DACW67-85-0029
Work Order 0001C
TC3090-02; Task 6
Final Report
DEVELOPMENT OF SEDIMENT QUALITY VALUES
FOR PUGET SOUND
Volume 2
by
Tetra Tech, Inc.
Prepared for
Resource Planning Associates
for
Puget Soimd Dredged Disposal Analysis
and Puget Sound Estuary Program
September, 1986
Tetra Tech, Inc.
11820 Northup Way, Suite 100
Bellevue, Washington 98005
-------
APPENDIX A
SEDIMENT DATA COMPILED
IN SEDIMENT QUALITY VALUES DATABASE
-------
APPENDIX A. SEDIMENT DATA
CONTENTS
Number
Page
Table A-l Commencement Bay - Main Sediment Survey A-3
Table A-2 Commencement Bay - Blair Waterway Dredging Study A-77
Table A-3 Eight Bay A-103
Table A-4 Duwamish River I A-119
Table A-5 Alki Extension A-122
Table A-6 TPPS Phases 3A and 3B A-130
Table A-7 Everett Harbor A-138
Table A-8 Duwamish River II A-140
Figure A-l Locations of Commencement Bay stations sampled for
benthic macroinvertebrates and sediment bioassays
during March and July A-100
Figure A-2 Locations of reference stations sampled in Carr Inlet A-102
Figure A-3 Sequim Bay sampling stations A-110
Figure A-4 Sinclair Inlet sampling stations A-lll
Figure A-5 Case Inlet sampling stations A-112
Figure A-6 Dabob Bay sampling stations A-113
Figure A-7 Elliott Bay - Fourmile Rock sampling stations A-114
Figure A-8 Samish Bay sampling stations A-115
Figure A-9 Everett Harbor - Port Gardner sampling stations A-116
Figure A-10 Bellingham Bay sampling stations (Inner Harbor) A-117
Figure A-ll Bellingham Bay sampling stations (Outer Harbor) A-118
Figure A-12 Sediment sampling station locations for dredged
material characterization A-121
A-11
-------
Figure A-13 Plot of total organic carbon with total volatile
solids A-124
Figure A-14 Regression of total organic carbon on total volatile
solids for total volatile solids <10 percent A-125
Figure A-15 Sediment collection stations offshore of Point
Williams, sampled May 26, 1984 A-126
Figure A-16 Sediment collection stations offshore of Alki Point,
sampled May 25-26, 1984 A-127
Figure A-17 Point Williams benthos reference sampling station
locations A-128
Figure A-18 Alki Point benthos sampling station locations A-129
Figure A-19 Map showing the 26 stations in the central basin of
Puget Sound and Elliott Bay sampled during Phase III
of the TPPS program A-137
Figure A-20 Sediment sampling locations in the East Waterway A-139
A-111
-------
Each study used for this report has been assigned a group number (i.e.,
1-9). This number is in the first column of Tables A-3 through A-8. The
study in Table A-l is group 1 and the study in Table A-2 is group 2. Station
numbers have been assigned for this study in addition to the original
investigators' station numbers. These station numbers are in column 3 of
Tables A-l and A-2 and in column 2 of Tables A-3 through A-8. The station
number prefix corresponds to the geographical location of the station as follows:
AP Alki Point
B Blair waterway, Commencement Bay
BH Belltngham Bay
BL Blair waterway, Commencement Bay
CI City waterway, Commencement Bay
CR Carr Inlet
CS Case Inlet
OB Dabob Bay
DR Ouwamish River (includes East and West waterways)
EB Elliott Bay
EV Everett Harbor
HY Hylebos waterway, Commencement Bay
MD Middle waterway, Commencement Bay
MI Milwaukee waterway, Commencement Bay
PW Point Williams
RS Ruston - Point Defiance Shoreline
SC Sinclair Inlet
SI Sitcum waterway, Commencement Bay
SP St. Paul waterway, Commencement Bay
SM Samish Inlet
SQ Sequim Bay
WP West Point
All organic compounds are expressed as ug/kg (ppb) dry weight and
metals are expressed as mg/kg (ppm) dry weight.
Toxicity, benthic, and microtox codes are indicated for all stations.
The toxicity code is defined as:
0 = No data available
1 - No significant3 oyster larvae abnormality or amphipod mortality
2 = Significant3 oyster larvae abnormality
3 = Significant3 amphipod mortality
4 = Both significant3 oyster larvae abnormality and amphipod
mortality.
A-l
-------
The benthic code is defined as:
0 = No data available
1 = No significant* depressions in benthic infaunal abundances
2 = Significant3 depressions in benthic infaunal abundances of one
major taxonomic group
3 = Significant3 depressions in benthic infaunal abundances of
more than one major taxonomic group.
The microtox code is defined as:
0 = No data available
1 = No significant3 decrease in bacterial luminescence
2 = Significant3 decrease in bacterial luminescence.
3 Significance implies statistically significant difference (P>0.05) from
reference conditions.
A-2
-------
A-l. COWENCEMENT BAY MAIN SEDIMENT QUALITY SURVEY'
STATION*
1 BL-11
1 BL-13
1 BL-21
1 BL-25
1 BL-2B
1 BL-31
1 CI-11
1 Cl-13
1 CI-16
1 CI-17
1 C1-2D
CI-22
CR-11
CR-12
CR-13
CR-14
HY-12
1 HY-14
1 HY-17
1 HY-22
1 HY-23
1 HY-24
1 HY-2S
1 HY-32
1 HY-37
1 HY-42
1 HY-43
1 HY-44
1 HY-47
1 HY-50
1 fD-12
1 MI-11
1 MI-13
1 MI-15
RS-12
RS-13
RS-14
RS-16
RS-19
RS-20
RS-22
1 RS-24
1 51-11
1 51-12
1 SI-15
SP-11
SP-12
SP-14
SP-15
TOX BENTHIC MICRO
CODE CODE CODE
1 SP-16
1
1
3
1
1
4
2
2
1
4
1
1
1
1
1
2
1
2
4
4
1
1
1
1
3
1
1
2
1
1
3
1
3
1
4
1
4
4
1
1
3
1
3
3
1
2
4
4
4
1
1
1
1
1
2
3
3
1
1
1
1
1
1
1
1
2
3
3
3
1
1
3
2
1
1
1
2
1
1
1
1
1
1
1
1
3
2
2
0
0
2
2
1
1
1
3
3
2
2
1
1
1
2
2
2
2
2
1
1
1
1
1
1
2
2
2
2
2
2
1
1
2
2
2
1
2
2
1
1
2
2
1
1
1
2
2
2
1
1
2
2
1
2
2
2
2
2
The 50 stations listed on this page have biological effects data and are used
for this report. Additional stations and associated chemical data are Include
on subsequent pages of this Table A-l. Where replicate data have been provided,
the mean value is used for calculations.
A-3
-------
MAIN SEDIMENT DUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
PHENOLS
Uralnaye
17110019-BL-UOO-
1711U019-BL-000-
1711U019-BL-000-
17110U19-8L-000-
1711U019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-UOU-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UUO-
*• 17110019-BL-OOO-
.*» 1711U019-BL-000-
17110019-BL-UOO-
1711U019-BL-UOU-
17110019-HY-UOO-
17110U19-CB-000-
17110U19-C8-000-
1711U019-CB-UOU-
17110U19-CI-UOU-
1711UU19-CI-OUO-
17110019-CI-OOU-
1711UU19-CI-UUU-
17110019-CI-OUO-
17110019-CW-OOU-
17110019-C1-OUO-
1711UU19-CI-OOU-
1711U019-CI-UUU-
17110U19-CI-OOU-
17110019-CI-OOU-
17110U19-CI-OOU-
17110019-CI-OOO-
17110U19-CK-UOU-
17110U19-CK-OOU-
171100]y-CK-000-
Survey
MSQS
MSU.S
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
BL-11 SOSC -
BL-1Z S01C -
BL-13 SOSC -
BL-14 S01C -
BL-lb S01C -
BL-16 S01C -
BL-17 SU1C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 S05C -
BL-22 S01C -
BL-23 S01C -
BL-24 S01C -
BL-25 SObC -
BL-26 S01C -
BL-27 SU1C -
BL-2U S05C -
BL-29 S01C -
BL-30 501C -
BL-31 SOSC -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
C8-13 S01C -
CB-14 501C -
CI-11 S02C -
Ul-12 S01C -
CI-13 SOSC -
CI-14 S01C -
Cl-15 S01C -
Cl-16 SOBC -
CI-17 SObC -
CI-17 SObC -
Ci-18 S01C -
CI-19 S01C -
Cl-20 SOSC -
CI-21 S01C -
Cl-22 SObC -
CR-11 S01C -
CR-12 SOSC -
CR-13 S01C -
Rep phenol
BIO
U25
BIO
B2b
15
220
01 84
02 20U
220
130
230
240
140
bSO
82
63
420
110
62
190
240
420
160
200
92
70
92
Z1100
Z270
Z70
Z130
Z240
U10
01 Z190
02 Z360
Z160
Z200
Z1200
Z20
Z30
U10
010
44
2,4-d1
methyl
phenol
U10
U10
U10
010
010
17
U10
010
010
U10
010
U10
010
010
U10
010
010
010
010
U10
010
010
010
23
010
010
19
050
U10
010
010
77
50
010
010
38
010
29
020
010
010
010
010
-------
MAIN SEDIMENT QUALITY SUKVEY ORGANIC CHEMICALS - Values tn ppb dry weight
PHENOLS
Drainaye
17110019-CR-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
Survey
MSQS
Msgs
MSQS
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
wsqs
Msgs
MSQS
Msgs
Msgs
Msgs
MSQS
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample Rep
CR-14 SOSC -
HY-11 S01C -
HY-12 S01C -
HY-13 S01C -
HY-14 SOSC -
HY-15 S01C -
HY-16 S01C -
HY-17 S01C -
HY-18 SU1C -
HY-19 S01C -
HY-20 S01C - 01
HY-20 S01C - 02
HY-21 S01C -
HY-22 SObC -
HY-23 S01C -
HY-24 S01C -
HY-25 S01C -
HY-26 S01C -
HY-27 S01C -
HY-28 S01C -
HY-29 S01C -
HY-30 S01C -
HY-31 S01C - 01
HY-31 S01C - 02
HY-32 S01C -
HY-33 S01C -
HY-34 S01C -
HY-3b S01C -
HY-36 S01C -
HY-37 S01C -
HY-38 S01C -
HY-39 S01C -
HY-40 S01C -
HY-41 S01C -
HY-42 S01C -
HY-43 S01C -
HY-44 S01C -
HY-45 S01C -
HY-46 S01C -
HY-47 SOSC -
HY-48 S01C -
HY-49 S01C -
HY-50 SOSC -
phenol
62
290
500
130
280
43
2100
250
120
Z190
Z6bO
ZbO
010
Z530
B25
B25
Z74
Z20
BIO
Z110
BIO
140
68
61
110
150
90
110
490
420
200
BIO
Z110
Z40
300
3bO
010
BIO
BIO
Z120
Z57
110
330
2,4-d1
methyl
phenol
U10
010
010
010
010
010
020
010
020
020 .
010
010
010
010
010
010
010
020
010
U10
010
010
010
010
010
010
14
22
010
010
010
010
010
020
U10
010
010
010
010
020
010
010
U10
-------
MAIN SEDIMENT QUALITY SURVEY UKGAN1C CHEMICALS - Values in ppb dry weight
PHENULS
Drainage
17110019-HY-OOO-
17110019-MD-OOO-
17110019-MD-OOO-
17110019-MO-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-UOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110U19-RS-UUU-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110U19-RS-UOO-
17110019-RS-OOO-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-SI-OOO-
17110019-S1-000-
17110019-SI-UOO-
17110019-SI-OOO-
17110019-SI-OOO-
17U0019-SP-UOO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-SP-UOO-
17110019-DP-OOO-
17110019-UP-OOO-
Survey
Msgs
MSQS
MSUS
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
HY-bl S01C -
MU-11 S01C -
MO-12 SU5C -
MD-13 S01C -
MI-11 S01C -
MI-12 SU1C -
MI-13 S01C -
MI-14 S01C -
M!-lb S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C -
RS-14 SU5C -
HS-15 S02C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-2U S01C -
RS-21 S01C -
RS-22 SU1C -
RS-24 SU5C -
SI-11 S05C -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 S01C -
SP-14 S01C -
SP-15 S05C -
SP-16 S05C -
WBS CTL -
WHS CTL -
Kep phenol
Z150
Z850
Z360
Z20U
02 Z60
BIO
Z150
BIO
Z20
260
51
230
01 270
02 250
130
420
110
310
99
56
220
49
43
Z160
Z190
Z220
Z16
Z120
Z130
Z25
Z160
Z1700
Z110
Z240
01 U10
02 U10
2.4-di
methyl
phenol
U10
010
010
010
U10
37
29
29
U10
U10
U10
U10
U10
010
U10
210
23
U10
010
010
U10
010
010
U10
010
19
26
010
U10
U10
71
U40
U10
U10
U10
010
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
PHENOLS
- Values 1n ppb dry weight
Drainage
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110U19-BL-OUO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-UUO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-UOU-
1711UU19-BL-OOU-
17110019-HY-OOO-
1711U019-CB-UUO-
17110019-CB-UOO-
17110019-CB-UOO-
17110019-CI-OOO-
1711U019-CI-UUU-
17110019-C1-UOO-
17110019-C1-000-
17110019-CI-OOO-
17110019-CW-OOO-
17U0019-CI-000-
1711U019-C1-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17U0019-C1-000-
17110019-C1-OUO-
17110019-CK-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample Rep
BL-11 S05C -
BL-12 S01C -
BL-13 S05C -
BL-14 S01C -
BL-15 S01C -
BL-16 S01C -
BL-17 S01C - 01
BL-17 S01C - 02
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 SObC -
BL-22 S01C -
BL-23 S01C -
BL-24 S01C -
BL-2b SObC -
BL-26 S01C -
BL-27 S01C -
BL-28 SObC -
BL-29 S01C -
BL-30 S01C -
BL-31 S05C -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
Cl-12 S01C -
CI-13 SObC -
CI-14 S01C -
Cl-lb S01C -
CI-16 SObC -
CI-17 SObC - 01
Cl-17 SObC - 02
Cl-18 S01C -
CI-19 S01C -
CI-20 SObC -
CI-21 S01C -
CI-22 SObC -
CR-11 S01C -
CR-12 SObC -
CR-13 S01C -
CR-14 SObC -
2-
methyl-
phenol
Lin
14
L10
U10
12
b2
12
62
29
Ib
010
010
11
12
010
010
14
26
16
010
Ib
L10
22
11
U10
L10
U10
UbO
74
36
49
67
46
L10
30
40
37
43
3b
38
U10
010
010
U10
4-
methyl-
phenol
48
84
120
62
63
320
80
410
410
110
110
420
160
92
180
170
120
240
190
660
240
230
340
130
47
210
61
460
550
270
310
940
1200
700
bOO
670
240
230
330
IbO
010
010
26
32
-------
M/UN SEDIMENT DUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
PHENOLS
Drainage
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
f 17110019-HY-OOO-
00 17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UUO-
17110019-HY-UUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OUO-
J7110019-MO-000-
Survey
MSQS
MSUS
MSQS
MSQS
Msgs
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
HY-11 S01C -
HY-12 S01C -
HY-13 SU1C -
HY-14 SObC -
HY-15 S01C -
HY-16 S01C -
HY-17 S01C -
HY-18 S01C -
HY-19 S01C -
HY-20 S01C -
HY-20 S01C -
HY-21 S01C -
HY-22 S05C -
HY-23 S01C -
HY-24 S01C -
HY-25 S01C -
HY-26 S01C -
HY-27 S01C -
HY-28 S01C -
HY-29 S01C -
HY-30 S01C -
HY-31 S01C -
HY-31 S01C -
HY-32 S01C -
HY-33 SU1C -
HY-34 S01C -
HY-35 S01C -
HY-36 S01C -
HY-37 S01C -
HY-38 S01C -
HY-39 S01C -
HY-40 S01C -
HY-41 S01C -
HY-42 S01C -
HY-43 S01C -
HY-44 SU1C -
HY-45 S01C -
HY-46 S01C -
HY-47 S05C -
HY-48 S01C -
HY-49 S01C -
HY-50 S05C -
HY-51 S01C -
MO-11 S01C -
2-
methyl-
Kep phenol
U10
U10
U10
U10
U1U
U20
U1U
U20
U20
01 95
02 U10
U10
15
U10
U10
U10
U20
U1U
U10
U10
U10
01 U10
02 U10
2b
U10
16
34
U10
11
U10
21
31
U20
L10
U10
U10
U10
U10
14
U10
14
27
15
68
4-
methyl
phenol
74
320
67
26
38
110
39
67
71
U10
U10
U10
88
21
110
70
160
110
180
U10
61
U10
U10
190
75
520
190
63
50
71
120
190
L20
45
45
U10
55
90
49
100
120
290
250
620
-------
MAIN SEDIMENT (DUALITY SURVEY ORGANIC CHEMICALS
PHENOLS
- Values 1n ppb dry weight
Drainage
17110019-MU-OUO-
17110019-MD-OOO-
17110019-MI-UOU-
17110019-MI-OUU-
17110019-MI-UOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
1711U019-RS-000-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110U19-RS-000-
17110019-KS-OOO-
f* 17110019-RS-OOO-
UD 17110U19-RS-000-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-Sl-OOO-
17110019-SI-OUO-
17110019-SP-OOO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-OP-OOO-
Survey
MSQS
MSQS
MSQS
Msgs
MSQS
MSO.S
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
MU-12 S05C -
MO-13 S01C -
MI-11 S01C -
Ml-11 S01C -
MI-12 S01C -
MI-13 S01C -
MI-14 S01C -
MI-15 S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C -
RS-14 S05C -
KS-15 S01C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 S05C -
SMI SObC -
SI-12 S01C -
Sl-13 S01C -
SI-14 S01C -
SI-15 S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 SOBC -
SP-14 S01C -
SP-15 S05C -
SP-16 S05C -
WBS CTL -
WBS CTL -
2-
methy) -
Rep phenol
63
36
01 18
02 15
30
19
23
22
19
13
72
01 U10
02 U10
U10
46
13
71
U10
U10
IS
U10
U10
14
17
19
18
L10
U10
35
100
U40
U10
U10
01 010
02 U10
4-
methyl -
phenol
670
450
150
140
260
140
250
220
380
130
560
500
270
U10
380
51
190
L10
15
79
U10
U10
110
180
230
96
73
250
390
1900
96000
2600
890
U10
U10
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
SUBSTITUTED PHENOLS
Uralnatje
17110019-BL-OOO-
17UU019-BL-OUO-
17110019-BL-OOU-
17110019-BL-OUO-
17110019-BL-UOO-
17110U19-BL-OOU-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-000-
1711U019-BL-000-
17110019-BL-UUO-
17110019-BL-OOO-
1711U019-BL-OUU-
17110019-BL-OOU-
17110019-BL-OOO-
** 17110019-BL-OOO-
*-* 1711U019-BL-000-
0 17110019-Bl-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-HY-UUO-
17110019-CB-OOO-
17110019-CB-UOO-
17110019-CB-OUO-
17110019-Cl-OOO-
17110019-CI-OOO-
17UU019-CI-OUO-
17110019-C1-000-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-C1-OUO-
17110U19-CI-UOO-
17110019-CI-OOO-
1711U019-C1-OUO-
17H0019-C1-000-
17110019-C1-OOU-
1711UU19-CI-OUU-
17I10019-CR-OOU-
171 1001 y-CR-000-
Survey
MSQS
MSQS
Msgs
Msgs
MSQS
Msgs
MSQS
MSQS
Msgs
Msgs
Msgs
Msqs
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
Station Sample
BL-11 SUbC -
BL-12 S01C -
BL-13 SObC -
BL-14 SU1C -
8L-lb S01C -
BL-16 S01C -
BL-17 S01C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 SObC -
BL-22 S01C -
BL-23 S01C -
BL-24 SO 1C -
BL-2b SObC -
BL-26 S01C -
BL-27 S01C -
BL-28 S05C -
BL-29 S01C -
BL-30 S01C -
BL-31 SObC -
BL-32 S01C -
CB-11 S01C -
CB-12 SO 1C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 SObC -
Cl-14 S01C -
Cl-lb S01C -
CI-16 SObC -
Cl-17 SObC -
CI-17 SObC -
CI-18 S01C -
CI-19 S01C -
CI-20 SObC -
CI-21 S01C -
Cl-22 SUbC -
CR-11 S01C -
CR-12 SObC -
2-
chloro-
Rep phenol
Ub
Ub
Ub
Ub
Ub
Ub
Ul Ub
02 Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U5
Ub
Ub
Ub
Ub
Ub
U2b
Ub
Ub
Ub
Ub
Ub
Ul Ub
02 Ub
Ub
Ub
Ub
UIO
Ub
Ub
Ub
2,4-d1-
chloro-
phenol
UIO
UIO
UIO
UIO
U1U
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UbO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U1U
UIO
UIO
U20
UIO
UIO
UIO
2,4,6-
4-chloro- tr1-
3-methyl
phenol
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UbO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U20
UIO
UIO
UIO
chloro-
phenol
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UbO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U20
U1U
UIO
UIO
penta-
chloro-
phenol
140
U2b
U2b
b8
44
U2b
81
UlUO
78U
UbO
UbO
U2b
UbO
U2b
140
U2b
U2b
UlOO
UbO
92
860
U2b
U2b
UbO
UlOO
UbO
UbO
U130
UlOO
64
77
67
b7
4b
U2b
b7
b6
48
U80
UbO
UbO
UbO
2-n1tro-
pheno)
UIO
UIO
UIO
U1U
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U1U
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UbO
UIO
UIO
UIO
U1U
UIO
UIO
UIO
UIO
UIO
UIO
U20
UIO
UIO
UIO
2,4- 4,6-
nltro- n1tro-o- 4-nitro-
phenol cresol
UlOO
UlOO
UlOO
UlUO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
U10U
UlOO
UlUO
UlOO
UlOO
UlOO
U10U
UlOO
UlOO
UlOO
UlOO
UlOO
UbOO
UlOO
UlOO
UlOO
UlUO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
U200
UlOO
UlOO
UlOO
phenol
UlOO
UlOO
UlOO
UlUO
UlOO
UlOO
UlOO
UlUO
UlUO
UlOO
UlOO
UlUO
UlOO
UlUO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlOO
UlUU
UlOO
UlOO
UlUO
UbOO
UlOO
UlOO
UlOO
UlUO
UlOO
UlUO
UlOO
UlUU
UlOO
UlOO
U200
UlUO
UlOO
UlUO
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n pub dry weight
SUBSTITUTED PHENOLS
Drainage
17110019-CR-OOO-
17110019-CR-OOO-
17110019-HY-OUO-
17110019-HY-OUO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-NY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUU-
17110019-HY-OOO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station
CR-13
CR-14
HY-11
HY-12
HY-13
HY-14
HY-15
HY-16
HY-17
HY-18
HY-19
HY-20
HY-20
HY-21
HY-22
HY-23
HY-24
HY-2b
HY-26
HY-27
HY-28
HY-29
HY-30
HY-31
HY-31
HY-32
HY-33
HY-34
HY-35
HY-36
HY-37
HY-38
HY-39
HY-40
HY-41
HY-42
HY-43
HY-44
HY-45
HY-46
HY-47
HY-48
Sample
S01C -
S05C -
S01C -
S01C -
SO 1C -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
SU1C -
S01C -
S01C -
SU1C -
S01C -
SO 1C -
S01C -
S01C -
S01C -
SO 1C -
S01C -
S01C -
S01C -
S01C -
SO 1C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
S01C -
2-
chloro-
Rep phenol
U5
U5
US
U5
U5
US
U5
U10
U5
U10
U10
01 US
02 U5
U5
U5
U5
U5
U5
U10
U5
Ub
Ub
U5
01 Ub
02 U5
U5
Ub
U5
U5
US
U5
Ub
Ub
Ub
U10
U5
U5
US
Ub
Ub
Ub
Ub
2,4-dl-
chloro-
phenol
U10
U10
U10
U10
U10
U10
U10
U20
U10
U20
U20
U10
U10
U10
010
U10
U10
U10
U20
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
010
U10
U10
U10
U10
U20
U10
U10
U10
U10
U10
U10
U10
2,4,6-
4-chloro- tri-
3-methyl
phenol
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
010
U10
U1U
U10
U10
U10
U10
010
U10
010
U10
U10
U10
U20
U10
U10
U10
U10
U10
U10
U10
chloro-
phenol
U10
U10
U10
U10
U10
U10
U10
U20
U10
U20
U20
U10
U10
U10
U10
U10
U10
U10
U20
U10
U10
U10
U10
160
140
010
U10
U1U
U10
U10
U10
U10
U10
U10
U20
U10
U10
010
U10
U10
U10
U10
penta-
chloro-
phenol
UbO
UbO
UbO
U2b
UbO
U100
UbO
UbO
UbO
UbO
UbO
UbO
U2S
UbO
U100
UbO
UbO
UbU
UbO
U2b
U2b
UbO
UbO
U2b
UbO
71
U2b
120
110
UbO
U2b
UbO
UbO
UbO
U100
U100
U100
U100
UbO
U2b
U100
UbO
2-n
phei
U10
U10
U10
U10
U10
U10
U10
020
U10
U20
U20
U10
U10
U10
U10
U10
U10
U10
U20
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U20
U10
U10
U10
U10
010
U10
U10
2,4-
di-
nitro-
phenol
4.6-
di-
nitro-o-
cresol
U100
U100
U100
U100
U100
U100
0100
U100
U100
U1UO
U100
0100
U100
U100
U100
U100
U100
U100
U100
U100
U100
0100
U100
U10U
U100
U1UO
U100
U100
0100
0100
U100
U100
U100
U100
0200
U100
U100
0100
U100
U100
U100
U1UO
4-n1tro-
phenol
U100
U100
U10U
0100
U100
U100
U100
U200
U100
U200
U200
U1UO
U100
0100
U100
U100
U100
0100
U200
0100
U100
0100
U100
U100
U100
0100
0100
U100
0100
0100
U1UO
U100
0100
0100
0200
U100
0100
0100
U100
0100
U100
U1UU
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
SUBSTITUTED PHENOLS
Drainage
17110019-HY-OOU-
17110019-HY-UOU-
17110019-HY-OOO-
17110U19-MU-000-
17110019-MD-OOO-
17110019-MD-OOO-
17110019-MI-OOU-
17110019-MI-OOU-
17110019-MI-OOU-
17110019-MI-OOO-
17110U19-M1-000-
17110U19-M1-UOU-
17110U19-RS-000-
17110U19-RS-000-
17110019-RS-OOO-
1711U019-RS-000-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
17110019-RS-OOO-
1711U019-RS-000-
17110U19-RS-UOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110U19-SI-UOO-
17110019-SI-OOU-
17110019-SI-UOU-
17110019-SI-OUO-
17110019-SI-OOO-
17110U19-SP-000-
17110019-SP-OOO-
1711U019-SP-UUU-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
1711U019-UP-OUO-
17110U19-DP-UOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
HY-49 S01C -
HY-50 SObC -
HY-51 S01C -
MD-11 S01C -
MD-12 SObC -
MO-13 S01C -
MI-11 S01C -
MI-11 S01C -
MI-12 S01C -
MI-13 S01C -
MI-14 S01C -
Ml-lb S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 SObC -
RS-14 SObC -
RS-lb S02C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 SObC -
SI-11 SObC -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 SObC -
SP-11 SObC -
SP-12 SObC -
SP-13 S01C -
SP-14 S01C -
SP-lb SObC -
SP-16 SObC -
WBS CTL -
WHS CTL -
2-
chloro-
Rep phenol
Ub
Ub
Ub
Ub
Ub
Ub
01 Ub
02 Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
01 Ub
02 Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U20
Ub
Ub
01 Ub
02 Ub
2,4-di-
chloro-
phenol
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U40
U10
U10
U10
U10
2,4,6-
4-chloro- tri-
3-methyl
phenol
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U40
U10
U10
U10
U10
chloro-
phenol
U10
U10
U10
U10
2b
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U40
U10
U10
U1U
U10
penta-
chloro-
phenol
IbO
UbO
U2b
620
77
49
UbO
UbO
U2b
U2b
UbO
U2b
UbO
UbO
U2b
U2b
U2b
UbU
U2b
34
U2b
U2b
U100
UbO
UbO
U2b
U7b
UlUO
L2b
UbO
U100
UbO
UbO
UlUO
U100
UbO
U2b
U2b
UbO
2-n
phei
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U1U
U10
U10
U10
U1U
U40
U10
U10
U10
U10
2,4-
di-
nitro-
phenot
4,6-
di-
nitro-o-
cresol
U100
U1UU
U100
U1UO
U100
U100
U100
U1UO
U100
U100
U100
U100
U100
U100
U10U
U100
U100
U1UO
U100
U10U
U100
U100
U100
U1UO
U10U
U10U
U1UO
U1UO
U100
U100
U100
U100
U100
U1UO
U400
U100
U100
U100
U10U
4-nitro-
phenol
U10U
U1UU
U100
U100
U100
U100
U100
U1UU
U100
U10U
U100
U1UO
U100
U100
U1UO
U10U
U100
U100
U100
U100
U100
U100
U1UO
U100
U100
U100
U100
U10U
UlUO
U1UO
UlUO
U100
U100
UlUO
U4UU
U10U
U100
UlUO
U100
Number of Observations: 123
-------
.MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
SUBSTITUTEU PHENOLS
Drainage
Survey Station Sample Rep
2,4.b-
tri-
chloro-
phenol
17110019-BL-OOO-
1711U019-BL-UOO-
17110019-BL-UOU-
17110U19-BL-000-
17110019-BL-UOO-
1711U019-BL-000-
17110019-BL-OOO-
1711UU19-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-UOO-
1711U019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-UOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOU-
i 171LU019-BL-000-
J-' 17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOU-
17110019-HY-OOU-
17110019-CB-OOO-
17110U19-CB-UOU-
17110019-CB-OOO-
1711UU19-CI-000-
17110U19-CI-OOU-
17110U19-CI-UOU-
17110019-CI-OOU-
1711U019-CI-UUU-
1711U019-CW-OUO-
17110U19-CI-UOU-
17110019-CI-UOO-
17110019-CI-OOU-
17110019-CI-UOO-
17110U19-CI-OOU-
17110019-Cl-OOO-
1711U019-CI-OOU-
17110019-CR-OOO-
1711UU19-CH-000-
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 SU5C -
BL-12 S01C -
BL-13 SUbC -
BL-14 SU1C -
BL-15 SU1C -
BL-16 SU1C -
BL-17 SU1C -
BL-17 SU1C -
BL-18 SU1C -
BL-19 S01C -
BL-2U SO 1C -
BL-21 S05C -
