ML20247F910
| ML20247F910 | |
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| Site: | FitzPatrick  |
| Issue date: | 12/31/1990 |
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5 FINAL TASK REPORT FUMIGATION FREQUENCY ANALYSIS:
NINE MILE POINT NUCLEAR POWER STATION LYCOMING, NEW YORK PREPARED FOR:
Empire State Electric Energy and Research Corporation 1155 Avenue of the Americas New York, NY 10036 ESEERCO Project No. EP 88-6 PREPARED BY:
Galson Technical Services, Inc.
6601 Kirkville Rd E. Syracuse, NY 13057
, r.al enn Project No.09-022 9905200096 990226 PDR ADOCK 05000333 P PDR _ ,
NEW YORK POWER AUTHORITY December 1990
. DOCUMENT REVIEW STATUS STATUS h .
1 7 - ACCEPTED 2 O AccaPTED AS NOTED REsUBurrTAL NOT REQUIRED 3 O ACCEPTED AS NOTED RESUBMfTTAL REQUIRED
, 4 O NOT ACCEPTED I Pinesson a Peased sees est consam anospance or apptw or essen estes, estadssons, anews, est momens e ameriale esveisped w smacInd by se suppner and does not seteue suppear hem lui comptance wtm contractal@= 0, n /-
nnemo sv.L&ibTmu:}( t.J" ', P mTo. alp N (f goj$pDQ hh
abstract The goal of the Eastern Lake Ontario Meteorological Study (ELOMS) is to develop quantitative techniques for predicting and evaluating characteristics of the vind field in the eastern Lake Ontario region in order to better estimate the dispersion of pollutants in the complex shoreline environment.
An analysis of the frequency of meteorological conditions favorable for shoreline fumigation along the southeast shore of Lake Ontario near Niagara Mohawk Power Corporation's Nine Mile Point Nuclear Power Stati n (9MP) in Lycoming, New York is presented.
Shoreline fumigation results when stable lake air (marine) moves onshore over unstable air resident over the land (nonmarine). Pollutants released into the stable marine air may be mixed downward should they intersect the boundary between the marine and nonmarine air (TIBL). The traditional Gaussian models are able to model such fumigation in a rather crude fashion. Data contained vithin the Eastern Lake Ontario Meteorological Study (ELOMS) vind field
_ database collected during Phase I of the study is used to develop the frequency of fumigation. Stability over land is assessed at a representative inland station uninfluenced by the lake air. Over water stability is !
determined by using data from a coastal tower that provides a reasonably unmodified measurement of meteorological parameters over the lake during i onshore flov (9MP Main Meteorological Tower).
The result of the analysis show that the southeastern Lake Ontario shoreline I i under the influence of strong fumigation conditions over 15% of the time during May, June, and July. On an annual basis, fumigation conditions are noted approximately 5.4% of the time. The best meteorological conditions for fumigation occur during the spring and early summer, and are limited, mainly, to daylight hours.
The results of this study show that along the southeastern shore of Lake l Ontario, shoreline fumigation is a statistically important consideration when describing pollutant dispersion from facilities in the area.
TABLE OF CONTENTS int List of Figures............................................... 11 Li s t o f Ta b l e s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i i i Executive Summary............................................. iv 1.0 Introduction............................................. 1 2.0 Method................................................... 3 2.1 Calculation of Stability Over Land.................. 3 2.2 Calculation of Stability over Vater................. 5 2.3 Vind Direction Criteria............................. 8 2.4 Frequency Calculations.............................. 8 3.0 Results.................................................. 11 3.l' Stability Frequency Over Land....................... 11 3.2 Stability Frequency over Vater...................... 11 3.3 Combined Stability Results and Fumigation Frequency. 11 4.0 Conclusion............................................... 20 5.0 References............................................... 21 APPENDIX A - POLYNOMIAL FIT TO CLIMATOLOGICAL LAKE ONTARIO TEMPERATURE DATA FOR USE IN COMPUTER APPLICATIONS APPENDIX B - MONTHLY FUMIGATION FREQUENCY ANALYSIS RESULTS
LIST OF FIGURES EAlt Figure 2-1. Schematic flovchart of approach used to determine j the frequency of shoreline fumigation conditions at 9MP from the ELOMS data base................... 4 Figure 2-2. Stability Classes defined as a function of surface roughness (z,) and the inverse of the Monin-Obukov length (1/L). After Golder (1972)................. 7 l
Figure 3-1. Frequency of occurrence of fumigation conditions at 9MP for all hours and classes of fumigation (Strong '
and Marginal)..................................... 18 Figure 3-2. Frequency of occurrence of fumigation conditions at 9MP differentiated by class....................... 19 1
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LIST OF TABLES f.4E1 Table 2-1. Stability classification using the Monin-Obukov Length (L)........................................ 8 Table 2-2. Fumigation classifications......................... 10 Table 3-1. Frequency of stability classes over land.......... 13 Table 3-2. Frequency of stability classes over water......... 14 Table 3-3. Monthly summary of fumigation frequency during daylight hours.................................... 15 Table 3-4. Monthly summary of fumigation frequncy during !
night hours....................................... 16 Table 3-5. Monthly summary of fumigation frequency for all hours............................................. 17 I
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_.--__________________________J
l EXECUTIVE
SUMMARY
l The goal of the Eastern Lake Ontario Meteorological Study (ELOMS) is to develop quantitative techniques for predicting and evaluating characteristics of the vind field in the eastern Lake Ontario region. Air quality models are routinely used to predict the trajectory, concentration, and deposition of emissions from electric utility sources. Generally, straight-line Gaussian models are used. These models assume uniform vind speed, vind direction, and stability throughout the area of interest. However, vind regimes near large bodies of water, and in areas of complex terrain are unsteady and exhibit pronounced local circulations. Therefore, the assumption of uniform meteorological conditions in these areas is inappropriate in many cases. In addition, air quality estimates for points further than 20 km from the source require consideration of spatial and temporal variabilities in the vind field.
