linear, as is appropriate for the model formulation. As formulated, following nonlinear processes would be inappropriate unless one allowed the quanta to interact in a nonlinear fashion. This would be both much more expensive computationally and also require understanding how the random transport should be interpreted. It would lose information to just develop a concentration field for each of the interacting species. While less obvious, the assumption of linearity impacts the method's ability to predict aerosol size distributions.

There is a weakness in how the random/fluctuating velocity component is found. The article used either zero or a constant value in the example. There is more variation than that. Also, the fluctuating component should include some knowledge of uncertainty.

An important component of the model is the development of the rate coefficients (e.g., transformation, and dry and wet deposition rates). These are not fundamental in nature, and as discussed in the article, the model is tuned to give optimal results for the period studied. Then that data are issued for evaluation. Obviously, this introduces some limitations and uncertainties and also limits how readily the technique can be applied in a new domain or to a new pollutant. The tuning does give viable results, though they show significant disagreement between the simulated and observed SO2 and sulfate fields. The sulfate wet deposition results look better. In doing the sulfate aerosol comparison, spatially averaged observations were used. This is bound to improve the performance, though it was suggested by the large meteorological grid. It is not apparent that a finer meteorological grid would yield significantly different results. Thus, the averaging may not be needed. Also, the tuning method is not well explained (e.g., what penalty function was used?).

The article describes the use of the technique to develop source-receptor relationships. While the description is brief, it is straightforward in practice (e.g., one just needs to mark how one quanta evolves versus another, and then do the appropriate averaging over time to identify which quanta impact a specific receptor). Again, this technique is limited to a pollutant whose behavior is well approximated by behaving linearly. A significant uncertainty in the ability of using this technique to conduct source apportionment is introduced by the tuning process. For example, if there is a significant uncertainty (and possible errors) in the emissions, the model is tuned to get rid of the errors. This can introduce other errors and biases in the model. This would be on top of the uncertainties found just from the base model application. For example, if to make up for an error in the dry deposition rate of SO2, they mis-tuned the transformation rate, the ability to correctly assess the source-receptor impact is diminished. Further, if the emissions from one area were off, this could lead to mis-tuning the transformation rates. This would also lead to following the evolution incorrectly and mis-quantifying the transfer matrix coefficients. It might be good to have a more fundamentally determined set of rate coefficients and minimize the model tuning required. Also, any error in the meteorological fields (winds, rain, solar radiation, or mixing height) will contribute significantly to the modeling error, uncertainties, and possible hidden biases.

In terms of answering specific questions about the model's ability to be used for source-receptor determination and identification of Areas of Influence:

The technique is available and can be used over any domain which has the appropriate information. It is appropriate only for compounds that behave linearly, or very nearly so. Thus, primary aerosol and sulfate (if one is not worried about the size distributions) are the two which would be most relevant. The technique could also be used for AOI-type calculations for those species with the same limitations.

The computational requirements of such a technique are small. They were applied successfully about two decades ago. Now, the desired meteorological data are more routinely available, and with the advent of the more advanced meteorological models (e.g., the mesoscale Eta model) being applied and having their results available, this type of approach is even more practical. The required data bases are minimal: wind fields for the area of interest over the period of interest and the emissions. It is hard to imagine a method with fewer requirements that is still even close to being valid. The expertise to use such a model is not great, and the technique could be made routine very quickly. The output is fields of pollutant concentrations, possibly marked as to their origin and also a deposition field.

The technique has very significant limitations and uncertainties. One major limitation is that the model assumes linear behavior in the pollutant. Further, as discussed, the model is primarily developed from tuning. The tuning process suffers from the errors in the inputs, and then masks those errors. It would be interesting to see how different the results are if one uses an alternative set of meteorological fields, (e.g., developed from a second model or just interpolated values). Further, do the tuned values change significantly between years and domains? If so, this raises concerns. Also, if the fineness of the meteorological model grid is an important limitation on the minimum resolution of the transport model, this further limits the method's applicability. On top of the uncertainties and errors introduced from the tuning process and the approximations made in how it follows the pollutant evolution, the uncertainties and errors are introduced from the meteorological model used to develop the input wind fields. The model evaluation as presented in the article is insufficient to support adoption. Such a step should include not only more extensive model evaluation (in the same domain, and elsewhere), but also a more thorough investigation of the uncertainties and limitations discussed above.

