Table A-1-.	Source Wood Characterization Data Summary

Table A-1Board IDBoard Length (in)Board Length (cm)Predominant Grain
TypePredominant Grain OrientationPredominant Ring SpacingPredominant
Wood TypePredominant Wood
SeasonA-AC181.4375460.940F/60EUpTightSapwoodLateA-AD182.75464.2Flat-Grai
nedUpWideSapwoodEarlyA-AE184.1875467.8Flat-GrainedUpMediumSapwoodEarlyA-
AF185.0469.9Flat-GrainedDown20H/80S70E/30LA-AG186.25473.1Flat-GrainedUpM
ediumSapwoodEarlyA-AH187.5476.340F/60EDownWideHeartwoodEarlyA-AJ190.1254
82.930F/70EUpTightHeartwoodLateA-AN178.375453.1Flat-GrainedDownWideSapwo
odLateA-AR161.25409.6Flat-GrainedUpWideHeartwoodEarlyA-AT147.0373.420F/8
0EDownMediumHeartwoodLateA-BC163.0414.0Flat-GrainedUpMediumSapwoodEarlyA
-BG193.25490.9Flat-GrainedDownWideSapwoodEarlyA-BW178.875454.3Flat-Grain
edDownMediumHeartwoodEarlyA-BY162.875413.730F/70EDownWideHeartwoodEarlyA
-F156.0625396.430F/70EUpMediumSapwoodLateA-I157.0398.8Flat-GrainedUpTigh
tSapwoodLateA-L160.625408.0Flat-GrainedDownWideSapwoodEarlyA-O164.25417.
2Flat-GrainedUpWideHeartwood60E/40LA-P165.375420.150/50UpMediumSapwoodEa
rlyA-Q166.3125422.440F/60EDownWideHeartwoodEarlyA-T170.4375432.9Flat-Gra
inedUpMediumSapwoodLateA-U171.6875436.1Flat-GrainedUpCloseHeartwood50/50
A-V172.875439.1Flat-GrainedUpHeartwoodLateA-X175.5445.8Flat-GrainedUpMed
iumSapwoodEarlyA-Y176.625448.6Flat-GrainedDownMediumSapwoodEarlyA-Z177.7
5451.5Flat-GrainedDownWideSapwood50/50C-AA160.0406.4Flat-GrainedDownMedi
umSapwoodLateC-AC191.5486.4Flat-GrainedUpTightSapwoodLateC-AD192.125488.
0Flat-GrainedUpMediumHeartwoodEarlyC-AE191.625486.7Flat-GrainedDownWideS
apwoodEarlyC-AI192.125488.0Flat-GrainedDownMediumSapwoodEarlyC-AJ193.625
491.8Flat-GrainedUpMediumSapwoodEarlyC-AK186.875474.730F/70EDownTightSap
woodEarlyC-AM191.9375487.5Flat-GrainedDownMediumSapwoodEarlyC-AN191.8754
87.430F/70EDownTightHeartwoodEarlyC-AP174.25442.6Flat-GrainedUpMediumHea
rtwood60E/40LC-BE178.375453.1Flat-GrainedUpMediumSapwood70E/30LC-BI188.1
25477.8Flat-GrainedUpWideHeartwoodEarlyC-BJ177449.6Flat-GrainedUpWideSap
woodEarlyC-BM192.625489.3Flat-GrainedUpTightSapwoodEarlyC-BO188.5478.8Fl
at-GrainedDownMedium-wideSapwoodEarlyC-BT192.625489.330F/70EDownTightHea
rtwoodEarlyC-BU156.375397.2Flat-GrainedUpWideSapwood40E/60LC-BW156.37539
7.230F/70EDownTightHeartwoodEarlyC-BX192.375488.630F/70EUpMediumHeartwoo
dEarlyC-BY174442.0Flat-GrainedUpWideSapwoodEarlyC-BZ192.125488.0Flat-Gra
inedUpMediumSapwoodEarlyC-CA156.625397.8Flat-GrainedUpMediumSapwoodLateC
-CC174442.0Flat-GrainedDownMediumSapwoodEarlyC-CD174.5443.2Flat-GrainedU
pTightHeartwoodLateC-CE174.5625443.4Flat-GrainedDownMediumSapwoodEarlyC-
E136345.440F/60EDownMediumHeartwoodEarlyC-N152.25386.7Flat-GrainedUpMedi
umSapwood50/50C-S189.1875480.5Flat-GrainedUpWideSapwoodEarly

Table A-2.	Baseline Wipe Sample Summary for Source Deck A

Table A-2Statistical ParameterDas(µg/cm2)DCr(µg/cm2)DCu(µg/cm2)Board
A-ACN555Average1.631.620.74Std. Dev.0.410.280.25RSD24.9%17.0%34.0%Board
A-ADN555Average2.793.051.43Std. Dev.0.600.440.45RSD21.4%14.4%31.4%Board
A-AEN555Average1.361.871.01Std. Dev.0.291.110.30RSD21.2%59.4%30.0%Board
A-AFN222Average1.581.480.83Std. Dev.0.940.830.56RSD59.3%56.1%67.4%Board
A-AGN666Average1.421.500.65Std. Dev.0.630.660.16RSD44.5%43.7%24.6%Board
A-AHN666Average0.700.720.46Std. Dev.0.290.240.15RSD41.2%33.8%33.1%Board
A-AJN666Average1.761.930.86Std. Dev.<IP_ADDRESS>RSD11.4%10.9%14.9%Board
A-ANN555Average3.894.940.63Std. Dev.1.261.860.14RSD32.4%37.6%22.6%Board
A-ARN555Average1.731.680.91Std. Dev.1.171.300.64RSD67.6%77.5%70.0%Board
A-ATN555Average1.321.570.75Std. Dev.0.280.400.13RSD20.9%25.2%16.7%Board
A-BCN555Average1.571.840.72Std. Dev.0.410.410.08RSD26.2%22.5%10.5%Board
A-BGN666Average1.541.500.73Std. Dev.0.270.350.12RSD17.3%23.0%16.9%Board
A-BMN333Average1.421.680.51Std. Dev.0.510.540.17RSD35.7%32.2%32.8%Board
A-BWN666Average0.810.710.50Std. Dev.0.280.250.17RSD34.3%35.2%34.3%Board
A-BYN555Average1.531.570.91Std. Dev.0.510.510.30RSD33.5%32.6%33.0%Board
A-IN555Average1.742.160.76Std. Dev.0.510.360.15RSD29.1%16.7%19.5%Board
A-LN555Average0.750.850.45Std. Dev.<IP_ADDRESS>RSD26.3%23.5%15.4%Board
A-ON555Average2.172.380.95Std. Dev.0.690.810.26RSD31.6%34.3%27.0%Board
A-PN555Average2.121.731.12Std. Dev.<IP_ADDRESS>RSD51.5%57.8%22.3%Board
A-QN555Average2.702.801.10Std. Dev.<IP_ADDRESS>RSD38.9%42.6%31.8%Board
A-TN555Average<IP_ADDRESS>Std. Dev.0.600.570.41RSD27.3%26.4%34.5%Board
A-UN555Average0.951.110.54Std. Dev.0.410.520.16RSD42.9%47.0%29.0%Board
A-VN555Average1.601.700.67Std. Dev.0.660.620.23RSD41.1%36.1%33.9%Board
A-XN555Average1.751.660.66Std. Dev.0.500.490.18RSD28.4%29.4%27.0%Board
A-YN555Average3.954.331.33Std. Dev.1.751.530.47RSD44.2%35.3%35.2%Board
A-ZN555Average2.342.610.96Std. Dev.0.290.400.14RSD12.3%15.4%14.7%All A
BoardsN130130130Average1.811.960.82Std.
Dev.<IP_ADDRESS>RSD43.4%39.3%56.5%Min0.380.310.24Max5.857.382.19

