This is a preliminary document prepared by a subgroup member of the National and Regional Strategies Work Group and the Science and Technical Support Work Group as part of the Federal Advisory Committee Act (FACA) Subcommittee process. It is not an EPA document. This document has seen only preliminary review by the work group and has been substantially changed since that review. Members of the NSWRG and STWG subgroups are still developing the issues and options for this topic. Interested people should forward their comments to Ted Russell of the National and Regional Strategies Work Group.

The Modeling Process and Emissions Inventories:

Their Availability, Evaluation, Use, and Limitations

Date:	July 22, 1997 Material added by Ned Meyer in italics & redlined

Contact:	Dr. Ted Russell <trussell@pollution.ce.gatech.edu>

Status:	Draft #3.3

Note: Readers are encouraged to read related issue papers for discussions concerning Area of Influence (AOIs), Areas of Violation (AOVs), Regionally Integrated Plans (RIPs), and Regional Air Management Partnerships (RAMPs). These terms are defined in Appendix A, taken from the 2/13/97 RAMP/AOI paper. These papers are all available from TTN and WWW. The following provides an initial list of papers directly related to this issue paper:

Designation Issues for New NAAQS (7/25/96)

Institutional Mechanism for Development and Implementation of Regional Strategies (2/13/97)

Area of Violation Boundaries (2/20/97)

Regional Air Management Partnerships and Areas Of Influence (2/13/97)

Introduction

This paper deals with issues relating to the use of models and emissions inventories in response to the FACA discussions. The paper first discusses some of the reasons this issue paper was suggested, then identifies and addresses some findings, which appear as "statements". Next, a number of issues upon which FACA can make recommendations are presented.

In the FACA process, the largest body of knowledge about demonstrating attainmnet (or continuing non-attainment) is the process of ozone modeling, including developing inventories of its precursors, meteorological modeling, and control strategy assessment. The process for modeling PMfine and regional haze is not so well developed and will require significant basic data acquisition and analysis, with the concurrent development of appropriate analysis techniques. This paper is meant to address specific issues dealing with emission inventory development and air quality modeling, particularly looking at guidance for the future. It is useful to compare future recommended directions for modeling/inventory assessments with current practices. Current practices are summarized in Appendix B.

Air quality modeling should be viewed as a process, including data collection (air quality, emissions, source activities and meteorological), emissions inventory construction, quality assurance of the data and inventory, air quality model input preprocessing (including construction of meteorological fields and emissions modeling), model development, model application, evaluation (of both the inputs and outputs), review of existing air quality control programs likely to affect the air pollutant of interest, and then, finally, control strategy analysis. The process is not as linear as this may make it appear and is evolving. There will be iteration, likely at every step. For example, evaluation of the inputs could well lead to reanalysis of those inputs. Then, model evaluation can lead to identification of other questions in the model inputs and/or model formulation. This iteration should be encouraged, and documented, to lead to a better final product. The iteration should also be done at a larger scale, that being every few years the process should be repeated both to improve the prospective estimates of how air quality will improve and what level of controls are necessary, but also to assess how well prior modeling exercises were able to simulate the response of the atmosphere to the controls applied during the interim. Peer-review of model capabilities is part of the model development and evaluation aspects of the process. The process should be evolutionary in a way to bring the best tools to bear in air quality management.

The following 7 statements concerning modeling/inventories appear appropriate.

Statement 1: Models are available for use in designing ozone and PM SIP's. However, quality of data bases and lack of rigorous performance evaluation should make one cautious in interpreting their output.

Statement 2: Availability of adequate emissions and other inputs is probably the greatest limitation to roles of models in regulatory applications. We need better emissions, meteorological and air quality data to apply the models with confidence to develop effective controls.

Statement 3: It is essential that emissions data be collected and reported consistently. This is necessary for strategies to be fair, to reduce biases in selection of control measures, and to evaluate/quality assure emissions information/collection procedures against independent data.

Statement 4: Regulatory demands on model outputs need to be reconciled with limits on the resources needed to develop technically defensible analyses. The resource problem consists of 4 dimensions: capable, well-trained personnel; money to collect needed information and apply the models; time and computer/data base management limitations.

Statement 5: Appropriate use of models in attainment planning should be considered as a process consisting of several essential steps which are iterative. The first step is to collect/analyze emissions, meteorological and air quality data. Often this may require special field studies. The second step is choice of appropriate episodes and domains to model using the data base collected in step 1. The third step is to run emissions and meteorological models to generate appropriate inputs to the air quality model. The fourth step is to evaluate/quality assure inputs/outputs of the meteorological and emissions models. The fifth step is to evaluate the air quality model's ability to predict physically realistic air quality values. The first 5 steps must be satisfactorally completed before the model can be used to evaluate prospective control strategies.

Statement 6: Performance tests for air quality models should be significantly more rigorous than current common practice. Greater attention should be given to the ability to predict precursors, key indicator species and to simulate physical processes in a realistic manner.

Statement 7: Model attainment demonstrations and RFP-related modeling requirements should be closely coordinated so as to provide a clear set of requirements to States in which a limited number of analyses meet several regulatory goals/requirements so as to limit resources demanded of the States. This implies that "mid-course reviews" required in the attainment demonstration process and retrospecitive analyses required for RFP should be timed so that they are one in the same.

The preceding discussion and concerns in the public and private sectors leave a number of issues needing further resolution. Important ones of these are addressed below. Options for resolving each issue are also identified along with tentative recommendations for consideration by the FACA subcommittee in making its recomendations to the EPA.