BL-22 S01C -
BL-23 S01C -
BL-Z4 SU1C -
BL-25 S05C -
BL-26 S01C -
BL-27 S01C -
BL-^8 SU5C -
BL-29 S01C -
BL-30 S01C -
BL-31 SUbC -
BL-32 SU1C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 S05C -
CI-14 S01C -
CI-1S S01C -
CI-16 S05C -
CI-17 SObC -
CI-17 SU5C -
Cl-18 S01C -
CI-19 S01C -
CI-2U S05C -
CI-21 S01C -
CI-22 SObC -
CR-11 S01C -
CR-12 S05C -
U10
U10
U10
U10
U10
U1U
01 U10
02 U1U
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
UbO
U10
U1U
U10
U10
U10
01 U1U
02 U10
U10
U10
U10
U20
U10
U10
U10
-------
MAIN SEOIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
SUBSTITUTED PHENOLS
Drainage
17110019-CK-OOO-
17110U19-CR-OOU-
1711U019-HY-OUO-
17110019-HY-UOO-
17110019-HY-OOU-
1711U019-HY-UOO-
17110019-HY-OOO-
1711UU19-HY-000-
1711U019-HY-OOU-
17110019-HY-OUU-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110U19-HY-000-
' 17110019-HY-OOO-
- 17110019-HY-OUO-
' 171100iy-HY-000-
17110019-HY-OOU-
17110U19-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
171I0019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711UU19-HY-000-
17110019-HY-OOO-
17110019-HY-OOU-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-UOO-
1711U019-HY-OOU-
17110U19-HY-UOO-
1711UU19-HY-UOO-
17110019-HY-UOO-
Survey Station Sample Kep
2,4,5-
tri-
chloro-
phenol
MSOS
Msgs
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSU.S
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
CR-13 S01C -
CR-14 S05C -
HY-11 S01C -
HY-12 S01C -
HY-13 S01C -
HY-14 S05C -
HY-15 S01C -
HY-16 SU1C -
HY-17 S01C -
HY-18 SU1C -
HY-19 SU1C -
HY-20 S01C -
HY-20 S01C -
HY-21 S01C -
HY-22 S05C -
HY-23 S01C -
HY-24 S01C -
HY-25 S01C -
HY-26 SOIC -
IIY-27 SOIC -
HY-28 SOIC -
HY-29 SOIC -
HY-30 SOIC -
HY-31 SOIC -
HY-31 SOIC -
HY-32 SOIC -
HY-33 SOIC -
HY-34 SOIC -
HY-35 SOIC -
HY-36 SOIC -
HY-37 SOIC -
HY-38 SOIC -
HY-39 SOIC -
HY-40 SOIC -
HY-41 SOIC -
HY-42 SOIC -
HY-43 SOIC -
HY-44 SOIC -
HY-4S SOIC -
HY-46 SOIC -
HY-47 S05C -
HY-48 SOIC -
U10
U10
010
U10
U10
U10
U10
010
U10
U20
U20
01 U10
02 U10
U10
U10
U10
U10
U10
U20
U10
U10
U10
uin
01 U10
02 U10
U10
U10
010
U10
010
UK)
U10
U10
010
U20
U10
U10
010
U10
010
U10
U10
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry welyht
SUBSTITUTED PHENOLS
Drainage
17110019-HY-UOO-
17110019-HY-UOO-
17110019-HY-OUO-
17110019-MD-OUO-
17110019-HU-OOO-
17110U19-MD-OUO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-UOO-
17110U19-MI-UOO-
17110019-M1-000-
17110019-MI-OOU-
1711U019-RS-000-
1711U019-RS-OOU-
1711U019-RS-000-
17110U19-RS-000-
17110019-RS-OOO-
17110019-RS-OOO-
3* 17110019-KS-OOO-
i— 17110019-RS-OOO-
01 17110019-RS-UOU-
17110019-RS-OUO-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-KS-OOO-
17110019-SI-OOO-
17110019-SI-OUU-
17110019-S1-OOU-
17110019-51-000-
17110019-SI-OOO-
17110019-SP-OOO-
1711U019-SP-000-
17110019- SP-000-
1711U019-SP-000-
17110019-SP-OUO-
17110019-SP-OOU-
17110019-DP-OUO-
17110019-OP-OOO-
Survey
MSQS
Msgs
MSUS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample Rep
HY-49 SO 1C -
HY-50 SOSC -
HY-bl S01C -
MU-11 S01C -
MD-12 SOSC -
MD-13 S01C -
Ml-11 S01C - 01
MI-11 S01C - 02
MI-12 S01C -
MI-13 S01C -
MI-14 S01C -
MI-15 S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C - 01
RS-14 SOSC - 02
RS-15 S01C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 SOSC -
SI-11 S05C -
SI-12 S01C -
SI-13 S01C -
Sl-14 S01C -
SI-15 S05C -
SP-11 SOSC -
SP-12 S05C -
SP-13 SOSC -
SP-14 S01C -
SP-15 SOSC -
SP-16 SOSC -
WBS CTL - 01
WBS CTL - 02
2,4,5-
trl-
chloro
phenol
U10
U10
U10
150
29
010
U10
U10
U10
010
U10
010
U10
U10
U10
010
010
010
U10
010
010
010
010
010
U10
010
U10
U10
010
010
010
010
U10
010
040
010
U10
010
010
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
•LOW MOLECULAR HEIGHT AROMATIC HYDROCARBONS
Drainage
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
' 1711U019-BL-000-
, 17110019-BL-OOO-
' 17110019-BL-OOO-
17110019-BL-OOO-
17110019-HY-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOU-
17UOU19-CI-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOO-
17110U19-CR-OUO-
17110019-CR-OOO-
- Values in ppb dry weight
Survey
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample Rep
BL-11 S05C -
BL-12 S01C -
BL-13 S05C -
BL-14 S01C -
BL-15 S01C -
BL-16 S01C -
BL-17 S01C - 01
BL-17 S01C - U2
BL-18 S01C -
BL-19 S01C -
BL-2U S01C -
BL-21 S05C -
BL-22 S01C -
BL-23 S01C -
BL-24 SU1C -
BL-25 SObC -
BL-26 S01C -
BL-27 S01C -
BL-28 S05C -
BL-29 S01C -
BL-30 S01C -
BL-31 SObC -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 S05C -
CI-14 S01C -
Cl-lb S01C -
CI-16 SObC -
CI-17 S05C - 01
CI-17 SObC - 02
ci-ie sole -
CI-19 S01C -
CI-20 SObC -
CI-21 S01C -
CI-22 S05C -
CR-11 S01C -
CR-12 SObC -
CR-13 S01C -
2-
metnyl
naphth-
alene
65
130
110
140
71
380
120
270
280
100
120
280
150
170
240
180
240
IbO
140
320
130
78
270
130
93
160
120
590
740
330
290
700
460
600
530
480
320
360
890
460
U5
05
U5
naphtha- acenaph-
lene
300
310
280
230
240
590
400
390
330
260
360
360
750
850
530
480
1100
110
380
870
410
150
750
330
68
85
83
1100
5500
1100
1700
2100
1300
1200
1900
950
830
980
2400
1200
7.4
13
U5
thylene
22
57
57
98
33
43
51
51
33
36
32
37
46
49
44
65
92
11
38
68
33
34
44
43
U5
21
25
180
250
170
110
250
190
230
330
180
140
190
650
330
U5
Ub
05
acenaph-
thene
U5
39
39
270
25
50
56
44
38
26
38
38
88
120
65
54
140
38
54
190
58
31
92
30
7.1
9.8
10
460
220
110
74
360
130
170
180
100
90
130
380
190
Ub
U5
U5
fluorene
19
52
48
220
35
73
62
62
62
34
45
47
84
120
76
69
120
43
68
190
48
110
110
53
14
16
16
U25
290
160
110
490
200
280
300
190
110
160
600
280
05
U5
05
phenan-
threne
98
240
220
1200
140
340
220
250
230
160
200
230
300
420
310
270
540
180
290
770
210
570
470
260
85
82
88
1800
1900
830
540
2500
790
1100
1200
760
510
670
2700
1500
14
12
16
anthra
cene/
anthra- phenan
cene threne
44
120
110
780
69
110
91
98
91
68
86
82
160
220
140
150
220
62
170
230
77
220
220
91
14
20
15
460
660
310
190
830
330
530
450
400
230
330
1600
960
6.7
8.6
05
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
LOW MOLECULAR WEIGHT AROMATIC HYDROCARBONS
- Values in pph dry weight
Drainage
Survey Station Sample Rep
17110019-CR-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOU-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-UUO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
, 17110019-HY-OOO-
•- 17110019-HY-OOO-
^ 17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711UU19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
MSgs
Msgs
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
CR-14 S05C
HY-11 S01C
HY-12 S01C
HY-13 SU1C
HY-14 S05C
HY-15 S01C
HY-16 S01C
HY-17 S01C
HY-18 S01C
HY-19 S01C
HY-20 S01C
HY-20 SO 1C
HY-21 S01C
HY-22 S05C
HY-23 S01C
HY-24 S01C
HY-25 S01C
HY-26 S01C
HY-27 S01C
HY-28 SO 1C
HY-29 S01C
HY-30 S01C
HY-31 S01C
HY-31 S01C
HY-32 S01C
HY-33 S01C
HY-34 S01C
HY-35 S01C
HY-36 S01C
HY-37 S01C
HY-38 S01C
HY-39 S01C
HY-40 S01C
HY-41 S01C
HY-42 S01C
HY-43 S01C
HY-44 S01C
HY-45 SU1C
HY-46 S01C
HY-47 S05C
HY-48 S01C
HY-49 SU1C
HY-bO S05C
!P
01
02
01
02
2-
methyl
naphth-
alene
U5
50
58
82
55
68
100
69
120
130
220
200
270
390
180
130
150
380
91
120
91
74
80
77
150
210
150
280
380
170
260
390
330
250
160
130
42
290
340
160
270
100
190
naphtha-
lene
7.5
79
160
190
13U
160
190
240
300
250
610
580
610
1600
380
360
2600
480
280
480
300
240
340
300
510
1100
740
740
840
920
740
1200
1100
750
530
490
23
850
980
670
760
200
400
acenaph-
thylene
U5
45
42
49
36
53
110
61
67
87
87
130
61
100
79
50
42
57
56
61
48
34
68
55
77
93
130
94
120
93
110
100
88
62
87
90
7.8
95
94
80
76
38
27
acenaph-
thene
U5
79
39
49
51
34
200
86
110
12U
61
71
97
450
88
61
40
43
44
79
39
34'
36
32
85
85
130
92
190
76
110
67
60
58
83
90
U5
73
100
51
110
22
54
f luorene
U5
82
55
85
120
53
280
120
110
150
89
87
110
480
160
94
65
63
58
100
57
45
48
59
85
98
140
100
200
100
150
75
92
85
160
110
8.6
95
22U
86
88
26
31
phenan-
threne
16
44U
420
510
700
490
1600
720
970
680
580
C
750
1200
2300
Z620
Z360
Z450
400
570
310
240
290
300
420
550
570
460
750
460
620
Z320
Z390
Z430
720
530
42
Z230
Z470
Z420
Z310
120
190
anthra
cene/
anthra- phenan-
cene threne
22
160
190
460
390
260
1300
440
700
680
Z290
C 550
Z360
Z580
Z930
Z230
Z170
Z330
160
240
190
110
120
270
220
260
280
260
430
220
310
Z170
Z180
Z150
380
220
26
Z130
Z200
Z150
Z130
56
83
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
LOW MOLECULAR WEIGHT AROMATIC HYDROCARBONS
- Values in pph dry weight
00
Drainaye
17110019-HY-OOO-
17110019-MU-OOO-
17110019-MO-OUO-
17110019-MU-OOO-
17110019-Ml-OOO-
17110U19-MI-000-
17110019-MI-OOO-
17110019-M1-000-
17110019-MI-OOO-
17110019-M1-000-
17110019-RS-OOO-
17110019-KS-OOO-
17110U19-RS-000-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
17110019-RS-OOO-
17110U19-RS-000-
17110019-RS-OOO-
17110019-SI-OUO-
17110019-S1-OOU-
17110019-SI-OUO-
17110019-S1-000-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17H0019-SP-000-
17110019-SP-OOO-
1711U019-SP-000-
17110U19-SP-OOU-
17110019-DP-OOO-
17110019-OP-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station
HY-bl
MD-11
MD-12
MD-13
MI-11
MI-11
MI-12
MI-13
MI-14
MI-15
RS-11
RS-12
RS-13
RS-14
RS-14
RS-15
RS-16
HS-17
RS-18
RS-19
RS-20
RS-21
RS-22
RS-24
SI-11
SI-12
SI-13
SI-14
SI-15
SP-11
SP-12
SP-13
SP-14
SP-15
SP-16
WBS
MBS
Sample Rep
S01C -
SU1C -
S05C -
S01C -
S01C - 01
S01C - 02
S01C -
S01C -
S01C -
soie -
S01C -
S01C -
S01C -
S05C - 01
S05C - 02
S02C -
sole -
SO 1C -
S01C -
S01C -
S01C -
S01C -
soic -
S05C -
S05C -
soic -
SOIC -
SOIC -
S05C -
S05C -
S05C -
SOIC -
soic -
S05C -
S05C -
CTL - 01
CTL - 02
2-
methyl
naphth-
al ene
170
910
670
320
360
320
450
270
310
220
320
190
440
350
180
12
830
250
1200
61
21
1100
05
33
270
180
230
720
380
130
200
390
810
70
110
U5
U5
naphtha- acenaph-
1 ene
250
2900
2100
1200
Z910
2890
1300
Z680
770
Z550
620
330
1200
55
540
36
1900
450
1900
150
83
1200
05
63
2910
Z740
530
1400
2860
2560
Z720
2700
4400
2270
2290
U5
190
thylene
40
530
560
600
150
170
170
110
110
56
120
76
140
120
97
6.4
210
94
290
22
26
120
U5
16
120
65
49
70
56
150
no
290
410
75
45
U5
U5
acenaph-
thene
21
350
500
190
160
150
150
120
110
60
no
160
390
180
140
11
900
140
2500
U5
14
790
U5
05
240
120
68
640'
87 .
73
120
290
270
36
27
U5
U5
f luorene
36
410
540
230
190
18
180
150
170
91
180
240
490
220
160
16
790
220
3100
140
22
1100
05
14
250
120
88
610
130
95
160
370
240
42
32
U5
U5
pnenan-
tnrene
170
2100
1100
830
2870
2890
740
2570
670
2340
820
940
1100
1000
740
89
1900
640
11000
570
210
1800
11
67
Z480
Z480
350
1800
Z460
Z330
2540
950
Z660
Z160
Z150
U5
26
anthra
cene/
anthra- phenan-
cene threne
62
440
380
380
440
500
390
310
350
110
500
410
330
740
380
43
710
330
1400
400
56
1200
8.7
43
220
210
140
460
160
120
140
450
285
236
239
U5
24
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry weight
HIGH MOLECULAR WEIGHT PAH
Drainage
Survey Station Sample Rep
17110019-BL-OUO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-OUO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOU-
17110U19-BL-OOU-
17110019-BL-OUO-
17110019-BL-OOO-
17110U19-BL-UOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-HY-OOO-
17110019-CB-OUO-
17110019-CB-UOU-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-Ct-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-C1-000-
17110019-CI-OOO-
17110019-C1-000-
17110019-C1-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
Msgs
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11
BL-12
BL-13
BL-14
BL-15
BL-16
BL-17
BL-17
BL-18
BL-19
BL-20
BL-21
BL-22
BL-23
BL-24
BL-25
BL-26
BL-27
BL-28
BL-29
BL-30
BL-31
BL-32
CB-11
CB-12
CB-13
CB-14
CI-11
CI-12
CI-13
CI-14
CI-15
CI-16
CI-17
CI-17
CI-18
CI-19
CI-20
CI-21
CI-22
CH-11
CR-12
CR-13
S05C -
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
SO 1C -
S01C -
S01C -
S05C -
SO 1C -
S01C -
S01C -
SObC -
S01C -
S01C -
S05C -
S01C -
S01C -
SObC -
SO 1C -
S01C -
S01C -
S01C -
S01C -
S02C -
S01C -
SObC -
SO 1C -
S01C -
S05C -
S05C -
S05C -
S01C -
S01C -
S05C -
S01C -
S05C -
S01C -
SObC -
S01C -
200
510
460
3600
290
530
01 440
02 420
390
320
430
480
740
1200
650
600
800
280
1200
1800
330
890
980
390
51
48
47
2400
2400
1200
800
2800
860
01 1300
02 2600
860
710
790
2700
IbOU
16
14
11
190
530
410
2900
270
470
420
380
330
320
360
420
600
1000
530
560
760
220
620
1200
290
620
730
350
51
52
b3
2200
3700
2000
1200
3600
1300
2100
1000
1500
1100
900
4700
2600
16
14
11
100
270
240
2200
140
220
180
150
210
140
170
200
370
600
290
230
320
95
350
770
130
260
510
170
25
21
27
980
1100
560
270
870
330
780
630
430
370
500
1900
1300
5.5
5.8
U5
200
b30
500
2700
310
390
290
330
400
250
320
330
520
740
550
420
600
120
510
850
180
490
560
330
51
43
51
1600
1500
810
400
1200
610
880
730
690
490
640
1500
1300
9.2
11
U5
C
C
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
benzo(a) benzo(b) benzo(k)
fluor- anthra- fluor- fluor-
anthene pyrene cene chrysene anthene anthene
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
benzo(a)
pyrene
b2
290
200
1200
140
420
240
240
270
230
250
300
440
740
350
350
420
100
450
770
150
280
510
200
34
51
32
1300
1100
860
460
1300
640
1200
1400
760
590
670
2400
1200
U5
7.1
U5
Indeno
(1,2,
3-cd)
pyrene
59
130
110
310
77
110
89
100
130
85
86
100
160
280
170
140
160
36
170
270
68
110
250
94
15
14
Ib
630
680
420
200
541
160
480
250
260
290
240
670
410
05
U5
U5
-------
M.AIN SEDIMENT qUALlTY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
HIGH MOLECULAR WEIGHT PAH
Drainage
17110019-CR-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
17110019-HY-OOO-
f" 17110U19-HY-000-
rs>17110019-HY-000-
°17110019-HY-000-
1711UU19-HY-000-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OUO-
17110019-HY-OOO-
1711U019-HY-OOU-
17110019-HY-OOO-
1711U019-HY-OUO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711UU19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
CR-14 S05C -
HY-11 S01C -
HY-12 S01C -
HY-13 S01C -
HY-14 S05C -
HY-15 S01C -
HY-16 S01C -
HY-17 S01C -
HY-18 S01C -
HY-19 S01C -
HY-20 S01C -
HY-20 S01C -
HY-21 S01C -
HY-22 S05C -
HY-Z3 S01C -
HY-24 S01C -
HY-25 S01C -
HY-26 S01C -
HY-27 S01C -
HY-28 S01C -
HY-29 S01C -
HY-30 S01C -
HY-31 S01C -
HY-31 S01C -
HY-32 S01C -
HY-33 S01C -
HY-34 S01C -
HY-35 S01C -
HY-36 S01C -
HY-37 S01C -
HY-38 S01C -
HY-39 S01C -
HY-40 S01C -
HY-41 S01C -
HY-42 S01C -
HY-43 S01C -
HY-44 S01C -
HY-45 S01C -
HY-46 S01C -
HY-47 S05C -
HY-48 S01C -
HY-49 S01C -
HY-50 S05C -
fluor-
Rep anthene
16
1300
680
1900
2500
1000
6400
3900
3300
5800
01 1800
02 2000
2000
3600
2500
1600
1400
2600
720
1300
600
790
01 450
02 520
800
830
1400
950
1400
740
1200
Z490
Z7HO
Z700
170
840
72
Z650
1100
Z690
Z660
340
250
pyrene
18
1200
1300
2200
3300
3400
4800
4300
3300
5800
1700
2000
2100
2600
1900
IbOO
1400
3500
790
1200
800
480
460
500
1000
1300
1300
900
1400
880
1100
Z710
Z720
Z610
140
710
65
Z59U
Z910
Z640
Z640
200
290
anthra-
cene
7.0
570
1300
1100
1600
1100
2200
1200
2300
3500
1300
C
1300
2300
1000
670
880
1300
370
770
360
370
320
300
400
560
700
480
760
500
400
260
380
350
830
560
26
330
520
310
310
90
110
chrysene
10
1200
1800
3300
2800
2600
6100
2700
3900
5500
2300
C
2300
2700
2300
2300
1500
2600
980
1100
950
900
700
540
940
1100
1100
1200
1600
940
950
Z570
Z760
Z650
1800
650
62
Z410
Z770
Z560
Z480
160
170
f
a
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
benzo(a) benzo(b) benzo(k)
fluor- fluor-
ne anthene
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
benzo(a)
pyrene
8.5
470
1200
1700
1300
5300
1800
2400
4200
2100
2600
2900
1400
6100
2000
1300
b30
1700
330
670
440
390
270
210
510
2800
510
520
1000
580
550
390
580
510
890
580
42
310
480
370
360
78
130
indeno
(1,2,
3-cd)
pyrene
U5
260
580
740
600
1200
1700
69
94U
1500
920
760
720
2700
150
690
350
630
230
240
270
160
120
110
200
390
350
420
410
260
290
210
210
210
300
380
20
130
220
170
190
68
71
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
HIGH MOLECULAR WEIGHT PAH
benzo(a)
Urainaye
17110019-HY-OOO-
17110U19-MO-OOU-
17110019-MD-UOO-
17110U19-MD-UOO-
17110019-MI-UOO-
17110U19-MI-000-
17110019-MI-OOO-
17110019-MI-UUO-
17110019-MI-OUU-
17110019-MI-OOO-
17110019-RS-OOO-
17110019-RS-UOO-
17110019-RS-OOO-
17110019-KS-OOO-
17110019-RS-UOO-
17110U19-RS-OOU-
17110019-RS-OOO-
17110019-RS-UUO-
17110019-RS-OOO-
** 17110019-RS-OOU-
ro 17110019-RS-OOO-
*~* 17110019-RS-OOO-
17110U19-RS-000-
17110019-RS-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SP-OOU-
17110019-SP-OUO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-OP-OOO-
17110019-UP-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station
HY-51
MO- 11
MD-12
MD-13
MI-11
MI-11
MI-12
MI-13
MI-14
Ml-15
RS-11
RS-12
RS-13
RS-14
RS-14
RS-15
RS-16
RS-17
RS-18
RS-19
RS-20
RS-21
KS-22
RS-24
SI-11
SI-12
SI-13
SI-14
SI-15
SP-11
SP-12
SP-13
SP-14
SP-15
SP-16
WBS
WBS
Sample Rep
S01C -
S01C -
SObC -
S01C -
S01C - 01
S01C - 02
SO 1C -
S01C -
SO 1C -
S01C -
S01C -
S01C -
S01C -
SObC - 01
SOSC - 02
S02C -
SO 1C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
SOSC -
S01C -
S01C -
S01C -
SOBC -
SObC -
SObC -
S01C -
SO 1C -
SOBC -
SOBC -
CTL - 01
CTL - 02
fluor-
anthene
210
2800
1600
1300
1BOO
1BOO
1300
980
1200
BbO
1000
1000
1300
1800
1600
79
1300
640
8100
8SO
180
3600
11
160
780
850
84D
1100
530
520
550
1700
Z300
IbO
140
U5
05
pyrene
190
2900
1600
1600
1600
1500
1200
Z970
1000
Z480
1300
1300
1200
1700
1600
100
1900
620
5600
680
210
2100
12
110
Z680
Z920
670
930
Z600
Z490
Z680
1400
Z290
Z330
Z110
05
U5
anthra-
cene
85
1200
710
530
620
560
450
330
420
100
650
480
1100
710
620
41
500
590
3200
350
76
1800
Lb
97
860
400
250
710
300
110
140
550
93
32
41
05
Ob
chrysene
160
1500
920
530
820
880
610
460
710
350
870
650
1400
1400
880
54
750
1000
4700
400
92
2300
7.0
95
1200
630
400
710
260
170
220
660
Z59
Z56
Z69
U5
05
benzo(b)
fluor-
anthene
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
U5
U5
benzo(k )
f luor-
anthene
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
05
05
benzo(a )
pyrene
110
1600
1600
770
980
920
630
480
560
180
1000
1000
980
880
790
43
540
880
4000
270
110
1400
5.8
100
1400
730
600
b70
410
84
160
370
100
21
45
U5
U5
Indeno
(1,2,
3-cd)
pyrene
30
710
100
J60
480
420
320
230
200
95
350
460
600
320
320
19
250
17
770
90
58
480
05
40
570
180
210
270
190
05
61
170
67
U5
23
OB
U5
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
HIGH MOLECULAR WEIGHT PAH
Drainage
17110019-BL-OOO-
1711U019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-8L-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
" 17110019-BL-OOO-
l 17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-HY-OOO-
17110U19-CB-000-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-CI-OOO-
1711U019-CI-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOO-
171J0019-CR-000-
Survey Station Sample Rep
total
dlbenzo- benzo- benzo- benzo(a)
(a,h)an- (ghi) fluor- anthracene/
thracene perylene anthenes chrysene
MSQS
MSQS
Msgs
MSU.S
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 S05C -
BL-12 S01C -
BL-13 SObC -
BL-14 S01C -
BL-15 S01C -
BL-16 S01C -
BL-17 S01C -
BL-17 S01C -
8L-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 S05C -
BL-Z2 S01C -
BL-23 S01C -
BL-24 S01C -
BL-25 S05C -
BL-26 S01C -
BL-27 S01C -
BL-28 S05C -
BL-29 S01C -
8L-30 S01C -
BL-31 SU5C -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
Ci-11 S02C -
CI-12 S01C -
CI-13 SObC -
CI-14 S01C -
CI-15 S01C -
CI-16 SOBC -
CI-17 S05C -
CI-17 S05C -
CI-18 S01C -
CI-19 S01C -
CI-20 SOBC -
CI-21 S01C -
CI-22 SOSC -
CR-11 S01C -
CR-12 SOBC -
22
34
30
120
23
41
01 36
02 24
S3
16
21
23
33
62
66
42
60
IB
36
110
26
43
90
21
UB
6.4
UB
U2B
260
160
69
160
60
01 180
02 95
110
110
67
270
150
U5
U5
78
150
120
240
81
100
100
110
130
77
89
90
150
240
150
130
170
38
130
210
50
89
190
91
14
12
15
780
740
390
200
420
230
480
400
290
290
260
640
380
UB
U5
260
1200
540
1100
330
410
400
420
480
510
360
420
630
1000
580
620
460
140
1300
560
110
150
800
330
51
33
31
3200
2SOO
2100
1100
2BOO
640
2600
7BO
1500
1600
690
4400
2800
18
17
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
H'IGH MOLECULAR WEIGHT PAH
Drainage
Survey Station Sample
17110019-CR-OOO-
171 1001 9-CK-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-UOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
.-, 17110019-HY-OOO-
i 17110019-HY-OOO-
£> 17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
CR-13 SOIC
CR-14 S05C
HY-11 SOIC
HY-12 SOIC
HY-13 SOIC
HY-14 S05C
HY-15 SOIC
HY-16 SOIC
HY-17 SOIC
HY-18 SOIC
HY-19 SOIC
HY-20 SOIC
HY-20 SOIC
HY-21 SOIC
HY-22 S05C
HY-23 SOIC
HY-24 SOIC
HY-25 SOIC
HY-26 SOIC
HY-27 SOIC
HY-28 SOIC
HY-29 SOIC
HY-30 SOIC
HY-31 SOIC
HY-31 SOIC
HY-32 SOIC
HY-33 SOIC
HY-34 SOIC
HY-35 SOIC
HY-36 SOIC
HY-37 SOIC
HY-38 SOIC
HY-39 SOIC
HY-40 SOIC
HY-41 SOIC
HY-42 SOIC
HY-43 SOIC
HY-44 SOIC
HY-45 SOIC
HY-46 SOIC
HY-47 S05C
HY-48 SOIC
Rep
01
02
01
02
dlbenzo-
(a,h)an-
thracene
U5
U5
82
260
280
230
470
580
58
280
480
340
240
270
1500
440
170
110
160
79
100
84
45
41
29
75
140
100
140
150
110
110
82
65
53
100
120
U5
35
86
45
58
benzo-
(ghl)
perylene
U5
U5
100
740
670
720
1100
1900
75
1000
1100
740
610
610
U5
1100
610
440
690
280
340
210
200
170
140
290
650
290
310
350
340
290
210
210
190
320
420
19
140
200
160
160
total
benzo-
f luor-
anthenes
Ub
15
1200
2100
3800
3600
5500
8800
3700
4800
5500
5800
5800
6100
8500
2400
2400
1500
6300
1200
1800
730
570
540
550
3200
2200
1100
1100
1600
900
1000
1400
920
810
1200
670
150
1100
1500
670
1200
benzo(a)
anthracene/
chrysene
3300
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
HIGH MULECULAR WEIGHT PAH
Dralnaye
Survey Station Sample Rep
total
dlbenzo- benzo- benzo- benzo(a)
(a,h)an- (ghi) fluor- anthracene/
thracene perylene anthenes chrysene
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-MO-OOO-
17110019-MD-OOO-
17110019-MD-OOO-
17110019-M1-000-
17110019-MI-OOO-
17110019-MI-UOO-
17110019-MI-OOU-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
17110019-RS-OOO-
17110019-RS-OOO-
** 17110019-RS-OOO-
ro 17110019-KS-OOO-
•** 17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110U19-RS-000-
17110019-Sl-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110U19-SI-000-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-DP-OOO-
MSQS
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
Msgs
MSQS
Msgs
Msgs
MSQS
MSQS
Msqs
Msqs
Msqs
Msqs
Msqs
Msqs
Msqs
MSU.S
Msqs
Msqs
Msqs
Msqs
Msqs
Msqs
MSQS
Msqs
Msqs
Msqs
MSQS
MSQS
Msqs
Msgs
MSQS
Msqs
Msqs
MSQS
HY-49 S01C -
HY-50 S05C -
HY-51 S01C -
MD-U S01C -
MO- 12 S05C -
MD-13 S01C -
MI-11 S01C -
Ml-11 S01C -
MI-1Z S01C -
Ml-13 S01C -
MI-14 S01C -
MI-15 S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C -
RS-14 S05C -
RS-lb S02C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 S05C -
Si-11 S05C -
Sl-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 S01C -
sp-14 soic -
SP-lb S05C -
SP-16 S05C -
WBS CTL -
WBS CTL -
18
19
U5
140
110
110
01 190
02 130
61
48
48
29
85
170
230
01 140
02 79
05
42
21
320
21
9.6
200
05
14
150
58
42
91
53
05
05
37
U5
U5
U5
01 U5
02 U5
54
63
49
740
670
340
360
320
240
160
180
100
320
370
460
250
260
17
230
23
U5
76
57
480
U5
37
570
280
170
220
180
U5
55
160
67
18
25
U5
U5
200
420
190
1800
1400
940
2400
2400
940
1300
830
910
940
1700
3000
1200
940
64
730
1000
4200
400
190
1900
21
250
1500
810
610
830
490
360
610
580
140
130
170
Number of Observations:
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry weight
CHLORINATED AROMATIC HYDROCARBONS
Drainage
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17 11001 9-BL-OOO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110U19-HY-000-
