Working Draft Paper - Do not cite or quote

This is a preliminary document prepared by a subgroup of members of the Science and Technical Support Work Group (STSWG) as part of the Federal Advisory Committee Act (FACA) subcommittee process. It is not an EPA document. It is subject to further work group discussion. Interested people should forward their comments to a member of the STSWG.

Issue Title: Scientific Issues Related to Defining the Areas of Violation (AOVs) and Areas of Influence (AOIs)

Contact: David Chock, Tom Moore, S. T. Rao, Ken Schere, Steve Ziman

Date: February 7, 1997

Preamble

In this Issue Paper, we discuss the general background, scientific understanding and tools available to date that may be useful for domain identification of an Area of Violation and its associated Area of Influence. While the tools may be applicable to both ozone and particulate matter (PM2.5 and PM10), it is important to appreciate that our knowledge of PM, especially PM2.5, is minuscule compared to that of ozone. Moreover, because PM contains multiple components from diverse sources and has a primary and a secondary fraction, substantially more monitoring of speciated ambient PM will have to be conducted before an AOV and its AOI can be adequately defined. At any rate, one must appreciate that the AOVs and AOIs of ozone can be quite different from those for PM10 and for PM2.5.

Background

The Area of Violation (AOV) is defined as an area within which one or more monitors register a violation of a given standard. The definition of a violation depends on how attainment and maintenance tests are defined in the final promulgation of the NAAQS and Regional Haze rules. For the purposes of establishing either a State Implementation Plan (SIP) within a state or a group of SIPs (as a category, SIPs would also include Tribal Implementation Plans (TIP), where appropriate) to support a Regionally Integrated Plan (RIP), a preliminary Area of Influence (AOI) boundary would need to be defined to represent the area whose emissions are potentially responsible for the violation of the standard(s) at the AOV(s). For a given set of violations chosen for control purposes, a control region (CR) within the preliminary AOI is to be established as the final AOI where emission reduction is imposed in an effort to achieve or approach the level of the relevant standards. Scientific guidelines, as they become available, should be used to help establish the domains of AOVs and refine the AOIs through both the preliminary and final analysis steps, in order to improve the cost-effectiveness of control and the likelihood of success. To avoid confusion in the definitions of AOI, we shall call the "preliminary AOI" as "AOI" and the "final AOI" as "CR" or the control region in this Issue Paper.

Air pollutant concentrations are strongly influenced by meteorology, which varies with a wide range of temporal and spatial scales (a scale means a domain within which the variable of interest tends to move up or down together). In addition, primary pollutant concentrations tend to be influenced by the microenvironments of the emission sources and thus can have very small spatial scales while secondary pollutants tend to have a much larger spatial scale which also tends to increase with averaging time. Past observations indicate that an hourly ambient ozone concentration observed at a monitor, in the absence of major NOx emission sources nearby, may represent the hourly concentration of an area around the monitor up to a radius of, say, around 10 to 15 km. No information is available for PM. But if the averaging time is 24h and the secondary component is dominant, the spatial representativeness of an ambient measurement may be fairly large. Note that these spatial ranges can vary considerably under different meteorological conditions. For example, under a stagnant condition, the concentrations of even the secondary pollutants can be quite localized, with a spatial scale of only a few km.

The scientific knowledge of tropospheric ozone has advanced significantly over the last decade or so. Knowledge of PM and regional haze characteristics has also continued to move forward over the past 10 years with significant advancement on the nature and causes of PM in the Western U.S. Ozone monitoring data commensurate with the recently proposed ozone NAAQS are much more comprehensive, by definition, than those for PM because insofar as the measurements are concerned, the new ozone NAAQS changes only the averaging time, whereas the revised PM NAAQS will contain a new size cut-point (PM2.5) whose federal reference method has just been proposed. A further complication with PM is that it is composed of a primary and a secondary fraction of multiple components.

For PM, including the constituents of regional haze, there have been special field studies in the Los Angeles Basin, Denver, Phoenix, and those conducted through the Grand Canyon Visibility Transport Commission. These studies have utilized a variety of ambient sampling and source apportionment techniques to characterize the nature and causes of elevated PM10, PM2.5 and haze episodes. Since no single ambient sampling technique has been universally applied for fine PM, baseline sampling data to support scientific analyses of cause and effect for likely future PM2.5 NAAQS violations urgently need to be collected. These data and their analyses are especially critical, as the PM2.5 NAAQS is an aggregate indicator whose magnitude and composition varies continuously in time and space. Further, data from the fine PM studies illustrate that no single sampling device can adequately capture, and retain for analysis, fine particulate aerosol. This limitation makes development of accurate, area-specific and cost- effective control strategies problematic. In contrast, for ozone, there have been several major studies in various parts of the country which have increased our knowledge of the ozone transport and transformation processes. More recently, two field studies -- the Southern Oxidant Study and the NARSTO-Northeast Study -- have provided even more valuable information and insights on the characteristics of ambient ozone. These insights actually help to reaffirm our previous understanding.