Table A-3.	Baseline Wipe Sample Summary for Source Deck C

Table A-3Statistical ParameterDas(µg/cm2)DCr(µg/cm2)DCu(µg/cm2)Board
C-AAN555Average0.630.840.31Std. Dev.<IP_ADDRESS>RSD27.4%23.5%22.2%Board
C-ACN666Average0.851.170.44Std. Dev.<IP_ADDRESS>RSD11.8%11.7%10.1%Board
C-ADN777Average0.680.910.34Std. Dev.<IP_ADDRESS>RSD23.8%18.6%21.3%Board
C-AEN666Average1.672.290.62Std. Dev.0.300.310.09RSD18.1%13.7%15.2%Board
C-AIN555Average0.951.300.49Std. Dev.<IP_ADDRESS>RSD15.7%8.9%10.0%Board
C-AJN555Average1.391.850.75Std. Dev.<IP_ADDRESS>RSD5.7%8.2%12.2%Board
C-AKN222Average0.590.880.48Std. Dev.<IP_ADDRESS>RSD12.3%6.1%7.4%Board
C-AMN666Average1.061.450.55Std. Dev.0.200.280.06RSD18.5%19.2%10.9%Board
C-ANN555Average1.241.360.54Std. Dev.0.310.720.09RSD24.9%53.0%16.7%Board
C-APN555Average0.530.830.41Std. Dev.<IP_ADDRESS>RSD28.6%23.2%30.9%Board
C-BEN555Average1.391.730.83Std. Dev.0.190.290.49RSD14.0%16.8%58.7%Board
C-BIN555Average0.811.090.63Std. Dev.<IP_ADDRESS>RSD21.0%19.0%23.6%Board
C-BJN555Average2.322.771.38Std. Dev.0.610.530.37RSD26.5%19.3%26.7%Board
C-BMN777Average0.670.980.33Std. Dev.<IP_ADDRESS>RSD24.7%22.7%19.7%Board
C-BON444Average0.701.010.40Std. Dev.<IP_ADDRESS>RSD7.6%11.3%17.5%Board
C-BPN555Average0.731.020.32Std. Dev.0.220.310.07RSD30.1%30.3%22.1%Board
C-BTN666Average0.530.740.35Std. Dev.<IP_ADDRESS>RSD15.6%15.9%23.7%Board
C-BUN333Average0.921.290.56Std. Dev.0.260.320.24RSD28.9%25.0%43.4%Board
C-BWN333Average0.480.640.32Std. Dev.<IP_ADDRESS>RSD42.1%28.0%9.1%Board
C-BXN555Average0.941.310.44Std. Dev.0.310.450.14RSD33.4%34.7%32.2%Board
C-BYN333Average0.650.960.36Std. Dev.<IP_ADDRESS>RSD8.1%6.7%7.1%Board
C-BZN777Average0.711.060.46Std. Dev.0.300.400.14RSD42.2%37.2%31.4%Board
C-CAN333Average0.670.960.47Std. Dev.<IP_ADDRESS>RSD30.7%25.5%11.4%Board
C-CCN444Average0.801.110.55Std. Dev.<IP_ADDRESS>RSD18.0%15.1%8.9%Board
C-CDN555Average1.401.791.02Std. Dev.0.470.560.34RSD33.9%31.2%33.5%Board
C-CEN333Average1.381.871.14Std. Dev.0.260.300.23RSD18.5%15.9%20.4%Board
C-EN555Average1.441.720.52Std. Dev.0.280.280.06RSD19.3%16.1%11.3%Board
C-NN555Average1.251.510.38Std. Dev.<IP_ADDRESS>RSD14.3%11.2%12.5%Board
C-SN555Average1.341.750.40Std. Dev.0.590.720.11RSD44.2%41.0%27.2%All C
BoardsN140140140Average1.001.330.54Std.
Dev.0.480.570.29RSD51.7%57.1%46.2%Min0.280.200.21Max3.053.561.96

Table A-4.	Brush Sample CCA Analyte Concentrations

Bristle TypeActual Concentration
(µg/L)AsCrCuNatural<0.20*0.404.2Natural<0.20*0.882.4Synthetic<0.20*0.49
1.7Synthetic<0.20*0.351.7*Reporting limit was later reduced by the
subcontracted laboratory to 0.1 µg/L

Table A-5.	Wood Core Sample Results for Source A

Table A-5Statistical ParameterWood Core As (mg/kg)Wood Core Cr
(mg/kg)Wood Core Cu (mg/kg)Board A-ABN333Average2148.42704.31698.4Std.
Dev.179.2224.987.9RSD8.3%8.3%5.2%Board
A-ACN444Average1499.91959.01224.0Std.
Dev.327.5204.7134.6RSD21.8%10.5%11.0%Board
A-ADN444Average2675.33280.51791.4Std.
Dev.75.5335.5191.6RSD2.8%10.2%10.7%Board
A-AEN444Average851.11023.2610.5Std.
Dev.977.91190.2656.1RSD114.9%116.3%107.5%Board
A-AFN333Average1424.41691.5982.9Std.
Dev.142.2188.9118.5RSD10.0%11.2%12.1%Board
A-AGN444Average1865.12227.31288.3Std.
Dev.395.9413.3323.4RSD21.2%18.6%25.1%Board
A-AHN444Average1106.01293.7770.8Std.
Dev.738.1871.1530.6RSD66.7%67.3%68.8%Board
A-AJN444Average1238.32007.81090.7Std.
Dev.886.2484.7501.1RSD71.6%24.1%45.9%Board
A-AKN444Average2008.62192.71256.0Std.
Dev.1115.41220.4661.9RSD55.5%55.7%52.7%Board
A-ANN333Average2116.72656.01667.6Std.
Dev.228.0222.489.7RSD10.8%8.4%5.4%Board
A-ARN444Average382.8499.1281.1Std.
Dev.444.9518.6275.6RSD116.2%103.9%98.1%Board
A-AZN444Average2501.02967.51781.7Std.
Dev.642.6544.5213.5RSD25.7%18.4%12.0%Board
A-BCN333Average1846.02543.71515.8Std.
Dev.144.2295.1229.7RSD7.8%11.6%15.2%Board
A-BEN333Average35.736.627.4Std. Dev.<IP_ADDRESS>RSD18.1%12.6%22.5%Board
A-BGN444Average2188.12589.01543.0Std.
Dev.370.5346.8202.2RSD16.9%13.4%13.1%Board
A-BNN111Average152117411079Std. Dev.---RSD---Board
A-BWN222Average1452.31692.51013.2Std.
Dev.126.1236.2193.5RSD8.7%14.0%19.1%Board
A-BYN333Average1149.71404.1859.3Std.
Dev.220.3220.7121.1RSD19.2%15.7%14.1%Board
A-IN333Average1953.02270.11218.6Std.
Dev.382.9285.5195.3RSD19.6%12.6%16.0%Board
A-LN333Average1510.11954.21206.2Std.
Dev.309.2276.7221.6RSD20.5%14.2%18.4%Board
A-NN444Average1117.71333.5825.0Std.
Dev.616.5722.8512.7RSD55.2%54.2%62.1%Board
A-ON333Average1598.42191.41292.2Std.
Dev.51.2297.7188.2RSD3.2%13.6%14.6%Board
A-PN333Average1763.62189.61209.0Std.
Dev.275.4416.1245.9RSD15.6%19.0%20.3%Board
A-QN333Average2549.23177.52002.8Std.
Dev.433.5554.1301.6RSD17.0%17.4%15.1%Board
A-RN333Average1913.62606.81558.4Std.
Dev.435.3135.5146.8RSD22.7%5.2%9.4%Board
A-SN333Average1559.71824.31027.1Std.
Dev.92.3138.3107.8RSD5.9%7.6%10.5%Board
A-TN333Average1927.82316.21375.7Std.
Dev.105.9140.767.6RSD5.5%6.1%4.9%Board
A-UN222Average885.01146.3711.3Std.
Dev.1193.51568.0943.0RSD134.9%136.8%132.6%Board
A-VN333Average759.81012.6535.1Std.
Dev.305.4364.8199.2RSD40.2%36.0%37.2%Board
A-XN444Average1711.82387.51461.6Std.
Dev.424.4325.8149.6RSD24.8%13.6%10.2%Board
A-YN333Average2696.83250.21847.6Std.
Dev.524.4614.1309.0RSD19.4%18.9%16.7%Board
A-ZN333Average2496.02921.61618.5Std.
Dev.710.0613.2327.1RSD28.4%21.0%20.2%For All A
BoardsN104104104Average1645.42045.31203.4Std.
Dev.787.7917.0543.5RSD47.9%44.8%45.2%Min283421Max344539332186

Table A-6.	Wood Core Sample Results for Source C

Table A-6Statistical ParameterWood Core As (mg/kg)Wood Core Cr
(mg/kg)Wood Core Cu (mg/kg)Board C-AAN444Average2185.72302.81517.3Std.
Dev.787.9707.9309.9RSD36.0%30.7%20.4%Board
C-ACN444Average2051.22171.91346.4Std.
Dev.520.2618.7405.0RSD25.4%28.5%30.1%Board
C-ADN444Average1730.61729.01075.2Std.
Dev.206.5250.8182.0RSD11.9%14.5%16.9%Board
C-AEN333Average2405.22323.81449.9Std.
Dev.939.4753.8592.8RSD39.1%32.4%40.9%Board
C-AHN444Average1546.61561.7941.9Std.
Dev.155.1186.678.4RSD10.0%12.0%8.3%Board
C-AIN333Average2650.32709.71607.3Std.
Dev.428.2612.8369.4RSD16.2%22.6%23.0%Board
C-AKN444Average225.6248.5151.7Std.
Dev.222.2206.2116.2RSD98.5%83.0%76.6%Board
C-AMN444Average2580.42597.11527.5Std.
Dev.207.0181.5194.8RSD8.0%7.0%12.8%Board
C-ANN444Average874.1886.8540.6Std.
Dev.484.3458.3239.0RSD55.4%51.7%44.2%Board
C-APN444Average2555.52653.51706.5Std.
Dev.667.7648.8387.1RSD26.1%24.5%22.7%Board
C-ASN444Average1490.21495.9921.2Std.
Dev.1261.61233.2733.4RSD84.7%82.4%79.6%Board
C-BEN444Average2784.72649.81612.9Std.
Dev.314.3251.9160.1RSD11.3%9.5%9.9%Board
C-BIN222Average1279.41203.1688.3Std.
Dev.114.0182.6125.7RSD8.9%15.2%18.3%Board
C-BJN333Average2037.81995.61260.2Std.
Dev.727.8745.1534.0RSD35.7%37.3%42.4%Board
C-BLN444Average3157.93162.51979.2Std.
Dev.573.8378.9204.9RSD18.2%12.0%10.4%Board
C-BMN333Average1463.81451.8933.4Std.
Dev.1317.71308.3893.2RSD90.0%90.1%95.7%Board
C-BNN111Average254426021648Std. Dev.---RSD---Board
C-BON444Average1518.71488.4888.8Std.
Dev.679.7655.8410.0RSD44.8%44.1%46.1%Board
C-BPN333Average2603.72748.11722.1Std.
Dev.680.8881.2555.3RSD26.1%32.1%32.2%Board
C-BRN333Average1431.91380.3809.9Std.
Dev.149.1142.4113.0RSD10.4%10.3%13.9%Board
C-BTN111Average13861367831Std. Dev.---RSD---Board
C-BUN333Average2855.03128.91867.3Std.
Dev.588.9684.7395.2RSD20.6%21.9%21.2%Board
C-BWN333Average562.0571.1347.5Std.
Dev.293.5304.6151.6RSD52.2%53.3%43.6%Board
C-BXN444Average2301.42309.61469.8Std.
Dev.993.6991.5626.1RSD43.2%42.9%42.6%Board
C-BYN333Average2507.92578.51790.5Std.
Dev.528.2459.8312.3RSD21.1%17.8%17.4%Board
C-BZN444Average1343.21486.0847.4Std.
Dev.439.5559.1337.1RSD32.7%37.6%39.8%Board
C-CAN333Average3171.33224.61983.8Std.
Dev.581.7490.2287.8RSD18.3%15.2%14.5%Board
C-CCN222Average1435.91356.5807.2Std. Dev.12.732.22.0RSD0.9%2.4%0.2%Board
C-CDN444Average1194.11162.8671.2Std.
Dev.798.9777.0449.8RSD66.9%66.8%67.0%Board
C-CEN222Average2425.52425.51520.8Std.
Dev.435.9435.9284.8RSD18.0%18.0%18.7%Board
C-EN333Average2946.32832.71627.9Std.
Dev.1236.11330.9451.5RSD42.0%47.0%27.7%Board
C-NN333Average2988.32966.61832.1Std.
Dev.549.8618.7465.0RSD18.4%20.9%25.4%Board
C-SN444Average3675.93736.52173.5Std.
Dev.1047.1840.9310.8RSD28.5%22.5%14.3%ALL C
BoardsN107107107Average2074.22095.81465.0Std.
Dev.990.1997.42016.1RSD48.9%48.8%138.6%Min659657Max4624462421179

