VT DEC Air Trajectory Analysis of Long-Term Ozone Climatology

Status Report to OTAG Air Quality Analysis Workgroup: 8/15/96

Rich Poirot and Paul Wishinski, VT DEC

Contents:


Abstract

This summarizes past reports, current status, and work in progress pertaining to applications of backward trajectory analyses conducted for the OTAG Air Quality Analysis Workgroup by VT DEC. The approach employs the NOAA HY-SPLIT model to calculate several thousand backward air trajectories (4/day for June-August, 1989-95) for each of 23 non-urban ozone monitoring sites with reasonably complete data capture over the past 7 Summers. Spatial characteristics of this long-term trajectory/ ozone data base are examined through sorting and aggregation techniques ('residence-time analysis'). The objective is to provide contextual information on 'ozone transport' by identifying locations which, over the long-term, have upwind statistical associations with high (or low) downwind ozone concentrations at ozone monitoring sites throughout the OTAG domain.

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Past Reports

The general methods for trajectory 'residence-time analysis' employed in the current project have been previously described in: Poirot and Wishinski (1985) in AWMA Spec. Conf. on Receptor Methods for Source Apportionment, T.G. Pace, Ed.; Wishinski and Poirot (1986) in AWMA Spec. Conf. on Visibility Protection: Research and Policy Aspects, P.S. Bhardwaja, ED; and Poirot and Wishinski (1986) Atmos.. Environ., 20: 1457-1469. These earlier assessments were based on the NOAA ARL-ATAD trajectory model (Heffter (1980) NOAA Tech. Mem. ERL-ARL-81), while the current results are based on the NOAA HY-SPLIT model (Draxler (1992) NOAA Tech. Mem. ARL-195). Otherwise, current methods for processing trajectory results are essentially the same as reported in these earlier studies. 'Residence-time analysis' involves tracking space/time characteristics of trajectories on a grid of 1440 80x80 km squares. Resultant trajectories and pollutant concentrations are sorted, aggregated and plotted in one of two general ways:

1. Concentration-Based Sorting: Trajectories are first sorted into subsets based on receptor site pollution concentrations (low, high, very high, etc.). For each subset, we track the time that associated trajectories reside over each grid square, and plot locations characterized by the largest number of hours in each subset. A standard plotting routine involves bounding the smallest areas that account for 25%, 50% and 75% of the residence time hours for a given subset of trajectories (see Figures 5 and , for example). These Residence-time probability plots address the question : "if the concentration at this site was high (or low or very high), where did the air come from?"

2. Location-Based Sorting: A descriptive statistic (mean, median, etc.) is calculated for each grid square based on receptor site concentrations associated with all trajectories arriving at the receptor which have passed through that square. Calculation of an average value for a square is weighted by each trajectory's residence time over the square. Average values have little meaning for squares traversed by few trajectories (few residence-time hours, small sample size, etc.), so results are only reported for squares which exceed a "threshold" minimum number of residence-time hours (see Figure 7, for example). Resulting plots display average concentrations at the receptor as a function of prior airmass location, and address the question: "if the air has previously been here (or there), what's the average concentration at this (or that) receptor?"

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Analysis of Data from High Elevation Sites

The initial phase of the current OTAG project (presented at the 2/96 OTAG meetings, and posted by Wishinski and Poirot (1996) at http://capita.wustl.edu/otag/Reports/Restime/Restime.html focused on a group of 6 high elevation ozone sites, located primarily along the spine of the Appalachian Mountains. The NOAA HY-SPLIT model (Draxler (1992) NOAA Technical Memorandum ARL-195) was run in a 'backward' mode using a subset of the NGM (Phillips (1975) NOAA Tech. Rpt. NWS-22) meteorological data, which covers most of North America. Initial trajectory starting "elevations" were input as "pressure-heights", as determined by the formula: Pressure (mb) = (1000-Height above MSL (m))/10. An alternative method of selecting a starting elevation (in meters above the terrain) was not employed because the model's gridded terrain is substantially lower than the actual elevation for the mountain-top sites. For lower elevation sites the difference between model terrain and actual elevation is minimal. Vertical trajectory motion is determined by the NGM omega vertical velocity fields (as opposed to an isobaric (pressure-following) or isosigma (terrain-following) mode, without exercising an option to interpolate meteorological sub-layers within the model's first sigma layer (based on recommendations from B. Stunder and R, Draxler, NOAA-ARL).