Issue 1. Appropriate Use of Models in Attainment Demonstration/ Reasonable Further Progress (RFP). (There may be areas where a realistic analysis can not show attainment. In that case, RFP demonstration may have to be used to replace attainment demonstrations. The latter is a policy issue.) One very pertinent question that is being asked with increased frequency deals with the appropriate use of models in demonstrating attainment/RFP. Currently air quality models are the backbone to demonstrating attainment/RFP. As described above, a model is evaluated by its ability to simulate an historical episode. After acceptable performance is obtained, it is used to simulate the impact of the application of controls. Attainment/RFP is demonstrated when the model results show that the set of controls lead to acceptable air quality. However, models are not perfect, nor are their inputs. In typical ozone model applications, the simulated ozone levels are generally within no better than 25% of the observations. The disagreement is likely due to a combination of the lack of spatial representativeness in the observations and the model uncertainties, with most of the disagreement due to the latter. Another set of question arises from the application of a deterministic model to a stochastic process. In order to evaluate a model, it is applied to historical episodes. However, that meteorology will never be repeated. Controls may produce different results in future meteorologies. These questions suggest that the air quality modeling process is flawed when applied to showing that future air quality will meet a air quality goal specified at the ppb level. (The uncertainties can be as large as the change in concentrations desired.) Another weakness, often cited, is that the changes in emissions expected due to growth and controls are also uncertain.

On the other hand, the models, particularly when applied correctly, are the scientifically most sound means of quantifying how air quality will respond to future emissions changes during similar meteorological episodes. Those episodes are chosen to represent meteorologies that lead to conditions that have, in the past, been tied to non-attainment, and thus, all else equal would likely be similar to periods in the future that would also have higher levels. If not a model, what other approach is preferable? How should models be used for attainment demonstration?

Option 1.	Do not use models for attainment demonstration/RFP since they are not accurate enough.

Pros: Addresses the issue of uncertainty and model limitations

Cons: Begs the issue as to how to determine the magnitude of control, or which sources should be controlled. Environmental regulatory analysis will always be done in the face of uncertainty and model limitations. Models are arguably the best approach, whose use can be made stronger by the development of better modeling process data (emissions, air quality, meteorological), and whose results can be supported with corroborative information.

Option 1b.	Use models for attainment demonstration, but only for directional information.

Pros: Addresses concerns about model limitations

Cons: Still begs the issue as to how to determine the magnitude of control, or which sources should be controlled. Environmental regulatory analysis will always be done in the face of uncertainty and model limitations. Models are arguably the best approach, whose use can be made stronger by the development of better modeling process data (emissions, air quality, meteorological), and whose results can be made stronger with supporting corroborative information.

Option 2.	Apply an appropriate model and use it for attainment/RFP demonstration.

Pro:	Similar to current structure so is more readily accomplished.

Provides a means for demonstrating attainment.

Cons:	Does not deal with the view that models are incapable of "demonstrating attainment."

Does not deal with what constitutes adequate performance.

Option 2a:	Apply an appropriate model, and attempt to get "performance" within some recommended guidelines. If the model performs adequately, it may be used for attainment.

Pro:	Similar to current structure so is more readily implemented.

Provides a means for demonstrating attainment when model performance is adequate.

Cons:	Does not deal with the view, held by some, that models are incapable of "demonstrating attainment."

Does not deal with poor model performance due to lack of appropriate data.

Requires the development of performance guidelines that are themselves not universally accepted.

Adequate performance can be region- and case-dependent.

Use of rigid performance guidelines leads to model gaming that is ultimately harmful to the process.

Hidden flaws

False confidence

Does not provide a mechanism to demonstrate attainment if model performance is inadequate or if appropriate data is unavailable.

Option 3:	Continue to use models similar to now: Apply an appropriate model using the most appropriate inputs for the region (AOI), based on the nature of the air pollution problem. Use the model results along with other air quality analysis. If good model performance is achieved, the model is then used in attainment demonstration. If poor performance is found, reasons for poor performance should be identified and quantified. (Reasons for good performance should also be established.) Further, if poor performance is found, greater weight would be placed on other information used in the attainment demonstration process (e.g., statistical analysis of the response of air quality to emissions controls, observation based methods, etc.).

Pro:	An extension of the current structure so is more readily implemented.

Provides a means for demonstrating attainment when model performance is adequate or not.

Provides a more robust approach to attainment demonstration

Cons:	Better deals with the view that models are incapable of "demonstrating attainment."

Requires the development of performance guidelines that are themselves not universally accepted.

Adequate performance can be region-dependent.

Recommendation: Option 3. Air quality models (and the air quality modeling process), when applied correctly and in conjunction with other information should provide the best means of assessing the necessary levels of controls. As discussed below, the process should be iterative and on-going, with periodic re-assessment.

Issue 2. Identification and Treatment of Uncertainty in the Emissions Inventorying and the Air Quality Modeling Process. Environmental decision making will be conducted in the presence of uncertainty, now and forevermore. Some of those uncertainties are inherent, while others exist due to the lack of effort to minimize them. In the past, the decision making process was not cluttered significantly by the knowledge of exactly how big those uncertainties (and biases) were (and continue to be). This was largely because of a lack of knowledge on the magnitude of the uncertainties, which is largely due to the perception that developing better, quantitative uncertainty estimates would not be used. On the other hand, it is now recognized that knowledge of such uncertainties is valuable for a number of reasons. Policy-makers can use such information to influence their decisions. The public can use it to understand the complexity of the issues involved. Scientists can use it to identify methods to reduce uncertainty in future studies. Finally, quantitative knowledge of uncertainties and variabilities can be used to identify better air quality management practice and design control strategies, much like it is used in the design of automobiles to improve product quality. However, development of a better knowledge of uncertainties takes resources. A major problem with not addressing uncertainty is that, while it helps define what we don't know or should not say, abstract discussion of its impacts cloud what we do know and can say with greater confidence. Further, if the agencies (EPA and others) are not actively assessing uncertainties, other parties will, and use such information to argue for increased or decreased controls. EPA can take the lead in this endeavor.