Table A-7.	Summary of Cross-Contamination Data for All Coatings

Table A-7Time = 1 monthTime = 3 monthTime = 7 monthTime = 11 monthTime =
15 monthTime = 20 monthTime = 24
monthCoat.As(µg/L)Cr(µg/L)Cu(µg/L)As(µg/L)Cr(µg/L)Cu(µg/L)As
(µg/L)Cr(µg/L)Cu(µg/L)As (µg/L)Cr(µg/L)Cu(µg/L)As
(µg/L)Cr(µg/L)Cu(µg/L)As (µg/L)Cr(µg/L)Cu(µg/L)As
(µg/L)Cr(µg/L)Cu(µg/L)1av2.073.2059.332.204.60143.77.3710.87103.024.0
020.00280.027.2724.60400.729.6723.33310.0262.0324.0470.0Std
Dev0.320.5<IP_ADDRESS>54.502.001.9612.1215.5911.36<IP_ADDRESS>7.162.0
83.5126.46414.0524.8268.9RSD15.6%16.5%10.2%52.4%33.5%37.9%27.2%18.1%11.8
%65.0%56.8%43.4%22.5%20.4%4.3%7.0%15.0%8.5%158%162%57.2%2av3.435.9039.67
2.376.40112.75.539.80103.011.0317.00313.38.7318.17355.78.0319.00256.7471
.1675.7396.7Std
Dev1.4<IP_ADDRESS>2.2131.640.782.3123.521.951.0020.821.515.16156.23.054
.5880.83804.51146.9288.9RSD41.2%36.2%18.2%13.6%34.6%28.1%14.0%23.5%22.8%
17.7%5.9%6.6%17.3%28.4%43.9%38.0%24.1%31.4%171%170%72.8%3av2.073.3767.33
2.804.83176.75.906.57<IP_ADDRESS>153.314.479.57161.08.1310.27160.012.80
15.33210.0Std
Dev0.600.613.511.111.595.770.871.9828.291.770.4240.418.594.9476.653.133.
1660.03.812.8951.96RSD29.2%18.1%5.2%39.8%33.0%3.3%14.8%30.1%24.9%19.4%5.
3%26.4%59.4%51.6%47.6%38.5%30.8%37.5%29.8%18.9%24.7%4av4.707.2328.003.13
4.87103.710.2715.33111.722.6724.67303.314.5027.07459.315.3326.33373.315.
6023.33210.0Std
Dev0.661.767.810.852.2446.694.273.2151.239.0713.50<IP_ADDRESS>162.43.21
7.0970.955.245.6930.55RSD14.0%24.4%27.9%27.1%46.0%45.0%41.6%21.0%45.9%40
.0%54.7%61.2%14.5%23.9%35.3%20.9%26.9%19.0%33.6%24.4%14.5%5av3.536.1730.
674.477.53113.310.6718.33117.018.0026.33383.39.1019.10312.08.9321.0230.0
17.0030.00263.3Std
Dev0.501.846.661.423.0015.282.525.0339.964.006.81100.72.436.25112.22.913
.4610.010.0012.4980.83RSD14.2%29.9%21.7%31.7%39.9%13.5%23.6%27.5%34.2%22
.2%25.8%26.3%26.7%32.7%36.0%32.6%16.5%4.3%58.8%41.6%30.7%6av4.677.2033.0
03.434.8386.6710.377.7767.6748.6722.33170.044.2717.70252.745.025.0336.72
46.3303.0393.3Std
Dev1.932.802.651.071.4843.501.101.2412.0110.2613.6526.469.456.89171.19.0
06.08150.4358.4482.4171.0RSD41.3%38.9%8.0%31.1%30.7%50.2%10.6%16.0%17.8%
21.1%61.1%15.6%21.3%38.9%67.7%20%24.3%44.7%145%159%43.5%7av2.774.8034.33
3.304.50102.79.379.2376.0040.0011.33176.749.5015.03218.367.022.3263.354.
6722.00250.0Std
Dev0.320.954.731.081.3124.680.461.6617.325.571.5328.8712.056.43109.47.81
0.58<IP_ADDRESS>87.18RSD11.6%19.9%13.8%32.8%29.1%24.0%4.9%18.0%22.8%13.
9%13.5%16.3%24.3%42.8%50.1%11.7%2.60%12.2%45.9%16.4%34.9%8av1.532.7034.6
72.103.73170.06.507.6782.008.838.80132.011.7315.032<IP_ADDRESS>76.788.0
7105.0280.0Std
Dev0.250.464.040.350.9534.641.351.8611.271.201.9362.393.742.8756.3110.22
7.3720.82131.6143.0112.7RSD16.4%17.0%11.7%16.5%25.3%20.4%20.7%24.2%13.7%
13.6%21.9%47.3%31.9%19.1%19.8%77.4%34.0%7.5%150%136%40.2%9av1.203.7341.3
31.182.23<IP_ADDRESS>53.676.333.7026.3312.374.4753.36.975.1348.678.505.
9341.33Std
Dev0.103.0212.580.200.2914.732.052.5223.462.311.3914.4712.462.3710.641.5
51.543.062.612.6611.5RSD8.3%80.8%30.4%17.1%12.9%13.8%49.7%51.5%43.7%36.5
%37.5%54.9%100.7%53.0%20.0%22.2%30.0%6.3%30.7%44.9%27.9%10av1.091.9326.3
31.673.77143.36.307.1086.335.504.1766.677.534.4392.17.374.3739.336.974.0
038.0Std
Dev0.120.455.860.510.9255.081.951.6712.343.251.4632.531.170.2522.460.420
.313.063.551.5918.0RSD11.1%23.3%22.3%30.8%24.5%38.4%30.9%23.5%14.3%59.1%
35.1%48.8%15.5%5.64%24.4%5.7%7.1%7.8%50.9%39.8%47.4%11av1.603.3056.003.7
74.47163.34.973.7736.677.375.7045.006.836.4386.97.679.8087.6712.0714.831
60.0Std
Dev0.2<IP_ADDRESS>.7283.861.110.607.641.651.3713.891.251.7932.20.501.3
131.533.906.4520.0RSD16.5%6.1%10.9%28.1%38.4%51.3%22.3%16.0%20.8%22.4%24
.1%30.9%18.3%27.8%37.0%6.5%13.4%36.0%32.3%43.5%12.5%12av4.436.1035.334.3
010.97102.36.978.3066.6712.6719.00233.317.1727.53340.314.2321.67336.729.
6358.33323.3Std
Dev4.565.9812.742.408.5922.503.742.1428.014.628.19<IP_ADDRESS>73.125.97
10.26179.532.3854.43189.3RSD102.9%98.0%36.1%55.8%78.3%22.0%53.8%25.8%42.
0%36.5%43.1%10.8%17.6%36.2%21.5%42.0%47.3%53.3%109%93.3%58.6%13av9.2313.
1757.336.279.00153.35.9311.10135.318.4329.00310.0231.2333.8667.310.023.6
7360.021.931.7373.3Std
Dev5.877.0112.424.275.2950.331.593.8565.7711.5415.3980.00379.2535.4332.0
2.003.0678.1011.312.5136.1RSD63.6%53.2%21.7%68.2%58.8%32.8%26.9%34.7%48.
6%62.6%53.1%25.8%164%160%49.8%20.0%12.9%21.7%51.6%39.4%34.5%