17110019-C6-000-
17110019-CB-OOO-
17110019-CB-OUO-
17110019-CI-OOO-
17110019-C1-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110U19-C1-OOU-
17110019-CW-OOO-
17110U19-CI-UOO-
17110019-CI-OOO-
17110019-Cl-OOU-
17110019-CI-OOU-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOO-
17110019-CK-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
Station
BL-11
BL-12
BL-13
BL-14
BL-lb
BL-16
BL-17
BL-17
BL-18
BL-19
BL-20
BL-21
BL-22
BL-23
BL-24
BL-2b
BL-26
BL-27
BL-28
BL-29
BL-30
BL-31
BL-32
CB-11
CB-12
CB-13
CB-14
CI-11
CI-12
CI-13
CI-14
Cl-lb
CI-16
Cl-17
CI-17
CI-18
CI-19
CI-20
CI-21
CI-22
CK-11
CH-12
Sample
SObC -
S01C -
SObC -
S01C -
SO 1C -
SO 1C -
S01C -
S01C -
SO 1C -
S01C -
S01C -
SObC -
S01C -
SU1C -
S01C -
SObC -
S01C -
S01C -
SObC -
S01C -
S01C -
SObC -
S01C -
S01C -
SO 1C -
S01C -
S01C -
S02C -
S01C -
SObC -
S01C -
S01C -
SObC -
SUbC -
SObC -
S01C -
sole -
SObC -
S01C -
SObC -
SO 1C -
SObC -
1.3-dl-
chloro-
Rep benzene
Ub
Ub
Ub
98
U10
UbO
01 Ub
02 UbO
UbO
210
200
E170
72
26
120
b6
18
U20
Ub
98
88
14
110
Ub
Ub
Ub
Ub
U2b
U20
Ub
Ub
Ub
b7
01 23
02 Ub
18
Ub
Ub
U10
Ub
Ub
Ub
1,4-di-
chloro-
benzene
U5
Ub
16
Ub
U10
UbO
27
UbO
UbO
34
48
U2b
100
94
67
46
76
U20
Ub
34
24
7.8
b9
32
Ub
Ub
Ub
290
U20
72
100
190
260
88
IbO
76
bl
64
U10
27
Ub
Ub
l,2-d1-
chloro-
benzene
Ub
Ub
Ub
Ub
U10
UbO
18
UbO
UbO
18
36
U2b
19
2b
2b
27
46
U20
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
37
U20
18
24
16
3bO
20
2b
17
14
16
U10
Ub
Ub
Ub
1,2,4-
tr1-
chloro-
benzene
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U2b
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U10
Ub
Ub
Ub
2-
chloro-
naph-
thalene
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U2b
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U10
Ub
Ub
Ub
hexa-
chloro-
benzene
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U1U
U10
U10
U10
U10
U10
U10
U10
40
U10
U10
U10
UbO
U10
U10
U10
U10
U10
U10
U10
U1U
U1U
U10
U20
U10
U10
U10
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
CHLORINATED AROMATIC HYDROCARBONS
Dralnaye
17110019-CR-OOO-
17110U19-CR-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
» 17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
Survey Station Sample
MSQS
wsgs
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
CR-13
CR-14
HY-11
HY-12
HY-13
HY-14
HY-1S
HY-16
HY-17
HY-18
HY-19
HY-20
HY-20
HY-21
HY-22
HY-23
HY-24
HY-25
HY-26
HY-27
HY-28
HY-29
HY-30
HY-31
HY-31
HY-32
HY-33
HY-34
HY-35
HY-36
HY-37
HY-38
HY-39
HY-40
HY-41
HY-42
HY-43
HY-44
HY-45
HY-46
HY-47
HY-48
S01C
SObC
SO 1C
S01C
S01C
SObC
S01C
S01C
S01C
S01C
SO 1C
S01C
SU1C
S01C
S05C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S01C •
S01C
S01C •
S01C •
S01C •
S01C •
S01C -
SOBC •
S01C •
Rep
01
02
01
02
1,3-di-
chloro-
benzene
U5
U5
Ub
U5
28
Ub
28
Ub
Ub
Ub
U5
Ub
Ub
Ub
8b
Ub
19
22
U20
33
Ub
36
150
110
210
U5
22
Ub
46
80
8.2
110
65
44
Ub
23
13
U5
40
100
19
U5
1,4-di-
chloro-
benzene
U5
Ub
Ub
19
Ub
21
U5
Ub
22
18
U5
87
68
94
180
53
39
67
U20
35
42
U5
U5
25
30
40
98
26
64
180
56
U5
150
150
81
64
71
U5
80
200
120
200
1,2-di-
chloro-
benzene
05
U5
U5
L5
Ub
Ub
Ub
Ub
U5
U5
Ub
U5
U5
Ub
73
U5
Ub
19
U20
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
05
8.8
U5
U5
U5
14
14
9.2
U5
U5
46
22
U5
1,2,4-
tri-
chloro-
benzene
U5
U5
U5
U5
U5
U5
Ub
U5
U5
Ub
U5
U5
U5
U5
260
U5
27
26
U5
U5
U5
Ub
U5
U5
Ub
31
U5
14
38
54
34
38
120
90
100
64
51
U5
110
260
51
39
2-
chloro-
naph-
thalene
Ub
U5
Ub
U5
U5
U5
U5
Ub
Ub
U5
Ub
U5
Ub
Ub
US
U5
Ub
Ub
U5
U5
U5
Ub
Ub
Ub
Ub
U5
U5
U5
Ub
Ub
U5
U5
U5
U5
Ub
Ub
U5
Ub
U5
Ub
U5
Ub
hexa-
chloro-
benzene
U10
U10
U10
22
31
U10
U10
U10
U10
U10
U10
61
63
78
730
U10
67
3b
33
U10
70
U10
U10
25
23
54
56
24
84
220
96
140
77
130
230
23U
130
U10
120
320
100
69
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry weight
CHLORINATED AROMATIC HYDROCARBONS
Drainage
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MO-OOO-
1711UU19-MD-000-
17110019-MD-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OUO-
17110019-MI-OOO-
17110019-M1-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-UOO-
17110019-SP-UOU-
17110019-SP-OOO-
17110019- SP-000-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-UP-OOO-
17110019-UP-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station
HY-49
HY-50
HY-51
MD-11
MO- 12
MD-13
Ml-11
MI-11
Ml-12
MI-13
MI-14
MI-15
RS-11
RS-12
RS-13
RS-14
RS-14
RS-1B
RS-16
RS-17
RS-18
RS-19
RS-20
RS-21
RS-22
RS-24
SI-11
Sl-12
SI-13
SI-14
SI-15
SP-11
SP-12
SP-13
SP-14
SP-15
SP-16
MBS
WBS
Sample
S01C -
S05C -
S01C -
S01C -
SOSC -
S01C -
S01C -
S01C -
SU1C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
SOSC -
SOSC -
S02C -
S01C -
S01C -
SO 1C -
S01C -
S01C -
S01C -
S01C -
SOSC -
SOSC -
S01C -
S01C -
S01C -
S05C -
SOSC -
SOSC -
SU1C -
SO 1C -
SOBC -
SU5C -
CTL -
CTL -
1,3-di-
chloro-
Rep benzene
16
U10
28
UB
UB
UB
01 US
U2 US
43
12
UB
36
UB
40
US
01 UB
02 UB
UB
US
UB
UB
UB
12
US
UB
UB
US
US
US
19
US
8.6
12
U5
US
US
15
01 U5
02 US
l,4-d1-
chloro-
benzene
10
U10
US
180
63
40
22
19
33
17
18
17
60
25
110
26
29
UB
40
38
250
10
U5
73
U5
US
24
24
23
30
17
11
13
UB
US
10
12
UB
US
1,2-dl-
chloro-
benzene
UB
U10
US
97
35
14
U5
UB
US
UB
UB
US
UB
U5
16
US
UB
UB
US
9.4
18
UB
US
UB
U5
UB
16
US
UB
US
US
US
US
UB
UB
UB
US
UB
US
1,2,4-
tr1-
chloro-
benzene
US
UB
UB
16
7.3
US
US
US
Ub
UB
US
US
US
U5
U5
UB
UB
UB
UB
US
UB
UB
US
UB
US
UB
US
US
UB
US
US
US
US
US
U5
US
US
UB
US
2-
chloro-
naph-
thalene
US
U5
U5
US
US
UB
US
US
US
US
US
UB
US
US
US
US
US
us
us
UB
UB
UB
UB
US
UB
UB
US
US
UB
US
US
US
US
US
US
UB
UB
UB
US
hexa-
chloro-
benzene
U10
U10
17
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U1U
U10
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
CHLORINATED ALIPHATIC HYDROCARBONS
..,
i
Drainage
17110019-BL-UOO-
17110019-BL-OOU-
1711U019-BL-OUU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-OUO-
17110019-BL-OOO-
17110019-BL-OUO-
1711UU19-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-HY-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-C1-000-
17110019-CI-OUO-
17110019-CI-OOO-
17110019-C1-000-
17110019-CW-OOO-
17110019-CI-UOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
1711U019-CI-000-
17110019-CI-OOO-
17110019-CI-OUO-
17110019-CR-OOO-
Survey
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
BL-11 S05C -
BL-12 S01C -
BL-13 SObC -
BL-14 S01C -
BL-1S S01C -
BL-16 S01C -
BL-17 S01C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 S05C -
BL-22 S01C -
BL-23 S01C -
BL-24 S01C -
BL-25 S05C -
BL-26 S01C -
BL-27 S01C -
BL-28 S05C -
BL-29 S01C -
BL-30 S01C -
BL-31 S05C -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
Cl-13 SOSC -
CI-14 S01C -
CI-15 S01C -
CI-16 SObC -
CI-17 SOBC -
CI-17 SOSC -
Cl-18 S01C -
CI-19 S01C -
CI-20 S05C -
CI-21 S01C -
CI-22 S05C -
CR-11 S01C -
hexa-
chl oro-
Rep ethane
UbO
U50
UbO
UbO
UbO
UbU
01 UbO
02 U50
U50
UBO
U50
UBO
0100
UBO
UBO
UBO
UBO
UbO
UBO
UBO
UbO
UbO
UbU
UbO
UbO
UBO
UbO
U2BO
U100
UbO
UbO
UbU
UBO
01 UBO
02 OSO
UBO
UBO
UBO
U100
UBO
UBO
hexa-
hexa- chloro
chloro- cyclo-
buta- penta-
diene diene
U2B
U2b
U2b
U2b
U2b
U2S
U2B
UBO
USO
026
U2B
U2B
U2B
U2B
U2B
U2B
U2B
UBO
U2B
U2B
U26
U2b
U2S
64
U2B
U2b
U2B
U130
U2B
U2B
U2b
U2B
U2B
U2B
U2B
U2S
U25
U2b
UbO
U2S
U2B
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
CHLORINATED ALIPHATIC HYDROCARBONS
Drainage
17110019-CR-OOO-
17110019-CR-OOU-
17110019-CR-UUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOU-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-UOO-
^" 17110019-HY-UOO-
ro 17110019-HY-OOO-
*° 17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
Survey
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
CR-12 S05C -
CR-13 S01C -
CR-14 S05C -
HY-11 S01C -
HY-12 S01C -
HY-13 S01C -
HY-14 S05C -
HY-15 S01C -
HY-16 S01C -
HY-17 S01C -
HY-18 S01C -
HY-19 S01C -
HY-20 S01C -
HY-20 S01C -
HY-21 S01C -
HY-22 S05C -
HY-23 S01C -
HY-24 S01C -
HY-25 S01C -
HY-26 S01C -
HY-27 S01C -
HY-28 S01C -
HY-29 S01C -
HY-30 S01C -
HY-31 S01C -
HY-31 S01C -
HY-32 S01C -
HY-33 S01C -
HY-34 S01C -
HY-35 S01C -
HY-36 S01C -
HY-37 S01C -
HY-38 S01C -
HY-39 S01C -
HY-40 S01C -
HY-41 S01C -
HY-42 S01C -
HY-43 S01C -
HY-44 S01C -
HY-45 S01C -
HY-46 S01C -
hexa-
chloro-
Rep ethane
U50
U50
U50
UbO
U50
U50
U50
UBO
U50
U50
U50
050
01 050
02 050
050
2800
050
140
050
050
U50
050
050
050
01 U50
02 050
050
050
050
U50
050
050
050
U50
U50
050
U50
ObO
050
050
050
hexa-
hexa- chloro
chloro- cyclo-
buta- penta-
diene diene
U25
U25
U25
U25
U25
U25
Elb
U25
U25
31
U25
U25
140
160
170
730
170
140
130
U25
U25
120
U25
U25
50
48
98
130
34
190
360
130
210
300
350
680
270
180
025
440
940
-------
MAIN SEDIMENT QUALITY SURVEY URGANIC CHEMICALS - Values 1n ppb dry weight
CHLORINATED ALIPHATIC HYDROCARBONS
Drainage
17110019-HY-DOO-
17110U19-HY-UOO-
17110019-HY-UOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-MD-OUO-
17110019-MO-OOU-
17110019-MU-OUU-
17110019-MI-OOO-
17110U19-MI-OOU-
17110019-MI-OOU-
17110019-MI-OOO-
17110019-MI-OUO-
1711U019-MI-OOU-
1711U019-RS-OOU-
17110019-KS-OUO-
17110019-RS-OOO-
J» 17110019-RS-OOU-
' 17110019-RS-OOO-
0 17110019-RS-OUO-
17110019-RS-OOO-
1711UU19-RS-000-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
17110019-SI-OOO-
1711U019-SI-OOU-
17110U19-S1-000-
17110019-SI-OUO-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17I10019-SP-000-
17110U19-SP-000-
Survey
Msgs
Msgs
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
MSQS
Station Sample
HY-47 SObC -
HY-48 SO 1C -
HY-49 S01C -
HY-bO SObC -
HY-bl S01C -
MD-11 SU1C -
MD-12 SObC -
MD-13 S01C -
MI-11 S01C -
MI-11 S01C -
MI-12 S01C -
MI-13 S01C -
MI-14 S01C -
Ml-lb S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 SObC -
RS-14 SObC -
RS-lb S02C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 SOSC -
Sl-11 SObC -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
Sl-lb SUbC -
SP-11 SObC -
SP-12 SObC -
sp-13 sole -
SP-14 S01C -
hexa-
chloro-
Rep ethane
UbO
UbO
UbO
UbO
UbU
UbO
UbU
UbU
01 UbO
02 ObO
UbU
UbO
UbO
UbO
UbO
UbO
UbO
01 UbO
02 UbO
UbO
UbO
UbO
UbO
UbO
UbO
UbU
UbO
UbO
UbO
UbO
UbO
UbO
UbU
UbU
UbU
UbO
UbO
hexa-
hexa- chloro
chloro- cyclo-
buta- penta-
dlene dlene
290
22U
U2b
U2b
32
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
U2b
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry welyht
CHLORINATED ALIPHATIC HYDROCARBONS
Drainage
17110019-SP-OOO-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-DP-UUO-
Survey Station Sample Rep
MSQS
MSQS
MSQS
MSU.S
hexa-
chloro-
ethane
SP-15 S05C -
SP-16 S05C -
WBS CTL -
W8S CTL -
U50
U50
01 U50
02 UbO
hexa-
chloro-
buta-
dlene
U25
U2b
U25
U25
hexa-
chloro-
cyclo-
penta-
diene
Number of Observations: 123
CO
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
HHTHALATES
di-n-
di methyl di ethyl butyl
Drainage
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
•J" 1 71 10019-BL-OOO-
W17110019-BL-000-
r°17110019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-Hr-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-C1-OUO-
17110019-C1-000-
17110019-C1-UUO-
17110019-CH-OOO-
17110019-CI-UOO-
17110019-CI-OOO-
17110019-CI-OUO-
17110019-CI-OOU-
17110019-LI-OOO-
17110019-CI-OOO-
1711U019-C1-000-
17110019-CK-OOO-
17110019-CR-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
BL-11 SOSC -
BL-12 S01C -
BL-13 S05C -
BL-14 SU1C -
BL-15 S01C -
BL-16 S01C -
BL-17 S01C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 SOSC -
BL-22 S01C -
BL-23 S01C -
BL-24 S01C -
BL-2S SOSC -
BL-26 S01C -
BL-27 S01C -
BL-28 SOSC -
BL-29 S01C -
BL-30 S01C -
BL-31 SOSC -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 SOSC -
CI-14 S01C -
CI-15 S01C -
CI-16 SOSC -
Cl-17 SOSC -
CI-17 SOSC -
CI-18 S01C -
CI-19 S01C -
CI-20 SOSC -
CI-21 S01C -
CI-22 SOSC -
CR-11 S01C -
CR-12 SOSC -
phtha-
Rep late
U50
L50
L50
USO
050
050
01 L50
02 UbO
050
U50
USO
U50
USO
USO
050
L50
050
U50
L50
y?
UbO
050
050
050
U100
USO
050
0250
84
58
66
L50
050
01 78
02 U50
L50
L50
050
L100
USO
USO
050
phtha-
late
010
43
U10
53
U10
010
U10
U10
U10
010
U10
U10
U10
010
U10
010
010
U10
010
010
U10
U10
U10
010
L20
U10
U10
USO
U10
U10
23
31
U10
38
U10
26
U10
U10
44
010
010
13
phtha
late
Z140
Z1200
Z210
Z1100
Z420
Z230
B25
Z370
Z1400
Z1200
Z5
Z530
Z760
Z1600
Z1000
B25
Z180
Z340
B25
B25
B25
Z420
Z85
Z9800
Z3700
Z150
Z280
050
Z510
Z50
Z15
Z70
Z1600
Z270
Z1000
B25
825
Z130
Z140
Z20
Z760
B25
butyl
benzyl
phtha-
late
63
93
83
64
025
U25
Z45
U25
U25
U25
U25
U25
U25
U25
U25
Z25
025
025
U25
L25
L25
025
025
025
USO
025
025
0130
660
210
130
150
U25
L25
U25
33
56
U25
LbO
L25
025
025
bis(2-
ethyl-
hexyl )-
phtha-
late
460
1000
760
780
120
B25
B25
025
U25
B2S
U25
025
B25
U25
U25
B25
B25
025
B25
B25
D2b
B25
B25
B25
B25
B25
B25
U130
6600
3100
1SOO
1800
860
550
700
790
930
U25
710
430
B25
B25
di -n-
octyl
phtha
late
025
025
025
27
U25
U25
025
025
U25
025
025
U25
025
U25
025
U25
U25
025
025
L25
025
025
025
U25
250
L25
025
U130
290
130
49
025
U25
025
025
U25
U25
U25
U50
U25
025
025
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
PHTHALATES
dl-n-
dimethyl dlethyl butyl
Drainage
17110019-CR-OOO-
17110019-CR-UOU-
17110U19-HY-000-
17110019-HY-UOU-
17110019-HY-UOO-
1711U019-HY-000-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
*» 17110019-HY-OOO-
Oj 17110019-HY-OOO-
00 17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUU-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OUU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
MSQS
Station
CR-13
CR-14
HY-11
HY-12
HY-13
HY-14
HY-15
HY-16
HY-17
HY-18
HY-19
HY-20
HY-20
HY-21
HY-22
HY-23
HY-24
HY-25
HY-26
HY-27
HY-28
HY-29
HY-30
HY-31
HY-31
HY-32
HY-33
HY-34
HY-35
HY-36
HY-37
HY-38
HY-39
HY-40
HY-41
HY-42
HY-43
HY-44
HY-45
HY-46
HY-47
HY-48
Sample Rep
S01C -
SOSC -
S01C -
S01C -
S01C -
S05C -
S01C -
S01C -
S01C -
SO 1C -
S01C -
sine - ui
sine - 02
S01C -
SOBC -
SO 1C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C - 01
S01C - 02
S01C -
S01C -
SO 1C -
S01C -
S01C -
S01C -
S01C -
S01C -
SO 1C -
S01C -
S01C -
S01C -
S01C -
S01C -
SU1C -
S05C -
S01C -
phtha-
late
U50
U50
L50
UbO
220
UBO
74
180
U50
550
200
890
680
1100
U50
350
120
U50
50
L50
UBO
73
71
L50
0100
60
UbO
L50
B2
1000
U50
64
LBO
270
610
UBO
UBO
LBO
LBO
LBO
UBO
85
phlha-
late
10
18
U10
U10
U10
U10
U10
U10
U10
U10
U10
37
74
42
U10
U10
47
U10
6S
U10
U10
U10
U10
27
U20
48
U10
26
22
U10
U10
33
U10
50
30
U10
U10
U10
U10
44
U10
120
phtha
late
B25
Z30
BIO
Z5100
BIO
Z270
010
BIO
B25
Z260
Z260
Z12
Z130
Z100
Z560
Z92
Z100
Z930
350
BIO
U10
BIO
810
B25
Z260
B25
Z450
BIO
BIO
BIO
825
810
Z15
Z180
Z140
B25
B25
B25
U10
Z5U
Z1SOO
Z530
butyl
benzyl
phtha-
late
U25
025
U25
025
67
U25
U25
U25
U25
U25
100
U25
U25
U25
U25
110
470
U25
33
300
U25
580
U25
U2B
U50
58
U2B
350
890
U25
U25
U25
130
100
620
U25
U2B
U2B
29
U25
U2B
250
b1s(2-
ethyl-
hexyl )-
phtha-
late
825
B25
825
825
Z360
825
B25
B25
B25
U25
1500
920
IJ25
86U
3000
710
810
530
U25
B25
B25
825
U25
825
Z200
B25
825
825
Z10
B25
B25
825
320
480
1500
Z90
Z40
B25
U25
440
U25
370
di-n-
octyl
phtha
late
U25
U25
39
U25
44
U25
U2b
025
U25
U25
U25
50
025
U25
U25
U2B
47
U2B
U25
U25
026
U2B
U50
L25
050
U25
U25
U25
L25
U25
U25
U2S
U25
U25
U25
U25
U2B
025
U25
U25
U25
U25
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
PHTHALATES
d1-n-
dimethyl dlethyl butyl
Drainage
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MD-OOO-
17110019-MD-OOO-
17110019-MO-OOO-
17110019-MI-OOO-
17110019-M1-000-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-M1-000-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
f 17110019-RS-OOO-
i, 17110019-RS-OOO-
*M7110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-DP-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
HY-49 S01C -
HY-50 S05C -
HY-51 S01C -
MO-11 S01C -
MO-12 S05C -
MO-13 S01C -
MI-11 S01C -
MI-11 S01C -
Ml-12 S01C -
MI-13 S01C -
MI-14 S01C -
MI-15 S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C -
RS-14 S05C -
RS-15 S02C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 S05C -
Sl-11 S05C -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 S01C -
SP-14 S01C -
SP-lb S05C -
SP-16 S05C -
WBS CTL -
WBS CTL -
phtha-
Rep late
66
050
68
050
050
050
01 050
02 82
59
110
110
050
050
71
050
01 L50
02 050
050
050
L50
050
050
U50
050
L50
050
050
050
050
88
050
L50
L50
050
050
050
L50
01 210
02 240
phtha-
late
010
010
010
010
010
010
010
010
010
010
010
010
010
U10
010
010
010
010
010
010
010
010
010
U10
010
U10
010
U10
010
010
U10
010
010
010
010
010
010
010
U10
phtha
late
Z460
Z1000
BIO
Z170
Z1400
Z350
825
825
BIO
B25
810
825
Z850
Z230
Z560
Z470
Z1200
Z1300
Z6700
Z30
B25
Z1600
Z740
Z1400
Z120
Z940
B25
B25
810
010
B25
B25
B25
810
010
B25
825
Z160
810
butyl
benzyl
phtha-
late
025
025
025
025
025
025
025
L25
025
025
025
025
025
L25
025
025
025
025
025
025
025
025
025
025
025
025
025
U25
U25
025
025
025
025
025
025
L25
025
025
025
bis(2-
ethyl-
hexyl )-
phtha-
late
825
825
825
1200
1900
300
B25
825
825
825
B25
B25
825
825
825
825
825
B2b
825
825
025
B25
825
B25
B25
B25
025
825
825
825
825
B25
B25
B25
825
825
825
B25
1325
d1-n-
octyl
phtha
late
025
025
025
025
025
025
025
025
025
025
025
025
025
L25
025
025
025
U25
230
L25
025
025
U25
025
025
025
U25
025
025
025
025
025
025
025
U25
25
025
025
025
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
MISCELLANEOUS OXYGENATED COMPOUNDS
Drainage
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
J> 17110019-BL-OOO-
' 17110019-BL-OOO-
ui 17110019-BL-OOO-
1711U019-BL-000-
17110019-HY-OOO-
1711U019-CB-OOU-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOU-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-UOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
'17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOU-
17110019-CR-OOO-
17110019-CR-OOO-
171I0019-CR-000-
Survey
Msgs
Msgs
Msgs
MSQS
MSQS
Msgs
MSUS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
BL-11 SU5C -
BL-12 S01C -
BL-13 S05C -
BL-H S01C -
BL-15 S01C -
BL-16 SU1C -
BL-17 SU1C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 S05C -
BL-22 S01C -
BL-23 S01C -
BL-24 S01C -
BL-25 S05C -
BL-26 S01C -
8L-27 S01C -
BL-28 S05C -
BL-29 SO 1C -
BL-30 S01C -
BL-31 S05C -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 S05C -
CI-14 S01C -
CI-15 S01C -
CI-16 S05C -
CI-17 S05C -
CI-17 S05C -
CI-18 SU1C -
CI-19 S01C -
CI-20 S05C -
CI-21 S01C -
CI-22 S05C -
CR-11 S01C -
CR-12 S05C -
CR-13 S01C -
CR-14 S05C -
benzyl
Rep alcohol
22
34
16
29
L10
36
01 16
02 58
27
L10
14
57
U10
U10
17
42
16
23
20
23
38
11
27
30
42
61
80
140
110
25
54
51
U10
01 33
02 12
29
44
18
31
29
U10
U10
U10
U10
benzole
acid
U25
260
390
U25
U25
8000
U25
450
360
250
18U
250
89
U25
250
330
1500
390
650
230
230
68
200
40
L25
U25
95
U130
790
690
U25
310
U25
330
U25
330
460
U25
350
U25
U25
430
210
200
dibenzo
furan
33
75
76
210
52
100
71
89
76
58
77
72
120
170
100
96
180
46
87
240
70
110
130
60
15
18
24
370
260
180
130
450
210
190
250
120
130
160
310
170
U5
U5
U5
US
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
MISCELLANEOUS OXYGENATED COMPOUNDS
Drainage
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
, 17110019-HY-OOO-
' 17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MO-OOO-
Survey
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
HY-11 S01C -
HY-12 S01C -
HY-13 S01C -
HY-14 S05C -
HY-lb S01C -
HY-16 S01C -
HY-17 S01C -
HY-18 S01C -
HY-19 S01C -
HY-20 S01C -
HY-20 S01C -
HY-21 S01C -
HY-22 S05C -
HY-23 S01C -
HY-24 S01C -
HY-25 S01C -
HY-26 S01C -
HY-27 S01C -
HY-28 S01C -
HY-29 S01C -
HY-30 SU1C -
HY-31 S01C -
HY-31 S01C -
HY-32 S01C -
HY-33 S01C -
HY-34 S01C -
HY-35 S01C -
HY-36 SO 1C -
HY-37 S01C -
HY-38 S01C -
HY-39 S01C -
HY-40 S01C -
HY-41 S01C -
HY-42 S01C -
HY-43 S01C -
HY-44 S01C -
HY-45 S01C -
HY-46 S01C -
HY-47 S05C -
HY-48 S01C -
HY-49 S01C -
HY-bU S05C -
HY-51 S01C -
MD-11 SU1C -
benzyl
Rep alcohol
45
14
25
U10
13
U10
U10
24
U10
01 95
02 66
500
010
41
33
010
U10
63
010
48
21
01 010
02 010
29
56
18
38
35
14
50
010
100
340
L10
010
U10
33
010
010
61
74
73
79
47
benzole
acid
025
U25
54
025
150
U25
U25
025
U25
U25
U25
250
025
470
025
U25
670
230
L25
170
U25
U25
025
U25
U25
220
025
550
U25
290
250
U25
U50
U25
025
02b
025
025
U25
025
140
U25
150
025
dlbenzo
furan
66
66
82
79
60
170
120
97
130
110
68
130
480
120
100
77
72
74
120
66
50
55
57
94
130
120
150
300
120
160
110
140
110
170
160
8.0
110
150
110
130
50
54
49
440
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
MISCELLANEOUS OXYGENATED COMPOUNDS
Drainage
17110019-MD-OOO-
17110019-MD-OOO-
17110019-M1-OOU-
17110019-M1-000-
1711U019-MI-000-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
JM7110019-RS-000-
(^ 17110019-RS-OOO-
^J 17110019-SI-OOO-
17110019-SI-OOO-
17110019-S1-000-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019- SP-000-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-DP-OOO-
Survey
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Station Sample Rep
MD-12 S05C -
MO-13 S01C -
MI-11 S01C - 01
Ml-11 S01C - 02
MI-12 S01C -
MI-13 S01C -
MI-14 S01C -
MI-15 S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C - 01
RS-14 S05C - 02
RS-15 S01C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 S05C -
SI-11 S05C -
Sl-12 S01C -
SI-13 S01C -
SI-14 S01C -
Sl-lb S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 S05C -
SP-14 S01C -
SP-15 S05C -
SP-16 S05C -
WHS CTL - 01
WBS CTL - 02
benzyl
alcohol
29
23
17
32
43
23
48
31
15
35
21
010
010
010
010
10
24
010
010
010
010
010
25
15
26
40
13
010
61
180
010
010
130
010
010
benzole
add
025
025
025
025
310
025
230
025
110
02t>
025
025
025
L25
250
200
U25
025
025
260
025
025
025
025
170
140
025
025
025
630
0100
025
025
025
025
dlbenzo
furan
540
190
280
260
320
2bO
250
130
150
150
400
210
150
12
920
190
2000
110
14
820
05
17
310
190
110
610
130
170
200
420
280
40
38
05
05
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
ORGANONITROGEN COMPOUNDS
Drainage
17110019-BL-OOO-
1711UU19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOU-
17.110U19-BL-OOU-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17UU019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-UOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OUO-
17110019-BL-OUO-
i 17110U19-BL-000-
W17110019-BL-000-
00 17110019-BL-UOO-
17110019-BL-OOO-
17llUU19-Hr-OUO-
17110019-CB-OOO-
17110U19-CB-OOU-
17110019-CB-OOO-
17110019-CI-OOU-
17110019-CI-UOO-
17110U19-CI-OOU-
17110019-CI-OOU-
1711U019-CI-000-
17110U19-CM-000-
17110019-CI-OOO-
17110019-Cl-OOU-
17110U19-CI-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOU-
17110019-CR-OOU-
1711U019-CR-000-
Survey
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
Msgs
Msgs
Msgs
MSQS
MSQS
Msgs
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Station Sample
BL-11 S05C -
BL-12 S01C -
BL-13 S05C -
BL-14 S01C -
BL-15 SU1C -
BL-16 S01C -
BL-17 SU1C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 SOSC -
BL-22 S01C -
BL-23 S01C -
BL-24 SU1C -
BL-25 SU5C -
BL-26 S01C -
BL-27 S01C -
BL-28 SUbC -
BL-29 S01C -
BL-30 S01C -
BL-31 S05C -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 S05C -
CI-14 S01C -
CI-15 S01C -
CI-16 S05C -
Cl-17 S05C -
CI-17 S05C -
CI-18 S01C -
CI-19 S01C -
CI-20 S05C -
CI-21 S01C -
CI-22 S05C -
CR-11 S01C -
CR-12 S05C -
n1 tro-
Kep benzene
U5
U5
U5
U5
Ub
U5
01 U5
02 US
US
US
US
US
U5
US
US
US
US
U5
US
US
US
US
us
us
us
us
Ub
U25
US
US
US
US
US
01 US
02 US
US
US
us
U10
us
us
us
N-
ni troso-
dipropyl -
amine
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
UIO
U10
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U50
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U20
UIO
UIO
UIO
2,6-d1-
nitro-
toluene.