Recognizing this shortcoming in the traditional Gaussian modeling approach, the Empire State Electric Energy Research Corporation embarked on ELOMS as an extensive program to develop methodologies for determining the mesoscale vind fields for different meteorological flow regimes in the Lake Ontario region.
As part of ELOMS Phase III, Galson Technical Services, Inc. performed an analysis of the frequency of meteorological conditions favorable for shoreline fumigation along the southeast shore of Lake Ontario near Niagara Mohawk Power Corporation's Nine Mile Point Nuclear Power Station (9MP) in Lycoming, New York. )
Shoreline fumigation results when stable lake air (marine) moves onshore over unstable air resident over the land (nonmarine). Pollutants released into the stable marine air may be mixed dovnvard should they intersect the boundary )
between the marine and nonmarine air (TIBL). The traditional Gaussian models are able to model such fumigation in a rather crude fashion. However, in order to better understand the significance of the phenomenon, an analysis was performed to determine hov often shoreline fumigation occurs.
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Data contained within the ELOMS vind field database collected during Phase I of the study is used to develop the frequency of fumigation conditions.
Stability over land is determined using a representative inland station (Syracuse, NY National Veather Service), uninfluenced by the lake air. Over vater stability is determined by using data from a coastal tover that provides a reasonably unmodified measurement of meteorological parameters over the lake during onshore flov (9MP Main Meteorological Tower).
The results of the analysis show that the southeastern Lake Ontario shoreline is under the influence of strong fumigation conditions over 15% of the time during May, June, and July. On an annual basis, fumigation conditions are noted approximately 5.4% of the time. The best meteorological conditions for fumigation occur during the spring and early summer, and are limited, mainly, to daylight hours.
Knowledge of shoreline fumigation frequency is necessary to assess the importance of the phenomenon in determining dispersien conditions in the coastal zone. Since many power producing facilities are located in coastal zones, the occurrence of shoreline fumigation is a concern of the utility industry. The results of this study show that along the southeastern shore of Lake Ontario, shoreline fumigation is a statistically important consideration when describing the pollutant dispersion from facilities in the area.
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4
1.0 INTRODUCTION
As a layer of air flows from one surface type to another, it is wodified at the bottom. The depth of the modified surface layer increases with distance
- over the "new" surface type. The boundary between the modified air near the surface and the unmodified air aloft is called the Internal Boundary Layer (IBL). The two most prominent and studied types of IBLs are those resulting from changes in surface roughness and those generated by stable air flowing over unstable air. The latter type or so-called thermal internal boundary layer (TIBL) is the focus of this study.
The specific TIBL of interest for this study is created when cold stable air over Lake Ontario flovs onshore over land heated by the sun along the southeast shore of the lake. This condition leads to a phenomenon known as fumigation, which is important to the transpert and diffusion of pollutants in a coastal zone. Fumigation also occurs when the nocturnal radiation inversion is destroyed by heating from belov af ter sunrise, however, this study is confined to shoreline fumigation which occurs within the TIBL.
The TIBL grows in height with distance inland from the coastline (ESEERCO, 1990a). If there is an elevated point source located near the coastline, its plume vould initially be emitted into the sta.ble layers above the TIBL provided the vind is onshore. However, the plume may eventually intersect the growing TIBL, where fumigation or downward mixing of the plume occurs in the unstable TIBL air. Throughout this report, the term fumigation used alone vill refer to shoreline fumigation resulting from plume intersection with a TIBL unless othervise indicated.
Fumigation conditions may occur under the following stability conditions:
Temperature gradients over water are stable, and Temperature gradients over land are neutral to unstable.
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L - - - - - - _ _ _ _ _ _ _ _ - - - - _ - - - - - - - - - - - - - - _ _ _ _ _ _
True TIBLs and fumigation conditions occur only with unstable overland temperature gradients. Fumigation under neutral stability classifications is possible, but most often results from mechanical mixing, rather than thermal imbalances.
Following are results of an analysis to determine the frequency of occurrence of fumigation conditions in the vicinity of Niagara Mohawk Power C1rporation's Nine Mile Point Nuclear Power Station using the Eastern Lake Ontario Meteorological Study (ELOMS) 2-year meteorological database. Details regarding the techniques for determining the occurrence of fumigation conditions are presented in Section 2.0 while results of the analysis are given in Section 3.0. Section 4.0 presents a discussion of the results.
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2.0 METHOD In order to determine whether fumigation is occurring, it is necessary to knov the values of the following parameters:
Stability over land, l Stability over water, and Wind direction.
First, stability over land is needed in order to determine if stability vichin the TIBL is neutral or unstable, the two classifications necessary for fumigation of pollutants from elevated levels to the ground (See Section 1.0).
Secondly, stability over the water is needed in order to determine whether the lapse rate is stable, the second requirement for fumigation. Finally, vind direction determines whether the flow is onshore, with fumigation defined as occurring under onshore flow conditions only.
A flovchart overview of the approach used to develop the frequency of fumigation regimes is shown in Figure 2-1.
2.1 Calculation of Stability Over Land Data to determine fumigation frequency was obtained using the ELOMS April 1986 l through March 1988 database (ESEERCO, 1989). Since the stability class for each monitoring site is not contained explicitly in the data base, Pasquill stability class was determined using methods frequently used in air quality i modeling applications. In the case of stability over land, Turner's method (Turner, 1970) vas used to classify the stability into one of the 7 Pasquill stability classes (A, B, and C: Unstable; D: Neutral; E, F, and G: Stable).
(Additional discussion regarding the Turner scheme is provided in ESEERCO, 1990b).
A representative inland station is needed to make the overland stability classification. During onshore flow, three types of air are present in the coastal zone: marine, modified marine, and nonmarine (ESEERCO, 1990b).
Stabilities are likely to be different in each zone. The marine air possesses 3
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em d I P e n
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m if catcstan ses s u s.
W TesNEs's Menos if catcstsu seresem STse et ets Metssa if asstMsts retssect assses tar >
ves
'Y catcstsu ratsumcits Figure 2-1. Schematic flovchart of approach used to determine the frequency of fumigation conditions at Nine Mile Poin't (9MP) from the ELOMS database.