Table A-8.	Coating Application Data Summary

Table A-8Minideck IDA Board App. Vol. (mL)C Board App. Vol. (mL)N Board
App. Vol. (mL)A Board App. Mass (g)C Board App. Mass (g)N Board App.
Mass
(g)1-A10060100------1-B1008090------1-C10080110------N333000Average100.0
73.3100.0------Std.
Dev.0.011.510.0------RSD0.0%15.7%10.0%------2-A70556553.54649.32-B604555
52.439.947.22-C60557046.650.557.1N333333Average63.351.763.350.845.551.2S
td.
Dev.<IP_ADDRESS>.75.35.2RSD9.1%11.2%12.1%7.3%11.7%10.2%3-A805011023.415.13
6.43-B80509022.312.9403-C706011<IP_ADDRESS>N333333Average76.753.3103.32
3.915.437.4Std.
Dev.5.85.8<IP_ADDRESS>.2RSD7.5%10.8%11.2%8.2%17.3%6.0%4-A60305538.338.342
.54-B60456047.335.547.64-C60408<IP_ADDRESS>N333333Average60.038.365.043
.239.048.5Std.
Dev.0.07.6<IP_ADDRESS>.5RSD0.0%19.9%20.4%10.6%9.9%13.4%5-A25305030.427.25
1.85-B25253032.928.452.15-C30356527.23362.3N333333Average26.730.048.330.
229.555.4Std.
Dev.<IP_ADDRESS>.93.16.0RSD10.8%16.7%36.3%9.5%10.4%10.8%6-A60407051.343.3
546-B50705<IP_ADDRESS>-C45404548.538.946.7N333333Average51.750.055.045
.343.450.0Std.
Dev.7.617.3<IP_ADDRESS>.7RSD14.8%34.6%24.1%17.7%10.6%7.4%7-A4035453833.94
3.77-B50455551.242.852.37-C50404553.63747.9N333333Average46.740.048.347.
637.948.0Std.
Dev.<IP_ADDRESS>.44.54.3RSD12.4%12.5%11.9%17.6%11.9%9.0%8-A50405045.930.64
6.48-B50306041.936.452.68-C45356034.832.450.7N333333Average48.335.056.74
0.933.149.9Std.
Dev.<IP_ADDRESS>.63.03.2RSD6.0%14.3%10.2%13.8%9.0%6.4%9-A35153034.527.952.
29-B35304<IP_ADDRESS>-C45301539.335.247.2N333333Average38.325.028.336.
032.148.6Std.
Dev.5.88.7<IP_ADDRESS>.1RSD15.1%34.6%44.4%8.0%11.7%6.5%10-A808011534.629.
358.410-B605010033.424.653.310-C706010031.624.449.5N333333Average70.063.
3<IP_ADDRESS>.7Std.
Dev.<IP_ADDRESS>.52.84.5RSD14.3%24.1%8.2%4.5%10.6%8.3%11-A9085110827380.
511-B80909577.657.8110.711-C80657580.952.6109.5N333333Average83.380.093.
380.261.1100.2Std.
Dev.5.8<IP_ADDRESS>0.617.1RSD6.9%16.5%18.8%2.9%17.3%17.1%12-A60408058.34
8.971.612-B60509063.544.785.312-C70509064.952.488.8N333333Average63.346.
786.762.248.781.9Std.
Dev.<IP_ADDRESS>.53.99.1RSD9.1%12.4%6.7%5.6%7.9%11.1%13-A------------13-B-
-----------13-C------------N000000Average------------Std.
Dev.------------RSD------------

Table A-9.	Average DAs (µg/cm2) vs. Sampling Interval

Table A-9 CoatingParameterBaseline1 Month3 Month7 Month11 Month15
Month20 Month24 Month1Average1.3720.0600.0340.0580.1170.1120.1350.364Std
Dev0.5870.0440.0250.0400.0520.0430.0610.164RSD42.8%73.5%74.4%68.7%44.6%3
8.4%45.3%45.0%2Average1.3610.1360.0530.0670.2450.2130.1750.351Std
Dev0.7290.0790.0320.0400.1270.1150.1150.212RSD53.5%58.1%61.3%60.1%51.8%5
4.1%66.1%60.2%3Average1.5120.0260.0140.0440.0890.1250.1550.420Std
Dev0.6410.0240.0110.0260.0290.0660.0860.195RSD42.4%94.6%81.8%60.4%32.1%5
3.1%55.6%46.4%4Average1.3580.1000.0510.0740.1850.1980.2270.524Std
Dev0.7400.0520.0180.0550.1040.1540.1630.286RSD54.5%52.4%35.1%74.3%56.4%7
7.8%72.0%54.7%5Average1.1590.1760.1260.1440.4850.3470.2850.624Std
Dev0.6720.0760.0650.0710.2440.3020.1590.385RSD58.0%43.3%51.7%49.3%50.3%8
7.0%55.7%61.8%6Average1.3430.1020.0510.1120.1620.1860.2630.521Std
Dev0.7340.1120.0740.1970.0920.2070.3380.430RSD54.7%110.3%146.3%176.1%56.
7%111.4%128.5%82.5%7Average1.3230.0940.0700.0690.2540.1920.2530.476Std
Dev0.6700.0450.0330.0500.1830.1300.2630.258RSD50.6%48.2%46.6%71.7%71.9%6
7.9%104.2%54.1%8Average1.3880.0530.0160.0230.0610.1520.2180.523Std
Dev0.4010.0270.0100.0070.0330.1140.1330.402RSD28.9%51.2%61.2%29.8%54.9%7
5.2%61.0%76.8%9Average1.4280.0090.0090.0200.0320.0550.0860.192Std
Dev0.9500.0100.0060.0140.0280.0500.0840.249RSD66.5%115.7%75.2%70.9%88.4%
90.2%98.0%129.8%10Average1.6070.0090.0080.0230.0380.0470.0570.080Std
Dev0.9550.0110.0060.0240.0220.0230.0630.040RSD59.4%119.5%82.0%103.5%58.3
%50.0%110.6%50.4%11Average1.2760.0050.0090.0340.0560.1430.2060.517Std
Dev0.5340.0050.0070.0230.0260.0700.0780.537RSD41.9%96.8%82.8%68.6%46.1%4
8.6%37.9%103.9%12Average1.4260.0770.0630.1230.3360.2920.2780.749Std
Dev1.2680.0980.0960.1850.4120.3740.2731.012RSD88.9%127.3%150.7%150.8%122
.8%128.0%98.4%135.1%13Average1.6580.4740.3210.3910.8890.8440.4861.008Std
Dev1.3770.3070.1400.2840.4450.6870.3980.703RSD83.0%64.8%43.7%72.7%50.1%8
1.4%81.9%69.8%

Table A-10.	Average DCr (µg/cm2) vs Sampling Interval

Table A-10 CoatingParameterBaseline1 Month3 Month7 Month11 Month15
Month20 Month24 Month1Average1.5540.0790.0600.0950.2560.2110.2220.524Std
Dev0.6290.0710.0410.0720.1390.0820.0990.190RSD40.5%89.9%67.6%75.8%54.2%3
8.7%44.8%36.3%2Average1.5400.2350.0920.0970.4340.3860.2540.529Std
Dev0.6280.1330.0490.0590.1710.1660.1330.247RSD40.8%56.5%53.6%61.0%39.5%4
3.0%52.4%46.6%3Average1.7500.0070.0050.0210.1260.1470.1450.385Std
Dev0.7560.0040.0020.0090.0540.0880.0910.153RSD43.2%50.6%42.1%42.6%43.2%6
0.2%62.5%39.7%4Average1.6010.1750.0870.1000.3990.2990.2700.663Std
Dev0.8140.0850.0300.0540.1780.1480.1480.275RSD50.8%48.2%34.2%54.1%44.6%4
9.7%54.8%41.5%5Average1.3520.3440.2550.2380.8900.5470.4240.920Std
Dev0.7040.1370.1150.1060.3400.3350.1710.423RSD52.1%39.7%45.2%44.6%38.2%6
1.2%40.4%46.0%6Average1.5420.1430.0640.1240.2260.2250.2760.576Std
Dev0.8070.1700.0910.2230.1120.2350.3380.473RSD52.3%118.3%141.1%178.9%49.
4%104.3%122.2%82.2%7Average1.5890.1470.1120.0920.3810.2840.3010.623Std
Dev0.7820.0610.0570.0470.2110.1450.2440.297RSD49.2%41.8%50.8%51.2%55.3%5
1.0%81.3%47.7%8Average1.6790.0400.0150.0240.0900.1840.2410.552Std
Dev0.4040.0200.0120.0100.0430.1240.1220.329RSD24.1%50.1%80.6%43.3%48.5%6
7.1%50.6%59.6%9Average1.6580.0020.0020.0060.0210.0350.0550.185Std
Dev1.1630.0010.0010.0030.0210.0370.0610.275RSD70.2%32.2%39.0%58.3%99.7%1
06.9%109.8%148.5%10Average1.8040.0040.0060.0120.0430.0430.0300.070Std
Dev0.9400.0040.0050.0080.0250.0270.0190.042RSD52.1%88.7%76.2%68.0%57.6%6
2.6%63.7%60.7%11Average1.5040.0020.0050.0260.0910.2030.2700.653Std
Dev0.6340.0010.0020.0190.0340.0920.0630.610RSD42.2%29.1%49.3%73.0%37.4%4
5.3%23.2%93.5%12Average1.8330.1310.1070.1760.5440.4610.3731.023Std
Dev1.6560.1510.1340.2380.4780.4700.2941.245RSD90.3%114.7%126.0%135.7%87.
9%102.0%78.8%121.7%13Average1.9180.7210.5030.4981.2621.1050.6031.227Std
Dev1.3300.3190.1760.2640.4910.7370.3890.658RSD69.3%44.3%35.1%53.0%39.0%6
6.7%64.5%53.6%

