Working Draft Paper - Do not cite or quote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 Influence (AOIs)

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

Date: November 12, 1996

Background

The Area of Violation (AOV) is defined as an area within which one or more monitors register an exceedance of the standard. For the purposes of establishing either a State Implementation Plan (SIP) or Spatially Integrated Plan (SpIP) air quality control planning process, the Area of Influence (AOI) is loosely defined as the area whose emissions are potentially responsible for the violation of the standard at the AOV. How the domains of AOVs and AOIs should be defined for the purpose of control implementation, and how eventual emissions control within the AOIs should be carried out depend on both scientific and nonscientific factors. Analysis of the nonscientific factors must lag in importance and timing behind acquisition and consideration of scientific knowledge. The state of scientific knowledge for specific, individual AOIs must be clearly established at the time of AOI boundary designation. If this procedure is followed, it is possible to devise and execute an effective and efficient SIP and/or SpIP air quality control plan. The AOVs ought to be defined according to the monitoring data, but supplemented by modeling if "sufficient" confidence exists in the modeling results for the exceedance conditions.

Air pollutant concentrations are strongly influenced by meteorology, which varies with a wide range of time and space scales (a scale means a domain within which the variable of interest tends to move up or down together). In addition, primary pollutants tend to be influenced by the microenvironments of the emission sources and thus can have very small space scales while secondary pollutants tend to have a much larger space scale which also tends to increase with averaging time. Past observations indicate that a recorded hourly ambient ozone concentration, in the absence of major NOx emission sources, may represent the hourly concentration of an area around the point of observation up to a radius of around 10 to 15 km. No information is available for PM. But if the averaging time is 24h and the secondary component is dominant, one expects the space scale to be fairly large as well (perhaps 10-20 of km). This type of information is useful in the design of an AOV. We shall not dwell on the AOV issue further.

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 pending revised version of the 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 is not yet available.

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. For ozone, there have been several major studies in various parts of the country. More recently, two field studies -- the Southern Oxidant Study and the NARSTO- Northeast Study -- have provided even more valuable information on ozone. They provide us important insights on the ozone transport issue which is critical in the design of the AOI. 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 exceedance 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 now detached upper-layer ozone 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 true trajectory of this ozone-rich layer. (2) It is likely that PM is not removed as readily as ozone at night in the presence of the ground surface. Accordingly, surface layer transport of PM may be more important for PM than for ozone. (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, there is a primary component which directly impacts the local PM concentrations near the sources. This primary component may have a relatively long lifetime and the fine aerosol portion 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 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 Southern California.

Scientific Tools

There are scientific tools that can assist us in defining the AOIs. We list them here together with the restrictions on their applicability.

Monitoring

Monitoring is essential for determining the AOV but is also helpful for designating the AOI. Monitoring should not be for the pollutant 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. 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 (say, NOy) of a species or a group of species is found to move following the appropriate wind velocity from source to destination. 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 either because the sampling duration is 24h, making it difficult to define the air mass carrying the observed PM concentrations. Thus, ground-level 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.

Trajectory modeling

Trajectory modeling is useful in identifying the short-term (less than one day) transport of air pollutants. Short-term transport is responsible for many high-pollution events especially those in the presence of a high pressure system.

The trajectory approach is more difficult to apply for long-distance transport. Because of the variation of the mixing height both in space and in time, the choice of the height of trajectory becomes 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, 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. 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 the only tool capable of 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 of, say, x percent or x amount would lead to a perturbation of the predicted ozone or PM at the AOV by at least y percent. The AOI 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 level must be defined for determination of significance.) Accordingly, the scientific domain of the AOI 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, and the accuracy of the numerical methods. Emissions inventories for VOC and NOx have improved significantly, but those for primary PM and ammonia remain poor. Three-dimensional meteorological data with the relevant time and space resolutions are not available. 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. 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 clustered so that the frequencies of occurrence of different episodic conditions can be established. Such clustering allows one to construct a relatively stable AOI domain, not one that varies with each individual episode. This analysis is absolutely necessary even if some simplistic approaches are to be used to prescribe the AOIs. 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 AOI, 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.