There are generally three types of high ozone (and perhaps, associated secondary PM as well) conditions. One consists of sunny days with high temperatures and low and often disorganized winds, typically associated with an atmospheric high pressure system. These are rather typical stagnant conditions where ozone (and perhaps secondary PM) concentrations are primarily generated by local emissions. (Note that in this case, even though the areas of violation may be large as a result of a large synoptic scale weather pattern, the emission sources leading to the ozone violation are local.) The second type are days with high ozone (and perhaps secondary PM) concentrations aloft that entrain into the mixed layer as the surface-based inversion erodes after sunrise. This high ozone (and secondary PM) aloft could have been generated in an upwind area due to the first set of conditions described above. The third type are days where both of the above conditions exist. These are likely days near the end of a large slowly passing high pressure system, as the disorganized wind flow becomes more organized and persistent before the advance of the next frontal boundary. For these days, a significant amount of local ozone is generated in addition to a downward mixing of a significant amount of upper-layer ozone.

In addition to the high ozone (and secondary PM) events above, local meteorological conditions may also be conducive to persistent pollution events that extend no more than tens of kilometers to several hundred kilometers from major source areas. Notable among these conditions are land-sea or land-lake breeze circulations, and complex recirculating wind flows in mountainous terrain. It is these conditions, among others, that have set up the potential for severe pollution events over the past several decades in the Los Angeles Basin.

Several interesting points should be mentioned:

(1) Ozone within the lower portion of the mixed layer is unlikely to participate in long-distance transport because it is separated from the upper portion at night due to the growth of a ground- based inversion layer and is scavenged by the ground surface and local sources of NOx. Yet the upper-layer ozone, detached from the surface layer at night, will likely travel with a different wind pattern than the wind pattern registered by the ground-based monitors. So, unless the vertical profile of the wind field is available via monitoring or modeling, it is difficult to predict the path of the air mass within this ozone-rich layer.

(2) The deposition rates for PM under various environmental conditions are not as well known as those for ozone. It is possible that some components of PM are not removed as readily as ozone at night in the presence of the ground surface. In that case, surface layer transport of these PM components may be more important than that of ozone. This comparison is not meaningful in the western U.S. where the PM epidoes tend to occur in late fall or in winter.

(3) Because of the relatively short lifetimes of the reactive VOCs (a few minutes to a few hours, much longer for less reactive but more diffused VOCs) and NOx (a few hours), primary pollutants generally do not participate in long-distance (say, over 100 km) transport during daytime. At night, however, both VOC and NOx have much longer lifetimes and may participate in long- distance transport, especially within elevated plumes.

(4) For PM, the primary fraction impacts the local PM concentrations near the sources. Yet the fine component of this primary PM fraction may have a relatively long lifetime and can be transported far downwind.

(5) The distance of transport within which a significant impact on local concentrations is registered depends on the relative differences between the locally generated surface concentrations of pollutants and their precursors at the destination and those transported aloft and mixed downward at the destination.

(6) Since PM is an aggregate variable, its components are impacted by sources that are controlled by a wide range of independent factors such as winter wood burning, fog and clouds, road dust, etc. High secondary PM concentrations can occur due to unique local environments. For example, ammonium nitrate plays a key role in elevating the PM concentrations during fall or winter in the western U.S..

Domains of AOVs and AOIs

There are three basic tools to help establish the domains of AOVs and AOIs: monitoring and data analysis, trajectory modeling, and air quality modeling. More details on these tools are presented in the next section. An AOV is determined given the form and the level of a standard and the attainment test considered. The only valid tool to determine the domain of an AOV is concentration monitoring. Air quality modeling, while essential for control planning, is not recommended because of the general lack of accuracy and precision in the input information. However, air quality modeling can help in locating ambient monitors needed to better define the boundary of an AOV (and an AOI). It is difficult to define the boundary of an AOV precisely especially in areas where there are few monitors. Perhaps a rough guideline may be described here. If an ambient monitor (or a group of monitors in the case of spatial averaging) registers one or more violations and a concentration reduction is deemed necessary, one or more episodes of violation may be selected for intended control simulations. For these episodes, it is then necessary to check the concentrations of neighboring monitors (or groups of monitors) downwind of the most likely control region to establish a rough boundary of the AOV. When a large region registers a violation of a given standard, meteorological data analyses can be very useful to ascertain if this region should be treated as one very large AOV or many smaller AOVs. For the predominant episodic conditions, it is meaningful to combine areas within this region as one AOV where an upwind-downwind relation can be demonstrated.