Table A-11.	Average DCu (µg/cm2) vs. Sampling Interval

Table A-11 CoatingParameterBaseline1 Month3 Month7 Month11 Month15
Month20 Month24 Month1Average0.7080.0410.0610.0580.1850.1530.1380.340Std
Dev0.2990.0150.0330.0220.0670.0510.0440.101RSD42.3%37.5%54.1%38.4%36.4%3
3.5%31.9%29.6%2Average0.7120.0980.0620.0560.2800.3030.1670.342Std
Dev0.3350.0400.0190.0160.1030.1790.0700.163RSD47.0%40.6%31.0%28.8%36.8%5
9.2%41.7%47.8%3Average0.8030.0260.0370.0410.1730.1720.0900.221Std
Dev0.3630.0050.0100.0160.0840.1560.0640.110RSD45.3%18.8%26.2%39.0%48.5%9
0.8%70.9%49.7%4Average0.6890.0590.0510.0530.2790.2060.1800.454Std
Dev0.3730.0210.0120.0210.1230.0590.0520.146RSD54.1%34.9%23.8%38.6%44.0%2
8.5%28.6%32.2%5Average0.6110.0790.1120.0840.3700.2800.2200.486Std
Dev0.3590.0310.0360.0250.1450.1310.0940.216RSD58.7%38.5%31.9%29.2%39.2%4
6.8%42.9%44.6%6Average0.5910.0630.0500.0550.1390.0800.0900.221Std
Dev0.1710.0190.0180.0320.0810.0440.0370.093RSD28.9%30.1%36.7%57.4%58.4%5
5.4%40.9%42.4%7Average0.6120.0710.0800.0550.2030.1580.1480.330Std
Dev0.2440.0260.0220.0190.0530.0750.0650.148RSD39.9%36.6%27.4%34.9%26.1%4
7.6%43.9%45.0%8Average0.6890.0330.0490.0310.0700.0940.0910.210Std
Dev0.2250.0130.0220.0120.0360.0420.0300.068RSD32.6%38.4%45.0%38.4%50.8%4
4.8%33.3%32.5%9Average0.6670.0110.0230.0130.0210.0250.0310.069Std
Dev0.3020.0020.0040.0030.0100.0120.0220.040RSD45.3%20.1%18.1%26.2%46.1%4
9.6%69.5%57.8%10Average0.8300.0130.0410.0270.0730.0570.0310.061Std
Dev0.4360.0040.0130.0100.0310.0250.0170.024RSD52.6%31.0%30.6%38.4%42.1%4
3.3%53.5%39.7%11Average0.6360.0180.0430.0300.0760.1280.1460.321Std
Dev0.2480.0040.0150.0090.0240.0630.0630.240RSD39.0%23.5%33.7%31.5%32.1%4
9.2%43.3%74.7%12Average0.6140.0380.0480.0610.2450.1830.1510.368Std
Dev0.2680.0140.0120.0220.1200.0790.0580.198RSD43.7%36.3%25.8%35.3%49.1%4
3.4%38.3%53.7%13Average0.6820.2260.1900.1640.4020.3180.1690.368Std
Dev0.3450.0890.0530.0540.1440.1760.0690.125RSD50.7%39.6%27.8%33.2%35.7%5
5.3%41.1%33.9%


bad weather but samples should still be collected according to the prescribed intensity ($"!", still
at an average frequency of once every month unless a deviation from the protocol is approved by
the Agency) and at times closest to the scheduled date that are feasible.
6.5  GROUND-WATER MONITORING
As with the soil pore-water sampling devices, monitoring well design decisions have been made
and described in the Monitoring Plan Design Report.  Problems or variances from these
procedures should be documented and discussed as they arise in each Quarterly Report and in the
Final Report.
6.51  Instrumentation
The ability to collect representative ground-water samples depends on the proper installation of
monitoring wells.  Most states require that a permit be filed prior to the installation of monitoring
wells and that well construction records are returned to the state within a specific time following
the emplacement of monitoring wells.  States may also have specific monitoring-well
construction requirements that should be followed.  Wells should be installed by licensed drillers
familiar with all relevant state and local requirements and supervised by an experienced
geologist, professional engineer or environmental scientist. In addition, before initiation of the
monitoring well installation program, an effort should be made to identify and locate utilities,
irrigation systems, tile drains, etc. to avoid damage and costly repairs.
All information pertaining to work performed during the well construction should be recorded in
detail in a bound project notebook using waterproof ink.  Field-book entries for each monitoring
well should be sufficiently detailed to allow the preparation of a detailed well log.  The following
minimum information should be provided on every well log:
·
Well number and permit number,
·
Well location,
·
Well depth,
·
Elevation of the top of the casing,

68
·
Depth to the water table,
·
Well construction details including: materials; length and diameter of the casing,
screen, and surface casing,
·
Well annulus construction details including:  depth, thickness, and materials
selected for the filter pack, plug, and surface pad,
·
Geologist's field observations of soil characteristics from continuous split-spoon
samples including: soil texture, color, moisture, and structure,
·
Geotechnical information, such as blow counts necessary to advance the split
spoon and sample recovery and
·
Names of the drilling contractor and supervising geologist and,
·
Weather conditions during the drilling activities.
Any deviations should be recorded and described in the First Quarterly Report and in the Final
Report.
6.52  Well Development
Well development procedures should be undertaken once a monitoring well has been installed
and sufficient time has passed to allow the annular seal to cure.  The purpose of developing a
monitoring well is to improve the hydraulic characteristics of the filter pack by removing fine-
grained materials from the pack, and causing coarser materials to settle around and stabilize the
screen.  Once this is accomplished, the monitoring well can be used to collect representative
ground-water samples.
Removal of fine materials may be more difficult if the surrounding soil itself is fine-textured, or
if the soil water is naturally turbid.  In these cases, indicator parameters such as pH and
conductivity should be monitored during development (Driscoll, 1986).  Development should
continue until parameters stabilize or the turbidity of the discharge water is less than 5
nephelometric turbidity units (NTU), as recommended in USEPA (1986b).  If the parameters do
not stabilize, this is an indication that the development method is not effective and an alternate
method is needed.  Information recorded during the development of each well should include:
date of development and development method, volume of water removed, and a log of
parameters monitored.

69
6.53  Decontamination
Unless dedicated pumps are used for sampling, all ground-water sampling equipment should be
cleaned before use, and between wells.  Non-dedicated sampling equipment should be washed
with a nonphosphate detergent, rinsed with tap water then distilled water, and finally rinsed with
a pesticide-grade acetone (or hexane or methyl alcohol) to aid in drying and to remove any
organic residue (Barcelona at al., 1985).  Other options are given in Driscoll (1986).  All solvents
used in decontamination activities should be disposed of in accordance with state or local
regulations.
If a single pump will be used to purge multiple wells or collect ground-water samples, tubing
should be dedicated to individual monitoring wells.  Any non-dedicated equipment should be
designed so that it can be disassembled for cleaning at the decontamination area before a
different well is sampled.  Tubing removed from a well after each purging or sampling event
should be rinsed with deionized water before placing in a clean storage container and sealed.
Field blanks should be collected from sampling equipment between wells to test the effectiveness
of decontamination.  Bailers should be filled once and then decanted into sample collection
bottles.  If non-dedicated pumps are used for sample collection, deionized water should be
pumped through equipment after decontamination and a sample collected for analysis.
Some States have developed guidelines for decontamination protocols (Mickam et al., 1989).
State regulatory agencies should be consulted to obtain current information on standard
decontamination practices for saturated and vadose zone monitoring programs.  These standards
should be used to supplement the guidelines outlined in this document.
The water purged from the wells should be discharged away from all well clusters.  Additionally,
purge water should not be disposed of in “ecologically sensitive” areas, such as wetlands or down
other wells.  Field personnel can take precautions beyond decontamination of sampling
equipment to ensure that cross-contamination between wells does not occur.  Decontaminated
equipment should be handled using rubber laboratory gloves, and should not be placed directly
on the ground.  It is best to store equipment in clean sealed containers after decontamination
(!"#", plastic bags or coolers) for relocation to the well site.  If bailers are used for purging and
sampling wells, bailer cords should be discarded after a single use.
6.54  Sample Collection
Prior to sampling any of the monitoring wells at a study site, field conditions should be described
in the field notebook.  This includes observations of weather and soil surface conditions, and the
height of the water table in all wells and piezometers.
In order to collect a ground-water sample from a monitoring well that is representative of the
water in the surrounding aquifer, standing water should be removed from the well casing, screen,
and surrounding filter pack.  This procedure is called purging.  The volume of water purged, the
rate at which it is withdrawn, and the location of the sampling intake all determine how
representative the sample is to the water in the aquifer.  Typically parameters such as dissolved

70
oxygen, specific conductivity, oxidation-reduction potential, turbidity and pH are continuously
monitored throughout purging.  When these parameters stabilize, the well is considered purged
(Driscoll, 1986).  Alternatively, a specific number of well casing volumes can be removed.
Meters used to monitor pH, temperature, and specific conductance should be calibrated before
each use.
Bailers should be gently lowered into the water column to a measured depth just below the water
table.  Samples for volatile pesticides should be headspace-free.  Peristaltic pumps, low-flow
submersible pumps and bladder pump are suitable for sampling for volatile pesticides.
Ground-water samples should be collected in containers that will not interact with the sample.
Consult with the laboratory that the samples will be analyzed at to determine if preservation
methods beyond the use of ice will be necessary to prevent additional degradation of the target
compounds.  Ensure that use of chemical preservatives will not interfere with analysis of target
compounds.  Ground-water samples should not be composited, but placed directly into sample
collection bottles.  The techniques used need to be validated for providing a representative
sample in which the analytes are stable under the conditions of transport and subsequent storage
before analysis. A trip blank sample should be included to ensure cross-contamination of
samples does not occur during shipment to the laboratory.
If the volume of available pore water or ground water is not sufficient for both pesticide and
tracer analyses the pesticide analyses should be given first priority.  The analytical procedures
described in the Monitoring Plan Design should be used and documented.  Duplicate samples
and field spike samples should be collected.
6.6  SAMPLE HANDLING AND TRACKING
Water and soil samples should be placed directly into coolers after collection.  To ensure against
breakage and temperature fluctuations in the samples, the following sample packing and shipping
procedures should be followed:
·
Samples should be shipped in insulated boxes or coolers.  Devices are available to
automatically monitor and record temperature while the samples are in transit.
·
Individual glass sample containers should be wrapped either in plastic bubble wrap,
placed in Styrofoam holders, or somehow packaged to separate the sample bottles
during shipping.
·
Gel-Cold packs or other materials to maintain stable temperatures can be placed in each
cooler, but should not be in direct contact with any of the glass sample containers.  To
help maintain the Gel-Cold pack temperature and keep the temperature of the coolers
around 4
°
C, ice sealed in plastic bags may be added.  The ice should be bagged to
prevent seepage during transport.  Soil samples should be packed in a container at or