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U1U
UIO
U50
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U20
UIO
UIO
UIO
2,4-di-
nitro-
toluene
U5
US
U5
Ub
US
Ub
US
US
US
US
US
US
US
U5
US
US
US
US
us
us
Ub
US
US
US
us
Ub
US
U25
US
US
US
Ub
US
US
US
US
US
US
U1U
us
us
us
N-
nltroso-
diphenyl -
ami ne
US
US
US
US
US
59
US
US
US
US
US
US
US
US
US
US
90
US
us
us
Ub
us
us
U5
US
US
us
U2S
US
US
us
us
220
US
US
120
US
US
UIO
US
us
us
1,2-di-
phenyl -
hydra- benzi
zine dine
US
1200
US
Ub
US
US
US
US
US
US
US
US
US
us
us
us
280
Ub
US
US
US
Ub
US
US
US
US
US
U25
US
US
US
US
US
us
us
us
us
us
UIO
us
us
us
3,3'-di- N-
chloro- nitroso-
benzi- dimethyl
dine amine
U100
U1UO
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U100
U1UO
U100
U100
uion
U1UU
U100
U5UO
U100
U1UO
U100
U100
U100
U100
U100
U100
U100
U100
U2uo
U100
U100
U100
-------
MAIN SEDIMENT DUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
ORGANONITROGEN COMPOUNDS
Drainage
Survey Station Sample
i
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
CR-000-
CR-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
HY-000-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
wsgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
CR-13
CR-14
HY-11
HY-12
HY-13
HY-14
HY-15
HY-16
HY-17
HY-18
HY-19
HY-ZO
HY-20
HY-^1
HY-22
HY-23
HY-24
HY-25
HY-26
HY-27
HY-28
HY-29
HY-30
HY-31
HY-31
HY-32
HY-33
HY-34
HY-35
HY-36
HY-37
HY-38
HY-39
HY-40
HY-41
HY-42
HY-43
HY-44
HY-45
HY-46
HY-47
HY-48
S01C
S05C
SO 1C
S01C
S01C
SObC
S01C
S01C
SO 1C
S01C
S01C
S01C
S01C
S01C
S05C
S01C
S01C
S01C
SO 1C
S01C
S01C
SO 1C
S01C
S01C
S01C
S01C
S01C
soic
S01C
soic
SOIC
soic
soic
SOIC
SOIC
SOIC
SOIC
SOIC
SOIC
SU1C
S05C
SOIC
Kep
01
02
01
02
nitro-
benzene
U5
05
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
Ub
U5
U5
Ub
U5
U5
U5
U5
U5
Ub
U5
Ub
Ub
U5
Ub
Ub
Ub
Ub
Ub
Ub
U5
Ub
Ub
Ub
Ub
Ub
U5
Ub
Ub
U5
N-
nitroso-
dipropyl -
amine
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U1U
U10
U1U
U10
2,6-di-
ni tro-
toluene
010
U10
U10
010
U10
U10
U10
U10
U10
U10
010
010
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
010
U10
U10
U10
U10
U10
U10
U10
U10
U10
U1U
U10
U10
U10
U1U
U10
U10
2,4-di-
ni tro-
toluene
Ub
Ub
Ub
U5
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U5
Ub
US
U5
U5
U5
U5
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
N-
nitroso-
diphenyl-
amine
Ub
Ub
Ub
Ub
U5
Ub
Ub
Ub
Ub
U5
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U5
Ub
Ub
Ub
77
bO
Ub
96
Ub
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U5
Ub
28
Ub
Ub
Ub
Ub
1,2-di-
phenyl -
hydra- benzi
zine dine
U5
U5
Ub
05
U5
Ub
Ub
Ub
Ub
Ub
Ub
Ub
U5
Ub
Ub
Ub
Ub
Ub
U5
U5
U5
Ub
Ub
Ub
Ub
Ub
U5
Ub
Ub
Ub
Ub
Ub
U5
Ub
U5
U5
Ub
34
U5
Ub
Ub
Ub
3,3'-di- N-
chloro- nftroso-
benzi- dimethyl
dine amine
U100
U100
U100
U100
U100
U100
U100
U100
U10U
U100
U100
U100
UIOO
U10U
UIOO
U10U
UIOO
U1UO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
UlUU
UIOO
UIOO
UIOO
UIOO
UIOO
UIOO
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
ORGANONITROGEN COMPOUNDS
ni tro-
Oralnaye Survey Station Sample Rep benzene
N-
nitroso- 2,6-d1- 2,4-dl-
dipropyl- nltro- nltro-
ainlne toluene toluene
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MO-OOU-
17110019-MU-UOO-
17110019-MO-OOO-
17110019-MI-UOO-
17110019-MI-OOO-
17110019-M1-OUO-
17110019-M1-UOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-RS-OUO-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
»»17110019-RS-000-
^17110019-RS-OOO-
017110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
17110019-RS-OOO-
171100I9-RS-000-
17110019-S1-000-
1711U019-SI-000-
17110019-SI-OUO-
17110U19-SI-000-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-UOO-
17110019-SP-OOO-
17110U19-SP-000-
1711U019-SP-OUU-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-DP-OOO-
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-49 S01C -
HY-5U S05C -
HY-51 S01C -
MD-11 S01C -
MD-12 S05C -
MD-13 SU1C -
MI-11 S01C -
MI-11 S01C -
MI-12 S01C -
MI-13 S01C -
Ml-14 S01C -
MI-15 S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C -
RS-I4 S05C -
RS-15 S02C -
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 S05C -
SI-11 S05C -
SI-12 S01C -
SI-I3 S01C -
SI-14 S01C -
SI-IS S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 S01C -
SP-I4 S01C -
SP-15 S05C -
SP-16 S05C -
WBS CTL -
WBS CTL -
U5
U5
U5
US
U5
US
01 US
02 US
Ub
US
US
US
U5
US
US
01 U5
02 U5
US
US
U5
US
U5
US
US
U5
US
US
US
US
us
us
us
us
us
us
us
us
01 US
02 US
U10
U10
U10
U10
U1U
U10
U10
U1U
U10
U10
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
40
U10
U1U
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
Ub
us
us
us
us
us
us
us
us
us
us
us
us
us
us
Ub
us
us
us
us
us
us
Ub
us
Ub
us
us
us
us
us
us
us
Ub
us
us
us
Ub
us
Ub
610
us
us
us
us
40
us
130
us
us
Ub
us
us
Ub
Ub
Ub
us
33
43
Ub
us
Ub
Ub
us
us
us
us
us
Ub
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
us
Ub
Ub
us
Ub
us
3,3'-di- N-
chloro- nltroso-
benzl- dimethyl
dine amine
U100
U1UU
U100
U1UO
U100
U10U
U100
U10U
U100
U100
U100
U1UO
U100
U10U
U100
U100
U100
U1UU
U100
U100
U100
U100
U100
U1UU
U10U
U100
U100
U1UO
U100
U10U
U100
U1UO
U100
U100
U100
U100
U100
U100
U100
lumber of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
PESTICIDES I
Urainaye
Survey Station Sample Rep 4,4'-DDE 4,4'-DDD 4,4'-DDT aldrln dleldrln a-HCH
b-HCH
d-HCH
g-HCH
17110019-BL-QOU-
17110019-8L-000-
17110019-BL-OOO-
17110019-BL-OOO-
171HW19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OUU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-HY-OOO-
17110U19-CB-000-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-UOO-
17110019-CI-UOO-
17110019-CI-OOO-
17110019-CI-OOU-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-Cl-OOO-
1711U019-CI-000-
1711U019-CI-OOU-
17110019-CI-OOO-
17110019-CI-OOO- '
17110019-CR-OOO-
17110U19-CR-UOO-
1711U019-CR-UOO-
17110019-CR-OOO-
17110U19-HY-000-
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 SOSC -
BL-12 S01C -
BL-13 SOSC -
BL-14 SU1C -
BL-lb S01C -
BL-16 SU1C -
BL-17 S01C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 S05C -
BL-22 S01C -
8L-23 S01C -
BL-24 S01C -
BL-25 S05C -
BL-26 S01C -
BL-27 SO 1C -
BL-28 S05C -
BL-29 S01C -
BL-30 S01C -
BL-31 SOSC -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 S05C -
CI-14 S01C -
CI-15 S01C -
CI-16 S05C -
CI-17 SObC -
CI-17 S05C -
CI-18 S01C -
CI-19 S01C -
CI-20 SU5C -
CI-21 S01C -
CI-22 SObC -
CR-11 S01C -
CR-12 SOSC -
CR-13 S01C -
CR-14 S05C -
HY-11 S01C -
U50
040
ObO
U25
U25
050
01 U50
02 050
U50
U25
025
U50
050
ObO
UbO
ObO
UbO
UbO
050
050
050
050
025
U25
U25
U25
U25
U50
U50
025
025
U25
UbO
01 U2b
02 ObO
U2b
U2b
UbO
UbO
02b
U2b
U2b
025
U2b
U2b
UbO
U40
UbO
025
U25
050
U50
U50
U50
U25
U25
U50
U50
050
ObO
UbO
050
U50
050
050
050
050
U25
U25
U25
025
U25
U50
050
02b
U2b
U25
UbO
U2b
UbO
02b
U2b
UbO
ObO
02b
U25
025
U25
025
U25
050
U40
U5U
U25
U2b
UbO
UbO
UbO
UbO
02b
025
050
050
050
U50
U50
U50
U50
050
UbO
050
U50
U25
U25
U25
U25
U25
U50
U50
U25
U25
U25
U50
U25
U50
U25
U25
U50
U50
U25
025
U25
U2b
U2b
U2b
UbU
U40
050
U2b
U2b
UbO
U50
U50
U50
U25
025
050
U50
UbO
U50
UbO
UbO
UbO
ObO
UbO
UbO
UbO
025
U25
U25
U25
U25
050
U50
U25
U25
U2b
UbU
U25
U5U
U25
U25
UbO
U50
U25
U25
U25
U25
U25
U25
UbO
U40
050
U25
U25
UbO
UbU
UbO
UbO
U2b
U25
U5U
U50
050
U50
050
050
050
UbU
U50
U50
050
025
U25
U25
U25
U25
U50
U50
U25
U25
025
U50
U25
UbO
U25
025
U50
050
025
025
025
025
U25
U25
U50
040
U50
U25
U25
U50
U50
UbO
UbO
U25
U2b
UbO
UbO
UbO
UbO
UbO
ObO
UbO
UbO
UbO
UbO
UbO
U2b
U2b
U2b
U25
U25
U50
U50
U2b
U2b
U2b
U50
02b
050
025
025
UbO
U50
025
U25
025
U25
U25
U25
U50
040
U50
U2b
U2b
UbO
UbO
ObO
ObO
02b
U2b
UbO
UbO
ObO
ObU
UbO
UbO
ObO
ObO
UbO
UbO
ObO
02b
02 b
U2b
U25
U2b
UbO
UbO
U2b
U2b
025
U50
025
UbO
02 b
U25
UbO
ObO
02b
U2b
U2b
U25
U25
U25
UbO
U40
UbO
U25
U25
U50
U50
U50
050
U25
U2b
UbO
ObO
UbO
UbU
ObO '
UbO
UbO
UbU
U50
U5U
U50
U25
U25
U25
U2b
U2b
UbO
UbO
U2b
U2b
U2b
UbO
U2b
ObO
02b
025
050
ObU
02 b
U2b
U2b
U2b
U25
U25
UbO
U40
UbU
02b
U25
UbU
UbU
UbO
UbO
025
02b
050
050
050
050
UbO
UbO
UbO
UbO
UbO
ObO
UbO
U2b
U2b
U25
U25
U25
U50
UbO
U25
U25
U25
UbO
U25
U5U
U25
U2b
U50
050
U25
U25
U25
025
U25
U25
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in pub dry weight
PESTICIDES I
Drainage
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-UUU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
171101J19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17U0019-HY-000-
17110019-HY-OOO-
17110019-HY-OUO-
" 17110019-HY-OOO-
> 17110019-HY-OOO-
i 1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MO-OOO-
17110019-MO-OOO-
17110019-MD-OOO-
Survey Station Sample Rep 4,4'-UDE 4,4'-DUD 4,4'-DDT aldrin dieldrin a-HCH
b-HCH
d-HCH
y-HCH
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-12 S01C -
HY-13 S01C -
HY-14 S05C -
HY-15 S01C -
HY-16 SU1C -
HY-17 SU1C -
HY-18 S01C -
HY-19 S01C -
HY-20 S01C -
HY-20 S01C -
HY-21 S01C -
HY-22 S05C -
HY-23 S01C -
HY-24 S01C -
HY-25 S01C -
HY-26 S01C -
HY-27.S01C -
HY-28 S01C -
HY-29 S01C -
HY-30 S01C -
HY-31 S01C -
HY-31 S01C -
HY-32 S01C -
HY-33 S01C -
HY-34 S01C -
HY-35 S01C -
HY-36 S01C -
HY-37 S01C -
HY-38 S01C -
HY-39 S01C -
HY-40 S01C -
HY-41 S01C -
HY-42 S01C -
HY-43 S01C -
HY-44 S01C -
HY-45 S01C -
HY-46 S01C -
HY-47 S05C -
HY-48 S01C -
HY-49 S01C -
HY-bO S05C -
HY-51 S01C -
MD-11 S01C -
MD-12 S05C -
MD-13 S01C -
U50
U25
UbO
U25
U25
U50
U25
U25
01 U2B
02 U25
U25
U50
U25
U50
U50
U50
U25
U50
U25
U25
01 U50
02 U50
U25
U50
U25
U25
U25
UbO
U25
U25
U50
U50
UbO
U50
U50
U25
U25
U50
U2B
U2B
U25
U25
U50
U50
UbO
U50
U25
UbO
U25
U25
UbO
U25
U25
U2S
U25
U25
U50
U25
UbO
UbO
UbO
U2B
UbO
U2b
U2b
UbO
UbO
U25
UbO
U2b
U2b
U2b
UbO
U2b
U2b
UbO
UbO
UbO
UbO
UbO
U2b
U25
UbO
U2b
U25
U25
U2b
UbO
UbO
U50
UbO
U2b
UbO
U2b
U25
USO
U25
U2b
U2S
U2b
U25
USO
U25
UbO
UbO
UbO
U2b
UbO
U2b
U2b
UbO
UbO
U2b
UbO
U2b
U2b
U2b
UbO
U2b
U2b
USO
USO
USO
UbO
UbO
U25
U25
USO
U25
U25
U25
U25
UBO
UbO
UbO
UbO
U2b
UbO
U2b
U2b
UbO
U2b
U2b
U2b
U25
U2S
UbO
U2B
UbO
UbO
UbO
U2b
UbO
U2b
U2b
UbO
USO
U2b
UbO
U2b
U2b
U25
UbO
U2b
U2b
UbO
UbO
UbO
USO
USO
U2b
U25
USO
U2B
U2S
U2b
U2b
UbO
UbO
UbO
UbO
U2b
UbO
U2b
U2S
UbO
U2b
U2b
U2b
U2b
U2b
UbO
U2b
USO
UbO
UbO
U2b
UbO
U25
U2b
UbO
USO
U2b
U50
U2b
U2S
U2b
UbO
U2b
U2b
UbO
UbO
UbO
UbO
UbO
U2S
U25
UbO
U2S
U2S
U2b
U2S
UbO
UbO
UbO
UbO
U2b
UbO
U2b
U2b
UbO
U2b
U2b
U2b
U2b
U25
UbO
U2b
UbO
UbO
USO
U2b
UbO
U2b
U2b
UbO
UbO
U2b
UbO
U2b
U2b
U2b
UbO
U2b
U2S
UbO
UbO
UbO
UbO
USO
U25
U25
USO
U25
U25
U25
U2b
UbO
UbO
UbO
UbO
U2b
UbO
U2b
U2b
UbO
U2b
U2b
U2b
U2b
U2b
UbO
U2b
UbO
UbO
UbO
U25
UbO
U2b
U2b
USO
UbO
U2b
UbO
U2b
U2b
U2b
UbO
U2S
U2b
UbO
UbO
UbO
USO
UbO
U2b
U25
UbO
U2b
U25
U2b
U2b
UbO
UbO
USO
UBO
U2b
UbO
U2b
U2b
UbO
U2S
U2S
U25
U25
U2B
USO
U2B
UBO
UbO
UbO
U2b
UbO
U2S
U2B
UbU
UBO
U2b
UbO
U2b
U2b
U2b
UbO
U2b
U2b
UbO
UbO
UbO
UBO
USO
U25
U2S
UBO
U2b
U2B
U2b
U2B
UbU
UbO
UbO
UBO
U2b
UBO
U2S
U2b
UBO
U2B
U2S
U2S
U2B
U2b
UbO
U2b
UbO
UbO
UbO
U2b
UbO
U2B
U2b
UbU
UbO
U2B
UBO
U2b
U2B
U2B
UBO
U2b
U2B
USO
UBO
UbU
UBO
UbO
U2b
U2B
UbO
U2b
U2b
U2b
U2S
UbO
UbO
UbO
-------
MAIN SEDIMENT DUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry weight
PESTICIDES I
Drainage
Survey Station Sample Rep 4,4'-DDE 4,4'-DDD 4,4'-DDT aldHn dleldrln a-HCH
b-HCH
d-HCH
g-HCH
17110019-Ml-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-M1-000-
17110019-M1-000-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OUO-
1711U019-RS-000-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
7"17110019-SI-000-
->17110019-SI-000-
°17110019-SI-000-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-DP-UOO-
17110019-DP-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MI-11
MI-11
MI-12
MI-13
MI-14
MI-15
RS-11
RS-12
RS-13
RS-14
RS-14
RS-15
RS-16
RS-17
RS-18
RS-19
RS-20
RS-21
RS-22
RS-24
Sl-11
SI-12
SI-13
SI-14
SI-IS
SP-11
SP-12
SP-13
SP-14
SP-15
SP-16
WBS
WBS
S01C -
SO 1C -
S01C -
S01C -
S01C -
S01C -
S01C -
sine -
S01C -
SObC -
S05C -
S02C -
S01C -
SU1C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
SO 1C -
S01C -
SO 1C -
SObC -
SObC -
S05C -
S01C -
S01C -
SD5C -
S05C -
CTL -
CTL -
01 U25
02 U25
U25
U25
025
025
025
025
025
01 025
02 025
025
025
025
050
050
025
025
025
025
ObO
050
025
025
050
U25
025
025
050
025
025
01 025
02 025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
050
U50
025
025
025
025
050
050
025
025
050
025
025
025
050
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
ObO
050
025
025
025
025
050
ObO
025
025
050
025
025
025
050
025
025
025
025
025
025
025
U25
025
025
025
025
025
025
025
025
025
025
050
050
025
025
025
U25
050
050
025
025
050
025
025
025
050
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
050
050
025
025
025
025
050
050
025
025
050
025
025
025
050
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
050
050
025
025
025
025
050
050
025
025
050
025
025
025
050
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
050
050
025
025
025
025
050
050
025
025
050
025
025
025
050
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
025
050
050
025
025
025
025
050
050
025
025
050
025
025
025
050
025
025
025
025
025
025
025
025
02b
025
025
025
025
025
025
025
025
025
050
050
025
025
025
025
050
050
025
025
050
025
025
025
050
025
025
025
02b
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In npb dry weight
PCBS
Total
Drainage Survey Station Sample Rep PCB-1016 PCB-1221 PCB-1232 PCB-1242 PCB-1248 PCB-12S4 PCB-1260 PCBs
17110019-BL-OOO-
1711UUly-BL-000-
17110019-8L-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-8L-000-
17110019-8L-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
, 17110019-BL-OOO-
17110019-8L-000-
J 17110019-BL-OOO-
17110019-HY-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-C1-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-HY-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 SU5C -
BL-12 S01C -
BL-13 S05C -
BL-14 S01C -
BL-15 S01C -
BL-16 S01C -
tiL-17 S01C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 SObC -
BL-22 S01C -
BL-23 S01C -
BL-24 S01C -
BL-25 S05C -
BL-26 S01C -
BL-27 S01C -
BL-28 S05C -
BL-29 S01C -
BL-30 S01C -
BL-31 SOSC -
BL-32 S01C -
CB-11 S01C -
CB-I2 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 SOSC -
CI-14 S01C -
CI-15 S01C -
CI-16 S05C -
CI-17 SObC -
CI-17 SOSC -
CI-18 S01C -
CI-19 S01C -
CI-20 SObC -
CI-21 S01C -
CI-22 SOSC -
CR-11 S01C -
CR-12 SOSC -
CR-13 S01C -
CR-14 SOSC -
HY-11 S01C -
01
02
01
02
U10
U10
U10
U100
U10
U100
U10
U100
U10
U10
U10
U10
U10
U10
U10
U10
U100
U7
U80
U80
U10
U10
U10
0100
U10
U10
U10
U1200
U130
U130
UL30
U130
U15
U15
U130
U130
U130
U10
U100
U80
U7
U7
U7
U7
U130
U10
U10
U10
U100
U10
U100
U10
U100
U10
U10
U10
U10
U10
U10
U10
U10
U100
U7
U80
U80
U10
U10
U10
U100
U10
U10
U10
U1200
U130
U130
U130
U130
U15
U15
U130
U130
U130
U10
U100
U80
U7
U7
U7
U7
U130
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
17
68
22
U110
IS
uuo
22
200
13
9.4
13
6.7
11
17
18
38
U100
6.8
84
140
30
3
14
U100
U8
U8
U8
15
79
140
57
51
36
50
100
95
49
19
U90
32
U7
U7
U7
U7
U130
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
PCBS
Total
Drainage Survey Station Sample Rep PCB-1U16 PCB-1221 PCB-1232 PCB-1242 PCB-1248 PCB-12b4 PCB-1260 PCBs
17110019-HY-OOO-
17110019-HY-OUU-
17110019-HY-OOO-
17110019-HY-OUU-
17110019-HY-OOO-
17110U19-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110Q19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
•f 17110019-HY-UOO-
-^ 17110019-HY-OOO-
^ 17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-MU-OOO-
1711U019-MD-000-
17110019-MD-OOO-
Msqs
Msqs
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
Msqs
Msqs
Msqs
Msqs
MSQS
Msqs
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
HY-12 S01C
HY-13 S01C
HY-14 S05C
HY-15 S01C
HY-16 S01C
HY-17 S01C
HY-18 S01C
HY-19 S01C
HY-20 S01C
HY-20 S01C
HY-21 S01C
HY-22 S05C
HY-23 S01C
HY-24 S01C
HY-25 S01C
HY-26 S01C
HY-27 S01C
HY-28 S01C
HY-29 S01C
HY-30 S01C
HY-31 S01C
HY-31 S01C
HY-32 S01C
HY-33 S01C
HY-34 S01C
HY-35 S01C
HY-36 S01C
HY-37 S01C
HY-38 S01C
HY-39 S01C
HY-40 S01C
HY-41 S01C
HY-42 S01C
HY-43 S01C
HY-44 S01C
HY-45 S01C
HY-46 S01C
HY-47 S05C
HY-48 S01C
HY-49 S01C
HY-50 S05C
HY-51 S01C
MO-11 S01C
MD-12 S05C
MO-13 S01C
01
02
01
02
U80
U130
U50
U100
U150
U130
UlbO
U150
U130
U1'30
U130
U1500
U150
U130
U130
U100
U130
U900
U100
U130
U100
U100
U100
U100
uino
U1000
U100
U1000
U130
U900
U1000
U100
U10000
U1000
U80
U900
U1000
U1000
U80
U100
U10
U100
U15
U10
U10
U80
U130
U50
U100
U150
U130
uisn
U150
U130
U130
U130
U1500
U150
U130
U130
U100
U130
U900
U100
U130
U100
UbO
UlOO
U100
UlOO
U1000
UlOO
UBO
U130
U900
U1000
UlOO
U1000
U1000
U80
U900
U1000
U1000
U80
UlOO
U10
UlOO
IJ15
U10
U10
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
110
77
85
130
330
170
150
210
210
320
330
2000
1500
25U
190
170
860
120
220
240
110
110
130
U90
200
140
110
420
210
190
U95
1100
UlOO
1165
130
280
U85
20
33
U95
29
130
38
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
PCBS '
- Values in ppb dry weight
Total
Drainage Survey Station Sample Rep PCB-1016 PCB-1221 PCB-1232 PCB-1242 PCB-1248 PCB-1254 PCB-1260 PCBs
C 14
C 56
C 87
C 13
C 63
C 16
C U7
C 16
C 14
C 18
C 15
C U7
C 580
C blO
C 26
C 14
C 4
C 580
C U6
C 17
C 20
C 18
C 35
C 140
C 16
C U90
C U100
C 79
C U180
C U90
C U90
C U6
C U7
17110019-MI-OOO-
17110019-MI-UOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-RS-OOO-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-UOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-KS-OOO-
17110019-RS-UOO-
17110019-S1-000-
17110019-SI-OOO-
17110019-SI-OOO-
1711U019-SI-OUO-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-OP-OOO-
17110019-DP-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MI-11 S01C -
MI-11 S01C -
MI-12 S01C -
MI-13 S01C -
MI-14 S01C -
MI-15 S01C -
RS-11 S01C -
RS-12 SU1C -
RS-13 S01C -
RS-14 S05C -
RS-14 S05C -
RS-15 S02C -
RS-16 S01C -
RS-17 SU1C -
RS-18 SU1C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 S05C -
Sl-11 S05C -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 SU1C -
SP-14 S01C -
SP-15 S05C -
SP-16 S05C -
WBS CTL -
WBS CTL -
01
02
01
02
01
02
U10
U100
U100
U10
U100
U10
U7
U80
U10
UlbO
U15
U7
U200
U100
U15
U7
U7
U150
U7
U7
U10
U10
U10
U100
U10
U100
U100
U130
U180
U100
U80
U6
U7
U10
U100
U100
U10
U100
U10
U7
U80
U10
U150
U15
U7
U200
U100
U15
U7
U7
U150
U7
U7
U10
U10
U10
U100
U10
U100
U100
U130
U180
U100
U80
U6
U7
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in nob dry weight
VOLATILE HALOGENATEU ALKENES
Drainage
Survey Station Sample Rep
vinyl
chloride
1,1-di-
chloro-
ethene
1.2-
trans-
dichloro
ethylene
c i s - 1 , 3 -
dl-
chloro-
propene
t rans-
1,3-di-
chloro-
propene
tri-
chloro-
ethene
tetra-
chloro
ethene
17110019-BL-OOO-
17110019-BL-OOO-
17110019-8L-000-
1711UU19-BL-OUO-
17110019-BL-OOO-
1711U019-BL-UUU-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-UUO-
17110019-BL-OOO-
1711UU19-BL-OOU-
17110019-BL-OOO-
17110019-BL-OUO-
1711U019-BL-UOO-
1711U019-BL-UOO-
3,17110019-BL-OOO-
' 17110019-BL-OUO-
517110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
1711U019-HY-000-
17110019-CB-OOU-
17110019-CB-UOO-
17110019-C8-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOU-
17110019-CI-UOO-
17110019-CI-UOO-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-UUU-
1711U019-CH-OUU-
1711U019-CR-OUO-
17110019-CR-OOO-
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSU.S
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11
BL-12
UL-13
BL-14
BL-15
BL-16
BL-17
BL-17
BL-18
BL-19
BL-20
BL-21
BL-22
BL-23
BL-24
BL-25
BL-26
BL-27
BL-28
BL-29
BL-30
BL-31
BL-32
CB-11
CB-12
CB-13
CB-14
CI-11
CI-12
CI-13
CI-14
CI-15
CI-16
CI-17
CI-17
Cl-18
CI-19
CI-20
CI-21
CI-22
CR-11
CR-12
CR-13
S05C -
S01C -
S05C -
S01C -
S01C -
S01C -
SU1C - 01
S01C - U2
SU1C -
S01C -
S01C -
SUbC - U10 U10
sine - uio uio
S01C -
S01C -
S05C -
S01C -
S01C -
SObC -
S01C -
S01C -
SOBC -
S01C -
S01C -
SO 1C -
soic -
SO 1C -
S02C - UIO UIO
SOIC -
S05C -
SOIC -
SOIC -
S05C - UIO UIO
S05C - 01 UIO UIO
S05C - 02 UIO UIO
SOIC -
SOIC -
S05C -
SOIC -
S05C -
SU1C -
S05C -
SOIC -
UIO UIO UIO UIO UIO
UIO UIO UIO UIO UIO
UIO UIO UIO UIO UIO
UIO UIO UIO UIO UIO
UIO UIO UIO UIO UIO
UIO UIO UIO UIO UIO
-------
MAIN .SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
VOLATILE HALOGENATEU ALKENES
- Values in ppb dry weight
Drainage
Survey Station Sample Rep
vinyl
chloride
1,1-dl-
chloro-
ethene
1,2-
trans-
dichloro
ethylene
cis-1,3-
di-
chloro-
propene
trans-
1,3-di-
chloro-
propene
tri-
chloro-
ethene
tetra-
chloro
ethene
17110019-CR-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
> 17110019-HY-OOO-
** 17110019-HY-OOO-
00 17110019-HY-OOO-
1711U019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-UOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
MSQS
MSQS
MSQS
Msgs
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
CR-14
HY-11
HY-12
HY-13
HY-14
HY-lb
HY-16
HY-17
HY-18
HY-19
HY-20
HY-20
HY-21
HY-22
HY-23
HY-24
HY-25
HY-26
HY-27
HY-28
HY-29
HY-30
HY-31
HY-31
HY-32
HY-33
HY-34
HY-35
HY-36
HY-37
HY-38
HY-39
HY-40
HY-41
HY-42
HY-43
HY-44
HY-45
HY-46
HY-47
HY-48
HY-49
HY-50
SU5C -
SOIC -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
SObC -
U10
01
02
U10
U10
U10
UK)
U10
01
02
010
U10
U10
U10
U10
U10
U10
010
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
U10
010
U10
010
U10
U10
U10
U10
U10
U10
U10
U10
.010
U10
010
U10
IJ10
U10
010
U10
U10
U10
U10
010
U10
U10
U10
U10
U10
U10
U10
010
010
U10
210
160
67
170
140
HO
47
78
140
57
68
-------
MAIN,SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
VOLATILE HALOGENATED ALKENES
- Values In ppb dry weight
Drainage
Survey Station Sample Rep
vinyl
chloride
1,1-dl-
chloro-
ethene
1,2-
trans-
dlchloro
ethylene
cls-1,3-
dl-
chloro-
propene
trans-
l,3-d1-
chloro-
propene
trl-
chloro-
ethene
tetra-
chloro
ethene
17110019-HY-OOO-
17110019-MO-OOO-
17110019-MD-OOO-
17110019-MD-OOO-
17110U19-MI-OOU-
17110019-M1-000-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-Ml-UOO-
17110019-MI-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
J.17110019-RS-000-
i 17110019-RS-OOO-
^17110019-RS-OUU-
17110019-RS-OOO-
