VT DEC Air Trajectory Analysis of Long-Term Ozone Climatology

Status Report to OTAG Air Quality Analysis Workgroup: 12/3/96

Rich Poirot and Paul Wishinski, VT DEC

Abstract

This provides an update of a backward air trajectory analysis project conducted for the OTAG Air Quality Analysis Workgroup by VT DEC. Previous status reports have summarized results of HY-SPLIT back trajectories calculated 4 times/day, June - August, 1989 - 1995 for 23 ozone monitoring sites distributed throughout the OTAG domain. The current report responds to suggestions and questions raised by other AQA workgroup members in the following areas: effects of "seasonality" on trajectory results; effects of sub-regional grouping on multi-site trajectory results; and relative influence from intra-regional and inter- regional transport. Also summarized here are results of inter-site and inter-regional correlations among afternoon ozone concentrations at the 23 trajectory sites, and areas of convergence among these and other OTAG AQA studies.


Contents:


Seasonality

Several commenters have asked about possible influences of seasonal variation in ozone levels on the trajectory results. Conceivably, there could be concurrent seasonal variations in ozone concentrations and synoptic scale meteorological flows, such that spatial differences between "clean and dirty" trajectories might be more reflective of seasonal patterns than of source-receptor relationships. Rao et al. (JAWMA 45: 57-61, 1995); Porter et al. (http://capita.wustl.edu/otag/reports/StatChar/otagrep.htm) and co-workers have developed methods (KZ filter) for statistical decomposition of ozone (and other) data which allows the seasonal component to be separated from the synoptic scale and long-term trend components. Although our trajectory sorting did not utilize the KZ filter to remove seasonal influence, we did attempt to minimize the influence of seasonal variation in ozone by limiting our "window of view" to only the summer season (June through August). In addition, we have "standardized" the ozone data from different sites and different hours of day by converting the raw ozone data to "Z-scores"

(C = MG * SGZ, where C is concentration, MG is geometric mean and SG is standard geometric deviation). Figure 1 shows the strong seasonal variation for a few years of daily maximum ozone at Whiteface Mtn. NY. Figure 2 is based on our Summer-only Z-scores (1989-1995) for the Whiteface site. Clearly, the seasonal variation is minimized when only Summer months are considered.

Figure 1. Daily Max Ozone at
Figure 2. 3 PM Ozone Z-Scores
Whiteface Mtn., NY: 1989-90
for Whiteface Mtn. NY June 1
(left) through August 31 (right), 1989-95

Figure 3. Incremental Residence Time Probabilities for Upper 25 Percentile Ozone Concentrations at Whiteface Mtn., NY, based on Ozone Z-score (left) and Rao et al. Short-Term Component (right)

Given the large resource requirements for re-sorting the more than 50,000 individual trajectories in this analysis, and the minimal degree of seasonal influence on the summer-only Z-scores, it is neither feasible nor warranted to re-examine all results with seasonality removed (i.e. with KZ filter). However, the potential influence of the (small) degree of seasonal variation on the trajectory results can be further evaluated by examining trajectory results for the Whiteface site (for which Rao et al. have kindly provided a file of their hourly "short-term" data - with seasonal and long-term trend components removed). Figure 3 displays incremental residence-time probabilities for upper 25th percentile ozone concentrations at Whiteface Mtn. for the past 7 summers (See Poirot and Wishinski 11/7/96 OTAG AQA status report). In the left hand side of Figure 3, trajectories were sorted by our original Z-score metric. On the right, the trajectories were sorted using the KZ-filtered short-term component.

Clearly, the differences (influences of seasonality on Summer trajectory results) are very minor. This "convergence" between different approaches also lends indirect support to the work of Rao, Porter and co-workers, as their short-term component is specifically intended to represent synoptic-scale influences. The trajectory-based assessment is also intended to identify synoptic-scale influences. The results of these two very different approaches are quite consistent with each other.