71
below 0
°
C as soon as possible after collection.  If dry ice is used to preserve soil
samples during shipment, field personnel should follow all hazardous material labeling
requirements.
·
It
is important that all sample tracking paperwork (i.e. chain-of-custody) be included
with the shipment and that the chest be sealed according to chain-of-custody procedures.
All samples should be stored after collection according to GLP-compliant procedures and the
stability should be validated by a storage stability study.  Coordination with the laboratory
concerning frequency and number of samples is important for sample preservation and to ensure
that sample holding times are not exceeded.  The laboratory should therefore be notified prior to
sample collection to ensure that samples will be processed and analyzed quickly.
The sample tracking procedure described in the Monitoring Plan Design portion of the Study
Protocol should be followed.  A number should be assigned to each sample collected.  That
number should be marked on the sample container and recorded on the tracking form and in the
project notebook.  Weather conditions or field comments should also be recorded on the tracking
form or in the field notebook.  A three-part label should be used that includes numbered
descriptive information to be placed on the sample container.  In general, sample labels should be
placed in duplicate on the sample container and should include:
·
Date and time of collection,
·
pH, temperature, and specific conductance,
·
Identification of sample location,
·
Analytes, and
·
Signature of field technician.
If samples are shipped to a laboratory, samples should be shipped in such a way as to avoid
breakage of sample containers and maintain sample integrity.

72
CHAPTER 7 - REPORTING
Quarterly status reports for each prospective ground-water monitoring study should be submitted
following the application of the compounds of interest.  Required elements of these status reports
are described in Section 7.1.  These status reports are required to allow adjustment to the study
design, if necessary, and for the determination of when the stated goals of the study have been
fulfilled.  However, in the interest of efficiency, quarterly reports will not be formally reviewed
(i.e. no formal write-up of review will be submitted to registrant from the US EPA) unless the
results presented warrant further action.
The monitoring plan implementation phase of a prospective ground-water study may end once
the EPA approves a Study Termination Report submitted by the registrant.  While ground-water
sampling in a prospective study would ideally be completed two years after the application of the
test chemical, the appropriate termination point of a study is a function of site-specific and
chemical-specific characteristics.  Section 7.2 describes milestones in the ground-water study that
should occur before the Study Director considers submitting a Study Termination Report.
At the conclusion of each study, a Final Study Report should also be submitted to EPA.
Required elements of this report are described in Section 7.3.  As this document should be able to
stand alone as a full report on the small-scale prospective study, it will include information
already presented in previous submissions.  This report is the vehicle in which the registrant can
provide interpretation of the study results, suggest further mitigation measures, and extrapolate
the results through modeling, or comparison to other available data.   All information referenced
to sampling locations should conform to EPA's minimum data elements standards which can be
found at the US EPAs’ Water Quality Data Submissions: OPP Standard Operating Procedure
home page
(http://www.epa.gov/oppsrrd1/registration_review/water_quality_sop.htm#appendix_a).  All
reports and supporting data should be submitted in an editable electronic format and as one hard
copy.
7.1  QUARTERLY REPORTS
Quarterly status reports should be submitted to EPA beginning with the first quarter following
the application of the compounds of interest to each study site.  The required elements of these
reports include:
·
A summary of the activities at the site during the quarter,
·
Concentrations or mass accounting for the conservative tracer and total pesticide residues
at each cluster,
·
Protocol deviations from the approved monitoring plan, and

73
·
Results of all chemical analyses for samples (including quality control samples)
collected during the quarter.  Individual results should be reported.  Results should not be
averaged over samples or time.
·
Provide graphical representation of chemical analysis
These report elements are described further in the following sections.  The submittal of
additional information that may impact the design, conduct, or findings of the study is
encouraged.
7.11  Summary of Site Activities
The summary of site activities should include all activities related to agronomic practices,
monitoring, and irrigation.  The application dates for fertilizers or other compounds applied to
the study area should be reported along with information on other agronomic practices conducted
during the quarter.  Similarly, the dates of sample collection should be reported along with
information on weather conditions during the quarter.  If irrigation water was applied to the study
site during the quarter, then the dates and amounts of water applied should be reported, and
compared to the targets set in the Irrigation Plan.  The results of field measurements such as soil
water content and matric potential should be reported along with a water budget indicating the
amount of net recharge to the site.
7.12  Mass Balance
A mass balance for the conservative tracer and total pesticide residues should be reported for
each cluster.  The mass balance at each lysimeter and monitoring well should be approximated.
7.13  Protocol Deviations
Any deviations from the protocol established and approved as part of the Monitoring Plan Design
should be reported for each quarter.  Reporting of deviations should include a discussion of the
proposed and approved procedures, a description of the revised procedures that were
implemented during the quarter, the reasons for the revisions, and the anticipated effects on the
study.
7.14  Analytical Results
The results of all chemical analyses ($"!", analyses for the test pesticide, degradates, and tracer)
conducted on samples should be reported in tabular format.  MDLs and the results of quality
control analyses should also be reported.  Comments and a brief discussion of the analytical
results for the quarter may be included.

74
7.2  STUDY TERMINATION REPORT
The sampling program of a prospective ground-water monitoring study would ideally last two
years.  However, in reality the duration of a successful study is a function of how long it takes for
applied water to recharge to ground water and of how long it takes for the pesticide and its
degradates to leave the vadose zone by degradation and/or dissipation.  This time frame is a
function of the soil type, properties of the pesticide, and the amount of net recharge at the site.
In order to consider proposing an end to the sampling phase of a prospective study, the study
director should assess whether the following criteria have been met:
·
The tracer has peaked in concentration in all monitoring wells at the site, and has shown
a marked decline for at least three months, and
·
Residues of the pesticide (parent and degradates of concern) have completely degraded
and dissipated in the entire profile (vadose and, if applicable, saturated zone).  This is
generally defined as no detections for three consecutive sampling times over an interval
of at least two months, or
·
Pesticide residues have clearly peaked (parent and degradates of concern) in ground
water beneath the treated field (for each of the well clusters) or concentrations have
leveled off for an extended period (usually about 4 to 8 months) while significant
pesticide residues and tracer substance no longer remain in the vadose zone.
The Study Termination Report should detail the fact that the above conditions have been met,
give a brief summary of study results, and propose a date for the termination of the sampling
phase of the monitoring study.  Sample collection should continue until EPA reviews the Study
Termination Report and concurs that sampling can end.  EPA will review the Study Termination
Report within sixty days of receipt of the report.  No study will be terminated before the
questions for which the study was designed have been reasonably answered.
7.3  FINAL REPORT
A  Final Report should be submitted to EPA at the conclusion of each study.  This report should
include
·
Documentation of the application of the compounds of interest, climatic monitoring, and
irrigation practices;
·
Documentation of all sampling and sample analyses activities;
·
Results of all chemical analyses and discussion of findings.  Individual results should be
reported.  Results should not be averaged over samples or time; and

75
·
Documentation of quality assurance activities.
These report elements are described in more detail in the following sections.  The submittal of
additional information that may impact the interpretation of study results is encouraged.
7.31  Field Practices
The agricultural practices conducted during the course of the study should be summarized in the
Final Report.  Information on the application methods, rates, and dates for the compounds of
interest should be reported.  The results of climatic monitoring at each study site should be
reported along with information on the irrigation methods used, and irrigation rates, dates, and
duration.
7.32  Sampling Activities
Information collected during each sampling episode should be reported, including
·
Static water levels in monitoring wells and piezometers,
·
Weather conditions and field comments,
·
Tensiometer readings and the amount of suction used to collect lysimeter samples, and
·
Soil moisture content at the time of sample collection.
7.33  Analytical Results and Discussion of Findings
The results of all chemical analyses should be presented.  Analytical results for all compounds of
interest should be presented along with information on analytical detection limits that were
achieved.  The study findings should be summarized, and the significance of the findings with
respect to the demonstrated environmental fate of the test pesticide and degradates should be
discussed.  The use of graphics and/or computer models to illustrate this discussion is
encouraged.
7.34  Quality Assurance
Field studies should be conducted in compliance with good laboratory practices (GLPs) and
should be properly documented.  For field activities, a description of the system for sample
numbering and/or identification should be presented
For laboratory operations, the following should be reported:
·
Identification of responsible party who acted as sample custodian,

76
·
Copies of laboratory sample custody logs consisting of serially numbered standard lab-
tracking report sheets, and
·
Description of laboratory sample custody procedures for sample handling, storage, and
dispersion for analysis.
7.4 ELECTRONIC DATA REPORTING GUIDELINES
Data generated from PGW studies have proven valuable to OPP scientists and risk managers in
better understanding the potential for a pesticide to: (1) impact ground water quality, (2)
contaminate drinking water, and (3) reach ecologically important surface water systems, when
used in accordance with label directions.  As part of an effort to update the Agency guidance for
the PGW studies OPP has developed specific guidance for data submissions to improve the
reliability, completeness, transparency, and consistency in the PGW reports and facilitate further
use of these data for complete assessments of exposure to pesticides through ground water (!"#",
using leaching models to explore the potential for the pesticide of interest to leach under a variety
of conditions).  Specific requirements on the submission of electronic data are a matter that will
be addressed for each study by EPA when Prospective Ground-Water study candidate site
information is submitted to the Agency for approval.  In general though, submission of this data
electronically is likely to be requested and will help the Agency not only in the specific
interpretation of the implications of the study results for the overall potential to impact ground
water of the test pesticide but also to develop higher tier models and continue the advancement of
pesticide risk assessments.
The following guidelines have been developed to assist in reporting of PGW monitoring
information in electronic format.  Listed below are eight major information fields which focus on
the key areas for prospective ground-water monitoring data collection and reporting.  These
major information fields are;
·
study area and conceptual site model;
·
application of pesticide and tracer;
·
crop information;
·
soil properties;
·
ground water data;
·
weather data;
·
irrigation application and
·
chemical monitoring results.
Each major information field lists specific data types and requested data formats which serve as
guidelines for submission of prospective ground-water monitoring study data.