4.6b Reviewer 2

1.	Does the method seem scientifically plausible?

The Monte Carlo approach abandons traditional numerical techniques to air quality modeling in that no explicit conservation equation is solved. The approach treats emissions as quantized units subjected to transport forces; the trajectory and fate of those quantized units from any particular source are then followed as though with a Lagrangian model. Within the mixed layer, the height of the quantized masses is randomly changed every two hours of the simulation by Monte Carlo methods using values from a uniform probability density function (PDF) to represent vertical mixing. Horizontal diffusion is treated by standard K theory eddy diffusivity techniques.

The meteorological module must transpose meteorological data to a Lagrangian airmass history database by computing transport of conserved species in three dimensions. The CAPITA model uses RAMS and NGM data and converts the meteorological state variables to a grided format, then transposes from the Eulerian to the Lagrangian frame. For source-receptor analysis, the Lagrangian databases used in the air mass simulations can be run either forward (i.e., from the source to the receptor) or backward (i.e., from the receptor back to the source).

The chemistry kinetics module uses the source-oriented Lagrangian airmass databases with additional emissions and surface meteorological data and derives specific mass weights for each species emitted according to known emissions rates. The kinetic rate equations are then integrated along each mass trajectory, accounting for transformation and loss over time. The rate coefficients for these equations are dependent on spatial and temporal characteristics of the simulated region and therefore vary over time.

Source-receptor calculations are made with the model by defining a receptor volume and summing the Lagrangian variables for all species mass in the volume to derive a concentration field, or by summing over a defined receptor area to derive a deposition field.

However, that generalized background is not particularized for source-receptor calculations in the method; and given the issues and concerns about the chemistry representation and the possibility of testing the model described below, the method as presented in this paper appears to have a weak scientific foundation.

2.	What are the method's key strengths and weaknesses?

One strength might be that this technique could give a fairly quick depiction of regional transport phenomena, although the length of time for the simulations reported is not given. Also, it should be noted that the procedure requires specifying an artificial probability distribution for the meteorological conditions which may not correctly reflect true conditions of the region.

Treating the chemistry and transport characteristics separately on the assumption that transport affects chemistry, but that chemistry does not appreciably affect transport, is the key to analyzing source-receptor relations. But this is not unique to the CAPITA model; Eulerian models with the advantage of having real chemistry can also be instrumented for outputting the results of transport processes separately from the results of chemistry.

The method makes a crucial assumption: since the kinetics rate coefficients are tuned against observations, the pollutant transport and emissions rates must be known with a high certainty. The example used in the paper reviewed here was for SO2, for which there may be less uncertainty in emissions than for NOx and VOCs which would be needed if source-receptor calculations were to be made using this model for ozone.

Note also that the relation of transport to chemistry for the SO2 case presented in the method might well be quite different from the NOx-VOC-O3 case, since their atmospheric lifetimes are not closely equal to their transport distance, as is the case for SO2.

3.	Has the method been adequately reviewed and demonstrated?

The paper reviewed here has not presented a compelling case that the methods can be successfully used. The described results for SO2 are not especially surprising, and may not be directly relevant to the NOx-VOC-O3 case [see above]. Moreover, the authors interpret the SO4 concentration isopleths presented in Figure 8 as showing that the model simulations "reproduce the general spatial pattern and magnitudes;" however, this seems to be at odds with the actual representation in that figure, where for the first quarter simulation the spatial orientation of the simulated fields looks quite different from the observed. The simulation for the third quarter fits the observed pattern more nearly, but that pattern is more wide-spread and dense than the one simulated in the first quarter. Additional quantitative pattern analysis may help in this regard.

There is little direct description of the actual model or the model set-up for these simulations in the supplied paper reviewed. Again, more details may be available in other documents concerning this method, but they are left out of this paper.

The method and any external review of its application are also not particularly well- documented in this paper. Concerning the model formulation, there is one reference to a 1986 gray literature document internal to the authors' research group, and one reference to a 1981 publication in the Journal of Applied Meteorology. There are two other gray literature references and one conference proceeding which are not directly relevant to use of the model for source-receptor attribution.

4.	What, if any, additional reviews/tests should be done?

No mention of any error analysis has been mentioned. There are real questions about how a model that makes such extensive use of observational data for tuning and set up can be successfully evaluated against those data, and no alternative evaluation method was proposed here.