Option 1. Continue to focus primarily on best estimates for developing emissions inventories and air quality modeling, as opposed to developing best estimates and quantitative uncertainty estimates.

Pros: Easiest to implement and least resource intensive

Cons: Does not allow good estimates of uncertainties in the overall process, or the	determination of more robust approaches to air quality management.

Inhibits identification of the most critical uncertainties in the process.

More likely to allow major errors to exist

Does not, by itself, provide significant impetus to reduce uncertainties.

Option 1a. Continue to focus primarily on best estimates for developing emissions inventories and air quality modeling with some qualitative estimates of uncertainties, as opposed to developing best estimates and quantitative uncertainty estimates. (This most closely approximates the current state)

Pros: Easy to implement and not significantly more resource intensive than option 1.

Cons: Does not allow good estimates of uncertainties in the overall process, or the	determination of more robust approaches to air quality management.

Inhibits identification of the most critical uncertainties in the process.

More likely to allow major errors to exist

Does not, by itself, provide significant impetus to reduce uncertainties.

Option 2. Require that quantitative uncertainty estimates be developed and documented for emissions, meteorological input and boundary condition estimates. Any modeling exercise that uses those estimates would also assess how those uncertainties impact the assessment of control effectiveness and the attainment/RFP demonstration. The results of that uncertainty exercise would then be communicated to the appropriate policy makers, as well as in the RIP/SIP/AOI determination.

Pros:	Begins to educate the community as to the magnitude and origin of uncertainties	in the modeling process (for attainment demonstration or AOI	determination)

Allows decision-makers to better assess what options are most appropriate.

Would ultimately reduce uncertainties in the process.

Cons:	Somewhat more resource intensive than options 1/1a, and may discourage or	impede firms or agencies from developing and reporting emissions estimates.

As stated, does not provide significant impetus to reduce uncertainties.

Option 3. Require that quantitative uncertainty estimates be developed and documented for emissions estimates, and other model inputs and parameters. Any modeling exercise that uses those emissions would also interate the emissions and monitoring uncertainties. Then a more realistic calculation could be made to assess how the individual and composite uncertainties impact the assessment of control effectiveness and the attainment/RFP demonstration. The results of that uncertainty exercise would then be communicated to the appropriate policy makers, as well as in the RIP/SIP/AOI determination. Further, attainment/RFP demonstration would be based on showing attainment with the uncertainty taken into account, e.g., the attainment/RFP based on the best emissions estimates would be demonstrated to attain by a certain date would be likely with a certain degree of confidence based upon knowledge of the uncertainties and biases in the inventory.

Pros:	Begins to educate the community as to the magnitude and origin of uncertainties	in the modeling process (for attainment demonstration or AOI	determination)

Allows decision-makers to better assess what options are most appropriate.

Encourages minimizing uncertainties, and would reduce uncertainties in the	process faster than other options.

Cons:	Somewhat more resource intensive than options 1/1a, and may discourage or	impede some bodies from developing and reporting emissions estimates.

Could lead to excessive control requirements if attainment/RFP demonstration is not based on the best emissions estimates.

Recommendation: Option 2, moving to option 3 aggressively. (There may be some areas where Option 1, temporarily, is appropriate.)

Issue 3. Flexibility in the Attainment/RFP Modeling Process. Currently, the attainment modeling process is rather tightly prescribed. For example, the modeling guidelines, given above, specify the default model to use (UAM-IV), and EPA has developed modeling guidelines and performance measures. A number of reasons are given for such prescription. First, a preferred model (UAM-IV) has been set to deter the problem of 'dueling' models. Second, this promotes consistency. On the other hand, it leads to the use of a sub-optimal approach. For example, the preferred model is urban scale, and it is now recognized that regional transport is important. Further, it represents what many believe to be a less advanced model. These two factors are typified by a current situation in North Carolina.

The state of North Carolina wants to a scientifically more advanced model than UAM-IV in their attainment demonstration process, including one that can also better handle regional transport. However, they are required to apply UAM-IV, and then show equivalent or better performance when using a more advanced approach (e.g., MAQSIP). First, this increases the amount of work necessary. If it is generally accepted that MAQSIP is a "better" model, why use a less good one first. Second, suppose that UAM-IV performs "better." This presents some significant complications and conundrums. There is general agreement in the modeling community that

MAQSIP is a more advanced and scientifically comprehensive model

Uncertainties in the process (e.g. the emissions inventories and meteorology fields) are so large that changes in the inputs within the range of uncertainties could change the performance of both models greatly.

The typical performance measures are inadequate, particularly if only ozone predictions are considered.

Thus, an agency could be saddled with doing more work, and then being forced to use a model that is considered to be less good. The better performance achieved by the older model may be due to an inadequacy that will bias the assessment of control strategies. One direction that North Carolina could choose is to tune MAQSIP, but that may introduce new biases. Thus, being strapped to a single model leads to the process having less integrity.