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Site Selection and Site Grouping

The trajectory analysis has been conducted in an iterative manner, beginning with a selection of 6 high-elevation ozone monitoring sites, and gradually expanding to a total of 23 sites based on recommendations from AQA workgroup members. The first 6 sites (predominantly located along the spine of the Appalachian Mountains) were chosen initially because they were relatively remote, and because they exhibited minimal diurnal variation. This allowed combination of ozone concentrations and trajectories without regard for the time of day. As the analysis was expanded to include lower-elevation sites, alterative ozone metrics (Z-Scores) were applied to "standardize" the ozone data at different hours-of-day and at different sites. Site selection criteria included: regional representativeness, spatial distribution throughout the OTAG domain, and reasonably complete data coverage over the Summers of 1989-95. All sites except the roof of the World Trade Center in NYC were either "rural" or suburban". Two sites (Grafton, WI and Seaford, DE were moved slightly during the 7-year period. About one third of the sites are in counties currently designated as non-attainment, and all of them have experienced at least occasional hourly concentrations of 120 ppb or higher. Site locations are displayed in Figure 4 and summarized in Table 1.

Figure 4. OTAG Back Trajectory Site Locations

Table 1. 23 Ozone Sites Included in OTAG Back-Trajectory Residence Time Analysis

Code
Site Name
Latitude
Longitude
Elev. (m)
AIRS Site #
wfmn
Whiteface Mtn., NY
44.36
73.90
1480
360310002
mgml
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

In previously reports, trajectories from multiple sites have been combined in an attempt to condense information, provide a degree of "triangulation", and show "regional" influences. For example, incremental residence-time probabilities for upper 5 percentile ozone concentrations for selected site groups are displayed in Figure 5. While certain of these site groupings help provide a useful degree of triangulation (ie. identify locations upwind when ozone is high at a number of widely scattered sites), they are not necessarily logical groupings from a "regional influence" perspective (particularly for the "high-elevation" and "Southern" site groups.

Figure 5. Incremental residence-time probabilities for upper 5 percentile ozone concentrations

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Site Selection for OTAG "Subregions"

In an attempt to improve on the regional relevance of the site groupings, we have regrouped selected sites into 6 smaller subregions - generally described as: Southeast, Southwest, Northwest, Midwest, New England, and Mid-Atlantic (Table 2 and Figure 6). This subregional grouping was based on geographical proximity and also on inter-site ozone correlations (among 3 PM Z-scores). Within each subregion, intersite correlations ranged from moderate (r = 0.3) to high (r = .8). We also calculated a mean 3 pm Z-score based on all sites in a subregion and considered each individual site's correlation to the sub-regional mean. These correlations ranged from 0.7 to 0.9, indicating that the sites are appropriately grouped (for sample size of about 650, r > 0.1 is significant at .01 level). Distances between subregions (calculated as average distance between all pairs of sites in each pair of subregions) and inter-regional ozone correlations (from 3 PM Z-Scores) are displayed in Table 3. The New England and Mid-Atlantic subregions are relatively close together (about 300 miles) and exhibit a moderate inter-regional correlation (about 0.4). The Midwest and Northwest are similarly close together and also exhibit a moderate inter-regional correlation. All other Subregions are separated by at least 400 miles and exhibit inter-regional correlations of less than 0.3 (consistent with the more comprehensive evaluation of inter-site correlation (of short-term component) by Rao et al. and Porter et al.).

Table 2. Sites Included in OTAG "Subregions" and

(Site Same-day Ozone Correlations with Sub-Regional Means)
Southwest Midwest Northwest
De Soto, MS (0.79) Boone Co., KY (0.86) Port Huron, MI (0.88)
Greg Co., TX (0.80) Fortville, IN (0.80) Grafton, WI (0.88)
Iberville Par., LA (0.82) Nilwood, IL (0.79)
New England Southeast Mid-Atlantic
Bennington, VT (0.83) Look Rock, TN (0.87) Ancora, NJ (0.92)
Mr. Greylock, MA (0.87) Granville Co., NC (0.72) Seaford, DE (0.82)
Port Clyde, ME (0.80) Lithia Spr., GA (0.81) World Trade, NY (0.81)
Rye, NH (0.83)