Internal error analysis would be possible and should be done for ensuring that the meteorological state variables transposed in several steps from other data sets do not introduce substantial error into the transport and kinetics calculations, since both depend so closely on them. There are multiple steps where spatial and temporal scales must be matched, and resolution interpolated. These seem straightforward but are not trivial elements of a model setup.

Sensitivity testing should also be done to indicate the responsiveness of the model to the range of values in NOx and VOC observations used to tune the kinetics equations [see above].

5.	Does implementation of the methodology require data and expertise currently unavailable to State/local air quality agencies or the U.S. EPA Office of the Air Quality Planning and Standards? If so, is it realistic to assume that appropriate measures can be taken to develop the necessary data and personnel resources to support implementation of the method in the future?

There is insufficient material in this paper to tell whether the method could be implemented by current staff at State/local air pollution control agencies. No details are given about the computational requirements for producing and analyzing these results (these details may well be given in other materials pertaining to this method, but are not included in the supplied paper).

The method is heavily dependent on accumulated databases of meteorological and air quality conditions, and on the transposition of these data into a format this model can use. That proposes a fairly low-level, but potentially cumbersome routine of data manipulation, as well as the more significant problem of error introduction in the transposition [see above]. However, such database manipulation, if standardized, should be within the ability of most air quality staff currently operating with other models. Visualization of the results is straightforward, but interpretation requires thoughtful analysis to account for the effects of using limited chemistry and relatively gross grid resolution.

6.	Given information about resource requirements, status of documentation and degree of portability, is the method practical for the short term (i.e., next 18 months)? Longer term (within the next 4 years)?

Judging only from the description given in the paper reviewed here, the model seems to be insufficiently supported in its science and inadequately tested to make implementation feasible in the short term. Feasibility in the longer term will depend on the ability of the authors to address the significant questions of model formulation and evaluation.

7.	For which of the following applications is the method appropriate (can be more than one). Please indicate briefly why.

7a.	for quality assurance of emission inventory estimates?

No. The method depends uniquely on these emissions inventory estimates, and so may not be suitable as an external test of them.

7b.	for helping to identify areas of influence (AOI), as defined in the 2/13/97 paper on regional air management partnerships and areas of influence prepared by the National/Regional Strategies Working Group for the FACA?

No. The utility of the method for the NOx-VOC-O3 case has not been established [see above].

7c.	for help in selecting episodes to consider with nested regional air quality models?

Perhaps. The method (with appropriate additional sensitivity tests, and a better grid size resolution) might be used for simulating a fuller range of meteorological conditions using Monte Carlo than is currently done in regional air quality modeling.

7d.	for prioritizing strategy selection for simulation with nested regional air quality models?

No. [same reason as for 7b]

7e.	for use in a weight of evidence determination as part of an attainment demonstration?

No. [same reason as for 7b]

7f.	for other purposes? Which ones?

Not discussed by the reviewer.

ABBREVIATIONS AND ACRONYMS

AOI	Area of Influence

AOV	Area of Violation

AIRS	Aerometric Information Retrieval System

ANATEX	Across North America Tracer Experiment

CAPITA	Center for Air Pollution Impact and Trend Analysis

DMOC	Daily Maximum Ozone Concentration

EKMA	Empirical Kinetics Model Approach

FACA	Federal Advisory Committee Act

GAMUT	Global Atmospheric Multi-layer Transport Model

GNYMA	Greater New York Metropolitan Area

HNO3	Nitric acid

H2O2	Hydrogen peroxide

HPS	High pressure system

MORS	Multi-Variate Ozone Response Surface

NAAQS	National Ambient Air Quality Standard

NARSTO	North American Research Strategy for Tropospheric Ozone

NGM	Nested Grid Model

NOx	Nitrogen oxides

NOy	Total reactive nitrogen

NOz	Sum of NOx reaction products

NWS	National Weather Service

O3	Ozone

OTAG	Ozone Transport Assessment Group

PDF	Probability Density Function

PM2.5	Particulate Matter less than 2.5 microns in aerodynamic diameter

QC	Quality Control

R2	Regression coefficient

RAMS	Regional Atmospheric Modeling System

ROG	Reactive Organic Gases

ROM	Regional Oxidant Model

SCION	Southeastern Consortium Intermediate Oxidant Network

SO2	Sulfur dioxide

SO4	Sulfate

SOS	Southern Oxidant Study

SOx	Sulfur oxides

UAM-V	Urban Airshed Model, Version 5

U.S. EPA	United States Environmental Protection Agency

VOC	Volatile Organic Compound