A second reason is that no single approach, or model, is best for all applications. Third, this can lead to stagnation in advancing the modeling process, and the current regulatory photochemical model (UAM-IV) is not the most scientifically advanced approach. There are compelling arguments for a more flexible process, the first of which is that it can allow an area to use the best approach. Some areas will have resources (time, money, tools, and data) that are not available to another area. Prescription would lead to taking the lowest common denominator. Further, as is evidenced historically, air quality management plans evolve, and the development of emissions inventories and the application of air quality models, are part of that evolution. An attainment/RFP demonstration developed at one time will, if attainment is not reached in the interim, be revised at a later time using better information, and hopefully better tools as well. Third, as mentioned above, it is preferable to bring as much information to bear in developing attainment plans without reliance on one approach alone. The dueling model syndrome can improve air quality management, and can in essence occur even now. If one allows multiple models to be applied, and that the choice of how much to base controls on specific model results is done by the quality of the model application (e.g. comprehensiveness of the modeling approach, model evaluation, etc.), then the system will evolve to use better approaches. (There is little to be gained by conducting an effort that is less defensible than other, "competing" efforts.) Thus, the potential of using multiple models, including some that may be very different, should be encouraged. In fact, this is recommended below (and is not too dissimilar to the ideals behind the weight of evidence directions being developed within the agency). Finally, models that are not being used for regulatory application at this time, and are in development most appropriately address many of the issues being addressed in this committee. This is likely to continue to be the case. Even with the advent of Models 3, similar problems will remain. The best approach (including air quality model(s)) may not be part of Models 3. Further, Models 3, by design, is supposed to represent a broad range of models, thus it is not a model, and a broad range of models may be supported by Models 3 that may not follow the guidelines suggested below. An associated issue is the use of proprietary and/or closed models, and this issue is discussed below in 2a.

Option 1. Continue to have a more prescribed approach to regulatory modeling.

Pros: Easiest to implement.

Easiest to monitor from a regulatory standpoint

Prevents dueling models

Cons: May not allow the most appropriate approach.

No current approach for integrated strategy development.

Current regulatory models not developed for integrated (PM/ozone/RH)	control strategy	development.

Stagnates the development of improved approaches

It is hard to argue that the development of a very costly control strategy is	required to use a sub-standard approach, or to place the hurdle of using a better	approach so high as to effectively block its use.

Option 2. Increase the flexibility in regulatory modeling, emphasizing using the best set of tools to do the job.

Pros: Should lead to the use of better air quality models

Will lead to more rapid development of air quality models

Provides greater range of possibilities to deal with air quality modeling for integrated strategies across multiple pollutants

Cons: Places greater burden on EPA staff to review demonstrations.

Could lead to dueling models.

May lead to states choosing an less appropriate model

Option 3. Increase the flexibility in regulatory modeling, emphasizing using the best set of tools to do the job. Commit to a frequent peer review and comparison(every five years?) of approaches taken in the modeling process, including how inventories are developed and manipulated, the applicability and strengths of available meteorological and air quality models, and how to interpret the results in regards to being able to demonstrate attainment. Possibly identify recommended models, and procedures to justify the use of other models. (One could develop user-based groups, structured in a manner similar to the Inter-Agency Workgroup on Air Quality Models (IWAQM). These groups could help share information and analysis techniques and intrerpretation.)

Pros: Would lead to the use of better air quality models (and an improvement in the air quality modeling process in general)

Will lead to more rapid development of air quality models

Provides greater range of possibilities to deal with air quality modeling for integrated strategies across multiple pollutants

Cons: Places greater burden on EPA staff to review demonstrations.

Could lead to dueling models.

Option 3a. Similar to Option 3, except that each RAMP or AOI have a Technical Advisory Panel (TAP) of modeling, emissions and meteorologists to review and advise on all aspects of the work from protocol development through interpretation of results and uncertainty analysis.

Pros: Would lead to the use of better air quality models (and an improvement in the air quality modeling process in general)

Will lead to more rapid development of air quality models

Provides greater range of possibilities to deal with air quality modeling for integrated strategies across multiple pollutants

Removes burden from EPA to review the details of the SIPs

Cons: May be difficult to identify or get persons with appropriate expertise to review, given limits on their time.

Could be problems with potential conflicts of interest.

Review may be time consuming and inconsistent with required regulatory timeframes.

Recommendation: Option 3 or 3a The modeling process for use in attainment demonstration should be flexible, with an emphasis on using the best approach suitable for the region and the specific problem. A set of default models could be suggested after careful cross comparisons and evaluations including benchmark application, and a minimally acceptable approach identified such that the agencies have a starting point, but the choice of emissions inventory modeling and air quality modeling tools would be flexible to allow the state, tribe, or RAMP to choose their preferred, and scientifically most appropriate, approach. In some cases, the supporting data may significantly limit model choice. The solution is not to specify a new default model, since any current choice will prove to be sub-optimal in the future (particularly as air quality managers deal with multi-pollutant and multi-objective concerns). Instead, the agencies should be encouraged to use the best approaches available. This may involve more than one model, using a range of inputs into a single model, extensive data analysis using time series techniques and/or observation-based approaches. The key is to make sure that the agencies have the flexibility to use the best approach and also provide a motivation to do so.

Issue 3a. The Use of Proprietary and/or Closed Models: (Here, proprietary model refers to its unavailability to any parties at a reasonable fee, and closed refers to the unavailability of the source code for inspection and modification for testing purposes.) In the OTAG process, there has been significant concern and controversy over the use of a proprietary and closed model, particularly for regulatory policy development. It can be argued that, on one hand, one should be allowed to use the best available approach, and at times that may be a proprietary or closed model. However, the use of such a model can be dangerous. First, errors may exist in the approach that are not readily apparent and would be less readily identified if the tools are not fully open to inspection. Second, it can minimize the ability of all the interested parties from developing their own knowledge base that could then be utilized in the process, and further inhibits the development of information within the community. It can also potentially put a monopoly on the ability of groups to be effectively involved by limiting access to such models either by the model's owners not allowing access or the group not having sufficient resources. Further, a recommendation made as part of this issue paper is to have periodic scientific peer review of the air quality modeling process, including the various models (emissions, meteorological and air quality) that are part of that process. This level of openness of a model should be expected, that is, any credible group that wishes to conduct an investigation of a model should be allowed, if that model is going to be part of the regulatory process.

Option 1.	Allow the use of proprietary models.

Pros: Can allow the use of the best tools, if in some case the best tool is proprietary.

Cons:	Will inhibit the open development of relevant information

Model errors and limitations are less readily identified

Inhibits widespread participation

Models will lack peer-review of science, uncertainties, etc.