77
7.41  Study Area and Conceptual Site Model
The following study area information is requested to provide background information as well as
to develop a study-site conceptual model.  The conceptual model should include graphs, charts,
maps and data collected during site characterization.  Integration of site hydrogeology, surface
hydrology, and historical climatic data is acceptable.
Study Area Information
·
Base map in GIS readable format (E.g., Arc shape files, *.dxf files, a format specifically
approved by EPA);
·
NRCS soil surveys;
·
Other historical information and
·
Site Characterization Data of Study Area.
·
Sample locations identified and reported in consistent coordinate system format (GPS; x,y
coordinates; northing, easting; lat.,long.).
·
Soil cores (coordinates) see soil properties below for listing of data to be reported,
unsaturated and saturated hydraulic conductivity measurements at a minimum of 6 locations
(coordinates) and soil horizons with depth (bgs, ft) and soil water content measurements at
same 6 locations.
·
Piezometers - if not placed in soil core holes need coordinates, measuring point surveyed
with U.S.  Geodetic Vertical Datum, hydraulic head measurements, hydraulic conductivity
(slug tests and/or pumping test results), potentiometric surface diagrams, background water
quality data from upgradient well and influence of existing irrigation wells in the area on
subsurface hydraulics.
7.42  Application of pesticide and tracer
The following pesticide and tracer information is requested.
Pesticide information
·
Chemical properties of test substance;
·
molecular weight;
·
CAS number;
·
formulation and
·
verification of formulation (method and results).
·
Date(s) of pesticide application;
·
Incorporation depth;
·
method of application;
·
total amount applied;
·
rate;
·
area covered (coordinates of locations where pesticide applied and/or boundary where
pesticide applied) and
·
soil monitoring results after application (field dissipation data).
·
Topographic map detailing results of all surface soil samples collected to confirm
application rate of pesticide.  Determine if any hot spots of pesticides were detected post-

78
application.  This may be used to determine if any detection of pesticides in monitoring
wells, lysimeters, etc. can be correlated to a “hot spot” detected in surficial soil samples.
·
If determined, any site-specific measurements of degradation or soil sorption should be
reported
Tracer information
·
Chemical;
·


UNITED STATES ENVIRONMENTAL PROTECTION AGENCY WASHINGTON, D. C. 20460 OFFICE OF PREVENTION, PESTICIDES AND TOXIC SUBSTANCES Memorandum SUBJECT: Analysis of Disulfoton Use on Fraser Fir Christmas Trees in Western North Carolina.

FROM: Colwell A. Cook, Entomologist Michael K. Hennessey, Entomologist Herbicide and Insecticide Branch Biological and Economic Analysis Division THRU: Arnet Jones, Chief Herbicide and Insecticide Branch Biological and Economic Analysis Division TO: Christina Scheltema, Chemical Review Manager Betty Shackelford, Branch Chief Reregistration Branch 3 Special Review and Reregistration Division DATE OF PEER REVIEW: July 3, 2002 Introduction The Special Review and Reregistration Division (SRRD) has requested that the following documents, originally prepared by Michael K. Hennessey and incorporated below, be reviewed prior to their being placed in the docket and on the web. These documents were originally written to answer if the organophosphate, disulfoton, was critical to the Fraser fir Christmas tree industry in western North Carolina.

BEAD has reviewed the documents and has determined that they reflect the state of knowledge for the time in which they were written, July 2000. BEAD feels compelled to mention that several of the alternatives listed in the documents are currently under review for reregistration and some chemicals are still in the registration pipeline and should not be considered as alternatives.

The following are Mike's documents submitted to SRRD on July 5, 2000. 2 Site: Fraser Fir Christmas Trees This analysis focuses on the level of need for OPs and alternatives for Fraser firs.

All Fraser firs are grown in the mountainous areas of North Carolina where they are the major agricultural commodity. They are perennial with a 6 10 year crop cycle. In 1996, 34 million trees were grown on 24,000 acres by 1,600 growers. Over 66% of the growers had <10 acres. About 10 20% of the trees are harvested annually around Christmastime. Seventy five percent of the harvest is for the wholesale market and 25% is for the retail, choose and cut market. The value for 1996 was $78 million and for 1999 was $122 million. Trees for harvest are graded either by USDA or according to the NC Christmas Tree Association standards. Fraser firs represented 27% of the US grown Christmas tree market in 1999. Commercial Christmas trees of other varieties are mainly grown in OR, MI, WA, OH, and WI. Six pests are of concern to growers. OPs are critically needed for four of the pests. Crop loss comes mainly from cosmetic damage to foliage visible in the last year or two before harvest or tree death (balsam wooly adelgid). Before the last two years, trees can usually compensate for foliage loss. Large trees (6 7 ft tall) are most valuable. Choice of pesticide for a given pest depends upon timing, row spacing, land slope, size of trees, and acreage. Generally, pesticides applied foliarly on small trees may not be suitable for large trees after canopies are closed. Small acreage (because of cost) and acreage planted without tractor rows are not generally amenable to foliar spraying that requires access by truck or tractor. Because of mountainous terrain, aerial spraying is not generally done.

Balsam twig aphid and spruce spider mite are widespread and perennial pests. Impacts are greatest near harvest when trees may be downgraded for cosmetic damage. Disulfoton, a systemic soil granular applied by either hand or motorcycle, is most used followed by foliarlyapplied chlorpyrifos and esfenvalerate. Disulfoton has the advantages of controlling both balsam twig aphid and spuce spider mite, and, being systemic, conserving predators. Disulfoton and esfenvalerate may be alternated if resistance management becomes an issue. Chlorpyrifos and esfenvalerate must be foliarly applied by commercial applicator by mist blower or high powered hose sprayer. The chlorpyrifos foliar spray is phytotoxic in some situations. Foliar applications are problematic because they are usually uneven, which significantly affects efficacy. Disulfoton is also the pesticide most used against spruce spider mite, followed by chlorpyrifos. Esfenvalerate has the advantage of also controlling balsam wooly adelgid to a limited extent but the disadvantage of causing spider mite populations to increase.

The other four pests are minor but may be important in spotty situations. Hemlock rust mite and rosette bud mite are mainly controlled with foliarly applied dimethoate. There is no alternative to dimethoate for rosette bud mite. Balsam wooly adelgid is a major pest of spotty distribution that is mainly controlled with esfenvalerate spraying. If it is not controlled, the tree is killed or culled. White grubs are not very widespread and are controlled with soil applied chlorpyrifos at plant.

Overall, for trees nearing harvest, twig aphids and spider mites are the critical pests 3 annually. Under those conditions, disulfoton is the OP that is critically needed because alternatives are somewhat ineffective. Without disulfoton, a significant amount of loss from downgrading and extra application costs would be sustained over the region annually. Disulfoton is important in resistance management and conserving beneficials.

Sources: USDA Crop Profile for Christmas Trees in the North Carolina Mountains. 11/ 99. Personal Observation. NC Christmas Tree Crop tour with NC Cooperative Extension Service. 6/ 25 27/ 00. Pesticide labels. Metasystox R, Dimethoate 4EC, Di Syston 15%, Lorsban 4E. 6/ 00. Sidebottom, J. Personal communication. 6/ 22/ 00. Sidebottom, J. 1999. NC Crop Profile Christmas Trees, Mountain. 1997 North Carolina Christmas Tree Survey. NCASS. 3/ 98. Www. agr. state. nc. us/ stats/ trees/ xmastree. htm Prepared by: Michael K. Hennessey, Entomologist, EPA/ OPP/ BEAD/ HIB, 7/ 5/ 00. OP TOLERANCE REASSESSMENT USE/ USAGE MATRIX CROP SUMMARY 4 Site: Fraser Fir Christmas Trees Overall Confidence Rating: H Background: this analysis focuses on OP use. North Carolina is the only producer. Perennial crop with 6 10 year crop cycle. 34 million trees were grown on 24,000 acres with 1,600 growers in 1996. 66% of the farms are <10 acres. 93% of the trees were grown in five NC counties. Trees are cut and shipped fresh for homeowner ornamental use around Christmastime annually. 25% of the trees sold are retail. Value was $78 million in 1996 and $122 million in 1999. Commodity represented 27% of all US grown Christmas trees sold in 1999. Commercial Christmas trees of other varieties are produced in OR, MI, WA, OH, and WI.