The maximum trajectory duration is 106 hours (4 days, 10 hours), although many trajectories are truncated sooner, if they encounter "holes" in the NGM data or exceed the model's spatial domain. Additional truncation is imposed by the 'trajectory-tracking" grid of 1440 80x80 km squares, and by the plotting routines for concentration-based and location-based residence-time plots (see discussion of "Trajectory Duration" under "Response to OTAG Comments"). The net effect of this truncation limits the average duration of the utilized portion of the trajectories to a typical range of 2.5 to 3.5 days.

The first 6 high-elevation sites were selected because (with the exception of the roof of the World Trade Center in NYC) they were inherently remote from local source influence (ozone concentrations result predominantly from "transport"). They also exhibit minimal diurnal variation in ozone concentrations (such that a "high" (or low) ozone concentration is equally likely to occur at any hour of the day). This latter characteristic allowed use of trajectories and associated ozone concentrations at all hours of the day, without the need to consider whether a "high" (or low) concentration threshold at 3 AM should be different from a threshold at 3 PM, etc.

Figure 1. Average 7-Summer Diurnal Cycles at High Elevation Sites

Because ozone formation requires sunlight, high ozone levels at night or early morning provide more "relevant" information on transport than mid-afternoon levels (a measured concentration at sunrise represents ozone formed at least a day earlier). Results were not included from the World Trade Ctr., NYC (#360610003) and Greenbriar Cnty., WV(#540250001) sites because ozone levels at these sites exhibited significant diurnal variation, and the absolute ozone concentration at different hours of day were not "comparable" (i.e. levels in the mid-PM were typically higher than early AM).

Figure 4. Residence-Time Probability Plot for All Whiteface Mtn. Trajectories: Summers 1989-95
(25% of trajectory hours in each separately shaded area)

An example trajectory arriving at Whiteface Mtn., NY and the 1440-square 'trajectory tracking' grid are displayed in Figure 2. The portion of all trajectories arriving at Whiteface during the past 7 summers residing over this 1440 k grid are displayed in Figure 3 (note: a plotting error truncates these trajectories at 100 degrees longitude, rather than at the 105 degree western edge of the grid).

Figure 3. All Whiteface Mtn. Back Trajectories:
June - August, 1989 - 1995

Figure 2. Example Back Trajectory for Whiteface Mtn. on 1440 80x80 km square 'Trajectory-Tracking' Grid

Figure 4 is a residence-time plot based on the same 2,120 trajectories displayed in Figure 3. These trajectories collectively resided for a total of 153,737 hours over the 1440-square grid. The separately shaded areas each contain 1/4 (38,434) of the total hours, and bound the smallest areas accounting for 25% (pink), 50% (grey) and 75% (tan) of the hours.The least probable 25% of the trajectory hours are distributed in the unshaded, white area of the map. This plot provides a trajectory-based answer to the question: "Where is the Summertime air at Whiteface Mtn. Most likely to have previously resided?"

Figures 5 and 6are examples of concentration-based sorting for Whiteface trajectories. 50% of the long-term ozone "dose" at Whiteface results from ozone concentrations above 51 ppb (and 50% from concentrations below 51 ppb). Figure 5 displays residence-time probabilities for 'low ozone' trajectories (< 51 ppb) and Figure 6 shows probabilities for 'hi ozone' trajectories (>51 ppb).

Figure 6. Whiteface Mtn., NY Summers 1989-95 Residence-Time for Ozone > 51 ppb (797 Traj.)