Even though it is difficult to define the domain of an AOV precisely, its associated AOI can still be determined. Here, meteorological data analysis and trajectory modeling can be useful in identifying areas or states that should participate in the associated AOI. The AOI can then develop control regions in order to improve the air quality observed by the monitors in the AOV for selected episodes. Concentration and meteorological data analyses are useful in identifying episodes for control planning. If the meteorological conditions for the violating episode days happen to have a relatively high frequency of occurrence, then the control plans devised for these episodes ought to improve the overall air quality of the AOV. On the other hand, if the selected sets of days or episodes do not represent the most common possible meteorological conditions under which high concentrations can occur, then the corresponding control plans may not necessarily be the most effective for more typical conditions that may still lead to the violation of a standard.

For the ozone and the PM short-term standards, the selection of episodes dictates the establishment of control regions. Indeed, for different sets of episodes, even of the same pollutants, different control regions will be required. Because PM is a multi-component mixture of different sources, different control regions may be necessary even for the same episodes.

Ideally, the control region within an AOI should satisfy at least two criteria:

(1) the transport of ozone/PM and their precursors from this region to the AOV can be demonstrated; and

(2) the transported amounts do indeed contribute significantly to a violation.

Existing tools are not sufficiently refined to establish control regions that fully satisfy the two requirements. The situation in PM is substantially worse than that in ozone.

The PM annual standards constitute a great technical challenge for constructing the control regions. Speciated concentration and meteorological data analyses are necessary to partition each day of the year into different meteorological/concentration regimes. Multiple control regions may have to be developed that are component dependent and meteorological-condition (such as season, etc.) dependent. The resulting control plans should then be weighted based on the frequency of occurrence of each meteorological condition to determine the impact on the annual PM concentration.

Scientific Tools

The scientific tools described below have their own unique advantages and limitations. The corresponding tools are much richer and better established for ozone than for PM. More detailed discussions of these tools should be available in the Conceptual Model Document.

Monitoring and Data Analysis

Concentration monitoring is essential for determining the AOV but is also helpful for designating the AOI. Monitoring should not be for the pollutant and precursor concentrations alone, but should include meteorological information, such as wind, temperature, humidity, atmospheric pressure, UV intensity, etc. Unfortunately, while it is the three-dimensional information that is most critical in assessing the transport and establishing the source of NAAQS violation, we have no routine upper air monitoring of air quality. Nor do we have upper air monitoring of meteorological variables with appropriate time and spatial resolution needed for reliable air quality modeling. The lack of three-dimensional data places a severe restriction on our ability to trace a NAAQS violation to its sources.

Monitoring alone cannot tell if transport has taken place unless the signature of a species or a group of species (like NOy) that are relatively conserved and predominantly of anthropogenic origin is found to move following the appropriate wind velocity from source to destination. A useful tool to detect such movement is the space-time correlation analysis. But the monitoring network should be sufficiently dense to make the analysis meaningful. For secondary pollutants like ozone and some components of PM, the pollutant time scale and the impact of nonlinear chemistry must be considered to interpret their space-time correlations. Because the ground-level ozone concentration tends to diminish at night, it is difficult to track the multi-day transport of ozone based on its ground-level concentrations alone. Upper-level ozone concentrations are almost a necessity if monitoring is to be used to arrive at a definitive conclusion on long-range ozone transport. The situation on PM is not obvious because the sampling duration is 24h, making it difficult to define the air mass carrying the observed PM concentrations. Overall, ground-level concentration monitoring is useful in corroborating or refuting the predictions of, say, grid modeling, but is not capable of single-handedly establishing the extent of pollutant transport.

Analysis of concentration and meteorological data is of course necessary for classification of different meteorological conditions and their frequencies of occurrence. This information is valuable in the selection of episodes for control planning purposes.

Trajectory modeling

Trajectory modeling is useful in identifying the short-term (less than one day) transport of air pollutants, especially when only the surface wind field is used. Short-term transport is responsible for many high-pollution events especially those in the presence of a high pressure system. Trajectory modeling is also useful in qualitatively grouping transport events for classification purposes. It is also useful in establishing the AOI or the "preliminary AOI."

The trajectory approach is more difficult to apply to identify the source of long-distance transport, especially in the presence of a complex mesoscale wind pattern. Because of the variation of the mixing height both in space and in time, the choice of the height of trajectory can become ambiguous. If we choose the trajectory within the mixed layer, then we will miss the flow above the mixed layer which may contain high concentrations of ozone and/or PM at night. If we choose two or more trajectories at different heights, then they will diverge in different directions and speeds while cross-trajectory vertical dispersion cannot be accounted for. Also, errors are compounded, leading to the tendency of high inaccuracy in multi-day back trajectories. Another problem is the inability to decide when the back trajectory should be stopped to ascertain the extent of pollutant transport. This is because the trajectory approach does not account for the change in species concentrations in the air parcels moving along the trajectory. If we cannot tell the species concentrations in an air parcel, then we cannot tell how much the air parcel contributes to the occurrence of an episode. In the case of PM, each component can have a different source; tracing back trajectories for a given time interval may miss the sources of some important PM components. Even if the trajectories could be properly identified, grid modeling should still be conducted to assess the control requirements.