17110019-KS-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
1711U019-SP-000-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-UOO-
17110019-SH-OOO-
17110019-SP-OOO-
17110019-UP-OOO-
17110019-DP-OOD-
MSQS
MSQS
MSQS
MSQS
Msgs
Msys
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-51 SU1C -
MO-11 S01C -
MO- 12 S05C -
MD-13 S01C -
MI-11 S01C - 01
MI-11 S01C - 02
MI-12 S01C -
MI-13 S01C -
MI-14 SOIC -
MI-15 SOIC -
RS-11 SOIC -
RS-12 SOIC -
RS-13 SOIC -
RS-14 S05C - 01
RS-14 S05C - 02
RS-15 S02C -
RS-16 SOIC -
RS-17 SOIC -
RS-18 SOIC -
RS-19 SOIC -
RS-20 SOIC -
RS-21 SOIC -
RS-22 SOIC -
RS-24 S05C -
SI-11 S05C - U10 U10 U10 U10 U10 U10
SI-12 SOIC -
SI-13 SOIC -
SI-14 SOIC -
SI-15 S05C -
SP-11 S05C -
SP-12 S05C - U10 U10 U10 U10 U10 U10
SP-13 SOIC -
SP-14 soic - uio uio uio uio inn uio
SP-15 S05C - UIO UIO UIO UIO UIO UIO
SP-16 SOSC -
WBS CTL - Ul
WBS CTL - 02
UIO
UIO
UIO
UIO
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
VOLATILE AROMATIC HYDROCARBONS
Drainage
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17H0019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
-J.17110019-BL-000-
i 17110019-BL-OOO-
g!7110019-BL-000-
17110019-HY-OUO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-Cd-OOO-
17110019-CI-OUO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-C1-000-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-UOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOU-
17110019-CK-OOO-
1711U019-CR-000-
17110019-CR-OOO-
17110U19-CR-000-
Survey Station Sample Rep benzene toluene
ethyl-
benzene
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 SUSC -
BL-12 S01C -
BL-13 S05C -
BL-14 S01C -
BL-15 SU1C -
BL-16 S01C -
BL-17 S01C - 01
BL-17 S01C - 02
8L-18 SU1C -
BL-19 S01C -
B'L-20 SO 1C -
BL-Z1 S05C - U10 010
BL-22 S01C - U10 U10
BL-23 S01C -
BL-24 S01C -
BL-25 S05C -
BL-26 S01C -
BL-27 S01C -
8L-28 S05C -
BL-29 S01C -
BL-30 S01C -
BL-31 S05C -
BL-32 S01C -
CB-11 S01C -
CB-12 SOIC -
CB-13 SOIC -
CB-14 SOIC -
CI-11 S02C - U10 U10
CI-12 SOIC -
CI-13 SOSC -
CI-14 SOIC -
CI-15 SOIC -
CI-16 SOSC - U10 U10
CI-17 SOSC - 01 U10 U10
CI-17 SOSC - 02 U10 U10
CI-18 SOIC -
Cl-19 SOIC -
CI-20 SOSC -
CI-21 SOIC -
CI-22 SOSC -
CR-11 SOIC -
CR-12 SOSC -
CR-13 SOIC -
CR-14 S05C -
U10
010
U10
U10
010
010
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
VOLATILE AROMATIC HYDROCARBONS
Dralnaye
Survey Station Sample Rep benzene toluene
ethyl -
benzene
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOU-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
J.17110019-HY-000-
t 17110019-HY-OOO-
£l7110019-HY-000-
17110019-HY-OOO-
17110U19-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OUO-
17110U19-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-MD-000-
MSQS
MSQS
MSUS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-11 S01C -
HY-12 S01C -
HY-13 S01C -
HY-14 SObC -
HY-15 S01C -
HY-16 S01C -
HY-17 S01C - U10 U10
HY-18 S01C -
HY-19 S01C -
HY-20 S01C - 01
HY-20 S01C - 02
HY-21 S01C - U10 U10
HY-22 S05C - U10 U10
HY-23 S01C - U10 U10
HY-24 S01C -
HY-25 S01C - U10 U10
HY-26 SU1C -
HY-27 SU1C -
HY-28 S01C - U10 U10
HY-29 S01C -
HY-30 S01C -
HY-31 S01C - 01
HY-31 S01C - 02
HY-32 S01C -
HY-33 S01C -
HY-34 S01C -
HY-35 S01C -
HY-36 S01C -
HY-37 S01C -
HY-38 S01C -
HY-39 S01C -
HY-40 S01C -
HY-41 S01C - U10 U10
HY-42 S01C - U10 U10
HY-43 S01C - U10 U10
HY-44 S01C - U10 U10
HY-45 S01C - U10 U10
HY-46 S01C -
HY-47 S05C -
HY-48 S01C -
HY-49 S01C -
HY-50 SOBC -
HY-51 S01C -
MD-11 S01C -
50
U10
17
31
30
33
L10
18
37
10
13
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
VOLATILE AROMATIC HYDROCARBONS
ethyl -
Drainage Survey Station Sample Rep benzene toluene benzene
17110019-MO-OOO- MSQS MD-12 S05C -
17110U19-MD-000- MSQS MD-13 S01C -
17110019-MI-OOO- MSQS MI-11 S01C - 01
17110019-MI-OUO- MSQS MI-11 S01C - 02
17110019-MI-OOO- MSQS MI-12 S01C -
17110019-MI-OOO- MSQS MI-13 S01C -
17110019-MI-OOO- MSQS MI-14 SU1C -
17110019-MI-UOO- MSQS MI-15 S01C -
17110019-RS-OOO- MSQS RS-11 S01C -
17110019-RS-OOO- MSQS RS-12 S01C -
17110019-RS-OOO- MSQS RS-13 S01C -
17110019-RS-OOO- MSQS HS-14 SObC - 01
17110019-RS-OOO- MSQS RS-14 S05C - 02
17110019-RS-OOO- MSQS RS-15 S02C -
17110019-RS-OOO- MSQS RS-16 S01C -
17110019-RS-OOO- MSQS RS-17 S01C -
17110019-RS-OOO- MSQS RS-18 S01C -
17110019-RS-OOO- MSQS RS-19 S01C -
17110019-RS-OOO- MSQS RS-20 S01C -
_ 17110019-RS-OOO- MSQS RS-21 S01C -
i 17110019-RS-OOO- MSQS RS-22 S01C -
"M7110019-RS-000- MSQS RS-24 SObC -
17110019-SI-OOO- MSQS SI-11 SOBC - U10 U10 010
17110019-SI-OOO- MSQS SI-12 S01C -
17110019-SI-OOO- MSQS SI-13 S01C -
17110019-SI-OOO- MSQS SI-14 S01C -
17110019-SI-OOO- MSQS SI-15 S05C -
17110019-SP-OOO- MSQS SP-11 S05C -
17110019-SP-OOO- MSQS SP-12 S05C - U10 U10 U10
17110019-SP-OOO- MSQS SP-13 S01C -
17110019-SP-OOO- MSQS SP-14 S01C - U10 U10 U10
17110019-SP-OOO- MSQS SP-15 S05C - U10 U10 U10
17110019-SP-OOO- MSQS SP-16 S05C -
17110019-DP-OOO- MSQS WHS CTL - 01
17110019-DP-OOO- MSQS WBS CTL - 02
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry weight
VOLATILE AROMATIC HYDROCARBONS
Drainage
Survey Station Sample Rep
total
styrene xylenes
o-xylene
17110019-BL-OOU-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110U19-BL-OUO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
•fl711U019-BL-000-
cnl7110019-BL-000-
W1711U019-BL-OUO-
17110019-HY-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CB-OUO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-UOO-
17110019-Ct-OOO-
17110019-CI-OOO-
17110019-CI-UOU-
17110019-CI-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 S05C
BL-12 S01C
BL-13 S05C
BL-14 S01C
BL-15 S01C
BL-16 S01C
BL-17 S01C
BL-17 S01C
BL-18 S01C
BL-19 S01C
BL-20 S01C
BL-21 S05C
BL-22 S01C
BL-23 S01C
BL-24 SU1C
BL-25 S05C
BL-26 S01C
BL-27 S01C
BL-28 S05C
BL-29 S01C
BL-30 S01C
BL-31 S05C
BL-32 S01C
CB-11 S01C
CB-12 S01C
CB-13 SU1C
CB-14 S01C
CI-11 S02C
CI-12 S01C
CI-13 S05C
CI-14 S01C
CI-15 S01C
CI-16 S05C
CI-17 S05C
CI-17 S05C
Cl-18 S01C
CI-19 S01C
CI-20 S05C
CI-21 S01C
CI-22 S05C
CR-11 S01C
CR-12 S05C
CR-13 S01C
CR-14 S05C
01
02
U20
U20
U20
U20
U20
U20
01 U20
02
U20
U20
U20
IJ20
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
VOLATILE AROMATIC HYDROCARBONS
Values in ppb dry weight
Dralnaye
Survey Station Sample Rep styrene
total
xylenes
o-xylene
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
1711UU19-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
J»17110019-HY-000-
' 17110019-HY-UOO-
*.17110019-HY-000-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OUU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
171I0019-HY-000-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-MD-OUO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-11 S01C -
HY-12 S01C -
HY-13 S01C -
HY-14 S05C -
HY-15 S01C -
HY-16 SU1C -
HY-17 S01C - U20
HY-18 S01C -
HY-19 S01C -
HY-20 S01C - 01
HY-20 S01C - 02
HY-21 S01C - U20
HY-22 S05C - U20
HY-23 S01C - U20
HY-24 S01C -
HY-25 S01C - U20
HY-26 SU1C -
HY-27 S01C -
HY-28 S01C - U20
HY-29 S01C -
HY-30 S01C -
HY-31 SU1C - 01
HY-31 S01C - 02
HY-32 S01C -
HY-33 S01C -
HY-34 S01C -
HY-35 S01C -
HY-36 S01C -
HY-37 SOIC -
HY-38 SOIC -
HY-39 SOIC -
HY-40 SOIC -
HY-41 SOIC - U20
HY-42 SOIC - U20
HY-43 SOIC - U20
HY-44 SOIC - U20
HY-45 SOIC - 020
HY-46 SOIC -
HY-47 S05C -
HY-48 SOIC -
HY-49 SU1C -
HY-50 S05C -
HY-51 SOIC -
MO-11 SOIC -
160 C
U20 C
70 C
110 C
98 C
100 C
L20 C
53 C
120 C
30 C
49 C
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry weight
VOLATILE AROMATIC HYDROCARBONS
total
Drainage Survey Station Sample Hep styrene xylenes o-xylene
17110019-MD-OOO- MSQS MD-12 SU5C -
17110019-MD-OOO- MSQS MD-13 S01C -
17110019-MI-OOO- MSQS MI-11 S01C - 01
17110019-MI-OOO- MSQS MI-11 S01C - 02
17110019-MI-OOO- MSQS MI-12 S01C -
17110019-MI-OOO- MSQS Ml-13 S01C -
17110019-MI-OOO- MSQS MI-14 S01C -
17110019-MI-OOO- MSQS MI-15 S01C -
17110019-RS-OUO- MSQS RS-11 S01C -
17110019-RS-OOO- MSQS RS-12 S01C -
17110019-RS-OOO- MSQS RS-13 S01C -
17110019-RS-OOO- MSQS RS-14 S05C - 01
17110019-RS-OOO- MSQS RS-14 S05C - 02
17110019-RS-OOO- MSQS RS-15 S01C -
17110019-RS-OOO- MSQS RS-16 S01C -
17110019-RS-OOO- MSQS RS-17 S01C -
17110019-RS-OOO- MSQS RS-18 S01C -
17110019-RS-OOO- MSQS RS-19 S01C -
17110019-RS-OOO- MSQS RS-20 S01C -
17110019-RS-OOO- MSQS KS-21 S01C -
17110019-RS-OOO- MSQS RS-22 S01C -
, 17110019-RS-OOO- MSQS RS-24 S05C -
17110019-SI-OOO- MSQS SI-11 S05C - U20 U20 C
17110019-SI-OOO- MSQS SI-12 S01C -
17110019-SI-OOO- MSQS SI-13 S01C -
17110019-SI-OOO- MSQS SI-14 S01C -
17110019-SI-OOO- MSQS SI-15 S05C -
17110019-SP-OOO- MSQS SP-11 S05C -
17110019-SP-OOO- MSQS SP-12 S05C - U20 020 C
17110019-SP-OOO- MSQS SP-13 S05C -
17110019-SP-OOO- MSQS SP-14 S01C - U20 020 C
17110019-SP-OOO- MSQS SP-15 S05C - U20 U20 C
17110019-SP-OOO- MSQS SP-16 S05C -
17110019-OP-OOO- MSQS WBS CTL - 01
17110019-DP-OOO- MSQS WBS CTL - 02
Number of Observations: 123
-------
MAIN.SEDIMENT TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
1-methyl -
Dralnaje
Survey Station Sample
Rep
methyl-
ethyl)
benzene
2-
methoxy
phenol
penta-
chloro-
cyclo-
pentane
1-
methyl
naphth-
alene
1,1'
biphenyl
2,6-cll
methyl
naphth
alene
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711UU19-BL-OUO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OUO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
IM7110019-BL-000-
6il7110019-BL-000-
OM7110019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-HY-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOU-
17110019-CI-OOO-
17110019-CI-OOO-
17110U19-CI-000-
1711UU19-CW-000-
17110019-CJ-OOO-
1711U019-CI-000-
17110019-C1-000-
17110U19-CI-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOU-
17110019-CR-OOO-
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 SOSC -
BL-12 S01C -
BL-13 S05C -
BL-14 S01C -
BL-15 S01C -
BL-16 S01C -
BL-17 S01C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 S05C -
BL-22 S01C -
BL-23 S01C -
BL-24 S01C -
BL-2b SObC -
BL-26 S01C -
BL-27 S01C -
BL-28 S05C -
BL-29 S01C -
BL-30 S01C -
BL-31 SOBC -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
C8-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
CI-13 S05C -
CI-14 S01C -
Cl-lb S01C -
CI-16 S05C -
CI-17 SUbC -
CI-17 S05C -
Cl-18 SU1C -
CI-19 S01C -
CI-20 S05C -
CI-21 S01C -
CI-22 S05C -
CR-11 S01C -
CR-12 S05C -
CR-13 S01C -
CR-14 SObC -
E81
£87
E31
E14U
E25
01 Eb6
02
E12
E47
E130
El. 7
E990
E110
E30
E42
E52
El. 3
Eb.5
E28
Eb3
E230
E120
E150
E95
£39
£160
£330
U
£86
E46
£86
£170
01 £180
02
Ebl
U
Eb60
E55
£85
E12
U
0
0
E20
£35
U
E28
E2y
E18
£12
E17
E23
E18
£29
E39
£43
E15
£47
£8.9
£14
Ell
E39
£49
£160
£100
£42
£53
U
U
U
£120
£150
U
0
£160
£51
U
E580
£230
£43
U
U
El. 3
U
U
U
U
0
U
0
U
U
U
U
U
U
U
U
£1.8
U
U
U
U
El.l
U
Ell
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
£9.0
£22
£33
Ell
U
£16
£12
£15
E18
£12
E32
E55
E34
£13
£34
£8.9
Ell
E30
Ell
Ell
£44
£21
£15
£170
E19
E110
E200
£76
£46
E110
£140
£150
E56
U
£270
£73
£85
U
U
0
U
-------
MAIN SEDIMENT SURVEY TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
1-methyl -
Drainage
Survey Station Sample
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17U0019-HY-000-
17110019-HY-OUU-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
3>17110019-HY-UOO-
' 17110019-HY-OOO-
i>Jl7110019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17H0019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MD-OOO-
MSQS
MSQS
HSl)S
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-11 S01C
HY-12 S01C
HY-13 SU1C
HY-14 S05C
HY-15 S01C
HY-16 S01C
HY-17 S01C
HY-18 S01C
HY-19 S01C
HY-20 S01C
HY-20 S01C
HY-21 S01C
HY-22 S05C
HY-23 S01C
HY-24 S01C
HY-25 S01C
HY-26 S01C
HY-27 S01C
HY-28 S01C
HY-29 S01C
HY-30 S01C
HY-31 S01C
HY-31 S01C
HY-32 S01C
HY-33 S01C
HY-34 S01C
HY-35 S01C
HY-36 S01C
HY-37 S01C
HY-38 S01C
HY-39 S01C
HY-40 S01C
HY-41 SU1C
HY-42 S01C
HY-43 S01C
HY-44 S01C
HY-45 S01C
HY-46 S01C
HY-47 S05C
HY-48 S01C
HY-49 S01C
HY-50 S05C
HY-51 S01C
MU-11 SO 1C
Hep
methyl -
ethyl )
benzene
2-
methoxy
phenol
penta-
chloro-
cyclo-
pentane
1-
methyl
naphth-
alene
E220
Ell
01
02
01
02
E970
EblO
E2000
E2800
E710
E230
E190
E1000
E87
E180
E53
E9.5
E300
E1100
E550
E44
E400
E13
E340
E210
£150
E310
E310
E140
E210
E120
E210
E82
E180
E7.7
£150
E190
E120
U
E33
E15
U
U
U
U
U
U
U
U
U
U
U
U
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
£48
E41
E69
£130
£4.9
E22
U
E8.4
U
E18
Ell
E17
E6.0
E13
E6.7
El. 9
E22
E1S
E21
U
E5.8
E2b
E30
E23
E29
£200
E54
E34
ES2
E31
E47
E68
E72
U
£44
EZ70
E58
E17
U
U
2,6-di-
methyl
1,1' naphth-
hiphenyl alene
E24
E23
E100
U
E36
E20
E50
E51
E110
U
U
E17
E9.1
£45
E26
E48
E8.4
E15
E110
E54
E46
E48
E310
E74
E63
E41
U
E31
E64
E63
El.3
E51
E41
E27
E30
E19
E150
-------
MAIN SEDIMENT SURVEY TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
1-methyl-
Drainage
17110019-MD-OOO-
17110019-MD-OOO-
17110019-MI-OOO-
17110019-M1-000-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOU-
17110019-RS-OOU-
17110019-RS-OOO-
17110019-RS-UOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
,, 17110019-RS-OOO-
i 17110019-RS-OOO-
Sg 17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-S1-000-
17110019-S1-000-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-OP-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample
MD-12 SOBC -
MD-13 S01C -
MI-11 S01C -
MI-11 S01C -
MI-12 S01C -
MI-13 S01C -
MI-14 S01C -
MI-15 S01C -
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C -
RS-14 SObC -
RS-lb S02C -
RS-16 S01C -
«S-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 SU1C -
RS-24 SObC -
SI-11 SOBC -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 SOBC -
SP-11 S05C -
SP-12 S05C -
SP-13 S01C -
SP-14 S01C -
SP-15 S05C -
SP-16 SOBC -
WBS CTL -
WBS CTL -
methyl -
ethyl)
Rep benzene
E10
E190
01 E210
02
E310
E260
E2bO
E73
E150
01 E170
02
E22
E2700
E140
E570
E57
U
E9S
U
E4.5
E2300
E190
E160
E210
E560
E600
EB30
E6600
El 400
E300
01
02
2-
methoxy
phenol
E930
E170
E110
E190
E180
E83
E32
E58
U
U
U
U
U
Eb.O
U
E29
U
El .5
E300
E310
E160
E1BO
E370
E360
EB60
E3900
El 500
E340
penta- 1-
chloro- methyl
cyclo- naphth-
pentane alene
U
U
U
U
U
0
U
U
U
U
U
U
U
U
U
U
U
E5.7
U
U
U
U
U
U
U
U
U
U
1,1'
blph
E260
E75
E100
E100
E40
E61
E34
E80
E84
E3.9
U
E57
E1100
E23
E5.2
E120
U
E3.0
E100
E56
E210
E72
E84
E85
E64
E310
U
E14
2,6-di-
methyl
naphth-
alene
E190U
Number of Observations: 123
-------
MAIN SEDIMENT SURVEY TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
Drainage
Survey Station Sample
2,3,5-
tri-
methyl
naphth-
al ene
dlbenzo-
thlo
phene
2-
methyl
phenan-
threne
1-
methyl
phenan-
threne
9-
hexa-
decenolc
add
Iso-
plmara
dlene
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-8L-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
3M7110019-BL-000-
«Jnl7110019-BL-000-
V017110019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-HY-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-C1-000-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 SOBC -
BL-12 S01C -
8L-13 SOBC -
BL-14 S01C -
BL-15 S01C -
BL-16 S01C -
BL-17 S01C -
BL-17 S01C -
BL-18 S01C -
BL-19 S01C -
BL-20 S01C -
BL-21 SObC -
8L-22 S01C -
BL-23 S01C -
BL-24 S01C -
BL-25 SOSC -
BL-26 S01C -
BL-27 S01C -
BL-28 SOBC -
BL-29 S01C -
BL-30 S01C -
BL-31 SOBC -
BL-32 S01C -
CB-11 S01C -
CB-12 S01C -
CB-13 S01C -
CB-14 S01C -
CI-11 S02C -
CI-12 S01C -
Cl-13 SOBC -
CI-14 S01C -
C1-1B S01C -
CI-16 SOSC -
CI-17 SOBC -
CI-17 SOBC -
CI-18 S01C -
CI-19 S01C -
CI-20 SOBC -
CI-21 S01C -
CI-22 SOSC -
CR-11 S01C -
CR-12 SOBC -
CR-13 S01C -
CR-14 SOBC -
01
02
01
02
EB4
E2B
E270
E86
E26
E72
Ell
E31
E20
E32
E16
E25
E23
E32
E46
E43
E20
E67
E19
E26
E46
E20
Ell
E41
E19
Ell
U
U
E190
E190
E110
E31
E130
E130
E180
E49
E89
E2BO
E10U
E99
U
U
U
U
E83
E98
EB1
E42
E18
E96
E63
EBO
E8B
E92
E62
E130
E31
E61
E38
E38
E71
E26
£46
E310
E26
E38
E40
EB3
U
E290
E73
EB4
E2BO
U
E210
E110
E180
E490
E110
E260
U
U
U
U
EB9
E92
E38
E44
E38
E67
£49
E48
E48
E37
EB9
E110
E42
E34
E24
E42
E82
£29
£90
E140
E42
£39
ESO
£71
U
E600
E110
U
E160
U
£310
£180
E180
0
E380
E270
U
U
U
U
£410
E720
U
E440
E180
E140
E110
E210
£160
E91
U
E85
E260
£340
E30
£100
E240
E170
£200
£160
0
U
£900
£220
E1200
E4300
E600
E460
E300
U
U
U
E720
U
E690
£970
E62
£(13
E53U
E280
E1600
0
E110
£230
E97
E92
E120
E270
£170
E230
£170
£240
£120
E180
E84
E110
E91
E140
£230
EB30
£280
£130
£160
£300
£240
E600
E200
£190
E1BO
E490
£530
£98
E43U
E1400
£190
£370
U
U
0
U
-------
MAIN SEDIMENT SURVEY TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
Drainaye
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
3> 17110019-HY-OOO-
^ 17110019-HY-OOO-
017110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17M0019-HY-000-
17110019-HY-OOO-
17H0019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
1711U019-HY-000-
17110019-HY-OOO-
17110U19-HY-OUO-
1711U019-MD-000-
Survey Station Sample
2,3,5-
tri-
methy]
naphth-
al ene
Msgs
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
Msgs
Msgs
MSQS
MSQS
Msgs
Msgs
MSQS
Msgs
MSQS
Msgs
MSQS
Msgs
Msgs
MSQS
Msgs
Msgs
MSQS
MSQS
HY-ll S01C -
HY-12 SU1C -
HY-13 S01C -
HY-14 S05C -
HY-lb SU1C -
HY-16 S01C -
HY-17 S01C -
HY-18 SU1C -
HY-19 S01C -
HY-20 S01C -
HY-2U S01C -
HY-21 SU1C -
HY-22 S05C -
HY-23 S01C -
HY-24 S01C -
HY-25 S01C -
HY-26 S01C -
HY-27 S01C -
HY-28 S01C -
HY-29 S01C -
HY-30 S01C -
HY-31 S01C -
HY-31 SOIC -
HY-32 SOIC -
HY-33 SOIC -
HY-34 SOIC -
HY-35 SOIC -
HY-36 SOIC -
HY-37 SOIC -
HY-38 SOIC -
HY-39 SOIC -
HY-40 SOIC -
HY-41 SOIC -
HY-42 SOIC -
HY-43 SOIC -
HY-44 SOIC -
HY-45 SOIC -
HY-46 SOIC -
HY-47 S05C -
HY-48 SOIC -
HY-49 SOIC -
HY-50 S05C -
HY-51 SOIC -
MD-11 SOIC -
01
02
01
02
2-
dibenzo- methyl
thio phenan-
phene threne
E110
E63
1-
inethyl
phenan-
threne
E170
9-
hexa- 1so-
decenolc plmara-
acid diene
E1700
E320
U
E190
U
E200
U
U
U
E320
E84
E100
E44
E33
U
U
U
E21
E43
E39
E74
U
U
U
E110
E74
E100
E39
E35
E170
E110
U
U
E31
E46
E19
E26
E260
E120
E270
E310
E360
E50
E62
E46
E740
U
E66
E51
E56
E45
E44
U
E23
E110
E67
E35
E100
E70
E380
E64
E84
U
£56
E38
E40
E140
E6.4
E100
E41
E75
E32
E180
U
E390
U
E260
E27
E72
E62
E530
U
E61
E82
E39
E26
U
U
E19
U
E78
U
E93
ElbO
E330
U
E94
E110
E93
E36
E74
U
E5.1
E130
E72
E!170
E69
E210
U
E16UO
E5700
E130U
El 300
E3500
E3800
E850
E1500
E760
E800
U
E820
El 100
E1100
U
U
U
E640
E2300
El 300
E7300
E760
E2300
E880
E680
E14UO
E1600
ElbOU
E590
U
E290
E710
E470
E760
E750
E290
EB20
E4700
E31U
E17U
EB5
E130
E480
E62
£260
E170
E190
U
U
U
£59
E300
£220
E200
£150
E300
E1200
E470
£270
£210
£430
£420
£620
£630
E28
£200
£140
£540
E190
E150
E230
-------
MAIN .SEDIMENT QUALITY SURVEY TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
Drainage
17110019-MD-OOO-
17110019-MO-OOO-
17110019-MI-OOO-
17110U19-MI-000-
17110019-MI-OOO-
17110019-MI-OOO-
1711U019-MI-UOO-
17110019-MI-OOO-
17110U19-RS-000-
17110019-RS-OOO-
17110019-RS-UOU-
1711U019-RS-OOU-
17110019-RS-OUO-
17110019-RS-UOO-
1711U019-RS-000-
1711U019-RS-000-
17110019-RS-OOO-
_17110019-RS-000-
i 17110019-RS-OOO-
*» 17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-SI-OOU-
1711U019-SI-000-
17110019-SI-OOU-
17110019-SI-OOO-
1711U019-SI-UUO-
17110U19-SP-OUO-
1711UU19-SP-000-
17110019-SP-OOO-
17110019-SP-UOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-OP-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station
MD-12
MO- 13
MI-11
MI-H
MI-12
MI-13
Ml-14
MI-15
RS-11
RS-12
RS-13
RS-14
RS-14
RS-15
RS-16
RS-17
RS-18
RS-19
RS-20
RS-21
RS-22
RS-24
SI-11
SI-12
SI-13
SI-14
SI-15
SP-11
SP-12
SP-13
SP-14
SP-15
SP-16
MBS
W8S
2,3,5-
tr1-
methyl
naphth-
Sample alene
S05C -
S01C -
S01C - 01
S01C - (J2
SO 1C -
SU1C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C - 01
S05C - 02
SU2C -
sine -
SO 1C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
S01C -
S01C -
SU1C -
S05C -
S05C -
SOSC -
S01C -
S01C -
S05C -
S05C -
CTL - 01
CTL - 02
2-
dlbenzo- methyl
thlo
phene
E240
E65
E110
E68
U
E70
E53
E98
U
E5.2
E130
E97
El 100
£97
E16
E190
U
U
E170
E76
E260
U
U
U
U
U
U
U
phenan-
threne
E470
E120
E110
E110
E63
E180
E83
E180
E86
E9.2
E130
E180
E2400
E100
E38
E360
U
U
E280
E130
E660
E230
E110
E140
E64
U
E52
E40
1-
methyl
phenan-
threne
E220
E15U
mn
E150
E160
E320
E89
E200
E160
E5.2
U
E120
El 300
E140
ES2
E310
U
E18
E130
U
E430
E370
U
E36
E64
U
E96
E61
9-
hexa-
1so-
decenolc plmara
add
E1DOO
U
E1600
E560
E310
E230
El 300
U
E1300
U
E160
E340
U
E670
E66
U
E770
E2800
E1100
U
E650
E540
E2100
E220U
El 300
E990
E2000
El 600
dlene
E930
E240
E43U
E560
EblO
E190
E150
E150
U
E14
E310
E170
U
U
E14
E81
U
E36
E870
E960
E350
E28U
E750
E710
E410
E5900
E550
E310
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values In ppm dry weight
Drainage Survey Station Sample Rep Antimony Arsenic Barium
Beryllium Cadmium Chromium
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
-e, 17110019-BL-OOO-
i 17110019-HY-OOO-
<*M7110019-CB-000-
17110019-CB-OOO-
17110019-CB-OOU-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-UOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CH-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110U19-CI-000-
17110019-Ct-OOU-
17110019-CI-UOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSgS
MSQS
MSQS
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
BL-11 S05C
BL-12 S01C
8L-13 S05C
BL-14 S01C
BL-15 S01C
BL-16 S01C
BL-17 S01C
BL-18 S01C
BL-19 S01C
BL-20 S01C
BL-21 SOBC
BL-22 S01C
BL-23 S01C
BL-24 S01C
BL-25 S05C
BL-26 S01C
BL-27 S01C
BL-28 S05C
BL-29 S01C
BL-30 S01C
BL-31 S05C
BL-32 S01C
CB-11 S01C
CB-12 S01C
CB-13 S01C
CB-14 S01C
CI-11 S02C
CI-12 S01C
CI-13 S05C
CI-14 S01C
CI-15 S01C
CI-16 S05C
CI-17 S05C
Cl-18 S01C
CI-19 S01C
CI-20 S05C
CI-21 S01C
CI-22 S05C
CR-11 S01C
CR-12 S05C
CR-13 S01C
CR-14 S05C
HY-11 S01C
HY-12 S01C
HY-13 S01C
HY-14 SOBC
HY-15 S01C
HY-16 S01C
0.36
0.52
0.70
0.61
0.50
0.78
0.70
0.50
0.68
0.50
0.52
0.66
0.58
0.58
0.60
0.48
UO.l
0.18
0.44
0.34
0.34
0.38
0.48
0.28
0.30
0.70
0.86
1.1
1.2
1.2
1.4
1.0
0.96
0.94
0.78
0.80
0.28
0.22
0.13
UO.l
UO.l
0.14
1.7
1.1
1.2
1.0
1.1
12
26
31
35
25
36
34
33
36
21
28
18
21
19
16
15
22
5.4
7.0
13
8.0
7.6
12
26
8.4
9.6
14
21
25
30
33
33
20.
28.
30.