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the characteristics of the airmass resident over the lake, with limited l diurnal variations. Modified ~ marine describes the marine air that has moved onshore and is being modified from below by travel over land. Nonmarine air has the characteristics of air overlying a land mass, with the accompanying large diurnal variability.
I The site chosen for estimating representative overland stability classes was j the National Veather Service station at Syracuse Hancock International Airport (SYR). The Turner scheme was used to estimate stability class at the site i using cloud cover, ceiling height and vind speed data measured at STR, which is located roughly 50 km south-southeast of the Nine Mile Point facility.
2.2 Calculation of Stability over Vater
~
The situation for determining stability over water is more complicated since the ELOMS databan does not contain any data measured explicitly over the j water. To ovngn this limitation, the technique used in the Offshore and Coastal Dispesf on Model (OCD) is employed (Hanna, et. al., 1984). OCD l estimates stability class from bulk aerodynamic principles and boundary layer formulas using over-vater observations of vind speed (U), air temperature (T,), dev point temperature (T,), and vater surf &ce temperature (T,). Since observations of these parameters are not available over the vater, OCD allows substitution of similar observations from true coastal stations or clim.atological values.
For this study, representative values of U, T., and T4 are obtained from data '
collected at the Nine Mile Point Nuclear Power Station primary meteorological tower (9MP) at an elevation of 9.1 meters. In general, 9MP is close enough to Lake Ontario so that during onshore flow, it vill provide a reasonable estimate of vind speed and temperature conditions over the water. Due to its proximity to the lake, 9MP is likely to have higher vind speeds and different turbulence intensities than sites several kilometers inland (ESEERCO,1990b).
It should be noted that some minor local modifications to the measured parameters vill result at 9MP due the land, despite the proximity of the tower to the lake.
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Several limitations exist with the 9HP data. First, during the period April 1986 through April 1987, a software limitation resulted in unreliable estimates of sigma-theta during vind directions of 330' through 30' (0' to 360' scale). Secondly, for vind directions from 10' through 110', the tower itself may perturb air flow, leading to the potential for inaccurate measurements of vind speed, vind direction, and sigma-theta. However, since the OCD technique does not require sigma-theta, this is not considered a limitation of this particular analysis. Vith respect to the 10' through 110' blockage, the OCD technique is sensitive to vind speed, and determining onshore flow requires accurate knowledge of vind direction. However, for the purposes of this study, this limitation was ignored in order to make use of as much data as possible. Users of the frequency analysis should note the potential for impacts resulting from the blockage.
Since no concurrent temperature data was available over the water, T, was obtained from the climatological lake surface temperatures proposed by Ballentine (1987). The climatological value of lake temperature for any julian day can be determined by using a polynomial fit to Ballentine's climatological temperature data. A description of the equation applied in this study is provided in Appendix A.
To estimate over-vater stability class the Monin-Obukhov length (L) is determined with the following formula:
L - (0,
- Cun '2)/(5.096e-3) * (U )/(0, - 0,,).
3 vhere L is in meters, U in meters /see, and 0 in 'K. Cv, is the momentum drag coefficient:
,Cv, = (0.75 + 0.067U)
- 10-3 0, is the virtual potential temperature over land while 0,, is the saturated 0, since relative humidity at the water surface is assumed to be 100%.
I Stability can be classified using the relationship between roughness length ~
(z.) and L developed by Golder (1972) shovn in Figure 2-2. Assuming z, is l i
j 6
i f
l j
v46-l i l
[
t 4
I
.4 -
4= l
.4
.0:. -
LO g g
'e (m) 3 "
a 7
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t =
5 -
M=
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- .12 .10 .04 . 06 . 06 . 04 0 .02 .04 .06 .08 uu.~5 Figure 2-2. Stability classes defined as a function of surface roughness (z.)
and the inverse of the Monin-Obuknv length (1/L). Af ter Golder (1972).
7 A . _ _ , _ . _ _ . _ _ _ _ _ _ _ - - . _ . _ _ . - _ .m. . _ _ _ _ . _ _
betveen 10-4 meters and 10-3 ' mete s (Typical values over water surfaces),
stability classification can be made using L as shown in Table 2-1.
Table 2-1. Stability classification using the Monin-Obukhov length (L).
Monin-Obukhov Length Stability Class
-10 m f L f -5 m B
-25 m f L f -10 m C lLl > 25 m D 10 < L f 25 m E 5 < L f 10 m F Note that the absolute value of L is not permitted to drop belov 5 meters in this procedure.
2.3 Vind Direction Criteria In order for shoreline fumigation to occt3r, the vind must be directed onshore.
At 9MP, enshore flow is said to be occurring whenever the vind direction at I the 9.1-meter level is between 240' and 69' (ESEERCO, 1990b). )
l 2.4 Frequency Calculations ]
l Lequency distributions by stability class were computed over vater and over land. Stability was only calculated for onshore flow conditions at 9MP. The data vere stratified by time of day (day / night), and fumigation likelihood j (All/ Strong / Marginal /None). Tiine of day is determined by the method given in l
the CRSTER meteorological pre-processor (EPA, 1977). ,
l l
l
! Fumigation occurrence was assessed by determining the joint frequency of over 8
l
land versus over water stability classes as described in Table 2-2. True fumigation resulting from TIBL effects is likely to occur only during STRONG conditions (Hanna, et al,1984). However, during thermally neutral conditions over land, some veak or limited fumigation may result doe to mechanical mixing or very localized thermal effects (e.g. patches of snow or varying vegetation and soil coverages). By including a MARGINAL class, a conservatively high estimate of fumigation frequencies is developed.
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_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - _ _ _ _ _ a
Table 2-2. Fumigation classifications.
FUMIGATION CLASS OVER-LAND STABILITY OVER-VATER STABILITY ALL A, B, C, or D E or F STRONG A, B, or C E or F MARGINAL D E or F NONE D, E, F, or G A, B, C, or D 10 l
4 E h+
3.0 RESULTS Following are the results of the stability frequency estimates over land and water, and the frequency of fumigation conditions. Note that calculations were carried out only during onshore conditions, however percent frequency of l occurrence relates to all possible hours (onshore and offshore flow).