Option 2.	Not allow the use of proprietary models in the development of SIPs and RIPS, though allow information derived in past efforts for regulatory policy development.

Pro's	Better ensures that environmental policy making will be an open process

Cons:	May not allow the use of the best approach.

Option 3.	Not allow the use of proprietary model in the development of SIPS, TIPS, RIPs, etc., and not allow the use of information derived from such models for regulatory policy setting.

Pros: Better ensures that environmental policy making will be an open process

Minimizes the temptation to use a proprietary or closed model with the hopes that such results would be used.

Cons: Would not allow information derived historically.

In some cases it may not allow the use of the best approach

Recommendation: Option 2 or 3.

Summary on Issue 2: Model Flexibility. It is the general view that the concept of guideline models should be dropped, and instead the use of the most appropriate set of models be used. Thus flexibility is strongly supported. Models should be peer-reviewed, and open to further exhaustive, independent peer-review and application by all parties involved in the process. (Note: This does not exclude models for which a modest license fee is required, but it would require that the source code is available and that other parties could modify the code to perform tests and peer-review can be done without licensure.) The burden of proof as to the applicability of a proprietary model falls on the owner, and such proof would be thorough evaluation. Further, it can not be closed, i.e., to inspection and outside evaluation.

Issue 4. Emissions Inventories are the Critical Uncertainty in the Attainment Demonstration Process, and Action is Needed to Alleviate the Problem. One of the most critical limitations in the air quality management modeling process are the large uncertainties in many parts of the emissions inventories, even for ozone precursors. For PM10 it is worse, and worse still for PMfine. Little attention has been paid to developing spatially and temporally disaggregated emissions inventories for ammonia and primary fine particulate matter. To do a credible job, models will require detailed information of those emissions, as well as SO2, NH3, VOCs (speciated) and NOx. There is limited information on the size and composition of PM from many sources; for example Prof. Cass noted that there are over 70 separate sources of primary carbonaceous PM. Developing an inventory requires accurate information on the number of emitters in each source category, their activity levels and emission rates in terms of particle size and chemical composition. This would be a significant undertaking, though provide the basis for greater confidence placed in the regulations emanating from such an effort. It is generally believed that one can devote the necessary resources now and do it right, or spend significantly more in the long run down the road making up for it.

Option 1. Use existing state and national inventories along with default information

Pros: Analysis can start quickly

Cons:	May misallocate control emphasis

Default information may not be appropriate in some locations

Option 2. Launch a major research initiative to improve inventories

Pro's:	Strategies will be based upon most up to date information and should provide much better guidance on which controls should be most effective

Able to better identify deficiencies in the models

Cons: May cause delays in the analysis

Costs may be substantial

Option 3. Start with existing inventories for preliminary AOI definition and switch to refined inventories for final RIP/TIP/SIP

Pros:	Analysis can start quickly

Final plans will be more credible

Con':	Time and resources needed to develop two, possibly inconsistent, inventories

Option 4. (Can be added to any of the above) Develop a mechanism to provide incentives to industry to better characterize their emissions. This can be done as a fee based on uncertainty levels or the requirement to increase control level to make up for uncertainties.

Pros:	Can lead to better inventory characterization more expediently

Con':	Not popular with affected sources

Best incentive mechanism not apparent

Suggestion: Recommendation 3. (?) + 4 (?)

An issue that is not addressed above is that of roles and responsibilities. In part this is because the roles and responsibilities of some of the more important entities is not yet defined. For example, does a RAMP identify a preliminary AOI, then forms a body to do the planning for that AOI, or does the RAMP maintain responsibility for the planning for all AOIs in that area. Further, will a RAMP have the resources (money/expertise) to conduct the emissions inventory development and modeling? These issues impact who takes what role in developing the emissions inventory and modeling. One approach is to give EPA much of the responsibility for facilitating the modeling process, including emissions inventory development, choice of models for AOI determination, (and possibly AOI determination guidelines), and identifying the resources necessary. Another is to give the RAMPs (or AOIs) such responsibility.

Resources is a constant problem in air quality management, and the emissions inventory/modeling process is not exception. However the stakes are big, and a number of stakeholders are involved. Involving the stakeholders early in the process can facilitate a successful outcome, and can be one vehicle for identifying the necessary resources. This has been seen in the execution of a variety of field campaigns and data analysis. Again, the more open the process, the better.

During OTAG, a new paradigm was used to provide the desired computational capabilities involving "Modeling Centers." This paradigm has been further suggested in a more recent EPA white paper. Each RAMP would have its center, though the "Center" could actually be virtual, i.e., it could be distributed amongst a number of organizations and locations. It is not apparent that this is the most effective approach (or given the fuzziness of what the centers might be, what this may or may not entail). In some instances, a consulting firm may be able to best provide the desired expertise and facilities, and other cases, an agency, industry or university (as is now the case). What is recommended is to open up the process (as was not the case in OTAG) to the community to provide both independent ideas and to act as centers if that is the chosen paradigm. What is of concern is that continuing with the current state of affairs will miss opportunities and suffer from some of the baggage of OTAG. A particular concern with not changing significantly is that the current centers are set up to use a particular model, and the above recommendations suggest against both that model (without significant changes) and lack of flexibility. Again, there is a resource issue, not just for the RAMPS to do "their job," but also to develop the level of understanding necessary to evolve rapidly, and to lead cooperative efforts across stakeholders to conduct major studies. For example, in California, this role is played by CARB. In other areas, it is not apparent what organization(s) will take the lead.

It is not apparent that there needs to be a limited number of modeling centers (e.g., one per RAMP, or so). For example, different organizations can take the lead (and do the modeling) for different AOIs. Further, one organization may take the lead in the meteorological modeling, another for emissions, and another for air quality.