28.
29.
11.
8.0
2.4
3.8
3.8
3.8
100
40
60
32
40
79
15
20
21
21
18
21
21
15
18
18
17
17
18
19
19
18
8.2
12
17
17
18
22
32
22
23
24
42
44
44
47
57
33
48
46
50
50
23
16
5.6
6.8
7.3
7.8
24
28
28
22
22
25
0.23
0.27
0.28
0.25
0.25
0.28
0,27
0.20
0.25
0.23
0.22
0.23
0.25
0.24
0.26
0.23
0.12
0.16
0.22
0.19
E0.18
0.21
0.26
0.22
0.24
0.25
0.22
0.27
0.29
0.29
0.29
0.21
0.26
0.29
0.25
0.27
0.16
0.12
0.082
EO.ll
E0.073
E0.082
0.32
0.39
0.36
0.30
0.33
0.41
3.0
3.3
3.4
3.1
3.1
3.3
3.5
2.8
3.1
2.9
2.8
3.0
3.1
2.9
3.2
3.0
1.6
2.0
3.0
1.9
2.1
2.5
2.8
2.3
2.4
2.4
4.7
6.2
6.7
6.5
6.9
5.7
5.8
6.5
5.0
5.1
2.7
1.5
1.1
1.4
1.1
1.5
3.4
2.9
3.0
2.5
2.3
3.6
11
13
13
12
11
13
13
10
12
11
11
11
12
13
12
11
5.8
7.7
11
7.6
7.3
8.3
10
7.1
7.6
9.1
36
37
35
34
35
27
29
31
26
27
12
8.4
9.9
11
9.6
11
22
29
27
22
23
28
-------
MAIN. SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppin dry welyht
Drainage Survey Station Sample Rep Antimony Arsenic Barium
Beryllium Cadmium Chromium
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
i 1711U019-HY-000-
<7»17110019-HY-000-
W171100iy-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MD-OOO-
17110019-MD-OOO-
17110U19-MO-000-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-17 SU1C
HY-18 S01C
HY-19 S01C
HY-20 S01C
HY-21 S01C
HY-Z2 S05C
HY-23 S01C
HY-24 S01C
HY-25 S01C
HY-26 S01C
HY-27 S01C
HY-28 S01C
HY-29 S01C
HY-30 S01C
HY-31 S01C
HY-32 S01C
HY-33 S01C
HY-34 S01C
HY-35 S01C
HY-36 S01C
HY-37 S01C
HY-38 SO 1C
HY-39 S01C
HY-40 S01C
HY-41 S01C
HY-42 S01C
HY-43 S01C
HY-44 S01C
HY-45 S01C
HY-46 S01C
HY-47 S05C
HY-48 S01C
HY-49 S01C
HY-50 S05C
HY-51 S01C
MD-11 S01C
MO- 12 S05C
MD-13 S01C
MI-11 S01C
MI-12 S01C
MI-13 S01C
MI-14 S01C
MI-15 S01C
RS-11 S01C
RS-12 S01C
RS-13 S01C
RS-14 S05C
RS-15 S02C
1.5
1.5
3.4
1.0
0.82
1.0
0.88
0.96
0.96
0.70
1.1
1.4
0.88
0.78
0.60
1.1
0.50
0.70
0.68
0.74
1.0
0.86
0.38
0.68
0.44
0.72
0.62
UO.l
0.34
0.32
0.56
0.40
2.4
1.3
0.30
0.90
1.2
1.9
0.38
0.54
0.48
0.40
0.48
0.54
1.2
1.4
3.1
0.19
86
80
70
52
67
90
66
85
53
44
28
39
26
28
18
27
20
25
22
30
20
18
20
25
22
15
14
5.8
20
32
25
20
16
12
12
15
39
67
10
12
12
10
9.5
16
16
20
32
16
36
50
29
35
30
32
35
36
27
23
23
27
27
27
18
27
19
13
30
37
28
24
22
47
33
33
39
5.1
23
27
32
35
25
27
24
36
36
26
40
50
41
32
28
40
31
28
22
11
0.40
0.40
0.35
0.41
0.37
0.37
0.39
0.45
0.40
0.31
0.34
0.36
0.31
0.50
0.23
0.30
0.23
0.23
0.29
0.31
0.32
0.24
0.26
0.32
0.26
0.29
0.28
0.10
0.27
0.31
0.29
0.24
0.24
0.25
0.24
E0.16
E0.16
E0.16
0.27
0.28
0.27
0.27
0.25
E0.15
0.22
E0.15
0.22
E0.099
3.6
4.0
3.5
3.5
3.0
3.6
2.8
3.4
2.3
2.0
2.2
2.5
2.2
2.1
1.3
2.1
1.7
1.8
1.8
2.1
2.0
.5
.5
.9
.7
.8
1.6
0.62
1.8
2.3
1.8
1.4
2.6
2.8
1.3
3.4
3.4
3.5
1.4
2.0
1.9
1.6
1.5
2.2
2.2
2.b
3.1
1.4
29
35
27
32
30
31
26
37
30
21
25
26
22
24
16
24
23
18
22
24
23
17
16
22
18
20
20
5.4
14
19
21
14
9.9
9.0
12
18
15
14
13
14
13
15
12
12
16
18
16
15
-------
MAIN -SEDIMENT gUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep Antimony Arsenic Barium Beryllium Cadmium Chromium
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-S1-000-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OUO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-DP-OOO-
17110019-DP-OOO-
Msqs
Msqs
Msqs
Msqs
MSQS
MSQS
MSQS
MSQS
Msqs
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
RS-22 S01C -
RS-24 S05C -
SMI S05C -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 S01C -
SP-14 S01C -
SP-15 S05C -
SP-16 S05C -
WBS CTL -
UBS CTL -
7.8
190
420
36
1.8
205
b.3
26
1.3
0.82
0.70
1.1
0.46
0.44
0.54
0.72
0.66
0.13
0.26
02 Ul
01 Ul
136
12200
9700
1550
90
9000
85
700
93
33
28
23
10
6.0
7.0
12
7.0
7.4
5.5
2.8
2.1
23
56
71
102
15
153
14
103
19
18
18
26
21
11
16
19
50
12
17
10
9.9
E0.17
0.48
0.33
0.20
E0.18
0.55
0.10
0.28
0.22
0.21
0.21
0.18
0.25
0.13
0.17
0.20
0.18
0.12
0.18
0.12
0.11
3.4
76
184
16
3.0
105
2.2
9.6
4.4
3.2
2.3
1.6
1.6
1.3
1.7
2.4
2.7
0.82
0.86
0.24
0.22
14
18
23
12
15
46
7.6
15
13
11
11
13
11
10
14
18
21
6.7
8.1
18
17
Number of Observations: 117
-------
MAIM SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep Copper Iron Lead
Manganese Nickel Selenium
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
3, 17110019-BL-OOO-
i 17110019-HY-OOO-
5 17110019-CB-OOO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
Msqs
MSQS
MSQS
MSQS
Msqs
Msqs
MSQS
MSQS
Msqs
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
Msqs
Msqs
MSQS
Msqs
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 S05C
BL-12 S01C
BL-13 S05C
BL-14 S01C
BL-15 S01C
BL-16 S01C
BL-17 S01C
BL-18 S01C
BL-19 S01C
BL-20 S01C
BL-21 S05C
BL-22 S01C
BL-23 S01C
BL-24 S01C
BL-25 S05C
BL-26 SO 1C
BL-27 S01C
BL-28 S05C
BL-29 SO 1C
BL-30 S01C
BL-31 SU5C
BL-32 S01C
CB-11 S01C
CB-12 S01C
CB-13 S01C
CB-14 S01C
CI-11 S02C
CI-12 S01C
Cl-13 S05C
CI-14 S01C
CI-15 S01C
CI-16 S05C
CI-17 S05C
Cl-18 S01C
CI-19 S01C
CI-20 S05C
CI-21 S01C
CI-22 S05C
CR-11 S01C
CR-12 S05C
CR-13 S01C
CR-14 S05C
HY-11 S01C
HY-12 S01C
HY-13 S01C
HY-14 S05C
HY-15 S01C
HY-16 S01C
41
74
63
59
57
71
64
49
54
52
54
54
53
101
57
53
20
28
48
32
29
41
101
25
27
36
155
203
185
176
188
158
168
166
156
158
71
40
4.9
7.3
5.2
7.8
125
143
150
114
114
220
17300
18000
19500
16500
17000
18100
18600
14500
16600
16400
15200
16200
16900
15300
17200
16900
9150
11100
17500
11700
12200
14500
14800
12800
13700
14400
16100
16800
19200
19600
22700
15800
17900
17400
16200
17600
11100
8360
6230
8150
6530
6980
25000
24300
23100
19900
19300
22800
46
66
64
53
54
73
61
41
53
53
49
52
55
58
56
52
19
27
47
35
32
39
54
26
27
30
725
595
450
388
453
240
300
291
204
211
88
49
11
13
10
12
82
93
96
71
76
129
79
91
99
81
94
101
103
76
97
99
92
93
104
100
106
105
75
96
128
101
114
137
163
121
139
151
116
128
136
145
178
107
154
139
140
150
85
74
83
132
80
76
204
145
144
131
124
134
12
13
13
12
12
13
13
10
12
11
12
12
13
13
12
7.8
9.2
13
9.7
9.9
11
13
11
12
13
40
33
28
27
25
19
21
22
20
21
11
9.0
11
14
12
13
22
27
26
22
24
32
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul .0
Ul .0
Ul.O
Ul .0
Ul.O
Ul.O
Ul .0
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
-------
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep Copper Iron Lead
Manganese Nickel Selenium
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
*M7110019-HY-000-
o» 17110019-HY-OOO-
°» 17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
1711U019-MD-000-
17110019-MO-OOO-
17110U19-MD-000-
17110019-M1-000-
17110019-MI-UOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-17 SU1C
HY-18 S01C
HY-19 S01C
HY-20 S01C
HY-21 S01C
HY-22 S05C
HY-23 S01C
HY-24 S01C
HY-25 S01C
HY-26 S01C
HY-27 SU1C
HY-28 S01C
HY-29 SU1C
HY-30 S01C
HY-31 S01C
HY-32 S01C
HY-33 SU1C
HY-34 S01C
HY-35 S01C
HY-36 S01C
HY-37 S01C
HY-38 S01C
HY-39 S01C
HY-40 S01C
HY-41 S01C
HY-42 S01C
HY-43 SU1C
HY-44 S01C
HY-45 S01C
HY-46 S01C
HY-47 S05C
HY-48 S01C
HY-49 SOlC
HY-50 S05C
HY-51 SOlC
MD-11 SOlC
Ml)- 12 S05C
MO- I 3 SOlC
MI-11 SOlC
MI-12 SOlC
MI-13 SOlC
MI-14 SOlC
MI-15 SOlC
RS-11 SOlC
RS-12 SOlC
RS-13 SOlC
RS-14 S05C
RS-15 S02C
204
264
186
177
153
239
147
192
122
91
118
125
107
98
60
110
79
62
99
106
98
76
73
262
78
96
115
14
65
112
83
71
42
40
46
176
311
554
58
77
71
60
46
69
41
67
155
55
26700
29900
22700
25700
21500
25500
22000
26400
20900
17800
18000
20100
18000
22400
13700
17000
14800
13700
15600
17100
17000
14400
13700
16500
15000
15600
15000
6790
20100
23100
14800
13600
14000
14900
14800
14800
12800
13500
13900
15700
14700
15100
14200
10800
12900
10900
14600
8600
114
131
117
125
109
181
110
139
78
64
93
92
79
56
46
91
56
53
83
106
76
69
63
102
73
172
81
8.3
56
134
69
48
42
42
23
188
303
190
49
75
78
62
48
80
58
99
104
38
197
250
135
204
171
205
203
203
165
132
131
148
136
210
98
118
105
88
127
143
134
120
110
141
160
151
128
55
140
171
133
149
122
132
161
95
86
106
103
120
118
136
145
110
150
106
109
119
30
39
29
36
38
52
56
39
30
24
25
23
22
25
15
21
22
14
20
20
23
15
18
21
18
21
20
6.9
20
39
19
14
12
12
13
13
11
12
12
13
12
14
12
12
20
19
20
17
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
Ul.O
-------
MAIN SEDIMENT QUALITY SURVEY INOKGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep Copper Iron Lead
Manganese Nickel Selenium
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OUO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-OP-OOO-
17110019-OP-OOO-
i
en
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
RS-16
RS-17
RS-18
RS-19
RS-20
RS-21
RS-22
RS-24
SI-11
SI-12
SI-13
SI-14
SI-15
SP-11
SP-12
SP-13
SP-14
SP-15
SP-16
WBS
WBS
S01C -
S01C -
SO 1C -
S01C -
SO 1C -
S01C -
S01C -
S05C -
S05C -
S01C -
S01C -
S01C -
S05C -
S05C -
S05C -
S01C -
S01C -
S05C -
S05C -
CTL -
CTL -
458
8320
11400
2240
137
14300
87
385
292
191
158
148
74
65
56
82
275
32
29
02 7.2
01 5.2
12100
50000
52900
24000
15900
115000
10300
37100
14200
12800
12300
11700
12800
8310
10600
11900
9270
6650
10000
9900
9420
155
2680
6250
1020
78
4970
98
531
661
496
310
212
128
24
29
52
60
11
11
2.5
3.2
186
202
748
137
232
746
99
484
114
118
102
112
105
70
88
107
556
83
85
132
128
16
64
93
23
19
350
10
28
12
11
10
12
11
8.3
10
12
40
7.7
8.9
29
28
Ul.
26
24
1.
Ul.
2b
Ul.
Ul.
Ul.
Ul.
Ul.
Ul.
Ul.
Ul.
Ul.
Ul.
Ul.
Ul.
Ul.
Ul
Ul
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Number of Observations: 117
IV-10
-------
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep Silver Thallium Zinc Cyanide Cobalt Mercury
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UUO-
17110019-BL-OOO-
17110019-BL-OOU-
1711U019-BL-000-
J»17110019-HY-000-
<^17110019-CB-000-
0017H0019-CB-000-
1711U019-CB-000-
17110019-CI-OOU-
17110019-CI-OOO-
17H0019-C1-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
171100 19-CI-OOO-
17110019-CI-OOO-
17110019-Cl-OOO-
17110019-CI-UOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
MSQS
MSQS
Msys
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
Msqs
Msqs
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
BL-11 S05C
BL-12 S01C
BL-13 S05C
BL-14 S01C
BL-15 S01C
BL-16 S01C
8L-17 S01C
BL-18 S01C
BL-19 S01C
BL-20 S01C
BL-21 S05C
BL-2Z S01C
BL-23 SO 1C
BL-24 S01C
BL-25 S05C
BL-26 S01C
BL-27 SU1C
BL-28 SU5C
BL-29 S01C
BL-30 S01C
BL-31 S05C
BL-32 S01C
CB-11 S01C
CB-12 S01C
CB-13 S01C
CB-14 S01C
CI-11 S02C
CI-12 S01C
CI-13 S05C
Cl-14 S01C
CI-15 S01C
CI-16 S05C
CI-17 S05C
CI-18 S01C
Cl-19 S01C
Cl-20 SObC
CI-21 S01C
CI-22 S05C
CR-11 S01C
CR-12 S05C
CR-13 S01C
CR-14 S05C
HY-11 S01C
HY-12 S01C
HY-13 S01C
HY-14 S05C
HY-15 S01C
HY-16 S01C
0.17
0.30
0.14
0.22
0.26
0.18
0.19
0.18
0.24
0.22
0.17
0.40
0.22
0.34
0.20
0.20
UO.l
0.13
0.18
0.20
0.15
0.22
0.34
UO.l
0.10
0.11
UO.l
0.11
0.11
0.11
UO.l
UO.l
UO.l
0.11
0.12
0.13
0.44
0.40
UO.l
UO.l
UO.l
UO.l
0.24
0.26
0.26
0.24
0.22
0.26
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
0.10
0.11
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
uo.
uo.
uo.
uo.
uo.
uo.
0. 1
UO.l
0.20
0.24
UO.l
UO.l
UO.l
UO.l
UO.l
0.16
63
91
85
88
70
88
82
67
68
65
65
69
68
90
70
66
29
37
62
39
35
44
72
26
28
33
325
282
247
234
270
254
227
236
164
165
72
44
15
19
15
17
176
198
207
180
186
317
0.069
0.078
0.20
0.22
.0.20
0.15
0.16
0.18
0.21
0.20
0.13
0.10
0.15
0.099
0.15
0.12
0.051
0.070
0.082
0.094
0.085
0.11
0.14
0.053
0.063
0.066
0.53
0.45
1.1
0.10
0.28
0.11
0.11
0.96
0.20
0.24
0.32
0.22
0.055
0.098
0.049
0.034
0.078
0.46
0.39
0.33
0.048
0.17
-------
MAIN-SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep Silver Thallium Zinc
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
T* 17110019-HY-OOO-
0\ 17110019-HY-OOO-
*° 17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MD-OOO-
17110019-MD-OOO-
17110019-MD-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-M1-UOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOU-
17110019-RS-OOO-
17110019-RS-OOO-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-17 S01C
HY-18 S01C
HY-19 S01C
HY-20 S01C
HY-21 SU1C
HY-22 S05C
HY-23 S01C
HY-24 S01C
HY-25 S01C
HY-26 S01C
HY-27 S01C
HY-28 S01C
HY-29 S01C
HY-30 S01C
HY-31 S01C
HY-32 S01C
HY-33 S01C
HY-34 S01C
HY-35 S01C
HY-36 S01C
HY-37 S01C
HY-38 S01C
HY-39 S01C
HY-40 S01C
HY-41 S01C
HY-42 S01C
HY-43 S01C
HY-44 S01C
HY-45 S01C
HY-46 SU1C
HY-47 SU5C
HY-48 S01C
HY-49 S01C
HY-50 SU5C
HY-51 S01C
MD-11 S01C
MD-12 S05C
MD-13 S01C
MI-11 S01C
MI-12 S01C
MI-13 S01C
MI-14 SU1C
MI-15 S01C
RS-11 S01C
RS-12 S01C
RS-13 S01C
RS-14 S05C
RS-15 S02C
0.46
0.24
0.40
0.48
0.42
0.40
0.40
0.42
0.36
0.28
0.24
0.26
0.17
0.16
0.17
0.32
0.18
0.16
0.24
0.36
0.24
0.24
0.32
0.42
0.36
0.30
0.26
0.20
0.26
0.38
0.42
0.34
0.26
0.26
0.24
0.22
0.56
0.26
0.40
0.46
0.50
0.36
0.28
0.26
0.20
0.28
0.44
0.24
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
268
294
273
255
202
242
190
258
149
123
144
146
130
120
77
143
84
105
112
124
109
88
75
116
91
115
107
21
69
89
95
74
43
41
46
178
208
158
105
135
120
88
63
91
60
80
91
32
Cyanide Cobalt Mercury
0.30
0.39
0.32
0.28
0.056
0.50
0.40
0.49
0.27
0.25
0.32
0.28
0.28
0.22
0.19
0.38
0.23
0.21
0.30
0.38
0.22
0.22
0.22
3.2
0.34
0.31
0.18
0.20
0.32
0.44
0.21
0.13
0.11
0.11
0.13
0.18
0.32
3.4
0.18
0.17
0.16
0.16
0.12
0.32
0.30
0.39
0.30
0.12
-------
MAIN -SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep Silver Thallium Z1nc
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-UOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-RS-UOO-
17110019-RS-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OUO-
17110019-S1-000-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-OP-OOO-
17110019-OP-OOO-
i
o
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
RS-16 S01C -
RS-17 S01C -
RS-18 S01C -
RS-19 S01C -
RS-20 S01C -
RS-21 S01C -
HS-22 S01C -
RS-24 S05C -
SI-11 S05C -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 SU5C -
SP-11 S05C -
SP-12 S05C -
SP-13 S01C -
SP-14 S01C -
SP-15 S05C -
SP-16 S05C -
WBS CTL -
MBS CTL -
0.26
0.36
0.30
0.22
0.17
0.20
U.28
0.44
0.50
0.46
0.60
0.54
0.38
0.22
0.30
0.40
0.26
0.13
0.17
02 U0.2
01 U0.2
0.11
UO.l
3.2
0.46
UO.l
0.80
UO.l
UO.l
0.16
0.14
UO.l
UO.l
UO.l
0.11
0.14
0.16
0.12
UO.l
UO.l
U0.5
U0.5
103
2040
3320
906
140
4210
201
1620
491
337
254
205
109
60
62
106
125
29
30
21
20
Cyanide Cobalt Mercury
0.75
29
52
3.2
0.59
17
0.14
0.41
0.29
0.20
0.19
0.16
0.16
0.17
0.35
0.36
0.14
0.094
0.10
5.1 UO.l
5.1 UO.l
Number of Observations: 117
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT GRAIN SIZE
Drainage
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-UOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-8L-000-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-UOO-
1711U019-BL-UOU-
17110019-BL-OOU-
17110019-BL-OOU-
17110019-BL-UOO-
17110019-BL-OOO-
1711U019-BL-UOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
3>17110U19-BL-000-
^,17110019-BL-OOO-
•-17110019-BL-000-
17110019-HY-OOO-
1711UU19-CB-000-
17110019-CB-OOO-
17110U19-CB-000-
17110019-CI-OUO-
17110U19-CI-000-
17110019-CI-OOO-
17110019-CI-OOU-
17110019-CI-OOO-
17110019-CW-OOO-
17110019-C1-000-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CR-OOO-
17110019-CR-OUO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-CR-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
Msqs
Msqs
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station
BL-11
BL-12
8L-13
BL-14
BL-15
BL-16
BL-16
BL-17
BL-1B
BL-19
BL-20
BL-21
BL-22
BL-23
BL-24
BL-25
BL-26
BL-27
BL-28
BL-29
BL-29
BL-30
BL-31
BL-32
CB-11
CB-12
CB-13
CB-14
CI-11
CI-12
CI-13
CI-14
CI-15
CI-16
CI-17
CI-18
Cl-19
CI-2U
CI-21
CI-22
CR-11
CR-12
CR-13
CR-14
CR-14
HY-11
HY-12
Sample
S05C -
SOIC -
SOSC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOSC -
SOIC -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
SOIC -
SOIC -
soic -
S02C -
SOIC -
S05C -
SOIC -
SOIC -
SOSC -
S05C -
SOIC -
SOIC -
SOSC -
SOIC -
SOSC -
SOIC -
SOSC -
SOIC -
SOSC -
SOSC -
SOIC -
SOIC -
%
Rep Rocks
0
0
0
2
0
02 1
01 0
0
0
0
0
0
0
0
0
0
0
0
0
02 0
01 0
0
0
0
0
0
0
0
2
0
0
0
4
1
0
0
0
0
0
0
0
0
0
01 0
02 0
0
0
.210
.806
.478
.144
.809
.370
.127
.189
.020
.000
.000
.140
.810
.000
.115
.350
.174
.625
.575
.000
.166
.420
.258
.000
.363
.055
.334
.164
.377
.168
.063
.077
.933
.626
.221
.071
.178
.500
.218
.191
.361
.110
.095
.086
.000
.173
.166
*
Sand
44
13
15
23
17
10
8
11
48
13
24
35
35
32
10
11
33
75
62
24
24
42
39
21
15
30
18
16
58
29
21
12
32
24
27
29
16
19
57
71
95
87
92
76
78
30
21
.587
.908
.517
.600
.430
.311
.317
.524
.129
.579
.321
.761
.995
.400
.957
.830
.481
.463
.920
.166
.570
.318
.845
.017
.375
.482
.955
.393
.270
.629
.631
.519
.350
.723
.466
.192
.968
.782
.578
.784
.299
.076
.243
.022
.696
.255
.307
*
Silt
39
60
61
54
61
58
65
63
36
63
54
45
43
39
66
63
49
18
26
62
62
46
50
64
61
56
66
67
29
55
60
66
47
54
54
54
65
63
32
22
4
12
7
18
21
46
45
.347
.738
.660
.416
.256
.713
.714
.287
.431
.669
.604
.848
.403
.864
.032
.811
.638
.904
.548
.603
.119
.146
.258
.089
.138
.743
.426
.213
.387
.196
.071
.046
.581
.482
.637
.278
.669
.443
.918
.396
.340
.814
.662
.495
.304
.828
.984
1
Clay
15
24
22
19
20
29
25
25
15
22
21
18
19
27
22
24
16
5
9
13
13
11
9
14
23
12
14
16
9
15
18
21
15
19
17
16
17
16
9
5
0
0
0
5
0
22
32
.855
.548
.345
.840
.505
.606
.841
.000
.420
.752
.074
.251
.792
.735
.895
.009
.708
.008
.957
.231
.145
.117
.639
.894
.123
.719
.286
.230
.965
.008
.234
.359
.136
.168
.676
.459
.185
.275
.285
.629
.000
.000
.000
.396
.000
.745
.543
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT GRAIN SIZE
Drainage Survey Station Sample Rep Rocks Sand Silt Clay
17110019-HY-OOO-
17110019-HY-UOO-
1711U019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-OOU-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOU-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
•P.17110019-HY-000-
i 17110019-HY-OOO-
J^17110019-HY-000-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-UOO-
1711U019-HY-OUO-
17110019-HY-OOO-
1711UU19-HY-000-
17110019-HY-OOO-
17110019-HY-UOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-MD-UOO-
17110019-MO-OUO-
17110019-MD-OOO-
17110019-MO-OOO-
171100X9-MI-000-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Msqs
Msqs
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
Msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-13
HY-14
HY-15
HY-16
HY-17
HY-18
HY-18
HY-19
HY-20
HY-21
HY-22
HY-23
HY-24
HY-25
HY-26
HY-27
HY-28
HY-Z9
HY-29
HY-30
HY-31
HY-32
HY-33
HY-34
HY-35
HY-36
HY-37
HY-38
HY-39
HY-39
HY-40
HY-41
HY-42
HY-43
HY-44
HY-45
HY-46
HY-47
HY-48
HY-49
HY-50
HY-51
HY-51
MO- 11
MO- 11
MD-12
MO- 13
Ml-11
S01C -
505 C -
SO 1C -
S01C -
SU1C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
soic -
S01C -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
SOIC -
SOIC -
S05C -
SOIC -
SOIC -
0
1
0
17
5
01 2
02 0
0
0
1
2
0
0
0
6
9
6
01 1
02 4
0
2
5
52
4
0
1
0
1
01 11
02 8
0
0
0
6
2
1
6
0
1
3
0
01 0
02 0
01 0
02 3
0
35
0
.625
.857
.384
.134
.925
.525
.411
.770
.114
.118
.106
.390
.281
.765
.838
.374
.976
.895
.460
.000
.329
.284
.933
.892
.914
.887
.811
.737
.709
.080
.499
.820
.604
.121
.448
.018
.278
.388
.235
.512
.302
.164
.093
.078
.774
.653
.355
.048
23
50
31
38
27
26
25
36
12
18
22
13
18
8
36
27
31
35
34
18
47
33
26
46
28
31
21
45
44
45
19
38
21
36
91
30
31
21
39
13
13
20
20
25
38
43
40
13
.290
.208
.464
.596
.142
.573
.585
.062
.986
.392
.367
.160
.203
.563
.391
.760
.924
.213
.812
.323
.634
.568
.409
.379
.926
.855
.703
.264
.429
.517
.040
.020
.330
.725
.935
.365
.627
.324
.689
.929
.975
;526
.980
.039
.572
.205
.046
.951
43
29
40
27
40
41
44
41
55
54
50
67
50
57
38
44
45
42
40
47
34
40
13
36
46
44
51
39
30
32
55
42
55
38
5
48
55
55
41
62
66
61
60
57
43
46
17
62
.
t
t
962
619
532
.828
t
,
,
,
,
.
,
•
,
•
.
.
.
.
.
.
•
,
•
.
.
.
•
.
.
.
•
.
.
•
.
•
833
934
353
647
027
634
364
965
772
722
192
214
226
976
258
035
548
623
940
213
545
182
081
495
287
295
157
857
699
156
617
912
.043
.
.
.
663
492
798
.546
.
.
.
179
327
903
.912
.
.
.