3.1 Stability Frequency Over Land Data for the period April 1986 through March 1988 from SYR vas analyzed to estimate the stability classification over land. The frequency results by month are presented in Table 3-1. The percentages are related to all possible hours. These compare favorably to the partial year results obtained in a previous analysis (ESEERCO, 1990b).
l 3.2 Stability Frequency.0ver Vater Data from 9MP concurrent to the SYR observations were used to estimate stability class over the lake using the method described in Section 2.2. Note
! that missing observations from either site resulted in the hour being disregarded from further analysis. The frequency results by month are l presented in Table 3-2. The results are as one vould expect over the course of a year with increased variability.
During late fall and vinter, the air over the water tends to be unstable, since cold air of ten overlies the varm vater, thus creating a thermally destabilized airmass over the lake. In the spring and summer, stability tends to be high since varmer air is frequently over the colder lake.
l 3.3 Combined Stability'Results and Fumigation Frequency Summary fumigation analysis results are provided in Tables 3-3, 3 4, and 3-5, and graphically depicted in Figures 3-1 and 3-2. The month-by-month results of the fumigation frequency analysis on the ELOMS data is presented in Appendix A.
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Table 3-3 shows fumigation frequsney for daylight hours by a nth for the 2-year period. Thermally induced fumigation is shown by the STRONG category, veak or low probability fumigation cases are provided in the MARGINAL category and ALL represents both.
Fumigation frequency during night hours is provided in Table 3-4. Note that by definition, STRONG fumigation can not. occur at night, since, unstable classifications are not allowed at night over land. Therefore, only marginal or weak fumigation cases can occur.
Table 3-5 presents a summary table of fumigation frequency for all hours.
Appundix A shows the detailed monthly results of stability over land versus stability over water, and the resulting fumigation frequency. The boxed results represent STRONG and MARGINAL fum'.gation observations.
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i Table 3-1. Frequency of stability classes over land .
1 ALL HOURS - OVER LAND STABILITY CLASS FREQUENCY (FERCENT)
MONTH A _]L 1 _IL _I_ 1 1-JANUARY 0.00 0.27 1.78 34.79 2.40 1.37 0.34 FEBRUARY 0.00 0.93 5.37 37.20 7.24 5.60 3.27 MARCH 0.00 2.67 7.10 37.81 7.66 5.62 2.53 APRIL 0.09 4.17 6.30 42.69 3.98 2.87 1.02 MAY 0.58 6.41 10.82 25.79 4.49 2.91 2.08 I JUNE 1.78 8.54 11.27 21.03 3.66 3.38 0.85 JULY 1.95 8.86 11.58 20.52 3.63 3.07 1.05 i AUGUST 0.51 4.96 9.11 21.21 4.52 4.59 1.17 SEPTEMBER 0.22 2.64 6.24 24.89- 3.23 3.01 1.47 OCTOBER 0.00 0.72 3.03 29.75 4.26 3.54 1.59 1 NOVEMBER 0.00 0.29 2.14 37.56 3.39 1.62 0.59 DECEMBER 0.00 0.07 1.00 44 20 2.72 1.43 0.14 ,
1 ANNUAL 0.41 3.24 6.16 31.49 4.26 3.24 1.33 l
1 Note 1: Stability is calculated for all hours of the day when concurrent data is available from both sites and flow is onshore at 9MP. Percentages are calculated using a base of all possible hours with concurrent data at coastal and inland sites regardless of flov direction.
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Table 3-2. Frequency of stability classes over vater 1.
ALL HOURT - OVER VAT 2R STABILITY CLASS FREQUENCY (PERCENT)
MONTH B C D E F JANUARY 13.29 7.74 19.52 0.27 0.14 FEBRUARY 26.85 7.86 22.27 0.70 0.93 MARCH 16.02 5.20 28.39 3.16 10.40 APRIL 2.31 0.83 15.19 8.06 34.63 MAY 0.33 0.42 5.74 4.66 42.01 JUNE 1.03 0.75 11.74 4.69 32.30 JULY 7.40 2.65 11.03 5.65 23.94 AUGUST 13.19 6.41 18.88 2.04 5.54 SEPTEMBER 20.85 6.09 13.00 0.95 0.81 OCTOBER 18.84 9.17 14.15 0.22 0.51 NOVEMBER 10.68 6.26 26.29 1.25 1.10 DECEMBER 12.39 7.31 29.01 0.43 0.43 ANNUAL 12.37 5.27 18.32 e.52 11.65 1
See Note 1, Table 3-1.
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Table 3-3. Monthly summary of fumigation' frequency during daylight hours.1 D&YLIGRT BOURS FUMIGATION FREQUENCY (PERCENT)'
MONTH _ STRONG MARGINAL' &
JANUARY .0.00 0.73 0.73-FEBRUARY 0.35 1.91 2,25 MARCH 7.28 9.17 16.45 APRIL 19.37 31.24 50.61 MAY 27.20 29.09 56.29 JUNE 28.27 17.71 45.98 JULY 24.75 14.49 39.24 AUGUST 8.52 -3.69 12.21
-SEPTEMBER 1.35 1.94 3.29 OCTOBER 0.32 1.13 1.45 NOVEMBER 0.76 2.86 3.63 DECEMBER 0.38 1.35 1.73 ANNUAL 11.01 6.68. 21.15 1
Percentages are calculated using a base of all hours will concurrent data at the coastal and inland site, regardless of flow direction.
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Table 3-4. Monthly summary of fumigation frequency during night hours.
NIGHT SOURS.
FUMIGATION FREQUENCY (PERCENT)
MONTH STRONG 1 MARGINAL A JANUARY 0.00 0.00 0.00 FEBRUARY 0.00 1.39 1.39 MARCH 0.00 6.70 6.70 APRIL 0.00 17.28 17.28 MAY 0.00 6.36 6.36 -i JUNE 0.00' 3.72 3.72 JULY 0.00 2.54 2.54 AUGUST 0.00 0.00 0.00 SEPTEMBER 0.00 0.30 0.30 OCTOBER O.00 0.16 0.16 NOVEMBER 0.00 1.72 1.72 DECEMBER 0.00 0.38- 0.38 ANNUAL 0.00 . 6.68 6.68 1
Strong fumigation classifications can not occur at night is ::tability over land at night is restricted to P-G classes D, E F, or G.