% Treated # Applications Rate (lb AI/ A) PHI (days)

Organophosphate Pesticides Max Avg Max Avg Max Avg Min Avg Disulfoton 72 65 2 1 4.5 3 2 >14 Chlorpyrifos 7 62 11 0. 2511 Oxydemeton methyl 2 1 2 1 1 0. 5 3 3 Dimethoate 11 10 2 1 0.67 0.67 2 2 Confidence Rating: H= high confidence = data from several confirming sources; confirmed by personal experience M = medium confidence = data from only a few sources; may be some conflicting or unconfirmed info. L = low confidence = data from only one unconfirmed source Organophosphate Target Pests for Fraser Fir Major balsam twig aphid, spruce spider mite Minor hemlock rust mite, rosette bud mite, balsam wooly adelgid, white grubs Major = 20+% of all OP usage on pest; Moderate = 5 20% of all OP usage on pest; Minor =< 5% of all OP usage on pest Sources: USDA Crop Profile for Christmas Trees in the North Carolina Mountains. 11/ 99. Personal Observation. NC Christmas Tree Crop tour with NC Cooperative Extension Service. 6/ 25 27/ 00. Pesticide labels. Metasystox R, Dimethoate 4EC, Di Syston 15%, Lorsban 4E. 6/ 00. Sidebottom, J. Personal communication. 6/ 22/ 00. Sidebottom, J. 1999. NC Crop Profile Christmas Trees, Mountain. 1997 North Carolina Christmas Tree Survey. NCASS. 3/ 98. Www. agr. state. nc. us/ stats/ trees/ xmastree. htm Prepared By: Michael K. Hennessey Date: 7/ 5/ 00 Time: 17: 00 OP TOLERANCE REASSESSMENT USE/ USAGE MATRIX  PEST SUMMARY Page 5 5 Pest Organophosphate Efficacy Mkt Class Alt. Pesticide List Efficacy Mkt Constraints of Alternatives Crop: Fraser fir Christmas Trees Region: North Carolina Timing: All plant stages balsam twig aphid (Major) disulfoton ( hi P esfenvalerate

  med Esfenvalerate has an advantage of also controlling adelgid but disadvantage of flaring spider mite.

Liquids (esfenvalarate and oxydemeton methyl, 2 apps required) are difficult to apply

–coverage/ efficacy is poor with hose or mist blower most of the time depending on spacing or size of trees. Chlorpyrifos causes some phytotoxicity.

Predators are important and are killed off by foliar sprays of esfenvalerate and chlorpyrifos. Tau

fluvalinate (good efficacy), imidacloprid, azadirachtin (good efficacy) and bifenthrin are registered but not used. Triazimate and pymetrozine are in EPA pipeline.

chlorpyrifos

  med O imidacloprid

  lo dimethoate   lo O cinnamaldehyde

  lo oxydemeton methyl   lo spruce spider mite (Major) disulfoton ( hi O abamectin   lo Oil, tau fluvalinate (good efficacy adults only),

pyridaben (good efficacy), and bifenthrin (not effective on eggs) are registered for this but not used.

Hexythiazox doesn't control adults. Bifenazate (good efficacy) and clofentazine are in research pipeline.

Chlorpyrifos causes some phytotoxicity.

oxydemeton methyl   lo O hexythiazox

  lo chlorpyrifos   med hemlock rust mite (Minor) dimethoate ( hi O sulfur   lo Pyridaben (good efficacy) is registered but not used.

Sulfur was experimental use only. Clofentazine in research pipeline.

rosette bud mite (Minor) dimethoate ( med Clofentazine in research pipeline.

balsam wooly adelgid (Minor) oxydemeton methyl   lo imidacloprid   lo Esfenvalerate took over for lindane that was cancelled. It must be applied by high pressure hose.

Older trees with infestations are culled.

Predators are important and they are killed off by sprays. Pymetrozine is in research pipeline.

Some phytotoxicity with chlorpyrifos, oil, and soap.

chlorpyrifos   lo esfenvalerate ( hi oil   lo soap   lo OP TOLERANCE REASSESSMENT USE/ USAGE MATRIX  PEST SUMMARY Page 6 Pest Organophosphate Efficacy Mkt Class Alt. Pesticide List Efficacy Mkt Constraints of Alternatives Crop: Fraser fir Christmas Trees Region: North Carolina Timing: All plant stages 6 white grubs (Minor) chlorpyrifos   lo O methyl bromide ( med Soil application. Preplant soil methyl bromide is for root rot but would kill grubs also. Halofenozide soil use in EPA pipeline.

ADDITIONAL INFORMATION: For above, ( excellent,   good,   poor. Market share: hi=> 20%, med= 5 20%, lo=< 5%.

Class: P= pyrethroid, O= other SOURCES: USDA Crop Profile for Christmas trees in North Carolina mountains (Fraser fir)

Sidebottom, J. 1/ 00. Registered alternatives for Fraser fir.

Sidebottom, J. 6/ 00. Alternatives to Di syston 15G.

Personal observation and talks with growers and extension personnel D. Hundley, J. Sidebottom, J. Moody, J. Owen. Extension tour of Fraser fir in NC. 6/ 25 27/ 00.

Prepared By: M. Hennessey, Entomologist, OPP/ BEAD, 703 308 7076.

Date: 7/ 5/ 00 Time: 17: 00


[Federal Register: March 3, 2010 (Volume 75, Number 41)]
[Notices]
[Page 9636]
[DOCID:fr03mr10-102]

-----------------------------------------------------------------------

DEPARTMENT OF TRANSPORTATION

Federal Aviation Administration

[Summary Notice No. PE-2010-03]

Petition for Exemption; Summary of Petition Received

AGENCY: Federal Aviation Administration (FAA), DOT.

ACTION: Notice of petition for exemption received.

-----------------------------------------------------------------------

SUMMARY: This notice contains a summary of a petition seeking relief
from specified requirements of 14 CFR. The purpose of this notice is to
improve the public's awareness of, and participation in, this aspect of
FAA's regulatory activities. Neither publication of this notice nor the
inclusion or omission of information in the summary is intended to
affect the legal status of the petition or its final disposition.

DATES: Comments on this petition must identify the petition docket
number involved and must be received on or before March 23, 2010.

ADDRESSES: You may send comments identified by Docket Number FAA-2010-
0010 using any of the following methods:
     Government-wide rulemaking Web site: Go to http://
www.regulations.gov and follow the instructions for sending your
comments electronically.
     Mail: Send comments to the Docket Management Facility;
U.S. Department of Transportation, 1200 New Jersey Avenue, SE., West
Building Ground Floor, Room W12-140, Washington, DC 20590.
     Fax: Fax comments to the Docket Management Facility at
202-493-2251.
     Hand Delivery: Bring comments to the Docket Management
Facility in Room W12-140 of the West Building Ground Floor at 1200 New
Jersey Avenue, SE., Washington, DC, between 9 a.m. and 5 p.m., Monday
through Friday, except Federal holidays.
    Privacy: We will post all comments we receive, without change, to
http://www.regulations.gov, including any personal information you
provide. Using the search function of our docket Web site, anyone can
find and read the comments received into any of our dockets, including
the name of the individual sending the comment (or signing the comment
for an association, business, labor union, etc.). You may review DOT's
complete Privacy Act Statement in the Federal Register published on
April 11, 2000 (65 FR 19477-78).
    Docket: To read background documents or comments received, go to
http://www.regulations.gov at any time or to the Docket Management
Facility in Room W12-140 of the West Building Ground Floor at 1200 New
Jersey Avenue, SE., Washington, DC, between 9 a.m. and 5 p.m., Monday
through Friday, except Federal holidays.

FOR FURTHER INFORMATION CONTACT: Anna Bruse,<PHONE_NUMBER>, or Laverne
Brunache,<PHONE_NUMBER>, Office of Rulemaking, Federal Aviation
Administration, 800 Independence Avenue, SW., Washington, DC 20591.
    This notice is published pursuant to 14 CFR 11.85.

    Issued in Washington, DC, on February 25, 2010.
Pamela Hamilton-Powell,
Director, Office of Rulemaking.

Petition for Exemption

    Docket No.: FAA-2010-0010.
    Petitioner: Federal Aviation Administration, Air Traffic
Organization's Administration & Talent Management Directorate.
    Section of SFAR Affected: SFAR 103(5)(b)(vii).

Description of Relief Sought

    Federal Aviation Administration, Air Traffic Organization's
Administration & Talent Management Directorate seeks an exemption from
SFAR 103(5)(b)(vii) to forgo inclusion of a statement identifying any
operational error, operational deviation or runway incursion in the
last 5 years while working a control position for Air Traffic
Controllers reaching the age of 56 through December 31, 2011.

[FR Doc. 2010-4357 Filed 3-2-10; 8:45 am]
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[Federal Register Volume 84, Number 208 (Monday, October 28, 2019)]
[Notices]
[Page 57725]
[FR Doc No: 2019-23493]

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DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Docket No. EL19-100-000]

PJM Interconnection, L.L.C.; Notice of Institution of Section 206
Proceeding and Refund Effective Date

    On October 17, 2019, the Commission issued an order in Docket No.
EL19-100-000, pursuant to section 206 of the Federal Power Act (FPA),
16 U.S.C. 824e (2018), instituting an investigation into whether PJM
Interconnection, L.L.C.'s minimum run-time rules and procedures are
just and reasonable. PJM Interconnection, LLC, 169 FERC 61,049 (2019).
    The refund effective date in Docket Nos. EL19-100-000, established
pursuant to section 206(b) of the FPA, will be the date of publication
of this notice in the Federal Register.
    Any interested person desiring to be heard in Docket No. EL19-100-
000 must file a notice of intervention or motion to intervene, as
appropriate, with the Federal Energy Regulatory Commission, 888 First
Street NE, Washington, DC 20426, in accordance with Rule 214 of the
Commission's Rules of Practice and Procedure, 18 CFR 385.214 (2019),
within 21 days of the date of issuance of the order.

    Dated: October 18, 2019.
Kimberly D. Bose,
Secretary.
[FR Doc. 2019-23493 Filed 10-25-19; 8:45 am]
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