Figure 5. Whiteface Mtn., NY Summers 1989-95
Residence-Time for Ozone < 51 ppb
(1323 Traj.)


Figure 7. Average Ozone by Prior Trajectory Location for (lowest of) 4 Mountaintop sites: Summers 1989-95

Figure 7 displays an example of 'location-based' trajectory sorting. In this case, an average ozone concentration was first calculated for each grid square for all trajectories passing through that square en route to each of 4 mountaintop receptor sites: Whiteface Mtn., NY, Mt Greylock, MA; Shenandoah NP VA; and Look Rock, TN. Thus, for each grid square, there were 4 sets of average ozone concentrations, one for each receptor. In Figure 7, the lowest of the 4 average concentrations is plotted, such that for each separately shaded area, average ozone concentrations are at least as high as the indicated value for all trajectories passing through that square and arriving at any of the 4 mountaintop sites. Green (outer) > 45 ppb; pink > 47.5 ppb; tan > 50 ppb; purple > 52.5 ppb; red (inner) > 55 ppb.

An average grid square value is not displayed for squares traversed by less than 100 trajectory hours.


Figure 8. Ozone Sites for OTAG Back-Trajectory Analysis


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Analyses of Additional Low-Elevation Sites

While high-elevation monitoring sites provide ideal platforms from which to observe ozone transport aloft, they are limited primarily to the Appalachian Mountains and provide minimal representation of lower elevation exposures or to other areas of the OTAG region. Based on recommendations from members of the OTAG Air Quality Analysis workgroup and Ad Hoc Air Trajectory group, similar long-term trajectory data sets were developed for 17 additional low-elevation sites distributed throughout the OTAG domain. Locations for the current total of 23 high and low elevation sites are displayed in Figure 8 and Table 1.

Table 1. Ozone Sites Included in OTAG Long-Term Back-Trajectory Analysis

Code Site Name Latitude Longitude Elev. (m) AIRS Site #

wfmn Whiteface Mtn., NY 44.36 73.90 1480 360310002

mglm Mt. Greylock, MA 42.64 73.17 1140 250034002

wtcn World Trade Ctr., NYC 40.71 74.01 503 360610063

shen Shenandoah NP, VA 38.52 78.44 1073 511130003

grbw Greenbriar County, WV 37.82 80.51 829 540250001

grsm Gt. Sm. Mt. NP, TN 35.63 83.94 793 470090101

benn Bennington, VT 42.90 73.25 216 500030004

ptcl Port Clyde, ME 43.92 69.26 9 230130004

rynh Rye, NH 43.00 70.75 10 330150012

ancr Ancora, NJ 39.67 74.86 35 340071001

seaf Seaford, DE 38.65 75.61 10 100051001(2)

graf Grafton, WI 43.43 87.92 299 550890008(5)

mktw Mark Twain SP, MO 39.47 91.79 213 291370001

nilw Nilwood, IL 39.40 89.81 201 171170002

fort Fortville, IN 39.94 85.84 265 180590003

boon Boone Cnty., KY 38.92 84.85 171 210150003

pthr Port Huron, MI 42.95 82.46 186 261470005

gran Granville Co., NC 36.14 78.77 91 370770001

semi Seminole Co., FL 28.75 81.31 18 121171002

lith Lithia Springs, GA 33.74 84.63 300 130970002

deso De Soto Co., MS 34.83 89.99 117 280330002

iber Iberville Par., LA 30.20 91.10 9 220470002

greg Gregg Co., TX 32.38 94.71 103 481830001


While none of the selected low-elevation sites were urban (all were rural or suburban), they all exhibit substantial diurnal variation. See, for example, the Figure 9 comparison of long-term average diurnal

Figure 9. Average Sumer 1989-95 Diurnal Ozone at Mt. Greylock, MA and Bennington, VT

ozone levels at high-elevation MT. Greylock, MA and the nearby low-elevation Bennington, VT site. To compare ozone levels and associated trajectories at different hours of the day from the low-elevation sites, we first calculated a long-term (7 summer) mean concentration for each site for each hour of day (3 AM, 9 AM, 3 PM, 9 PM), and then re-expressed each hourly ozone value as the deviation (in ppb) from the diurnal mean for that hour.