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Table 3-5. Monthly summary of fumigation frequency for all hours.
ALL HOURS FUMIGATION FREQUENCY (PERCENT)
MONTH STRONG MARGINAL A JANUARY 0.00 0.27 0.27 FEBRUARY 0.15 1.46 1.61 MARCH 3.51 7.66 11.17 APRIL 10.28 25.74 36.02 MAY 16.72 21.80 38.52 JUNE 17.84 13.52 31.36 JULY 15.49 10.61 26.10 AUGUST 4.52 2.48 7.00 SEPTEMBER 0.66 1.10 1.76 OCTOBER 0.14 0.58 0.72 NOVEMBER 0.29 1.77 2.06 DECEMBER 0.14 0.64 0.79 ANNUAL 5.40 6.68 12.08 l
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4.0 CONCLUSION
S Review of the fumigation frequency analysis presented in Section 3.0 reveals that the phenomenon is quite common, especially in the spring and early summer months. This is not surprising since this is the time of year with the greatest temperature differences between the land and the lake. The land l l
heats up quickly in the spring while the lake temperature lags behind by 1 or J 2 months. This results in extreme lake air stabilities concurrent with l i
unstable conditions onshore. The opposite occurs during the late summer '
through vinter period, when the lake is better able to retain the thermal ,
energy collected during the summer while the land cools off rapidly. The result is stable conditions onshore, and unstable over-vater vertical temperature gradients; conditions unfavorable for fumigation.
Fumigation has been observed in numerous field studies and is recognized as an important part of the dispersion process of pollutants released in a l shoreline environment (Lyons, 1973). At 9MP, meteorological conditions favorable for possible fumigation (ALL) are observed in the ELOMS 2-year meteorological database on over 30% of the hours in April, Hay, and June.
Fumigation conditions clearly resulting from thermal effects (STRONG) are )
noted over 'L5% of the time in May, June, and July. On an annual basis, STRONG I i
fumigation occurs on 5.4% of the hours while favorable conditions exist 12.1% '
of the time. Clearly, from this analysis, it can be concluded that fumigation is an importa st phenomena in describing the dispersion meteorology of the Nine Mile site, especially during the spring and early summer months. l 20 GF
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5.0 REFERENCES
1 Ballentine, B.J., 1987: Formulation and testing of an index to predict the
~
onset of lake breezes along the south shore of Lake Ontario. Final Report to the New York Power Authority. 28 pp. ;
t .. EPA, 1977: User's manual for single source (CRSTER) model. EPA-450/2 l 013, Environmental Protection Agency, Research Triangle Park, NC.
ESEERCO, 1989: Eastern Lake Ontario meteorological study - Phase 1: Vind j field monitoring program. Volume III - User's guide to the ELOMS 1 meteorological database. Final task report by Galson Technical Services, Inc. for Empire State Electric Energy Research Corporation, New York, NY 10036. ESEERCO Project EP 85-24. 32 pp.
ESEERCO, 1990a: Review of formulas and observations of thermal internal )
boundary layers in shoreline environments. Final task report by Sigma '
Research Corporation for Empire State Electric Energy Research Corporation, New York, NY 10036. ESEERCO Project EP 88-6. 38 pp.
I ESEERCO, 1990b A comparison of turbulence classification schemes near eastern Lake Ontario. Final task report by Sigma Research Corporation for Empire State Electric Energy Research Corporation, New York, NY 10036.
ESEERCO Project EP 88-6. 73 pp.
l' Golder, D., 1972: Relations among stability parameters in the surface layer.
Boundary Layer Meteorology, 3, 47-58.
1 Hanna, S. R., L. L. Schulman, R. J. Paine, and J. E. Pleim, 1984: The J offshore and coastal dispersion (OCD) model user's guide. OCS Study, MMS 84-0066. Environmental Research and Technology, Inc., Concord, MA (NTIS PB 86-159803). 277 pp. I Lyons, V. A., 1973: Fumigation and trapping on the shores of Lake Michigan during stable onshore flov. Journal of Applied Meteorology, 3, 494-510.
Turner, D. B., 1970: Vorkbook of atmospheric dispersion estimates. Office of air programs publication No. AP-26. U.S. Environmental Protection Agency. U.S. Government Printing Office. 84 pp.
l 21 I ,
l
1
- APPDIDII A -
Polynomial fit to climatological Lake Ontario Temperature data for use in Computer Applications l
6
i l
- APPENDII A -
Polynomial fit to climatological Lake Ontario Temperature data for use in Computer Applications Ballentine (1987) proposed a table of climatological lake surface temperatures l for Lake Ontario for use when the measured temperature is not available. The l data was collected from infrared satellite images of the Lake Ontario surface provided by the Canadian Atmospheric Environmental Service. The coding of this data for use in computer applications is a bit cumbersome, due to the number of data points (n=365). In order to improve the efficiency of using the climatological temperatures in computer applications, optional techniques for specifying the climatological lake temperature for each day of the year vere explored.
A plot of the climatological lake temperature data (Figure A-1) shows that the response of lake surface temperature to the time of year is nearly sinusoidal, with coldest temperatures in late vinter/early spring, and varmest temperatures in late summer /early fall. The annual variation of lake temperature lags the atmosphere due mainly to the high heat capacity of the lake. From inspection of this plot, it was determined that the lake temperature could be described by a polynomial, with day-of-year (julian day) as the independent variable, and lake surface temperature as the dependent variable.
The best fit to Ballentine's data was achieved using a seventh-order polynomial. The polynomial represents a model for calculating lake surface temperature (T,) for each julian day (Ja,y) as follows:
T, ('F) = A + Bx + Cx +2 Dx3 + Ex4 + Fx3 + Cx8 + Ex 7 where A = 38.06223044 B = -2.36392550 C = 0.818558979 D = -0.16674588 E = 0.016869448 F = -0.00078948 G = 1.69247e-05 H = -1.3571e-07 and x = J,,y/10 The seventh-order pol nomial explains 99.8% of the annual variation in climatological lake temperature (ie. R2 = 0.998). It is suggested that the polynomial be run in double precision for computer applications. Analysis of Variance (ANOVA) statistics calculated for the polynomial fit are provided in Table A-1. The Polynomial is plotted against the climatological data in Figure A-1.