Appendix A

DEFINITION OF TERMS

Area of Influence (AOI) - An AOI is a geographical area containing anthropogenic and natural sources that contribute to Areas of Violation (AOV). The identification of AOIs is divided into two phases:

Phase 1 - Preliminary AOI - The Preliminary AOI is the area which contains an adequate array of sources to prepare a Regionally Integrated Plan (RIP). Inclusion within a Preliminary AOI does not mean that a location will necessarily be subject to regulation.

Phase 2 - Final AOI - The Final AOI is the area in which regulatory control of emissions will be needed to bring an AOV back into attainment and ensure maintenance of the standards. The Final AOI is synonymous with a control region.

Area of Violation (AOV) - The AOV is the area which is determined to be violating the NAAQS. Class I areas will be treated in the same manner as AOV's for the purposes of defining AOI's and developing plans. Contrary to the current nonattainment designation process, an AOV is not necessarily assumed to be responsible for the observed violation. Thus, designation of an AOV does not carry with it automatic control requirements for the designated area, as is currently the case for a nonattainment area. However, the initial presumption is that the AOV will be a part of the preliminary AOI and final AOI. This presumption can be rebutted only upon a clear and convincing demonstration that anthropogenic sources in the AOV are not contributing to the violations being experienced. (Process for making the demonstration to be defined later).

Control Region - A control region (CR) is an area within an AOI for which emission reductions of PM, its precursors and/or precursors of ozone are deemed necessary to meet the NAAQS/RH Rule. Controls are not necessarily applied uniformly across the CR. (A CR may be associated with one or more proximate areas of violation and Class I areas included with the AOI.) The control region is synonymous with the final AOI's.

Control Target - A control target is the amount of reduction and/or the emissions budget estimated as necessary in a control region to be consistent with an identified cohesive strategy for meeting PM and ozone NAAQS in areas of violation. The target can be a reduction relative to an agreed upon base level of emissions or it can be in the form of an emissions cap.

Regional Air Management Partnerships (RAMP) - Contiguous multi-state areas that have common air quality characteristics and share common air quality concerns. The RAMP will have the responsibility to undertake appropriate air quality analysis needed to identify the Preliminary Area of Influence. There are numerous options for the number and size of the RAMPs.

Regionally Integrated Plan (RIP) - This has been referred to as a "spatially integrated plan (SPIP)" in earlier versions of the FACA Regional Partnerships/AOI issue paper. Regionally Integrated Plan (RIP) - This has been referred to as a "spatially integrated plan (SPIP)" in earlier versions of the FACA Regional Partnerships/AOI issue paper. A RIP is prepared for a preliminary AOI. A RIP will define:

the Final AOI/control region,

in general terms, what level of control should be applied in the various portions of the AOI,

where transportation conformity should apply,

the structure and type of market-based incentives programs (where appropriate)

New Source control recommendations, and

other elements as appropriate.

The RIP is a set of recommendations to EPA and is to serve as the basis upon which EPA can develop SIP and TIP calls to the individual states and tribes. The RIP is not enforceable on its face. The RIP may also contain recommendations to EPA for action at the national or subnational level.

State Implementation Plan (SIP) - A State implementation plan is a plan submitted by individual States to reduce emissions consistently with the regionally integrated plan. It contains specific measures and identifies mechanisms by which the measures are enforceable by that State.

Tribal Implementation Plan (TIP) - A tribal implementation plan is a plan submitted by individual tribes to reduce emissions consistently with the regionally integrated plan. It contains specific measures and identifies mechanisms by which the measures are enforceable by that tribe.

Appendix B. Current Modeling Practices

Current policy regarding regulatory modeling for ozone is spelled out in three documents: "US EPA (1991) Guideline for Regulatory Application of the Urban Airshed Model, EPA-450/91- 013", "US EPA (1994) Guidance on Urban Airshed Model (UAM) Reporting Requirements for Attainment Demonstrations, EPA-454/R-93-056", and "US EPA (1996) Guideline on Use of Modeled Results to Demonstrate Attainment of the Ozone Standard, EPA-454/B-95-007." In addition, the EPA identifies "guideline models" for various applications (see Guideline on Air Quality Models (Revised), 40 CFR Part 51, Appendix W). The Urban Airshed Model, version IV is identified as the EPA Guideline model for use in attainment demonstrations. EPA policy is to allow use of other photochemical grid models in place of UAMIV if two criteria are met by those wishing to use an alternative model:

(1) There must be a discussion of why use of the alternative model is likely to be more sound scientifically than use of the UAM-IV and the EPA must agree with the reasoning presented, and

2) The proposed alternative model must perform at least as well as the UAM-IV in replicating observed data. Comparisons performed in making this determination are decided on a case by case basis.

Requirement 2, as discussed later, has been the subject of concern in the community, and is addressed in this paper. It has led to a relatively limited number of other models approved for use in attainment demonstrations. These include SAQM in the San Joaquin Valley applications and UAM-V in the LADCO and Atlanta applications. A request to use CALGRID in the New England application is pending. To date, use of urban scale analyses (e.g., horizontal grid resolution <~5km) has been recommended for all attainment demonstrations in the area which is the focus of the demonstration.

The EPA guidance for performing modeled attainment demonstrations for ozone addresses 10 subjects. They are listed below, and a number of the issues in this paper address aspects of the guidance. Subgroup commenmts on the 10 points are added paranthetically.

Development of a Modeling Protocol. This step requires the lead agency to describe the general procedures which will be followed in completing the 9 additional steps, as well as who will be participating and what arrangements will be followed in coordinating/communicating efforts. In cases where the domain encompasses more than one political entity, EPA urges participation of each in developing the protocol and in implementing subsequent steps.