Figure 10 displays the hourly ozone levels at Bennington and Mt. Greylock during a 3-day period of increasing ozone concentrations during June, 1991. Concentrations increase at both sites, and both sites exhibit similar mid-afternoon peak levels. But while the mountaintop concentrations increase "smoothly", a strong diurnal pattern is evident at the Bennington site. The mid-afternoon concentration at Bennington on 6/24 is higher than the midnight concentration on 6/26. In Figure 11, the data from both sites have been re-expressed as the deviations from their diurnal mean concentrations. Now both sites exhibit relatively smooth increases; their nighttime levels are more comparable; and the 6/26 midnight value at Bennington is higher (and a positive deviation) than the mid-afternoon value on 6/24 (a negative deviation). Re-expressing (standardizing) the hourly data in this manner allows use of data (and trajectories) from different hours and from different sites on a more directly comparable basis.

Figure 10. Hourly Ozone at Mt. Greylock , MA
and Bennington, VT on 6/24-26/91

Figure 11. Ozone Deviations from Diurnal Means at
Mt. Greylock, MA and Bennington, VT on 6/24-26/91

Having "standardized" the ozone concentrations for each site as deviations from the average (for that site and hour of day), we next employed "trajectory-based sorting" for each receptor site to calculate average ozone deviations as a function of prior trajectory location. That is, for each of the 1440 grid squares, we calculate an average receptor site ozone deviation for all trajectories passing over that grid square en route to the receptor. The average is time-weighted, such that a trajectory residing for 8 hours over a square is given twice as much weight as a trajectory residing over the square for 4 hours. Average values are for squares characterized by less than 100 hours of total trajectory time (approximately equivalent to a minimum of 25 trajectories) are not displayed.

Currently available results using these methods (as presented at 7/96 OTAG meetings)have been processed into a series of 23 movie animations (one for each receptor site) which plot locations associated with trajectories resulting in low and high deviations from the site's mean ozone levels. Each movie has 62 frames; begins with locations (if any) associated with large negative deviations (15 ppb below the mean) and ends with locations (if any) associated with large positive deviations (15 ppb above the mean). Frame 31 of each movie shows all locations with trajectories associated with negative deviations (below average ozone concentrations) at the receptor, while frame 32 shows all locations associated with positive deviations (above average ozone) at the receptor. Movies are created in .avi format (viewable with CAPITA movie program or Microsoft Media Player). Movie names follow the form xxxxdev.avi, where xxxx is the 4-character site code listed in the first column of Table 1 (benndev.avi for Bennington, VT). All 23 single-site movies are compressed into the zipfile: 1sitedev.zip (1.2 Megs compressed; 3.3 Megs uncompressed) and posted on the OTAG AQA website.

Example frames 31 and 32 from the benndev.avi movie are pasted below in Figure 12. These show locations upwind of Bennington, VT for which trajectories are associated with below average (left) and above average (right) ozone deviations at Bennington.

Figure 12. Locations Associated with Negative (left) and Positive (right) Ozone deviations at Bennington, VT

Figure 13 a, b, c and d display example frames from 'Average Ozone Deviation' Movies for 4 sites:Pt. Huron, MI; Rye, NH; Mk. Twain SP, MO and Gt Smk. Mtn., NP, TN - in diverent sections of the OTAG domain (from 4sitedev.avi movie).Clockwise from upper left, frames show locations for which trajectories arriving at specified receptors are associated with ozone levels: a. Less than Average; b. Greater than Average; c. At least 5 ppb Greater than Average; d. At Least 10 ppb Greater than Average. Low ozone concentrations are associated with areas external to (North, South, East and West) of the OTAG domain, while high ozone levels are associated with trajectories internal to OTAG, with areas in the industrial Midwest being upwind of high ozone deviations at all 4 sites..