268
543
500
32
18
27
16
26
28
29
21
31
25
25
18
30
32
18
18
15
19
20
34
15
20
6
12
23
22
26
13
13
14
25
18
22
18
0
19
7
22
17
19
19
18
18
16
13
9
7
23
.122
.316
.619
.442
.101
.969
.651
.521
.874
.856
.163
.485
.744
.951
.579
.652
.873
.917
.469
.642
.490
.525
.718
.516
.615
.075
.405
.504
.575
.108
.304
.303
.366
.998
.000
.706
.051
.625
.585
.762
.177
.131
.600
.980
.743
.874
.056
.501
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT GRAIN SIZE
Drainage
17110019-MI-OOO-
17110U19-MI-000-
17110019-MI-OOO-
1711U019-MI-UOO-
1711U019-RS-OUO-
17110019-RS-OOU-
17110019-RS-OOO-
17110019-RS-OOU-
•17110019-RS-OOO-
17110019-RS-OOO-
1711U019-RS-000-
1711UU19-RS-000-
17110019-RS-UOO-
17110019-RS-OOO-
17110019-RS-OOO-
1711U019-RS-OUO-
17110019-RS-OUO-
17110019-SI-OUO-
17110019-SI-OOO-
17110019-S1-000-
>17110019-SI-UOO-
' 17110019-SI-OOO-
W17110019-SP-000-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
17110019-SP-OOU-
17110019-SP-OOO-
17110019-DP-OOO-
1711U019-OP-000-
1711U019-DP-000-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station
MI-12
Ml-13
MI-14
MI-15
RS-11
RS-12
RS-13
RS-14
RS-15
RS-16
RS-17
RS-1B
RS-19
RS-20
RS-21
RS-22
RS-24
SI-11
SI-12
SI-13
SI-14
SI-15
SP-11
SP-12
SP-13
SP-14
SP-15
SP-16
WBS
WBS
MBS
Sample
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
SOSC -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
SObC -
S05C -
S01C -
S01C -
S01C -
S05C -
S05C -
SObC -
SOSC -
S01C -
S05C -
SOSC -
CTL -
CTL -
CTL -
*
Rep Rocks
0
0
0
0
3
1
0
29
0
12
9
7
7
5
1
38
8
0
0
0
1
1
6
3
1
1
1
0
01 0
02 0
03 0
.144
.103
.121
.000
.878
.723
.389
.864
.080
.157
.912
.744
.339
.193
.844
.860
.961
.068
.172
.081
.531
.155
.015
.140
.118
.500
.014
.000
.000
.000
.000
t
Sand
8
10
14
14
68
68
87
46
96
64
67
58
89
88
48
59
74
20
23
18
47
18
65
47
29
31
73
45
97
97
97
.833
.432
.095
.892
.839
.998
.043
.359
.259
.969
.661
.912
.469
.993
.593
.863
.976
.059
.747
.261
.859
.328
.887
.518
.538
.900
.085
.149
.188
.971
.274
%
Silt
63
64
68
69
18
21
12
12
3
14
14
25
3
5
36
1
11
62
59
65
40
61
19
40
57
50
25
46
2
2
2
.658
.234
.297
.155
.251
.220
.569
.456
.661
.292
.666
.209
.192
.814
.375
.277
.828
.754
.367
.779
.848
.876
.746
.175
.223
.750
.901
.759
.812
.029
.726
*
Clay
27
25
17
15
9
8
0
11
0
8
7
8
0
0
13
0
4
17
16
15
9
18
8
9
12
15
0
8
0
0
0
.365
.231
.486
.953
.031
.060
.000
.321
.000
.582
.762
.134
.000
.000
.188
.000
.235
.119
.714
.880
.762
.641
.352
.166
.121
.850
.000
.092
.000
.000
.000
Number of Observations: 126
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT CONVENTIONALS
Total
Drainage Survey Station Sample Rep Solids
Total Total
Volatile Oryanic Carbon-
Sol ids % Carbon J ate *
17110019-BL-OUO-
1711U019-BL-000-
17110U19-BL-000-
17110019-Bl-OOO-
17110U19-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-Bl-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-UOU-
17110019-BL-OOO-
1711UU19-BL-UUO-
>17110019-BL-000-
' 17110019-BL-UOO-
!^17110019-BL-000-
17110U19-HY-OUO-
17110019-CB-OUO-
17110019-CB-OOO-
17110019-CB-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-Cl-OOO-
1711UU19-CI-UOU-
17110019-CW-OOO-
17110019-CI-OUO-
17110019-CI-OOO-
17110019-CI-OOO-
17110019-CI-OOO-
17110U19-CI-UOO-
17110019-C1-OUO-
17110019-CW-OUO-
17110019-CR-OOU-
17110U19-CR-000-
17110019-CR-OOO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
\711OO19-HV-OOO-
MSQS
MSQS
MSqS
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
Msgs
Msgs
MSQS
Msgs
MSQS
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSU.S
MSUS
BL-11 S05C
BL-12 S01C
BL-13 SObC
BL-14 S01C
BL-15 S01C
BL-16 S01C
BL-17 SU1C
BL-18 S01C
BL-19 S01C
BL-20 SO 1C
BL-21 S05C
BL-22 S01C
BL-23 S01C
BL-24 S01C
BL-25 S05C
BL-26 S01C
BL-27 S01C
BL-28 S05C
BL-29 S01C
BL-30 S01C
BL-31 SObC
BL-32 SO 1C
CB-11 S01C
CB-12 S01C
CB-13 S01C
CB-14 S01C
CI-11 S02C
CI-12 S01C
CI-13 S05C
CI-14 S01C
Cl-lb S01C
CI-16 S05C
CI-17 SOBC
CI-18 S01C
CI-19 S01C
CI-20 S05C
CI-21 SU1C
CI-22 S05C
CR-11 S01C
CR-12 SObC
CR-13 S01C
CR-14 S05C
HY-11 S01C
HY-12 S01C
HY-13 S01C
HY-14 S06C
55.5
48.6
49.2
48.8
53.1
47.9
49.6
59.5
53.6
56.5
59.9
59.6
56.1
49.3
54.2
55.5
78.7
73.2
63.4
64.5
65.8
60.8
56.7
66.4
64.3
63.6
45.3
42.0
38.2
35.0
40.6
28.6
41.8
42.8
41.7
44.7
57.7
70.4
75.7
75.6
76.8
73.8
42.9
41.2
40.8
bl .b
3.2
4.9
5.1
5.2
3.8
5.0
4.4
3.1
3.7
3.4
3.2
3.2
3.8
4.1
3.4
3.4
1.4
1.9
2.6
2.5
2.7
3.4
5.0
3.4
3.3
3.4
13.5
12.3
11.4
11.0
11.0
17.3
9.8
10.3
8.8
8.5
0.3
3.2
0.9
1.0
0.77
1.2
16.0
11.7
12.3
8.4
1.29
2.21
2.03
2.12
1.39
1.96
1.71
.03
.40
.35
.14
.27
.49
.76
.47
.43
0.48
0.73
1.12
1.09
1.09
1.42
2.46
1.28
1.27
1.24
8.86
7.71
6.50
6.24
5.94
10.9
5.64
5.94
4.90
4.59
2;82
1.21
0.35
0.26
0.19
0.43
6.81
5.72
5.49
4.51
Hydrogen Nitrogen
% *
0.16
0.19
0.088
0.10
O.U63
0.10
0.10
0.062
0.082
0.077
0.064
0.069
0.089
0.10
0.083
0.087
0.046
0.047
0.072
0.067
0.066
0.082
0.71
0.076
1.15
0.073
0.35
0.34
0.29
0.34
0.28
0.49
0.27
0.22
0.22
0.20
0.13
0.16
0.24
0.035
0.024
0.052
0.25
0.21
0.^1
0.17
-------
MAIN SEDIMENT QUALITY SURVEY SEUIMENT CONVENTIONALS
Total
Urainaye Survey Station Sample Rep Solids
Total Total
Volatile Organic Carbon- Hydroyen
Sol ids % Carbon % ate % t
1711U019-HY-000-
17110019-HY-OUO-
1711UU19-HY-000-
17110019-HY-OUO-
17110019-HY-OUU-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-UOO-
17110019-HY-OOO-
17110U19-HY-UOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OOO-
17110019-HY-OOO-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110U19-HY-000-
17110019-HY-OOO-
1711UU19-HY-000-
1711U019-HY-000-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OOO-
17110019-HY-OUO-
17110019-HY-OUO-
17110019-HY-OUU-
17110019-HY-OOO-
17110U19-HY-000-
1711UU19-HY-000-
17110019-MD-OOU-
17110019-MO-OOO-
17110019-MO-OOO-
17110019-MI-000-
17110019-M1-000-
17110019-MI-OOO-
17110019-MI-OOO-
17110019-M1-000-
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-15 S01C
HY-16 S01C
HY-17 SU1C
HY-18 S01C
HY-19 S01C
HY-20 S01C
HY-21 S01C
HY-22 S05C
HY-23 S01C
HY-24 S01C
HY-25 S01C
HY-26 S01C
HY-27 S01C
HY-28 S01C
HY-29 S01C
HY-30 S01C
HY-31 S01C
HY-32 S01C
HY-33 S01C
HY-34 S01C
HY-35 S01C
HY-36 S01C
HY-37 S01C
HY-38 S01C
HY-39 S01C
HY-40 S01C
HY-41 S01C
HY-42 S01C
HY-43 S01C
HY-44 S01C
HY-45 S01C
HY-46 S01C
HY-47 S05C
HY-48 S01C
HY-49 S01C
HY-bU S05C
HY-51 S01C
MU-11 SU1C
MO- 12 S05C
MO-13 SU1C
MI-11 S01C
MI-12 S01C
MI-13 S01C
MI-14 SU1C
Ml-15 S01C
49.0
38.7
39.1
37.2
33.8
40.1
37.1
37.6
36.9
38.0
45.1
bl.8
46.5
38.5
47.6
40.6
59.9
51.1
59.3
52.6
52.6
47.8
51.5
47.7
61.4
51.4
57.8
49.7
55.2
78.4
54.5
51.6
51.8
63.4
5b.5
b6.9
59.9
38.8
49.2
63.0
55.9
53.3
57.4
5b.7
60.7
8.0
25.9
12.2
15.1
20.9
11.0
11.4
11.3
10.5
11.4
7.8
7.5
8.7
14.6
8.6
8.0
4.4
8.1
6.1
10.8
6.6
7.7
6.4
9.4
4.2
6.7
5.4
6.3
5.7
1.3
5.2
6.5
6.5
4.6
6.0
5.7
4.8
11.4
6.8
5.3
8.8
10.1
11.0
b.3
4.7
3.78
12.21
5.22
6.39
9.01
4.45
4.59
4.44
3.78
5.12
3.15
3.24
2.24
3.10
2.84
2.28
1.65
3.83
1.81
6.47
2.62
3.74
2.48
4.09
1.57
2.25
1.96
2.39
2.89
0.26
1.55
0.43
1.84
2.39
2.36
2.25
1.81
7.27
4.04
7.26
2.30
2.26
2.17
1.97
1.52
N1troyen
0.15
0.22
0.18
0.22
0.24
0.19
0.19
0.18
0.16
0.22
0.17
0.13
0.13
0.13
0.12
0.11
.092
0.12
0.075
0.16
0.12
0.15
0.12
0.14
0.079
0.11
0.10
0.12
0.11
0.021
0.085
0.11
0.89
0.12
0.11
0.11
0.091
0.44
0.16
0.04
0.11
0.13
0.12
0.11
0.076
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT CONVENTIONALS
Ot
Drainage
17110019-RS-OOU-
17110019-RS-OOO-
17110U19-RS-UOO-
17110019-RS-OOO-
17110019-RS-OOO-
17110U19-RS-000-
17110U19-RS-OUO-
1711UU19-RS-000-
1711U019-RS-000-
17110019-RS-OOO-
17110U19-RS-000-
17110019-RS-OOO-
17110019-RS-OOO-
17110019-S1-000-
17110019-S1-000-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SI-OOO-
17110019-SP-OOO-
17110019-SP-OOO-
1711U019-SP-000-
17110019-SP-OOO-
17110019-SP-OUU-
17110019-SP-OOO-
17110U19-UP-000-
17U0019-OP-OUO-
Survey
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
Station Sample Hep
RS-11 S01C -
RS-12 S01C -
RS-13 S01C -
RS-14 S05C -
RS-15 S02C -
RS-16 SO 1C -
RS-17 S01C -
RS-18 S01C -
RS-19 SU1C -
RS-20 SO 1C -
RS-21 S01C -
RS-22 S01C -
RS-24 S05C -
SI-11 S05C -
SI-12 S01C -
SI-13 S01C -
SI-14 S01C -
SI-15 S05C -
SP-11 S05C -
SP-12 S05C -
SP-13 S01C -
SP-14 S01C -
SP-15 S05C -
SP-16 S05C -
MBS CTL - 01
WBS CTL - 02
Total
Solids
I
72.4
68.3
60.1
40.6
76.3
30.8
59. b
42.0
79.4
77.3
42.1
85.3
66.9
60.0
62.2
62.4
68.1
61.5
57.2
55.0
46.8
30.5
6b.8
64.4
80.6
80.2
Total
Volatile
Solids %
3.7
4.8
9.1
22.2
1.2
28.6
3.9
19.6
1.0
1.5
11.2
0.8
2.7
4.8
3.7
4.1
3.7
4.9
7.9
8.6
13.2
44.7
4.3
3.6
0.56
0.57
Total
Organic Carbon
Carbon i ate I
2.35
2.57
0.69
15.1
0.36
20.5
1.90
8.83
0.58
0.28
3.13
0.28
0.80
2.10
1.56
1.79
1.61
2.46
3.50
4.67
5.67
16.0
2.06
1.47
0.07
0.17
Hydrogen Nitrogen
% I
0.59
0.087
0.11
0.28
0.028
0.29
0.079
0.20
0.032
0.024
0.16
0.020
0.070
0.11
.095
1.60
.084
0.13
0.12
.16
.19
.79
.084
1.19
Q0.01
Number of Observations: 117
a. Reference:
Tetra Tech, Inc. 1985. Commencement Bay nearshore tideflats remedial
investigation. Final Report Prepared for Washington Department of Ecology and
U.S. EPA by Tetra Tech, Inc. Bellevue, WA.
-------
Table A-2. COMENCEMENT BAY - BLAIR WATEFWAY DREDGING STUDY
STATION*
2 BOS
2 804
2 BD9
2 310
2 B12
TOX BENTHIC MICRO
CODE CODE CODE
1 C
1 0
1 D
a
o
The 6 stations listed on this page have biological effects data and are used for
this report. Additional stations and associated chemical data are include on
subsequent pages of this Table A-l. Where replicate data have been provided,
the mean value is used for calculations..
A-77
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in ppb dry weight
PHENOLS
2,4-dl-
methyl -
Drainage Survey Station Sample Rep phenol phenol
3>
I
00
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17H0019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
ORS001
URS001
URS001
URS001
URS001
URS001
URS001
UHS001
URS001
ORS001
URS001
URS001
URS001
URS001
URS001
ORS001
ORS001
URS001
ORS001
ORS001
ORS001
URS001
B02
802
803
804
804
804
804
804
BO 7
807
809
B09
BIO
BIO
811
811
812
B14
815
B17
817
818
S01C
S01C
S05C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S05C
S05C
S05C
S05C
S01C
S01C
S05C
S01C
S05C
S01C
S01C
S01C
010
Z40
01 Z65
02 Z29
03 Z43
04 010
05 010
U10
010
4.8
4.2
9.8
U2
010
010
010
01
02
02
01
01
02
020
Z400
Z370
Z4
Z22
Z56
Z53
111
Z34
Z30
020
2.3
Ul
02
01
3.0
7.2
02
2.3
U2
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in ppb dry weight
PHENOLS
2- 4-
methyl- methyl -
Drainage Survey Station Sample Rep phenol phenol
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
URS001
URS001
URS001
URSOU1
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
802
803
804
804
804
B04
B04
807
809
810
810
Bll
B12
814
BIS
B17
817
B18
S01C
SObC
SO 1C
S01C
S01C
S01C
S01C
S01C
S05C
S05C
S05C
S01C
S05C
S01C
S05C
S01C
S01C
S01C
.
-
.
-
.
-
.
.
-
.
-
-
-
-
.
.
.
.
01
02
03
04
05
01
02
01
01
02
U10
U10
U10
U10
U20
U10
160
240
110
92
vo Number of Observations:
18
-------
8LAIR UREOING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS
SUBSTITUTED PHENOLS
- Values 1n ppb dry weight
Drainage
oo
o
17110019-
17110019
17110019-
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
1711U019
17110019
17110019
17110019
•BL-000-
•8L-000-
•BL-000-
-BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-8L-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
Survey Station Sample Rep
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
UKS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
802
802
BU3
B04
804
804
804
804
B07
B07
809
809
810
810
811
811
812
B14
815
817
817
818
S01C
S01C
S05C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S05C
S05C
505 C
S05C
S01C
S01C
S05C
S01C
S05C
S01C
S01C
S01C
?p
01
02
03
04
05
01
02
02
01
01
02
2-
chloro-
phenol
U5
01
U2
U2
U2
05
U5
05
U10
01
01
03
01
02
03
Ul
01
U2
2,4-di-
chloro-
phenol
010
02
U4
04
US
U10
010
010
020
02
01
06
02
02
U4
U5
01
03
4-chloro-
3-methyl
phenol
010
6.1
Ul
81
02
010
U10
010
020
02
01
U2
Ul
Ul
Ul
U2
Ul
Ul
2,4.6-
tri-
chloro-
phenol
U10
01
01
01
03
U10
U1U
010
U20
Ul
01
02
01
Ul
Ul
Ul
Ul
Ul
penta-
chloro-
phenol
U25
S
02
U6
. 03
31
50
U25
UbO
Ul
Ul
U3
U2
Ul
Ul
Ul
01
U2
2-nitro-
phenol
010
U2
U5
U3
U5
U10
U10
010
U20
U2
01
06
U2
U3
05
02
Ul
04
2,4-
di-
ni tro-
phenol
0100
U150
U2
U150
060
U60
U190
uyo
U90
0100
U130
U60
UBO
4,6-
di-
ni tro-o-
cresol
U100
U5
U60
U2
0100
U100
U100
0100
0100
U30
U30
U100
U40
U40
050
070
01
U40
4-nitro
phenol
0100
070
U1300
020
U1500
U100
U100
U100
U200
U6
U20
U1900
09
U23
U22
01300
U13
080
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in pph dry weight
LOW MOLECULAR WEIGHT AROMATIC HYDROCARBONS
2-
methyl
naphth- naphtha- acenaph- acena
alene lene thylene thene
Drainage
Survey Station Sample Rep
fluorene
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
*• 17110019-BL-OOO-
oo 17110019-BL-OOO-
1-1 17110019-BL-OOO-
17110019-BL-OOO-
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
UHSU01
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
B02
B02
BU3
BO 4
B04
BO 4
B04
B04
B07
BO 7
B09
B09
BIO
BIO
Bll
Bll
812
B14
B15
B17
817
B18
S01C -
SO 1C -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
SO 1C -
SObC -
S05C -
S05C -
S05C -
S01C -
S01C -
S05C -
SO 1C -
S05C -
SO 1C -
S01C -
SO 1C -
01
02
03
04
05
01
02
02
01
01
02
75
50
67
110
100
240
180
100
100
46
51
27
24
65
44
27
34
48
Z590
200
680
45
710
Z970
Z570
B5
85
81
80
79
110
310
210
66
62
370
20
15
13
20
35
70
80
40
40
5.9
6.0
11
7.8
17
14
2.0
11
5.0
13
44
140
220
310
230
240
52
86
10
11
14
19
76
93
14
12
140
20
63
150
190
320
210
230
84
140
16
17
22
27
92
120
15
16
110
78
330
540
690
1400
44U
430
290
280
110
120
150
170
430
570
97
82
350
33
130
260
310
720
230
260
120
84
18
22
65
48
130
290
28
30
95
Number of Observations:
22
-------
BLAIK DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in ppb dry weiyht
HIGH MOLECULAR WEIGHT PAH
Drainage
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019-
17110019
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
^ 17110019-
00 17110019-
1X0 17110019-
17110019
-BL-OUU-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
BL-000-
BL-000-
BL-000-
BL-000-
BL-000-
•BL-000-
fluor-
Survey Station Sample Rep anthene
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URSU01
URS001
URS001
URS001
URSOU1
URS001
URS001
URS001
UHS001
URS001
URS001
URS001
URS001
URS001
B02
BO 2
803
B04
B04
804
B04
B04
BO 7
BO 7
B09
BU9
BIO
BIO
Bll
Bll
B12
B14
BIS
B17
817
B18
SO 1C
SO 1C
S05C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
S05C
S05C
S05C
S05C
S01C
S01C
S05C
S01C
S05C
S01C
S01C
S01C
01
02
03
04
05
01
02
02
01
01
02
210
480
1400
1900
2900
2200
2300
670
910
120
120
250
320
670
1100
160
140
470
pyrene
220
470
1100
1700
2100
1600
1800
580
640
110
110
240
260
550
730
150
130
310
benzo(a)
anthra-
cene
82
160
680
710
880
810
750
230
310
41
42
110
110
220
340
70
62
140
chrysene
160
400
680
1300
1600
980
1100
410
350
81
92
300
260
430
720
130
110
190
benzo(b)
f luor-
anthenq
C
C
630
C
C
C
C
C
C
C
C
C
C
C
380
C
87
C
benzo(k )
f luor-
anthene
C
C
580
C
C
C
C
C
C
C
C
C
C
C
300
C
72
C
benzofa )
pyrene
120
140
540
570
750
850
860
360
360
45
49
120
130
210
250
73
69
110
i ndeno
(1.2,
3-cd)
pyrene
55
120
310
330
510
280
390
130
150
Ul
29
86
b8
130
150
55
48
52
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEOIMENT ORGANIC CHEMICALS - Values In ppb dry weight
HIGH MOLECULAR HEIGHT PAH
Dralnaye
dlbenzo-
(a,h)an-
Survey Station Sample Rep thracene
J,
1
CO
CJ
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UUO-
17110U19-BL-000-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
1711UU19-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
URS001
URS001
URSU01
URSU01
URS001
URS001
URS001
URSU01
URSOU1
URS001
ORS001
URS001
URS001
URS001
URS001
URSOU1
URS001
UK SOU1
URS001
URS001
URS001
URS001
B02
BO 2
BO 3
B04
B04
804
B04
B04
807
BO 7
B09
B09
BIO
BIO
Bll
Bll
B12
B14
BIS
817
B17
B18
S01C
S01C
S05C
S01C
SO 1C
S01C
S01C
S01C
S01C
SO 1C
S05C
S05C
S05C
S05C
S01C
S01C
S05C
S01C
S05C
S01C
SO 1C
S01C
_
.
.
.
-
.
.
-
.
-
.
-
-
.
-
-
-
.
.
-
-
-
01
02
03
04
05
01
02
02
01
01
02
16
45
130
120
160
93
110
32
56
Ul
14
40
Ul
44
50
19
6.6
9.8
57
110
310
320
470
250
320
130
140
25
26
83
Ul
120
130
23
41
45
240
550
1700
2400
1300
1400
bUU
490
110
110
490
330
640
730
260
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in ppb dry weight
CHLORINATED AKOMATIC HYDROCARBONS
Dralnaye
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
7 17110019
0017110019
*" 17110019-
17110019-
-BL-000-
-BL-000-
-BL-000-
-8L-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
-BL-000-
•BL-OUO-
•BL-000-
BL-000-
•BL-000-
BL-UOO-
BL-000-
BL-000-
BL-000-
BL-000-
BL-000-
1,3-di-
chloro-
Survey Station Sample Rep benzene
URS001
URS001
URS001
URS001
URS001
ORS001
URS001
ORS001
URS001
URS001
UK SOU 1
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
802
602
803
804
804
B04
804
804
807
807
809
B09
BIO
810
811
Bll
812
814
B15
817
B17
818
S01C
S01C
S05C
SO 1C
S01C
S01C
S01C
S01C
S01C
S01C
S05C
S05C
S05C
SOSC
S01C
S01C
SOSC
S01C
SObC
S01C
S01C
S01C
U10
25
01 Ul
02 U2
03 Ul
04 130
Ob 17U
U5
U5
01 Ul
U2 Ul
02
01 U2
Ul
Ul
Ul
01 Ul
02 Ul
Ul
1,4-di-
chloro-
benzene
U10
21
21
32
25
110
63
U5
U5
Ul
Ul
U2
Ul
Ul
Ul
Ul
Ul
Ul
1,2-di-
chloro-
benzene
U10
50
Ul
Ul
Ul
U5
U5
U5
U5
Ul
Ul
U2
Ul
Ul
Ul
Ul
Ul
Ul
1,2,4-
tri-
chloro-
benzene
U5
Ul
Ul
Ul
Ul
U5
U5
U5
U5
Ul
Ul
U2
Ul
Ul
U2
Ul
Ul
Ul
2-
chloro-
naph-
thalene
US
Ul
Ul
Ul
Ul
U5
U5
U5
U5
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Ul
hexa-
chloro-
benzene
U10
Ul
Ul
Ul
U30
U10
U10
U10
U10
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in ppb dry weight
CHLORINATED ALIPHATIC HYDROCARBONS
Drainage
hexa-
chloro-
Survey Station Sample Rep ethane
hexa-
chloro-
buta-
diene
17110019-BL-OUU-
17110019-BL-UUU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
j= 17110019-BL-OOO-
• 17110019-BL-OOO-
U> 17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
URS001
URS001
URS001
UK SOU1
URSU01
URSUU1
URSU01
URSU01
URS001
URS001
URSOU1
URS001
URSU01
URS001
URS001
URS001
URS001
URS001
URSU01
URS001
URS001
URSU01
BO 2
BU2
BO 3
B04
B04
•BU4
B04
B04
B07
B07
B09
B09
BIO
BIO
Bll
Bll
B12
814
B15
817
817
B18
S01C -
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
S05C -
S05C -
S01C -
S01C -
S05C -
S01C -
S05C -
S01C -
S01C -
S01C -
01
02
03
04
05
01
02
02
01
01
02
U50
04
U5
080
U6
U50
050
UbO
U50
020
020
080
04
030
U40
05
025
030
025
02
02
04
03
025
025
025
025
02
01
06
02
03
04
02
01
03
hexa-
chloro-
cyclo-
penta-
diene
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values In ppb dry weight
PHTHALATES
Drainage
Survey Station Sample Rep
171 1001 9-BL-OOO-
1711U019-BL-000-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-UOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110U19-8L-UOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-Bt-OOO-
3>17110019-BL-000-
' 17110019-BL-OOO-
S17110019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
URS001
URSU01
URS001
URS001
UHS001
URS001
URS001
URS001
URS001
UHS001
URS001
URS001
UKS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
UKS001
URS001
802
BO 2
BO 3
804
B04
804
804
804
B07
BO 7
809
B09
BIO
BIO
Bll
Bll
B12
814
815
817
817
818
S01C
S01C
S05C
S01C
S01C
S01C
S01C
S01C
S01C
S01C
SObC
S05C
S05C
S05C
S01C
S01C
S05C
S01C
S05C
S01C
S01C
S01C
dimethyl diethyl
phtha- phtha-
late late
B50
24
01 29
02 48
03 23
04 850
05 Z47
B50
Z160
01 Ul
02 Ul
02
01 9.7
15
40
41
01 Ul
02 2.6
5.6
U10
Z73
Ul
Ul
Ul
U10
U10
U10
U10
Ul
1.9
8.8
4.7
4.8
2.9
Ul
Ul
1.6
di -n-
butyl
phtha
late
Z67
Z3U
Z170
Z43U
Z26U
Z34
Z64
U10
Z1300
81
Z40
81
1260
Ul
Bl
Z60
61
Z120
butyl
benzyl
phtha-
late
73
Z46
Z19
Z42
Z420
61
81
26
U25
122
Z60
Z160
Z50
Z17
Z14
Bl
Z3
Z80
bis(2-
ethyl-
hexyl )-
phtha-
late
Z380
Z13UO
Z580
Z370
Z1300
Z760
1200
Z230
Z180
Z110
Z80
Z600
Z330
Z260
Z240
Z10
Bl
Z160
di-n-
octyl
phtha
late
U25
Zl?
Ul
Z22
Ul
U25
U25
U25
420
Z7
Z5
Z10
Z6
Z10
26U
Z6
Ul
Bl
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values 1n ppb dry weight
MISCELLANEOUS OXYGENATED COMPOUNDS
benzyl
Drainage Survey Station Sample Rep alcohol
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
1711U019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-8L-000-
17110019-BL-OOO-
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URSOU1
URS001
URS001
802
803
604
804
BO 4
B04
804
807
809
810
810
811
812
814
B15
817
817
B18
S01C -
SObC -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
S05C -
S01C -
S05C -
S01C -
S05C -
SU1C -
S01C -
S01C -
01
02
03
04
05
01
02
01
01
02
U10
010
U10
U10
U10
025
025
U25
U25
U25
benzole dlbenzo-
acld furan
25
190
220
80
IK)
i Number of Observations:
oo
18
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values 1n ppb dry weight
ORGANONITROGEN COMPOUNDS
Drainage
00
00
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019
17110019-
17110019-
17110019-
17110019
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
BL-000-
BL-000-
BL-000-
•BL-000-
BL-000-
•BL-000-
BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
•BL-000-
BL-000-
BL-000-
BL-000-
BL-000-
Survey Station Sample Rep
ORS001
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
URSOOl
802
802
803
B04
804
804
B04
BO 4
807
807
809
809
BIO
BIO
811
811
812
814
815
817
817
B18
S01C
S01C
S05C
SO 1C
S01C
S01C
S01C
S01C
S01C
SO 1C
S05C
S05C
S05C
S05C
S01C
SU1C
S05C
S01C
S05C
SO 1C
S01C
S01C
!P
01
02
03
04
05
01
02
02
01
01
02
nitro-
benzene
U5
Ul
Ul
Ul
Ul
U5
U5
U5
U5
Ul
Ul
U2
Ul
Ul
Ul
Ul
Ul
Ul
N-
nitroso-
dlpropyl-
amine
U10
Ul
Ul
U2
Ul
U10
U10
U10
U10
Ul
Ul
U2
13
Ul
Ul
Ul
4.5
Ul
2,6-d1-
ni tro-
toluene
U10
Ul
U2
Ul
U20
U10
U10
U10
U10
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Ul
2,4-d1-
ni tro-
toluene
U5
U10
U10
U2
Ul
U5
U5
U5
U5
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Ul
Ul
N-
nltroso-
diphenyl -
amine
U5
24
Ul
46
37
U5
U5
U5
U5
6.7
Ul
9.9
13
15
20
Ul
3.9
5.0
1,2-di-
phenyl -
hydra- benzi
zine dine
U5
Ul
Ul
Ul
Ul
U5
U5
U5
U5
Ul
6.1
Ul
Ul
Ul
Ul
Ul
Ul
Ul
3,3'-di- N-
chloro- nitroso-
benzi- dimethyl
di ne attiine
U100
U100
U100
U100
U100
Number of Observations:
22
-------
BLAIR OHEOGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values In ppb dry weight
PESTICIDES I
Drainage
Survey Station Sample Rep 4,4'-DDE 4,4'-DDD 4,4'-DDT aldrln dieldrin a-HCM
b-HCH
d-HCH
g-HCH
17110019-Bl-OOO-
17110019-BL-OUO-
1711U019-BL-UOO-
17110019-81-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-Bl-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
^ 17110019-BL-OOO-
oo
vo
URS001
URS001
URS001
URSOD1
URS001
URS001
URS001
URS001
URS001
URSUOl
URS001
URSUOl
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
BO 2
B02
BU3
B04
B04
B04
804
B04
BO 7
BO 7
B09
B09
BIO
BIO
Bll
Bll
B12
B14
B15
817
817
B18
S01C -
SO 1C -
S05C -
SO 1C -
SU1C -
S01C -
S01C -
S01C -
S01C -
S01C -
SU5C -
S05C -
S05C -
S05C -
S01C -
S01C -
S05C -
S01C -
S05C -
SO 1C -
S01C -
S01C -
01
02
03
04
05
01
02
02
01
01
02
UbO
U0.02
0.27
O.bb
1.7
UbO
UbO
UbO
UbU
U0.01
UO.Ul
UU.02
U0.01
U0.02
0.73
U0.01
U0.01
0.12
UbO
UO.Ob
U0.04
3.1
0.8
UbO
UbU
UbO
UbO
U0.04
U0.04
U0.04
U0.04
U0.04
1.7
U0.03
U0.03
1.3
UbO
1.3
2.2
7.1
2.b
UbO
UbO
UbO
UbO
U0.04
1.6
UO.Ob
1.4
3.0
b.8
1.2
U0.03
Ib
UbO
U0.02
U0.01
U0.01
IJ0.01
UbO
UbO
UbU
UbO
U0.01
U0.01
U0.01
U0.01
U0.01
0.44
U0.01
U0.01
U0.01
UbO
U0.02
U0.01
U0.01
U0.01
UbO
UbO
UbO
UbO
U0.01
U0.01
U0.01
U0.01
U0.01
U0.01
U0.01
U0.01
U0.01
UbO
U0.02
U0.02
U0.02
UO.U2
UbO
UbO
UbO
UbO
U0.01
U0.01
U0.02
U0.01
U0.02
U0.01
U0.01
U0.01
U0.01
UbO
UO.Ob
UO.Ob
00 .05
UO.Ob
UbO
UbO
UbO
UbO
UO.U4
U0.04
UO.Ob
UO.Ob
UO.Ob
00 .Ob
U0.03
U0.03
U0.03
UbO
00.03
IJ0.03
UO.OB
IJ0.03
UbO
UbO
UbO
UbO
U0.03
00.03
00.03
00.03
U0.03
U0.03
00.02
U0.02
U0.02
UbO
UO.U2
U0.02
U0.02
U0.02
UbO
UbO
UbO
UbO
U0.02
U0.02
UO .02
UO.U2
UO .02
U0.02
00.01
U0.01
00.01
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values 1n ppb dry weiynt
PCBS
Total
Dralnaye
Survey Station Sample Rep PCB-1016 PCB-1221 PCB-1232 PCB-1242 PCB-1248 PCB-1254 PCB-1260 PCBs
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17HOU19-BL-000-
17110019-BL-OOO-
1711U019-8L-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-8L-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
3=. 17110019-BL-OOO-
^ 17110019-BL-OOO-
o
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URS001
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
URSOOI
B02
802
B03
B04
B04
B04
804
B04
B07
BO 7
809
809
810
BIO
611
Bll
812
B14
815
817
817
B18
SO 1C
S01C
S05C
sole
S01C
S01C
S01C
S01C
S01C
SO 1C
S05C
S05C
S05C
S05C
S01C
SO 1C
SObC
S01C
S05C
S01C
S01C
S01C
U22
01 U20
02 U20
03 U20
04
05
U22
U20
U20
U20
U22
47
U20
UZO
U22
U20
U20
U20
U22
46
69
210
84
60
C
U22
U20
U20
U20
19
16
U22
U20
U20
U20
01
02
02
01
01
02
U17
U17
U21
U19
U20
U18
U14
U14
U14
U17
U17
U21
U19
U20
U18
U14
U14
U14
U17
U17
14
U19
UZO
U18
U14
014
U14
U17
U17
U21
U19
UZO
U18
U14
U14
U14
U17
U17
U21
U19
26
36
U14
8.2
U14
U17
U17
U21
U19
UZO
U18
U14
U14
U14
U17
U17
U21
U19
UZO
U18
U14
U14
U14
7.0
11
U5
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values tn pph dry weight
VOLATILE HALOGENATED ALKENES
Drainage
Survey Station Sample Rep
vinyl
chloride
1,1-dl-
chloro-
ethene
1.2-
trans-
dlchloro
ethylene
ds-1,3-
di-
chloro-
propene
trans-
1.3-di-
chloro-
propene
tri-
chloro-
ethene
tetra-
cMoro
ethene
17110019-BL-OOO-
17110019-8L-000-
17110019-6L-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-8L-000-
17110019-BL-OOO-
*» 17110019-BL-OOO-
vo 17110019-BL-OOO-
•^ 17110019-BL-OOO-
17110019-BL-OOO-
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
B02
B02
B03
604
B04
BOA
B04
B04
BO 7
807
609
609
BIO
BIO
61 1
Bll
B12
614
815
B17
B17
818
S01C -
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
SObC -
S05C -
SOBC -
S06C -
S01C -
S01C -
S05C -
S01C -
S05C -
S01C -
S01C -
S01C -
01
02
03
04
05
01
02
02
01
01
02
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
Ub
U5
U5
U5
U5
U5
D5
65
U5
U5
U5
U5
U5
B5
85
U5
U5
U5
Z0.50
U5
B5
85
85
85
85
U5
85
85
85
Zl.l
85
85
85
BS
B5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
U5
Ub
U5
U5
U5
U5
Number of Observations:
22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in pjjb dry weight
VOLATILE AROMATIC HYDROCARBONS
Drainage
Survey Station Sample Rep
ethyl-
benzene toluene benzene
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17UU019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
J>17110019-BL-000-
^17110019-BL-OOO-
ro
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
UKS001
URS001
URS001
URS001
URSOU1
URS001
URSOOl
UKS001
URSOOl
802
B02
BO 3
804
B04
804
B04
BO 4
BO 7
B07
B09
B09
810
BIO
Bll
Bll
B12
B14
815
B17
817
B18
S01C -
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
SO 1C -
S01C -
S05C -
S05C -
S05C -
S05C -
SO 1C -
S01C -
S05C -
S01C -
S05C -
S01C -
S01C -
S01C -
01
02
03
04
05
01
02
02
01
01
02
85
B5
Z1.6
85
U5
Z0.35
B5
85
Z0.15
85
85
85
U5
Z3.2
85
U5
Z0.19
Z0.36
U5
U5
U5
B5
U5
U5
U5
U5
Z0.35
U5
U5
85
U5
0.08
U5
U5
Ub
U5
U5
U5
U5
U5
Ub
U5
U5
U5
U5
Number of Observations:
22
-------
BLAI-R DREDGING PROJECT SURFACE SEDIMENT TENTATIVELY IDENTIFIED ORGANIC CHEMICALS - Values In ppb dry weight
1-me t hy1 -
Drainage
Survey Station Sample
Rep
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
*• 17110019-BL-OOO-
10
U)
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URSU01
URSU01
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
802
803
B04
B04
B04
804
B04
BO 7
809
BIO
BIO
811
812
814
Bl!>
B17
817
B18
SO 1C -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
S05C -
S01C -
S05C -
SO 1C -
S05C -
S01C -
S01C -
SO 1C -
01
02
03
04
05
01
02
01
01
02
methyl-
ethyl)
benzene
2-
methoxy
phenol
penta-
chloro-
cyclo-
pentane
1-
methyl
naphth
alene
68
23
130
140
53
58
30
6.9
88
80
32
35
17
2,6-di-
methyl
1,1' naphth-
biphenyl alene
64
77
130
94
69
66
33
16
78
76
8.8
45
42
Number of Observations:
18
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT TENTATIVELY IDENTIFIED ORGANIC CHEMICALS - Values 1n ppb dry weight
Drainage
Survey Station Sample
2-
dlbenzo- methyl
thlo phenan-
phene threne
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
i
*.