Episode Selection. This is currently tailored toward the 1 ExEx NAAQS. Guidance suggests that exceedance days be divided into meteorological regimes. Within each regime days are ranked according to severity of the exceedances. Episodes should be chosen from among the top three ranked days in each regime. Exceptions have been granted to allow simulation of days with intensive databases. (This guideline should be modified for the new standard. In particular, the period containing the annual fourth highest ozone concentration should be considered. However, periods from more prevalent meteorological regimes should be considerecxd as long as the ozone concentrations are not substanitally difference from the observed annual fourth highest value.)

Preparation of Emissions Inputs. The starting point in our guidance is a 1990 emission inventory compiled by the participating States for a "typical summer day". These estimates are then adjusted to take account of VOC-speciation, day-specific meteorological conditions, day of the week and spatial/temporal variability patterns. Emissions preprocessor models are used for this purpose. The EPA has supported the EPS-2 processor for use for this purpose. However, other similar preprocessors (e.g., EMS-95) may also be used. BEIS1 is to be used to estimate biogenic emissions with CB4.2 chemistry. BEIS2 is recommended for use if the isoprene chemistry in CB4.2 has been revised to be consistent with the BEIS2 estimates.

Selection of Model Domain/Horizontal & Vertical Resolution. Size of the modeling domain is a function of the days selected to model. Guidance suggests the domain should be large enough to contain emissions originating in the center until at least 8 pm on the day of primary interest. Horizontal grid resolution of 2-5 km and use of at least 5 vertical layers within the planetary boundary layer are recommended. (The subgroup recommends that in coastal areas, the domiain should encompass the extent of the return flows.)

Boundary/Initial Conditions. Use of a regional scale model is identified as the preferred approach for identifying boundary conditions. Interpolation of appropriate monitored data is suggested as an alternative. Default estimates are identified for clean areas. The simulation should begin at least one day prior to the day of primary interest in order to diminish importance of assumed initial conditions. (Default values must be more realistic than just the annual mean values because the clean area pollutant concentrastions fluctuate with the weather conditions.)

Preparation of Meteorological Inputs. The preferred approach is to use regionally derived windfields (for regional models) and observations as a starting point for the more detailed winds needed for urban scale analysis. This should be compared with winds generated from the UAM's diagnostic wind model. Use the procedure, which seems to produce most realistic winds. Rawinsonde data are suggested for use in estimating mixing heights. Dynamic meteorological models are mentioned, but not particularly encouraged. Since publication of the guidance, these have been used with increasing frequency.

Performing Diagnostic Analyses. The guidance suggests these should be undertaken to increase confidence that results make physical sense and as a means for quality assuring inputs to the air quality model. Guidance is flexible regarding what these tests should be. Sensitivity tests are recommended as means for anticipating which components of the model's inputs are likely to be most influential in choice of control strategies.

Evaluating Model Performance. Use of computer graphics is recommended as qualitative means for assessing performance. Criteria for three numerical metrics are suggested to evaluate model performance in predicting ozone. These criteria are based on a series of urban analyses performed during the 1980's. EPA also encourages evaluation of ability to predict measured precursor levels. However, EPA has not placed much emphasis on this due to a lack of data and uncertainty about the spatial representativeness of the data that do exist.

Attainment Test. Guidance recommends evaluating severity of selected episodes based on a climatological database. Subject to some limitations, predicted exceedances are allowed on severe episodes. Guidance suggests use of corroborative analysis in cases that are close to meeting the air quality goal. The corroborative analyses can be used in a weight of evidence determination to justify approval of a demonstration not quite meeting the goal. Guidance requires use of a mid-course review as a means for dealing with uncertainty. EPAe recommends that modeling also be done for the intermediate period at which the mid-course review is conducted in order to facilitate the corrections.

Archiving/Reporting Information. Requires a description of procedures followed and results obtained. Also requires maintaining input and output files, documenting procedures for accessing them, identification of compatible hardware and ADP contacts for the application.

PM Modeling

To this point, EPA guidance on NAAQS attainment has, of course, focused exclusively on PM10. It is described in the following document:

US EPA, (1987), PM10 SIP Development Guideline, EPA-450/2-86-001.

Three approaches are outlined in the following order of preference:

Use dispersion models and receptor models in concert

Use dispersion models alone

Use receptor models alone, under limited circumstances (e.g., homogeneity of ubiquitous sources, etc).

A number of Model Guideline (40 CFR 51 Appendix W) Gaussian models are suggested for various types of applications.

One immediate observation is that ozone modeling practice is more developed. This is an important observation as the community must deal with PMfine air quality management and in multi-pollutant integrated strategies. Further, the ozone guidelines were developed for the old, one hour standard, and new guidelines are expected in the summer of 1998 to address modeling for the new standard.

Current policy regarding regulatory modeling for ozone was originally spelled out in two documents: "US EPA (1991) Guideline for Regulatory Application of the Urban Airshed Model, EPA-450/91-013" and "US EPA (1994) Guidance on Urban Airshed Model (UAM) Reporting Requirements for Attainment Demonstrations, EPA-454/R-93-056." The policy regarding tests used in the attainment demonstration described the first reference recently has been superseded by guidance in "US EPA (1996) Guideline on Use of Modeled Results to Demonstrate Attainment of the Ozone Standard, EPA-454/B-95-007." In addition, the EPA identifies "guideline models" for various applications (see Guideline on Air Quality Models (Revised), 40 CFR Part 51, Appendix W). The Urban Airshed Model, version IV is identified as the EPA Guideline model for use in attainment demonstrations. EPA policy is to allow use of other photochemical grid models in place of UAMIV if two criteria are met by those wishing to use an alternative model:

(1) There must be a discussion of why use of the alternative model is likely to be more sound scientifically than use of the UAM-IV and the EPA must agree with the reasoning presented, and

2) The proposed alternative model must perform at least as well as the UAM-IV in replicating observed data. Comparisons performed in making this determination are decided on a case by case basis.