Note that leap-year is not expected to have a significant impact on the specification of lake temperature.
A-1 W
Table A-1. Polynomial regression statistics.
POLYNOMIAL REGRESSION Z: Jox,/10 T LAKE TEMPERATURE DF: R: R-SOUARED ADJ. R-SOUARED: STD ERROR:
364 .999 .998 .998 .632
- Analysis of Variance Table -
Source: DF: Sum Squares Mean Square: F-Test:
REGRESSION 7 63802.692 9114.67 22836.859 RESIDUAL 357 142.486 .399 p = .0001 TOTAL 364 63945.178 Param: Value Std. Err.: t-Values a 38.06223044 0.276379746 137.7171481 b -2.36392550 0.272025512 -8.69008749 c 0.818558979 0.085740721 9.546910374 d -0.16674588 0.012053163 -13.8342009 e 0.016869448 0.000869524 19.40079196 f -0.00078948 3.34892E-05 -23.5742517 g 1.69247E-05 6.54380E-07 25.86375923 h -1.3571E-07 5.09538E-09 -26.6344582 A-2 E_____________ _ _ _ _ _ _ . _ _ _ . _ _ _ _ _ _ . _ _ _ _ _ _ _
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- APPENDII B-Monthly Fusigation Frequency Analysis Results I
i l
I i
e 1
. l Y \. -
1
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: APRIL YEAR: 1986 l _
VATER B C D E F A 0 0 0 l 0 0 j l I l B 0 0 0 l 3 8 l 1 l C 1 0 0 l 2 13 l S l l YD 11 8 100 j 10 63 l R ----_--------
E O O 8 0 10 F 1 0 0 0 7 l G 0 0 0 a 4 TOTAL NUMBER OF HOURS: 720 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 99 (25.1%)
NUMBER OF HOURS WITH FAVORABLE VIND DIRECTION: 253 (64.2%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRICTION: 141 (35.8%)
NUMBER OF MISSING HOURS: 326 (45.3%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH:MAY YEAR: 1986 VATER B C D E F A 0 0 0 l 0 4 l l l B 0 0 0 l 0 28 i
C 2 2 2 l 3 50 S l YD 0 3 34 l 19 138 R -------------
E O O 9 0 20 F 0 'O 1 1 8 G 0 0 0 0 6 I
TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 242 (43.0%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 330 (58.6%)
NUMBER OF HOURS WITH UNFAVORABLE VIND DIRECTION: 233 (41.4%)
NUMBER OF MISSING HOURS: 181 (24.3%)
B-1 1
l
f FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: JUNE YEAR: 1986
]
VATER -
B C D E F A 0 0 1 1 5 l l '
B 0 0 6 0 10 l l
C 4 4 19 0 17 S l YD 3 3 42 12 40 l R -------------
E 3 1 11 1 0 F 1 0 3 0 0 G 0 0 0 0 0 TOTAL NUMBER OF HOURS: 720 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 85 (22.7%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 187 (49.9%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 188 (50.1%)
NUMBER OF MISSING HOURS: 345 (47.9%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH JULY YEAR: 1986 VATER B C D E F A 2 1 1 l3 4 i
B 7 0 10 l 1 17 I
C 12 4 14 l 3 35 S l YD 36 14 50 l 15 98 R ----- - --
E 5 4 7 5 3 I
F 3 '4 7 0 0 G 0 0 0 0 0 l
TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 176 (24.9%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 365 (51.6%)
l- NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 342 (48.4%)
NUMBER OF MISSING HOURS: 37 ( 5.0%)
B-2
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH AUGUST YEAR: 1986 VATER B C D E F A 0 0 0 l 0 4 l l l B 15 3 6 l 0 8 l I l C 25 11 14 l 1 11 l S l 'l YD 41 18 75 l 9 5 l R -------------
E 16 4 10 0 2 F 9 8 3 1 0 G 1 1 1 0 0 TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 38 ( 5.7%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 302 (44.9%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 370 (55.1%)
NUMBER OF MISSING HOURS: 72 ( 9.7%)
FUMIGATION FREOUENCY ANALYSIS FOR 9MP MONTH: SEPTEMBER YEAR: 1986 VATER B C D E F A 3 0 0 l 0 'O l B 10 1 2 1 1 C 27 13 3 l 4 2 S
YD 98 23 54 5 4 l R _-__--__--
l- E 14 5 7 0 0 l F 13 'l 2 0 0 G 4 0~ 0 0 0 TOTAL NUMBER OF HOURS: 720 NUMBER OF HOURS WITH FUMIGATION PREDICTED: 17 ( 2.6%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 297 (46.1%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 347 (53.9%)
NUMBER OF MISSING HOURS: 76 (10.6%)
B_3
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ w
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH:0CTOBER YEAR: 1986 VATER B C D E F A 0 0 0 l 0 0 l l l B 2 0 0 l 0 0 l I I C 15 4 1 l 0 0 l S l l YD 86 42 77 l 2 3 l R ---- == --
E 20 6 3 0 0 F 15 0 4 0 0 G 2 3 3 0 0 TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 5 ( 0.8%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 288 (44.4%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIPICTION: 360 (55.6%)
NUMBER OF MISSING HOURS: 96 (12.9%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTE: NOVEMBER YEAR: 1986 VATER B C D E F A 0 0 0 l 0 0 l l 1 B 0 0 0 l 0 0 l l l C 8 1 6 l 1 0 l S l l YD 57 31 160 l 5 1 l R -
E 16 5 12 1 0 F 10 'O 2 0 0 G 0 0 0 0 0 TOTAL NUMBER OF HOURS: 720 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 7 ( 1.0%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 316 (46.0%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 371 (54.0%)
NUMBER OF MISSING HOURS: 33 ( 4.6%)
B-4 9
l l
FUMIGATION FREQUENCY ANALYSIS FOR 9MP l MONTH: DECEMBER YEAR: 1986 i
VATER- l B C D E F A 0 0 0 l 0 0 B 1 0- 0 0 0 I
C 4 0 0 1 0 0 S l YD 65 37 195 l 0 0 )
n .............
l E 7 1 3 0 0 l 1'
F 8 0 0 0 0 G 1 0- 0 0 0 .,
TOTAL NUMBER OF HOURS: 744 i NUMBER OF HOURS WITH FUMIGATION PREDICTED: 0 ( 0.0%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 322 (49.2%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 332 (50.8%)
NUMBER OF MISSING HOURS: 90 (12.1%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: JANUARY YEAR: 1987 VATER l B C D E F !