Figure 13a. Locations with
Ozone Deviations of < Average
Figure 13b. Locations with
Ozone Deviations of > Average
Figure 13c. Locations with
Deviations at least 5 ppb > Avg.
Figure 13d. Locations with
Deviations at least 10 ppb > Avg.


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Response to OTAG Comments/ and Work in Progress

Most of the results presented on the preceding pages have been presented for review at meetings of the OTAG Air Quality Analysis Workgroup and Modeling and Assessment Subgroup (and have been posted on the OTAG AQA Website. Various comments and suggestions have been received and will be addressed in a final report. Following are some preliminary responses, and an outline of the future plans for completing this analysis project.

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Trajectory Uncertainty

It has been suggested that we provide some discussion of the uncertainty inherent in trajectory calculations. A backward trajectory is a meteorological estimate of the history of airmass motion of air arriving at a pre-specified location and time. Sources of uncertainty in a trajectory calculation include the meteorological data which drives the model and the physical assumptions by which the trajectory model operates on that data. In this work, we have applied the NOAA HY-SPLIT (Hybrid Single-particle Lagrangian Integrated Trajectories) model, driven by meteorological data archived from the National Meteorological Center's (NMC) Nested Grid Model (NGM). Draxler has estimated a HY-SPLIT-NGM trajectory error (after 24 hours) in the range of 20 to 30 % of trajectory distance, based on model evaluation during the CAPTEX (Draxler (1987) J. Appl. Meterol. 26:1577-1578) and ANATEX (Draxler (1991) J. Appl. Meterol. 30:1466-1467) tracer experiments.

At an average wind speed of about 5 meters per second, a trajectory would travel roughly 400 km after 24 hours (and would pass through about 5 of our 80 km grid squares. At this 400 km distance, an error of 20 to 30 % would be in the range of 80 to 120 km, and thus the "actual" trajectory position might be off by 1 or 2 grid squares. At a distance of 1600 km (about 4 days), a 20-30% error might displace the "actual" trajectory position by 320-480 km - equivalent to 4-6 of our 80 km grid squares. As the trajectory error increases with distance, it should also be considered that the potential for a trajectory to reside over any particular location decreases. There are more than 100 80x80 km grid squares at a distance of 400 km from a receptor, and hence a probability of less than 1% that a random trajectory will reside over any particular square at this distance. With large trajectory numbers, the probability that many of them will have erroneously passed over an individual distant square decreases with distance. These potential errors are assumed to apply to any individual trajectory. Unless there is a systematic bias in the trajectory estimates (ie. They always move too fast and always turn too far to the right) we assume that the errors for large numbers of trajectories (in our case more than 2000 for each of 23 receptor locations) tend to be offsetting.

If there are systematic biases in the HY-SPLIT calculations, it is unlikely that such biases would somehow tend to "force" the trajectories associated with high (or low) ozone levels to pass over any specific locations prior to arriving at a large number of different receptor locations.

Another potential source of trajectory error is the presumption that a single trajectory represents the path of all the pollution molecules experienced at the receptor. HY-SPLIT tracks the backward path of a single "particle" and does not explicitly incorporate mixing into the trajectory calculations. Under certain atmospheric conditions the "representativeness" of a single trajectory pathway is questionable. The CAPITA Monte Carlo model (Schichtel and Husar (1995) Regional simulation of atmospheric pollutants with the CAPITA Monte Carlo model. J. Air & Waste Manage. Assoc. Accepted for publication), simulates effects of atmospheric mixing through release of multiple particles and application of vertical and horizontal mixing algorithms which result in multiple trajectory pathways. Schichtel and Wishinski (1996 ) conducted a detailed comparison of HY-SPLIT and CAPITA Monte Carlo Results(posted on OTAG Website at http://capita.wustl.edu/otag/Reports/Trajcomp/trajcomp.html). They found "individual trajectories at times compared very well and at other times predicted substantially different airmass pathways". When they applied residence time analysis to three months of trajectories, they found that "there were no critical differences between the residence time plots from the HY­SPLIT model and the Monte Carlo model. This is an indication that there are no systematic differences between the back trajectories from the two models, and the differences between the individual trajectory tend to average out when aggregated over "long" time periods".