Figure 6. Locations of Ozone/Trajectory Sites included in 6 OTAG "Subregions"



Table 3. Distances and 3 PM Ozone Correlations between OTAG "Subregions"

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Local Characteristics of Selected Subregions


Figure 7. Areas of greatest Local Residence-Time Influence for OTAG "Sub-regions" as defined by Grouped Trajectory Sites

Various "local" characteristics of these 6 selected subregions are displayed in Figures 7 - 10. In Figure 7, we attempt to define "areas of greatest local importance" for each subregion. An "everyday residence-time probability field" is calculated for each subregion. Then, grid squares are identified which have a higher residence-time probability for one subregion than for any other subregion (see Figure 7). For example, areas shaded light blue in Figure 7 represent grid squares for which the residence-time probabilities are higher for the grouped Mid Atlantic sites (Ancora, NJ, World Trade Ctr., NY, and Seaford, DE) than for any other subregional group. It is emphasized that this exercise is not intended to objectively define logical geographic boundaries of subregions in OTAG, but rather to show - as a function of preselected site groups and trajectory results - those areas most probabalistically upwind of each predefined subregion. These areas might be considered as representing regions of unique local importance for each subregion.

These unique local areas appear generally logical, with the notable exception of the "Southwest", which has higher everyday probabilities extending surprisingly far to the North and East of the Southwestern sites. This is likely due to inclusion in the Southwest group of sites extending further South than any in the Southeast group, and further West than any sites in the Midwest Group. Also, a few grid squares with highest probabilities in the Northwest region appear to be geographically misplaced.

Figure 8 provides an indication of the "seasonality" associated with the ozone Z-scores in each subregion. In each plot, the 3 PM data for all 7 summers are sorted from June 1st on the left through August 31 on the right (ie, the 1st 7 data points are all June 1 - 1989 - 1995). Figure 9 displays the incremental residence-time probabilities for upper 50th percentile ozone for each subregion.

Figure 8. 'Seasonality' in 3 PM Ozone Z-Scores from Six Sub-Regions: June - August, 1989-95

Data are Sorted from June 1 (all years) on left, to August 31 (all years) on right

Figure 9. Incremental Residence-time Probabilities for Upper 50% Ozone for Six Subregions

The ozone Z-Scores (see page 1) have the unique statistical characteristics of an (arithmetic) mean of 0 and standard deviation of 1. Assuming an approximately log-normal distribution to the original ozone data, a Z value of zero is approximately equal to the median ozone concentration, 68% of the values will fall within Z = + 1, and 95 % of the values within Z = + 2. Note the occasional very low (negative) Z-scores for some subregions in Figure 8. There is no equivalent Z value for a concentration of zero (Z approaches negative infinity as concentration approaches zero). The large negative Z-scores represent very low ozone concentrations, and most likely occur on rainy days. The large annual seasonal variation in raw ozone data displayed in Figure 1 is minimized in Figure 8 when only summer data is considered. The strongest (but not very strong) seasonal variations are evident in the Mid-Atlantic region (upper right and the Southwest (Gulf Coast) region (lower right). The seasonal pattern in these subregions is most evident in the lowest concentrations (and is not apparent in the higher concentrations - above Z = 0). The Mid-Atlantic (Coast) region exhibits lower (negative Z's) concentrations in the early and latter periods of the (June - August) Summer season, while the Southwest (Gulf Coast) region exhibits lowest concentrations (highest negative Z's) in mid-Summer. This mid-summer dip in concentrations along the Gulf Coast is consistent with, and described in additional detail in Porter et al.