Requirement 2, as discussed later, has been the subject of concern in the community, and is addressed in this paper. It has led to a relatively limited number of other models approved for use in attainment demonstrations. These include SAQM in the San Joaquin Valley applications and UAM-V in the LADCO and Atlanta applications. A request to use CALGRID in the New England application is pending. To date, use of urban scale analyses (e.g., horizontal grid resolution <~5km) has been recommended for all attainment demonstrations in the area which is the focus of the demonstration.

The EPA guidance for performing modeled attainment demonstrations for ozone addresses 10 subjects. They are listed below, and a number of the issues in this paper address aspects of the guidance. Subgroup commenmts on the 10 points are added paranthetically.

Development of a Modeling Protocol. This step requires the lead agency to describe the general procedures which will be followed in completing the 9 additional steps, as well as who will be participating and what arrangements will be followed in coordinating/communicating efforts. In cases where the domain encompasses more than one political entity, EPA urges participation of each in developing the protocol and in implementing subsequent steps.

Episode Selection. This is currently tailored toward the 1 ExEx NAAQS. Guidance suggests that exceedance days be divided into meteorological regimes. Within each regime days are ranked according to severity of the exceedances. Episodes should be chosen from among the top three ranked days in each regime. Exceptions have been granted to allow simulation of days with intensive databases. (This guideline should be modified for the new standard. In particular, the period containing the annual fourth highest ozone concentration should be considered. However, periods from more prevalent meteorological regimes should be considerecxd as long as the ozone concentrations are not substanitally difference from the observed annual fourth highest value.)

Preparation of Emissions Inputs. The starting point in our guidance is a 1990 emission inventory compiled by the participating States for a "typical summer day". These estimates are then adjusted to take account of VOC-speciation, day-specific meteorological conditions, day of the week and spatial/temporal variability patterns. Emissions preprocessor models are used for this purpose. The EPA has supported the EPS-2 processor for use for this purpose. However, other similar preprocessors (e.g., EMS-95) may also be used. BEIS1 is to be used to estimate biogenic emissions with CB4.2 chemistry. BEIS2 is recommended for use if the isoprene chemistry in CB4.2 has been revised to be consistent with the BEIS2 estimates.

Selection of Model Domain/Horizontal & Vertical Resolution. Size of the modeling domain is a function of the days selected to model. Guidance suggests the domain should be large enough to contain emissions originating in the center until at least 8 pm on the day of primary interest. Horizontal grid resolution of 2-5 km and use of at least 5 vertical layers within the planetary boundary layer are recommended. (The subgroup recommends that in coastal areas, the domiain should encompass the extent of the return flows.)

Boundary/Initial Conditions. Use of a regional scale model is identified as the preferred approach for identifying boundary conditions. Interpolation of appropriate monitored data is suggested as an alternative. Default estimates are identified for clean areas. The simulation should begin at least one day prior to the day of primary interest in order to diminish importance of assumed initial conditions. (Default values must be more realistic than just the annual mean values because the clean area pollutant concentrastions fluctuate with the weather conditions.)

Preparation of Meteorological Inputs. The preferred approach is to use regionally derived windfields (for regional models) and observations as a starting point for the more detailed winds needed for urban scale analysis. This should be compared with winds generated from the UAM's diagnostic wind model. Use the procedure, which seems to produce most realistic winds. Rawinsonde data are suggested for use in estimating mixing heights. Dynamic meteorological models are mentioned, but not particularly encouraged. Since publication of the guidance, these have been used with increasing frequency.

Performing Diagnostic Analyses. The guidance suggests these should be undertaken to increase confidence that results make physical sense and as a means for quality assuring inputs to the air quality model. Guidance is flexible regarding what these tests should be. Sensitivity tests are recommended as means for anticipating which components of the model's inputs are likely to be most influential in choice of control strategies.

Evaluating Model Performance. Use of computer graphics is recommended as qualitative means for assessing performance. Criteria for three numerical metrics are suggested to evaluate model performance in predicting ozone. These criteria are based on a series of urban analyses performed during the 1980's. EPA also encourages evaluation of ability to predict measured precursor levels. However, EPA has not placed much emphasis on this due to a lack of data and uncertainty about the spatial representativeness of the data that do exist.

Attainment Test. Guidance recommends evaluating severity of selected episodes based on a climatological database. Subject to some limitations, predicted exceedances are allowed on severe episodes. Guidance suggests use of corroborative analysis in cases that are close to meeting the air quality goal. The corroborative analyses can be used in a weight of evidence determination to justify approval of a demonstration not quite meeting the goal. Guidance requires use of a mid-course review as a means for dealing with uncertainty. EPAe recommends that modeling also be done for the intermediate period at which the mid-course review is conducted in order to facilitate the corrections.

Archiving/Reporting Information. Requires a description of procedures followed and results obtained. Also requires maintaining input and output files, documenting procedures for accessing them, identification of compatible hardware and ADP contacts for the application.

PM Modeling

To this point, EPA guidance on NAAQS attainment has, of course, focused exclusively on PM10. It is described in the following document:

US EPA, (1987), PM10 SIP Development Guideline, EPA-450/2-86-001.

Three approaches are outlined in the following order of preference:

Use dispersion models and receptor models in concert

Use dispersion models alone

Use receptor models alone, under limited circumstances (e.g., homogeneity of ubiquitous sources, etc).

A number of Model Guideline (40 CFR 51 Appendix W) Gaussian models are suggested for various types of applications.

One immediate observation is that ozone modeling practice is more developed. This is an important observation as the community must deal with PMfine air quality management and in multi-pollutant integrated strategies. Further, the ozone guidelines were developed for the old, one hour standard, and new guidelines are expected in the summer of 1998 to address modeling for the new standard.