A 0 0 0 0 0 l i i
B 3 0 0 0 0 l 1
C 13 2 0 O O i S i !
YD 63 55 171 l 3 1 )
R -------------
E 6 7 1 0 1 F 5 ~3 0 1 0 G 4 1 0 0 0 TOTAL NUMBER OF HOURS: 744 l
NUMBER OF HOURS VITH FUMIGATION PPEDICTED: 4 ( 0.6%)
NUMBER OF HOURS WITH FAVORABLE VIND DIRECTION: 340 (47.4%) 1 NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 377 (52.6%) :
NUMBER OF MISSING HOURS:~ 27 ( 3.6%)
i B5 I
_ , . _ _ _ _ _ _ _ . _ _ _ _ _ _ _ _ _ _ _ . _ . _ _ _ . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ . _ _ _ _ . _ _ _ _ _ _ _ _ _ _ _ _ _ . _ _ _ . _ _ _ _ _ _ _ _ _ . _____.._____a
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: FEBRUARY YEAR: 1987 VATER B C D E F A 0 0 0 0 0 B 9 0 01 0 0 C 40 3 2 0 0 S
YD 96 28 110 1 0 01 R -------------
E 33 6 25 0 0 F 39 5 3 0 0 G 20 5 5 0 0 TOTAL NUMBER OF HOURS: 672 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 0 ( 0.0%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 429 (68.2%)
NUMBER OF HOURS VITH UNFAVORABLE WIND DIRECTION: 200 (31.8%)
NUMBER OF MISSING HOURS: 43 ( 6.4%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: MARCH YEAR: 1987 VATEL B C D E F A 0 0 0 0 0 B 2 1 4 2 13 C 13 7 7 5 21 S l YD 47 23 105 ; 20 54 R -------------
E 19 4 28 2 11 F 17 '2 10 1 3 G 7 1 0 0 5 TOTAL NUMBER OF HOURS: 744
- NUMBER OF HOURS WITH FUMIGATION FREDICTED: 115 (16.9%)
l NUMBER OF HOURS WITH FAVORABLE VIND DIRECTION: 434 (63.6%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 248 (36.4%)
NUMBER OF MISSING HOURS: 62 ( 8.3%)
B-6 l
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ . . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ . . _ _ _ _ _ .J
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: APRIL YEAR: 1987 I I
VATER B C D E F A 0 0 0 l 0 1 l l
B 0 0 0 l 4 30 l l C 0 0 2 7 43 5
YD 13 1 50 l 51 154 l R -------------
E O O 4 2 19 F 0 -0 0 4 19 )
G 0 0 0 0 3 TOTAL NUMBER OF HOURS: 720 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 290 (42.3%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 407 (59.3%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 279 (40.7%)
NUMBER OF MISSING HOURS: 34- ( 4.7%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH:MAY YEAR: 1987 l VATER l B C D E F A 0 0 0 0 3 l B 0 0 5 1 43 C 0 0 2 7 62 S
l YD 0 0 12 l 22 83 l R -------------
E O O 2 0 23 l F 0 0 2 1 22 l
l G 2 0 0 2 15 l
l TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION FREDICTED: 221 (34.6%)
NUMBER OF HOURS VITH FAVORABLE VIND' DIRECTION: 309 (48.4%)
NflMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 330 (51.6%)
FUMBEP. OF MISSING HOURS: 105 (14.1%)
, B' - 7
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: JUNE YEAR: 1987 l
l VATER B C D E F A 0 0 0 b 12 l 1.
.B 0 0 1 3 71 l l
C 0 0 5 9 62 S
YD 0 0 32 12 80 R -------------
E O O 4 4 15 l
1 1
F 0 0 1 7 G 0 0 0 1 o i TOTAL NUMBER OF HOURS: 720 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 249 (36.1%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 351 (50.9%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 339 (49.1%)
NUMBER OF MISSING HOURS: 30 ( 4.2%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: JULY YEAR: 1987 l VATER B C D E F A 1 0 4 , 3 9 l
'l B 6 2 7 8 69 l
C 11 3 14 j 11 59 S l YD 11 1 30 l 12 27 l R ---------
E 3 3 12 4 6 F 7 2 2 11 8 G 2 0 0 5 8 6
TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS WITH FUMIGATION PREDICTED: 198 (27.3%)
i NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 361 (49.7%)
le NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 365 (50.3%)
NUMBER OF MISSING HOURS: 18 ( 2.4%)
B-8 W %.
R.___ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ . . _ _ . _ _ _ _ _ _ . . _ . _ . _ _ _ . .
FUMIGATION FREQUENCY ANALYSIS FOR 9MF MONTH: AUGUST YEAR: 1987 l VATER B C D E .F i
\
l A 0 1 1 1 0 B 8 5 8 4 11 C 11 2 23 h 7 20 S l YD 37 23 68 l 2 13 R -------------
E 6 4 18 2 0 F 9 5 25 1 2 G 3 3 7 0 0 TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION FREDICTED: 58 ( 8.3%)
NUMBER OF HOURS WITH FAVORABLE VIND DIRECTION: 330 (47.1%)
NUMBER OF HOURS WITH UNFAVORABLE VIND DIRECTION: 370 (52.9%)
NUMBER OF MISSING HOURS: 44 ( 5.9%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP NONTH: SEPTEMBER YEAR: 1987 VATER B C D E F A 0 0 0 0 0 B 11 5 5 0 0 C 20 5 10 0 1 S
YD 70 15 64 3 3 R -------------
E 7 3 8 0 0 F 4 8 13 0 0 G 3 4 9 0 0 TOTAL NUMBER OF HOURS: 720 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 7 ( 1.0%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 271 (37.7%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 447 (62.3%)
NUMBER OF MISSING HOURS: 2 ( 0.3%)
t B-9 W 4
h l.'