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Incremental Residence-Time Probabilities (Distribution Among Subregions)

Incremental residence-time probabilities for upper 50th percentile ozone concentrations for each sub-region are displayed in Figure 9. Using the trajectory-based definition of "areas of greatest local influence (Figure 7), the percentage of incremental probability associated with each region's "local influence" area can be determined. These percentages range from a low of about 14% in New England to a high of about 57% in the Midwest (see Figure 10).

Figure 10. Percent of Upper 50% Incremental Probability in Local Subregions

The full distribution of incremental residence time probabilities for upper 50% ozone concentrations among the 6 subregions is summarized in Table 4. This is based on the areal definition of subregions displayed in Figure 7. For the New England region for example, 14% of its incremental probability is located within the pink area labeled "New England" in Figure 7, and 43.2% New England's incremental probability is located within the light blue area labeled "Mid-Atlantic". It is emphasized that this distribution of incremental probabilities is in no way intended to represent a quantitative estimate of inter-regional ozone contributions. Rather, the objective is to provide a more quantitative estimate of the graphic patterns displayed in Figure 9. Percentages of > than 15% are highlighted for emphasis.

Table 4. Distribution of Upper 50% Incremental Residence-Time Probability among Subregions

To/

From
NE
MA
NW
MW
SE
SW
NE
14.0
0.1
1.8
1.3
2.5
1.5
MA
43.2
36.0
8.7
5.4
6.5
4.3
NW
15.4
15.2
27.8
4.1
21.9
14.4
MW
16.4
16.1
42.1
56.7
23.7
26.0
SE
9.8
28.0
6.6
16.1
36.6
18.2
SW
1.2
4.5
13.0
16.4
8.7
35.6

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Duration of Ozone Episodes (Subregional Ozone Auto-Correlations)

The site grouping of subregions described in Figures 6, 7 and 8 and Tables 2 and 3 was based on geographical proximity and inter-site correlation. In Figure 11, the 3 PM ozone Z-scores in each subregion are correlated with the 3 PM Z-scores in the same subregion with lag-times of 1, 2 and 3 days (forward or backward - it doesn't matter when a region is correlated with itself). This provides an indication of the duration of ozone episodes in each subregion. With a 1-day lag-time, all regions exhibit moderate to high (r = 0.4 to 0.7) auto-correlations (with this sample size of about 650, an r value of about 0.1 is significant at the 0.01 level). After 2 days, autocorrelations in the New England, Mid-Atlantic and Northwest subregions become insignificant, while significant correlations persist even after 3 days for the Southwest and Southeast subregions. Clearly, the duration of ozone episodes increases (from 1 to 3+ days) as one moves from Northeast to Southwest in the OTAG region.

Figure 11. Lagged Auto-Correlations for OTAG Subregions (3 PM Ozone, 1989-95)

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Lagged Inter-Regional Correlations

In Figure 12, average 3 PM ozone Z-scores from each subregion are correlated with the 3 PM Z-Scores from other subregions on the same day, and 1 and 2 days earlier. For example, afternoon ozone in the Northwest region (Figure 12a) has positive, significant correlations (r > 0.1 for n = 644) with ozone measured in the Midwest and Southwest 1 day earlier, and with the Southwest 2 days earlier. New England ozone (Figure 12b) correlates with ozone measured 1 day earlier in the Mid-Atlantic, Northwest and Midwest regions 1 day earlier, and also with Northwest and Midwest ozone measured 2 days earlier. Note also that the Southwest and Southeast subregions - which exhibited the highest lagged auto-correlations (Figure 11) - generally exhibit the poorest inter-correlations with other subregions. Significant, positive, lagged correlations from Figure 12 are summarized in Table 5.