It should also be emphasized that the NGM meteorological grid (composed of 182.9 km squares) is particularly inappropriate for evaluation of local-scale influences (use of higher resolution surface winds would be more effective). Also, our trajectory -tracking grid (80 km squares) is too coarse for evaluation of local-scale effects. Our 'location-based sorting techniques are also inappropriate for identifying local influences. The emphasis here in on identifying influences over larger, synoptic scales.

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Trajectory Length

Several reviewers have commented on the "excessive length" of our trajectory calculations. The initial trajectory calculations were conducted for a maximum duration of 106 hours (4 days, 10 hours). It should be noted that this is the nominal trajectory duration, and that a majority of the initial trajectories are effectively terminated after shorter durations. This truncation (implemented as a function of distance rather than time) occurs for the portion of the trajectory which exceeds our 'trajectory tracking' grid of 1440 80x80 km squares. The radial distance for this grid is approximately 1000 miles. For our 'concentration-based sorting residence-time plots (see Figures 4, 5, 6), the locations of the outermost 25% of residence time hours (the least probable trajectory locations) are not plotted, imposing a further truncation on the 'effective" length of the trajectories. Figures 3 and 4 (all trajectories for Whiteface Mtn. are initially based on 153,737 hours from 2,210 trajectories (an average of about 72 hours per trajectory). Figure 4 excludes the outer 25% of these trajectories, and consequently represents an average trajectory length of 2 to 3 days.

For our location-based residence-time plots (see Figures 12 and 13), additional truncation of trajectory length is imposed by exclusion of grid squares traversed by fewer than 100 trajectory hours. In Figure 5, the plot displays locations of all squares with greater than 100 trajectory hours (234 squares < average and 195 squares > average. Thus the utilized portion of trajectories within the grid is limited to about 30% of the total 1440 square grid area. When these 100 hour limits are examined for all 23 sites, the plotted areas have a radial distance of about 600 miles, and reflect average trajectory durations in the range of 2.5 to 3 days. This "effective truncation" is conducted on the basis of distance, rather than time, and so there are some very slow-moving trajectories which extend back the full 106 hours included within all of these plots.

We do not believe there is an objective basis for terminating a trajectory calculation at any specific length or time. The potential trajectory error increases with distance, incrementally, and not suddenly at some specific distance. It is not logical to "trust" a trajectory completely at 72 hours, but mistrust it completely at 73 hours. Truncation of a trajectory at a specific time step does however, have the effect of pre-determining the maximum transport time. If we truncate them at 12 hours, we have pre-determined that transport does not occur (or that we will disregard its effects) over longer time periods.

The effects of arbitrarily trajectory truncation at 106, 88, 70 and 52 hours have been determined for the Mark Twain receptor site, and are summarized in a series of movies submitted to the OTAG website. Some example frames from the movie mktw1836.avi are pasted in Figure 14. From upper left, to lower right, the frames show (as in figure 13) locations with average ozone deviations of <average, > average, at least 5 ppb > average, and at least 10 ppb > 0. In each frame, the 4 panels show, from upper left to lower right, results when trajectories are truncated at 106 hours, 88 hours, 70 hours and 52 Hours.

Figure 14. Effects of Trajectory Truncation
(106, 88, 70, 52 hours) on Average Deviation
Plots for Mark Twain SP,MO


With each progressively severe truncation, the implicated areas associated with low and high deviations become slightly smaller. Truncation clearly has minimal effect on the directional characteristics of the results. With the exception of the most extreme 52 hour truncation, the spatial extent and locations of the plotted areas remain similar. A certain extent of this "shrinkage" is due to our 100 hour minimum threshold for plotting (This effect can be seen in the upper 2 frames which together show all locations with at least 100 trajectory hours. So part of the difference is due to a reduction of the number, rather than the length of the trajectories considered in each square. Even when we pre-determine that ozone transport cannot occur beyond 2 days (or that we will disregard it) the results are similar. At about 3 days, the results show very minor differences from our standard procedure based on an initial maximum 106 hour calculation.