Grid modeling

In principle, this approach is the most comprehensive. It is an essential tool for identifying the sources contributing to the AOV for a given high pollution event. The model can be used to define an AOI boundary within which a selected emission control would lead to a significant perturbation of the predicted ozone or PM at the AOV. (One should note the model and input uncertainty and machine roundoff errors in evaluating what constitutes a "significant" perturbation.) The CR boundary can be further reaffirmed by testing the sensitivity of the AOV ozone/PM concentrations to the change of the boundary conditions of the AOI. Should this sensitivity be significant, then the domain of the AOI should be expanded, until the sensitivity becomes insignificant. (Of course, a cutoff concentration change must be defined for determination of significance.) Accordingly, the scientific domain of the CR can change according to the individual episodes and pollutants of interest. Other approaches using grid models may also be possible, including selectively omitting upwind source areas to study impacts on the AOV, or through the use of tracer species tagged to particular source areas.

However, the grid model approaches are dictated by the quality and resolution of the input data, the validity of the model assumptions, the model grid resolution, and the accuracy of the numerical methods. Emissions inventories for VOC and NOx have improved significantly, but those for primary organic aerosols, ammonia and other PM precursors remain poor. Three-dimensional meteorological data with the relevant time and space resolutions are not available, especially for the non-summer episodes. Air quality observations to provide reliable initial and boundary conditions are incomplete. Even if we could model a large region with clean air boundary conditions, the model results could still not be easily confirmed, again due to lack of speciated air quality observations of ozone- and PM-precursor species. Model grid resolution is known to provide dramatically different control strategies for ozone. Localized emissions cannot be readily handled. While tropospheric ozone chemistry has been studied for over forty years and is on relatively firm ground, our knowledge on secondary PM chemistry and size classification are still far from adequate, especially for secondary organic carbons. Much research and developmental work is needed to improve the models to the skill level comparable to that for ozone.

The performance of a grid model for a certain set of high pollution events should first be evaluated against observations before confidence can be gained on its applications. Even then, one may still not be sure of the model performance because of the possible presence of compensating errors in the model. Accordingly, confidence in the model can be strengthened if other tools are used to corroborate its results.

Data Needed

For certain areas of the country, ozone or PM episodes tend to occur under a certain range of meteorological conditions. It is important that ten or more years of meteorological data for the high-pollution days be analyzed and grouped so that the frequencies of occurrence of different episodic conditions can be established. Such grouping or clustering allows one to construct a relatively stable CR domain for control planning purposes. This analysis is absolutely necessary even if some simplistic approaches are to be used to prescribe the CRs. It should be noted that these episodic conditions may be different for ozone than for PM.

To model an episode well, it is almost a necessity to acquire a reliable 3D wind field and pollution field data. The importance of 3D data cannot be overemphasized according to the recent NARSTO-Northeast study and the Southern Oxidant Study. Three dimensional wind field data may reveal the presence of a nocturnal jet which can transport ozone (and PM) long distance downwind. The spatial and temporal resolution of the existing 3D meteorological network is not adequate, especially for the prediction of individual pollution events.

Obviously, we are in dire need of more speciated PM-fine data and speciated VOC data. We also need more reliable emissions inventories, to say the least. Assuming that model assumptions are adequate (which is a big assumption), model performance is totally at the mercy of input data.

Important Considerations

(1) It is important to realize that pollution events occur on different time and space scales. Even for the high pollution events, the time and space scales also vary. A large space-scale event does not necessarily indicate regional transport of pollutants. Similarly, a small space-scale event does not automatically exclude the possibility of regional pollutant transport.

(2) To establish a stable control region, it is necessary to analyze the meteorological data of high ozone (and PM) events in the past 10 to 15 years to establish the probability of occurrence of meteorological conditions responsible for given sets of pollution events, and devise controls for conditions that reduce, say, the largest number of high pollution events.

(3) Pollution events are substantially influenced by meteorology and its variability. This variability extends to concentrations in remote or background areas. In designing a comprehensive air quality program, one must not ignore the role of this variability. A possible implication is the question of attainability of the standard.

(4) Air quality modeling development and its applications are an iterative process. Improvement is possible only when the necessary research is carried out and the needed input data become available. It is desirable that new knowledge be continuously utilized as interim improvement in control planning development.