Table 5. Significant Inter-regional Correlations with 1 to 2 Day Lag-times

Reference

Region
No Significant Correlation
Significant Correlation 1 or 2 Days Backward
Significant Correlation 1 or 2 Days Foreword
SE
NE, NW
SW, MW
MA, MW, SW
SW
NE, MA
SE
MW, SE, NW
NW
SE
MW, SW
MW, NE, MA
MW
NW, SW, SE
NW, NE, MA
MA
SW
SE, MW, NW, NE
NE
NE
SE, SW
NW, MW, MA
MA

Note: Color indicates Regions where lagged correlation exceeds same day Correlation

It should be cautioned that although the seasonality in the Summer-only subregional Z-scores is relatively minor, some degree of seasonality remains, and may influence these inter-regional correlations (Ideally, this potential influence might be eliminated by applying the KZ filter). Also, it should be noted that near-by (and some more distant) subregions exhibit significant correlations on the same day (without lag-time). Ideally, the potential influences of this "auto-correlation" should be removed (by someone more clever than us). Presumably, the lagged inter-regional correlations might improve if the data were stratified by wind direction and wind speed. In this case the correlations are presented independent of meteorology to provide an independent test on the trajectory results. Despite these limitations, the results of the inter-regional correlations are very consistent with the back trajectory results (Figure 9 and Table 4). The comparative results of the Inter-regional incremental residence-time probabilities and the inter-regional lagged correlations are summarized in Table 6.

Figure 12. Inter-Regional Ozone Correlations (3 PM) as Function of Lag Time
a. Inter-Regional Ozone Correlations
with Northwest (Compared to Other Regions 0, 1 and 2 Days Earlier)
b. Inter-Regional Ozone Correlations
with New England (Compared to Other Regions 0, 1 and 2 Days Earlier)


















c. Inter-Regional Ozone Correlations
with Midwest (Compared to Other Regions 0, 1 and 2 Days Earlier)
d. Inter-Regional Ozone Correlations
with Mid-Atlantic (Compared to Other Regions 0, 1 and 2 Days Earlier)


















e. Inter-Regional Ozone Correlations
with Southwest (Compared to Other Regions 0, 1 and 2 Days Earlier)
f. Inter-Regional Ozone Correlations
with Southeast (Compared to Other Regions 0, 1 and 2 Days Earlier)


















Table 6. Comparative Results of "Inter-Regional Transport" as derived from:

A. Trajectory Residence Time Analysis and B. Lagged Inter-Regional Ozone Correlations

To/

From
NE
MA
NW
MW
SE
SW
NE
-
B
MA
A, B
-
NW
A, B
A, B
-
B
A
MW
A, B
A, B
A, B
-
A, B
A
SE
A, B
A, B
-
A, B
SW
B
A, B
B
-

A. Greater than 15% of upper 50% ozone incremental probability in "source region"

B. Significant correlation with ozone measured 1 or 2 days earlier in "source region"

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Conclusions

1. Degree of Seasonality is small in Summer-only ozone data measured at trajectory sites.

2. Seasonality has no discernable influence on trajectory residence-time analysis results.

3. Trajectory sites in close geographical proximity (within 350 miles) exhibit moderate to high (r = 0.3 to 0.8) same-day ozone correlations, and can be logically grouped into 6 geographic OTAG "subregions" described as: New England, Mid-Atlantic, Northwest, Midwest, Southwest and Southeast.

4. Each subregion has a unique area of local importance. With the exceptions of the Northwest and New England subregions, the highest fraction of a receptor region's high ozone incremental residence-time probability is contained within the region's "local area".

5. Time-lagged auto-correlations within each subregion indicate that the average duration of ozone events is shortest (1 to 2 days) in the Northwest, New England and Mid-Atlantic Regions, and longest (2 to 3 days) in the Southwest and Southeast.

6. Neither residence-time analysis nor time-lagged inter-correlations suggest substantial inter-regional transport contributions between the following sub-regions: to NW from NE, MA or SE: to MW from NE or MA; to MA from SW; to NE from SE or SW; to SW from NW, NE or MA; to SE from NE or MA.

7. Both residence-time analysis and time-lagged inter-correlations suggest substantial inter-regional transport contributions between the following sub-regions: to NW from MW; to MW from SW and SE; to MA from SE, NW, and MW; to NE from MA, NW, and MW; to SW from SE; to SE from MW.


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