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Use of Alternative Ozone Metrics (geometric mean and standard deviation)



Figure 15. Comparison of Location Based Sorting using absolute deviations (left) and geometric deviations (right)

Following presentation of the "average deviation" results at the July, 96 OTAG meetings, there was some discussion of the ozone metrics employed for calculation of average ozone deviations as a function of prior trajectory location. For each site and each hour of day we calculated an arithmetic mean ozone concentration, and re-expressed each hourly concentration as an absolute deviation (in ppb) from that mean. A number of alternative metrics were discussed including use of "percent deviations", and use of the geometric mean and standard geometric deviation. These metrics are primarily intended to provide an objective answer to the simple questions: "What is a high (or low) ozone concentration?" and "How high or low is it?"

We have tested several alternative metrics and believe that the use of geometric mean and standard deviation represents a statistically superior way to "standardize" the ozone data for different hours of day and for different sites (although it is more difficult to explain). For log-normally distributed data (which ozone approximately is), the mean is approximately equal to the median. Using the geometric mean as a threshold between "high" and "low" essentially divides the long-term data set in half. We can also re-express each hourly concentration as a "Z-score" by the formula: Concentration = Mg sgZ, solving for Z. A given Z-score also relates statistically to a specific percentile of the population. For example, the 98th percentile concentration is approximately 2 standard deviations above the mean.

We have subsequently re-expressed the ozone data for each site and each hour of day as Z scores, and plan to use this metric in future determinations of "high", "low", "how high", etc. While the metric and threshold are different from, and not directly comparable to, the previously applied "absolute deviation" concept, the effects on the results appear to be minor. This is illustrated by the movie ancrcomp.avi (posted on OTAG Website) which compares deviation plots based on absolute ozone deviations to comparable plots based on geometric means and standard geometric deviations (see Figure 15). While this alternative metric essentially has no effect on the patterns for location-based sorting for individual sites, we believe it provides for a more equitable standardization among data from different sites - which varies considerably in terms of absolute concentration. The ancrcomp.avi movie and several "multsiite" movies (which combine geometric deviations from multiple receptors are posted on the OTAG Website in the file geomdev.zip. Example frames from several of the multi-site aggregations are pasted below. Note that while the spatial areas are similar in all 4 frames, the Z values are quite different (0.2 for the Southern sites, 0.3 for the Midwestern sites, 0.4 for the Northeastern sites and 0.5 for the high elevation sites.

Figure 16. Example Plots from
multi-site Geometric deviation movies for
clusters of 6 receptors


Conversely, if equal Z values were compared for the different groups of sites, progressively smaller areas have high deviations as on moves from Northeast to Midwest to South . High (or low) ozone levels at the Compared to the Northeast, Southern sites all appear to be characterized by substantially more ambiguous "source region influence", and few "source regions" are appear to contribute persistently to high (or low) ozone concentrations at multiple sites in the South. ...to be continued...

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Influence of Seasonality (and/or long-term trends)

One reviewer suggested that our residence time results may be influenced by the seasonal variation that occurs between the middle and ends of the 3-month (June-August) "Summer" period examined in our 7-Summer data set. We are exploring the separate potential effects of "seasonality" (and long-term trends), through application of residence-time methods to different components of ST Rao's statistically ozone data for the Whiteface Mtn. site. Rao and co-workers have developed methods to statistically separate seasonal, long-term trend and short-term synoptic variation from the raw data. We feel this approach will help evaluate the influence (if any) of seasonality and trend on our results, and may also provide a useful perspective on the extensive body of work already conducted by Rao et al.

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