FUMIGATION FREQUENCY ANALYSIS FOR 9MP -
MONTH:0CTOBER YEAR: 1987 VATER B C D E F l
A 0 0 0 l 0 0 l B 6 2 0 0 0 [
C 14 5 1 0 2 S
YD 78 47 74 j 1 2 R -------------
E 12 8. 10 0 0 F 6~ 6 18 0 0 G 5 4 5 0 0 TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION FREDICTED: 5 ( 0.7%)
NUMBER OF HOUT.'l VITH FAVORABLE VIND DIRECTION: 306 (41.5%)
' NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 431 (58.5%)
NUMBER OF MISSING HOURS . 7 ( 0.9%)
-FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTE NOVEMBER TEAR '1987 VATER B C D E F A 0 0 0 l 0 0 l
B 2 1 1 l 0 0 I l'
C 3 2 5 0 3 5
YD 42 41 155 7 11 R - - - - - - - - - - - -
E 3 2 6 1- 0 F 1 'l 6 2 'O G 3 1 4 0 0 TOTAL NUMBER OF HOURS: 720 NUMBER OF HOURS VITH FUMIGATION FREDICTED: 21 ( 3.1%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 303 (45.2%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIPECTION: 368 (54.8%)
NUMBER OF MISSING HOURS: 49 ( 6.8%)
B - 10 W
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: DECEMBER YEAR: 1987 VATER B C D E F A 0 0 0 l 0 0 l l
5 0 0 0 0 0 l
[ l C 7 0 1 1 1 S
YD 68 63 180 5 4 l R -------------
E 5 1 20 0 1 F 6 0 6 0 0 G 1 0 0 0 0 TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 11 ( 1.5%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 370 (49.9%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 372 (50.1%)
NUMBER OF MISSING HOURS: 2 ( 0.3%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH JANUARY YEAR: 1988 VATER B C D E F A 0 0 0 0 0 l B 0 1 0 0 0 C 9 1 1 0 0 S-YD 67 37 111 0 0 R -------------
E 14 5 1 0 0 F 10 1 0 0 0 G 0 0 0 0 0 TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 0 ( 0.0%).
l NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 258 (34.7%)
l NUMBER OF HOURS WITH UNFAVORABLE VIND DIRECTION: 485 (65.3%)
NUMBER OF MISSING HOURS: 1 ( 0.1%)
B - 11 l
(
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH FEBRUARY YEAR: 1988
_ VATER B C D E F A 0 0 0 1 0 0 l l l B 3 0 0 , 0 0 l l
C 18 3 1 2 0 S
YD 50 45 130 7 12 l R -------------
E 9 4 16 0 0 F 16 2 7 0 0 G 12 0 0 0 0 TOTAL NUMBER OF HOURS: 696 NUMBER OF HOURS VITH FUMIGATION PREDICTED: 21 ( 3.1%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 337 (49.9%)
NUMBER OF HOURS WITH UNFAVORABLE VIND DIRECTION: 338 (50.1%)
NUMBER OF MISSING HOURS: 21 ( 3.0%)
FUMIGATION FREQUENCY ANALYSIS FOR 9MP MONTH: MARCH YEAR: 1988 VATER B C D E F A 0 0 0 0 0 l l
B 11 0 01 0 5 l C' 29 3 12 0 4 5
YD 50 25 179 , 10 25 R - - - - - - - - - - -
E 11 6 25 1 2 F 9 2 31 2 3 G 13 0 3 2 2 TOTAL NUMBER OF HOURS: 744 NUMBER OF HOURS VITH FUMIGATION FREDICTED: 44 ( 5.9%)
NUMBER OF HOURS VITH FAVORABLE VIND DIRECTION: 465 (62.8%)
NUMBER OF HOURS VITH UNFAVORABLE VIND DIRECTION: 276 (37.2%)
NUMBER OF MISSING HOURS: 3 ( 0.4%)
B - 12 9
- __ _ __ - - - ~ u m, o ~ , -o y - ;
_ e,cyypuuvu,ogy , g tr 4 EMPIRE STATE ELECTRIC ENERGY RESEARCH CORPORATION 418 Deedser, New Vet. NewYe4100M TeLt8188031318 hou113-33 477 hlW//www.eeeersten May 13,1998 Ms Joann McKee New York Power Authority 123 Mein St White Pleins, NY 10601 Sut$ect: EP 89-14, Review of Forgnules and Observations of Thermal internal soundary Layers in Shoreline Environments EP 9128, On Shore Air Plow Field Study Dear Jeann His letter shall serve as authortration from ESEERCO to allow the NRC to make copies of the subject reports for the purpose of dispieying copies in the Public Documents Room, NRC File Center, and input into NUDOCS. This letter in no way shall dHute ESEERCO's rights and interests in such reports end/or their contents to either tne Nuclear Regulatory Commission or users of the Pubik: Documents Room. {
if you have any questions, please contact me or Daniel Zaweski (ESEERCO's Manager of Finance, Contracts & Personnel Services).
Thank you.
Sincerely k
Debra M DiMeo Program Manager l
LMmWCOWwl89eMQK$$ che Oecers Merminers oer*c8 MWisen ese & Westle Copparuhen 90ew Yo4 sole Eoctdo S eos cegporeRon JOMM t.14MX le. Pteedent a nous Ptcriss, vlos preendere Canneedneed lateen Co.etNL Inc. Crunge end Assutenal utsees, tre.
ten 0leland WWilleg cargemy Rochesear ses eel osoms corposomen PAlfL J.foWEY,teseWive elsecour New Ye* Power Ausnamy TOTAL P.22
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