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
UKS001
URS001
URS001
URS001
URS001
URS001
B02
B03
B04
B04
B04
B04
B04
BO 7
809
BIO
BIO
Bll
B12
B14
B15
817
817
BIB
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
S05C -
SO 1C -
S05C -
S01C -
S05C -
S01C -
S01C -
S01C -
01
02
03
04
05
01
02
01
01
02
23
50
62
50
29
42
29
18
66
56
16
31
24
32
60
85
120
8.5
Ul
11
15
37
49
40
9.7
38
1-
methyl
phenan-
threne
82
47
76
90
60
36
48
23
16
69
14
34
42
9-
hexa- iso-
decenoic pimara-
acid diene
Number of Observations:
18
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENTS INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep Antimony Arsenic Barium Beryllium Cadmium Chromium
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
1711U019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URSU01
URS001
URS001
URS001
802
B03
B04
804
B04
807
809
BIO
Bll
B12
B14
BIS
B17
B18
SO 1C -
S05C -
S01C -
S01C -
SO 1C -
S01C -
S05C -
S05C -
S01C -
S05C -
SO 1C -
S05C -
S01C -
S01C -
0.1
0.5
01 0.7
02 0.7
03 0.7
0.4
0.3
0.3
0.5
0.5
0.4
0.8
0.3
0.3
38
4y
53
52
53
35
15
25
48
44
39
46
16
9.5
23.1
25.1
22.4
23.0
23.5
24.1
14.5
25.2
21.5
22.6
28.1
29.0
16.1
10.9
0.14
0.15
0.12
0.13
0.15
0.11
0.08
0.14
0.13
0.14
0.15
0.13
U0.05
U0.05
0.18
0.44
0.50
0.60
0.51
0.17
00.10
U0.10
0.30
0.27
0.28
0.50
U0.10
U0.10
18.9
20.0
18.0
19.6
18.6
18.5
11.1
13.0
18.2
17.5
18.7
18.4
10.4
11.2
Number of Observations:
14
i
UD
in
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENTS INORGANIC CHEMICALS - Values in ppm dry weight
Drainage
Survey Station Sample Rep Copper
Iron
Lead
Manganese Nickel Selenium
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
URS001
URS001
URSU01
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
B02
BO 3
B04
B04
B04
BO 7
B09
BIO
Bll
B12
814
B15
B17
B18
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
S01C -
S05C -
S01C -
S05C -
S01C -
S01C -
52.9
75.7
01 62.8
02 62.5
03 66.0
60.6
25.8
31.7
55.5
53.2
68.0
11.0
23.6
15.2
20700
20400
19800
19600
20500
19800
12000
15800
22000
19600
21800
20200
11600
9620
31.9
46.0
49.4
52.2
52.4
36.7
15.5
18.1
38.0
31.3
43.0
59.0
12.6
8.1
114
110
111
112
115
135
87.4
125
108
103
142
138
83.7
75.7
13.6
12.5
12.3
12.3
12.7
12.5
9.0
11.0
12.4
12.2
12.7
12.1
8.4
8.5
U0.05
U0.05
UO.Ob
U0.05
U0.05
U0.05
U0.05
U0.05
U0.05
U0.05
U0.05
U0.05
U0.05
U0.05
Number of Observations:
14
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENTS INCWGANIC CHEMICALS - Values In ppcn dry weight
Dralnaye Survey Station Sample Rep Silver Thallium Z1nc Cyanide Cobalt
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-UOU-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URSOU1
URS001
URS001
URS001
URS001
URS001
URS001
802
803
804
B04
804
BO 7
809
BIO
Bll
B12
814
815
817
B18
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
SO 1C -
S05C -
S01C -
S05C -
S01C -
S01C -
0.14
0.32
01 0.27
02 0.28
03 0.27
0.26
0.08
0.16
0.19
0.18
0.27
0.22
0.08
0.05
UO.l
UU.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
UO.l
79.7
91.2
89.3
90.6
90.8
72.3
37.6
40.3
87.8
73.9
91.0
118.3
33.3
26.6
Mercury
0.15
0.22
0.23
0.15
0.14
0.21
0.08
0.13
0.19
0.12
0.22
0.22
0.28
U0.04
Number of Observations:
14
i
vo
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT GRAIN SIZE
Drainage
Survey Station Sample Hep
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OUO-
1711U019-BL-UOO-
17110U19-BL-000-
17110U19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
URS001
URS001
URSU01
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
URS001
BO 2
803
B04
BO 7
809
BIO
Bll
812
B14
B15
817
818
S01C
S05C
S01C
S01C
S05C
SObC
S01C
SObC
S01C
SObC
S01C
S01C
%
Rocks
0.606
1.316
0.397
0.212
0.032
0.008
0.372
0.219
0.356
0.014
0.196
0.192
%
Sand
18.025
15.900
29.892
11.992
61.591
27.553
29.980
18.997
8.964
28.714
67.464
75.608
%
sm
61.001
63.552
53.739
70.239
32.398
61.803
48.970
61.325
70.387
54.846
27.815
21.189
*
Clay
20.367
19.232
15.972
17.557
5.979
10.636
20.678
19.459
20.293
16.427
4.525
3.010
Number of Observations:
12
i
VO
00
-------
BLAIR DKEUUNG PROJECT SURFACE SEOIMENT CONVENTIONALS
Total
Drainage Survey Station Sample Rep Solids
1
17110019-BL-OOU-
17110019-BL-OUO-
17110U19-BL-UOO-
17110019-BL-UOO-
17110019-BL-UOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OUO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-UOO-
17110019-BL-UOO-
1711UU19-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOO-
y> 17110019-BL-OOO-
^ 17110019-BL-OOO-
v£> 17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-OOU-
17110019-BL-OOO-
17110U19-BL-000-
URSOU1
URS001
UKSU01
URSU01
URSUU1
UKS001
UKSOU1
URS001
URS001
URS001
UKS001
URS001
URS001
URS001
URSOU1
URSOU1
URS001
URS001
URS001
URS001
URSU01
URS001
URS001
URSU01
URSOU1
B02
BO 3
B04
B04
B04
BO 7
B09
BIO
Bll
B12
B14
B02
B03
B04
B04
604
BO 7
B09
BIO
Bll
812
B14
Bib
B17
BIB
S01C -
S05C -
S01C -
SO 1C -
S01C -
sole -
S05C -
S05C -
S01C -
SObC -
S01C -
S01C -
S05C -
S01C -
S01C -
S01C -
S01C -
S05C -
S05C -
S01C -
S05C -
S01C -
S05C -
S01C -
S01C -
52.1
46.0
01 51.1
02 51.4
03 51.1
51.6
66.3
58. b
48.4
53.6
49.5
52.1
46.0
01 51.1
02 51.4
03 51.1
51.6
66.3
58.5
48.4
53.6
49.5
54.6
80.0
72.8
4.04
5.66 <
5.02
4.b4
4.64
4.19
2.14
3.90
4.82
3.97
4.53
4.04
5.66
5.02
4.54
4.64
4.19
2.14
3.90
4.82
3.97
4.53
4.91
1.91 (
1.29 (
.23
M8
.70
.69
.63
.49
.73
.32
.72
.41
.69
.23
.18
.70
.69
.63
.49
.73
.32
.72
.41
.69
.48
).70
1.29
Hydrogen NHroyen
0.065
0.104
0.079
0.077
0.095
0.083
0.031
0.069
O.U85
0.082
0.083
0.065
0.104
0.079
0.077
0.095
0.083
0.031
0.069
0.085
0.082
0.083
0.202
0.136
0.006
Number of Observations:
14
a. Reference:
Tetra Tech, Inc. 1985. Commencement Bay nearshore tideflats remedial
investigation. Final Report Prepared for Washington Department of Ecology and
U.S. EPA by Tetra Tech, Inc. Bellevue, WA.
-------
HY-50
COMMENCEMENT
BAY
STATIONS SAMPLED FOR BENTHOS
AND BIOASSAYS DURING MARCH
STATIONS SAMPLED FOR BENTHOS
AND BIOASSAYS DURING JULY
HY-44
o
o
CITY
WATERWAY
Figure A-l. Locations of Commencement Bay stations sampled
for benthic macroinvertebrates and sediment
bioassays during March and July.
Reference: Tetra Tech 1985.
-------
RS-22 (BKMSSAY ONLY)
I
I—"
o
• RS-24 (BtOASSAT ONLY)
RS-ia
RUSTON
• RS-20
•RS-19
0 4000
I I I I I FEET
- METERS
1000
• STATIONS SAMPLED FOR BENTHOS
AND BIOASSAYS DURING JANUARY
• STATIONS SAMPLED FOR BENTHOS
AND BIOASSAYS DURING JULY
COMMENCEMENT
BAY
TACOMA
HS12
Figure A-l. (Continued).
-------
SURFICIAL SEDIMENT CHEMISTRY — JANUARY
SURFICIAL SEDIMENT CHEMISTRY. BENTHIC
MACROINVERTEBRATES, AND SEDIMENT
TOXICITY — MARCH
FISH HISTOPATHOLOGY AND BIOACCUMULATION
JUNE
NOTE: ONLV CONVENTIONAL SEDIMENT VARIABLES
WERE MEASURED AT CR-02 AND CR-OS.
Figure A-2- Locations of reference stations sampled in Carr
Inlet.
Reference: Tetra Tech 1985.
A-102
-------
TABLE A 3. EIGHT BAY3
total
STATION*
3 SQ-14
3 SQ-17
3 SQ 18
3 SQ-20
3 SC 06
3 SC-07
3 SC 08
3 SC-14
3 SC 17
3 SC-18
3 SC-19
3 SC-20
3 CS 01
3 CS-11
3 CS-15
3 CS-17
3 DB-01
3 DB-05
3 DB-07
3 DB-15
, 3 EB-09
•-• 3 EB-10
5 3 EB-12
3 EB-17
3 EB-20
3 EB-22
3 EB-23
3 EB-24
3 SM-01
3 SH-03
3 SM 07
3 SH-20
3 EV-01
3 EV-02
3 EV-03
3 EV-04
3 EV-05
3 EV-06
3 EV-07
3 EV-11
3 BH 03
3 BH-04
3 BH-05
3 BH 07
3 BH-11
3 BH-12
3 BH-23
3 BH-24
PCBsD
Uc
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
0
20
20
20
20
1253
588
646
1672
231
229
60
384
20
20
20
20
20
20
20
20
330
279
78
646
640
687
148
69
20
20
20
20
445
84
516
965
394
124
155
171
74
54
27
31
54
53
20
20
4,4'- 4,4'
DDT DDE
U 1 U 1
U 1 U 1
U 1 U 1
U 1 U 1
U 1 01
U 1 1) 1
U 1 U 1
U 1 U 1
U 1 U 1
U 1 U 1
U 1 U 1
U 1 U 1
01 01
01 01
01 01
01 01
01 01
01 01
01 01
01 01
01 01
01 01
U 1 01
01 01
01 01
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
0 U 1
0 0 1
0 01
U U 1
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
U 0 1
0 0 1
U 0 1
0 0 1
0 0 1
0 0 1
01 01
01 01
2,4.6-
trl-
4.4' ch loro-
DDD phenol
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
U
0
0
0
0
0
0
0
0
0
U
0
0
0
0
0
0
0
U
U
0
0
0
0 500
0 500
1) 500
0 500
U 400
0 400
U 400
U 500
U 200
0 500
0 500
0 500
U 500
U 500
U 500
V 200
0 200
U 200
0 200
0 400
0 400
0 400
0 400
0 200
0 200
U 200
0 200
U 200
0 200
U 200
0 200
0
U
0
U
0
0
0
U
0 200
0 200
U 200
0 200
0 100
U 100
I) 100
0 100
2, 4, -di-
methyl
phenol
0 250
0 250
0 250
0 250
0 200
0 200
0 200
0 250
0 100
0 250
0 250
0 250
0 250
0 250
U 250
0 100
0 100
0 100
0 100
U 200
0 200
U 200
0 200
0 100
0 100
0 100
0 100
0 100
0 100
0 100
0 100
0
0
0
0
0
0
0
0
0 100
0 100
0 100
0 100
U 50
0 50
0 50
0 50
penta-
chloro-
phenol
01500
01500
01500
01500
01600
01600
01600
01500
0 600
01500
01500
01500
01500
01500
01500
0 600
0 600
U 600
0 600
01200
01200
01200
01200
0 800
0 800
0. 800
0 800
0 600
0 600
0 600
0 600
0
U
0
U
0
0
0
0
U 600
0 600
0 600
0 600
0 300
0 300
U 300
0 300
phenol
0 100
I) 100
U 100
U 100
Ld200
0 200
220
0 100
0 40
0 100
0 100
560
0 100
0 100
0 100
U 40
0 40
0 40
0 40
0 80
0 80
0 80
0 80
0 100
U 100
0 100
U 120
0 40
U 40
U 40
0 40
250
190
0
1400
0
0
0
U
U 40
I) 40
U 40
0 40
0 20
U 20
0 20
0 20
arenaph
thene
0 100
V 100
U 100
0 100
U 200
I 200
U 200
0 200
0 100
0 40
0 100
01200
0 100
0 100
0 100
0 100
0 40
0 40
0 40
0 40
0 80
630
0 80
I) 80
U 100
L 100
0 100
L 100
0 40
0 40
U 40
0 40
370
110
280
3300
240
120
480
0
0 40
150
0 40
110
I 20
0 20
0 20
0 20
1 .2,4
trj-
chloro-
benzene
U 200
0 200
0 200
0 200
0 200
0 200
0 200
0 200
0 200
0 80
0 200
0 200
0 200
U 200
0 200
0 200
0 80
0 80
0 80
0 80
0 160
U 160
0 160
0 160
0 100
0 100
0 100
0 100
0 80
0 80
0 80
0 80
U
0
0
0
U
0
0
0
0 80
0 80
0 80
II 80
II 40
0 40
0 40
0 40
hexa-
chloro-
benzene
0 800
U 800
U 800
U 800
U 800
0 800
0 800
0 800
01000
0 400
01000
01000
0 800
0 800
U 800
0 800
0 400
0 400
0 400
0 400
0 800
0 800
0 800
0 800
0 400
0 400
0 400
0 400
0 400
0 400
0 400
0 400
0
0
0
0
0
0
0
0
0 400
0 400
U 400
0 400
0 200
0 200
U 200
U 200
-------
STATION*
3 SQ 14
3 SQ- 17
3 SQ-18
3 SQ-20
3 SC-06
3 SC-07
3 SC-08
3 SC-14
3 SC 17
3 SC 18
3 SC-19
3 SC-20
3 CS-01
3 CS-11
3 CS-15
3 CS-17
3 DB 01
3 DB-05
3 DB-07
3 DB-15
3 EB-09
3 EB-10
3 EB-12
3 EB-17
3 EB-20
3 EB 22
3 EB-23
3 EB 24
3 SH-01
3 SM-03
3 SH-07
3 SH-20
3 EV-01
3 EV-02
3 EV-03
3 EV-04
3 EV-05
3 EV-06
3 EV-07
3 EV-11
3 BH-03
3 BH 04
3 BH 05
3 BH 07
3 BH-11
3 BH 12
3 BH-23
3 BH-24
1,2 di
chloro-
benzene
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 200
U 40
U 200
(I 200
U 100
U 100
0 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U
U
U
I)
U
U
U
U
U 40
U 40
U 40
IJ 40
U 20
U 20
U 20
U 2O
1 ,3 di
chloro-
benzene
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 200
U 40
U 200
U 200
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
0 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U
0
U
U
U
U
U
U
U 40
U 40
U 40
U 40
U 20
U 20
U 20
U 20
1 .4 di
chloro-
benzene
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 200
U 40
U 200
U 200
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
I) 100
U 40
U 40
U 40
U 40
U
U
U
U
u
II
u
u
U 40
U 40
U 40
U 40
U 20
U 20
U 2O
U 2O
2 , 6 d i -
ni tro-
toluene
U 500
U 500
U 500
U 500
U 400
U 400
U 400
U 400
U 500
U 200
U 500
U 500
U 500
U 500
U 500
U 500
U 200
U 200
U 200
U 200
U 400
U 400
U 400
U 400
U 200
U 200
U 200
U 200
U 100
U 100
U 100
U 100
U
U
U
u
u
u
u
u
U 100
U 100
U 100
U 100
U 100
II 100
U 100
U 100
1,2-di-
pheny]
hydra-
zine
U 250
U 250
U 250
U 250
U 200
U 200
U 200
U 200
U 250
U 100
U 250
U 250
U 250
U 250
U 250
U 250
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 100
U 100
U 100
U 100
U 100
U 100
U 100
U 100
u
u
0
u
u
u
u
u
U 100
U 100
U 100
U 100
U 50
U 50
U 50
U 50
f luor-
anthene
U 100
U 100
U 100
U 100
200
1300
490
650
520
640
200
5200
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
610
2300
220
U 50
820
370
240
190
76
97
68
U 40
4800
770
830
4100
1800
790
1400
200
710
1400
480
1500
550
200
200
U 20
hexa-
chloro-
bnta -
diene
U 400
U 400
U 400
U 400
U 400
U 400
U 400
U 400
U 400
U 160
U 400
U 400
U 400
U 400
U 400
U 400
U 160
U 160
U 160
U 160
U 320
U 320
U 320
U 320
U 200
U 200
U 200
U 200
IJ 160
II 160
U 160
U 160
U
U
U
U
U
U
U
U
U 160
U 160
U 160
U 160
I) 80
II BO
IJ 80
U 80
hexa
chloro-
cyclo
penta -
diene
1)1500
III 500
U1500
Ul 500
U1200
U1200
U1200
in 200
in 500
U 600
U1500
U1500
U1500
in 500
U1500
in 500
U 600
U 600
U 600
U 600
IJ1200
U1200
U1200
U1200
U 600
U 600
U 600
U 600
U 600
U 600
U 600
U 600
U
U
U
U
U
U
IJ
U
U 600
U 600
U 600
U 600
U 300
IJ 300
IJ 300
U 300
iso-
phorone
IJ 100
U 100
U 100
U 100
U 200
I) 200
U 200
U 200
U 100
U 40
U 100
U 100
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
I)
U
U
U
u
u
u
u
U 40
U 40
U 40
U 40
U 20
I) 20
II 20
U 20
naphtha-
lene
U 100
U 100
U 100
U 100
I 200
L 200
1, 200
U 200
U 100
57
U 100
1200
U'100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
420
U 80
I) 80
120
100
L 100
L 100
U 40
U 40
U 40
U 40
1300
750
1800
5900
590
790
1400
IJ
220
370
260
290
91
100
95
MO
n ni tro
sod i
pheny 1 -
amine
U2500
U2500
U2500
U2500
1)2000
IJ2000
IJ2000
U2000
U2500
U1000
U2500
U2500
U2500
112500
U2500
U2500
Ul 000
U1000
tnooo
in ooo
U2000
U2000
IJ2000
U2000
U1000
U1000
inooo
in ooo
U1000
in ooo
in ooo
U1000
u
u
u
u
u
u
• u
u
in ooo
in ooo
in ooo
U1000
II 500
U 500
U 500
1) 500
bis
(2 ethyl
he.xy 1 )
phtha
late
U 200
U ZOO
V ZOO
U 200
940
420
380
740
U1100
U 600
U 100
U 780
U 200
U 200
U 200
U 200
U 120
U 190
U 310
U 360
310
U 80
530
U 80
U 100
210
U 100
L 100
2800
340
180
740
U
U
U
II
830
190
290
870
390
290
260
310
U 70
U 160
U 200
U 760
-------
STATION*
3 SQ-14
3 SQ-17
3 SQ-18
3 SQ-20
3 SC-06
3 SC-07
3 SC-08
3 SC-14
3 SC-17
3 SC-18
3 SC-19
3 SC-20
3 CS-01
3 CS-11
3 CS-15
3 CS-17
3 DB-01
3 DB-05
3 DB-07
3 DB-15
ja 3 EB-09
1 3 EB-10
^ 3 EB-12
CTI 3 EB-17
3 EB-20
3 EB-22
3 EB-23
3 EB-24
3 SM-01
3 SM 03
3 SM-07
3 SM-20
3 EV-01
3 EV-02
3 EV-03
3 EV-04
3 EV-05
3 EV-06
3 EV-07
3 EV-11
3 BH-03
3 BH-04
3 BH-05
3 BH-07
3 BH-11
3 BH-12
3 BH-23
3 BH-24
benzyl
butyl
pht ha -
late
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 100
U 40
U 100
U 100
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U
u
u
440
U
u
u
u
U 40
U 40
U 40
U 40
U 20
U 20
U 20
U 20
dJ-n-
butyl
phtha-
late
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 100
U 40
U 100
U 100
U 100
U 100
U 100
U 100
U 40
U 75
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U
U
U
U
U
U
U
0
U 40
U 40
U 40
U 40
U 20
U 20
U 20
U 20
di-n-
octy 1
phtha-
latn
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 100
U 40
U 100
U 100
U 100
U 100
U 100
U 100
0 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
U 100
69
160
U 40
100
U
u
60
U
U
U
U
U
U 40
U 40
U 40
590
U 20
U 20
300
U 20
dl-
ethyl
phthn
late
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 100
U 40
U 100
U 100
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
U 100
0 40
U 40
U 40
U 40
U
U
U
U
U
U
U
U
U 40
U 40
U 40
U 40
U 20
U 20
U 20
U 20
di-
methyl
phtha-
late
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 100
U 40
U 100
U 100
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U
U
U
U
U
U
U
U
U 40
U 40
U 40
U 40
U 20
U 20
U 20
U 20
benzo(a)-
anthra-
nene
U 100
U 100
U 100
U 100
L 200
1000
380
490
350
550
230
1900
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
350
1400
U 80
V 80
690
320
U 100
150
U 40
U 40
U 40
U 40
810
160
U
370
910
U
540
U
250
430
U 40
700
150
80
U 20
U 20
benzo(a)
pyrene
U 250
U 250
U 250
U 250
350
1100
360
480
380
220
I) 250
1100
U 250
U 250
U 250
U 250
U 100
U 100
U 100
U 100
U 200
1400
U 200
U 200
U 100
200
U 100
140
U 100
U 100
U 100
U 100
250
U
U
U
U
U
U
U
U 100
U 100
U 100
U 100
U 50
U 50
U 50
U 50
total
benzo
f luor-
anthenes
U 200
U 200
U 200
U 200
570
1600
590
980
980
630
U 100
1900
U 200
U 200
U 200
U 200
U 40
U 40
U 40
0 40
490
2200
U 80
U 80
700
370
U 100
240
U 40
U 40
U 40
U 40
620
150
U
310
1000
U
460
U
U 40
U 40
U 40
770
190
U 20
U 20
II 20
chrysene
U 100
I) 100
U 100
U 100
L 200
1000
340
510
370
450
190
2000
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
370
1500
U 80
U 80
560
220
U 100
140
U 40
U 40
U 40
U 40
650
160
210
340
750
U
460
U
250
540
U 40
820
170
93
U 20
U 20
acenaph
thy lene
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 100
U 40
U 100
U 100
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
L 100
U 40
U 40
U 40
U 40
U
U
U
770
U
U
120
U
U 40
U 40
U 40
V 40
U 20
U 20
U 20
U 20
anthra -
eerie
U 100
U 100
U 100
U 100
U 200
580
U 200
U 200
U 100
200
U 100
U 100
U 100
u' 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
460
U 80
U 80
180
L 100
U 100
L 100
U 40
U 40
U 40
U 40
700
27
110
410
890
U
480
U
U 40
110
U 40
280
130
38
U 20
1) 20
ben/o
(ghi)
pery lene
U 500
(J 500
U 500
U 500
U 800
L 800
U 800
U 800
U 500
U 200
U 500
1000
U 500
U 500
0 500
U 500
U 200
U 200
U 200
U 200
U 400
U 400
U 400
U 400
U 400
U 400
U 400
t 400
U 200
U 200
U 200
U 200
U
U
U
u
u
u
u
u
U 200
U 200
U 200
U 200
U 100
U 100
U 100
U 100
-------
STATION*
3 SQ-14
3 SQ-17
3 SQ-18
3 SQ-20
3 SC-06
3 SC-07
3 SC-08
3 SC-14
3 SC-17
3 SC-18
3 SC-19
3 SC-20
3 CS-01
3 CS-11
3 CS-15
3 CS-17
3 DB 01
3 DB-05
3 DB-07
3 DB-15
j., 3 EB-09
I 3 EB-10
Q 3 EB-12
ON 3 EB-17
3 EB-20
3 EB-22
3 EB-23
3 EB-24
3 SM-01
3 SM-03
3 SM-07
3 SM-20
3 EV-01
3 EV-02
3 EV 03
3 EV-04
3 EV-05
3 EV-06
3 EV-07
3 EV-11
3 BH-03
3 BH-04
3 BH-05
3 BH-07
3 Bll 11
3 Bll 12
3 BH 23
3 BH-24
f luorene
U 100
U 100
0 100
U 100
U 200
L 200
U 200
U 200
U 100
U 40
U 100
980
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
490
U 80
U 80
U 100
L 100
U 100
L 100
U 40
U 40
U 40
U 40
410
140
290
2100
250
U
400
U
U 40
210
U 40
150
32
66
U 2O
U 2O
phenan-
threne
U 100
U 100
U 100
U 100
150
1200
L 200
520
1200
510
110
8900
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
300
2100
210
U 80
480
190
U 100
200
170
U 40
U 40
U 40
1900
380
970
4700
0
630
1600
370
240
570
210
880
170
170
110
230
dlbenzo-
(a,h)an-
Indeno-
(1 ,2.3-cd)
thracene pyrene
U 500
U 500
U 500
U 500
U 800
U 800
U 800
U 800
U 500
U 200
U 500
U 500
U 500
U 500
U 500
U 500
U 200
U 200
U 200
U 200
U 400
U 400
U 400
U 400
U 400
0 400
U 400
L 400
U 200
U 200
U 200
U 200
U
U
U
U
U
U
U
U
U 200
U 200
U 200
U 200
U 100
U 100
U 100
U 1OO
U 500
U 500
U 500
U 500
U 800
L 800
U 800
U 800
U 500
U 200
U 500
640
U 500
U 500
U 500
U 500
0 200
0 200
U 200
U 200
U 400
U 400
U 400
U 400
U 400
U 400
U 400
L 400
U 200
U 200
U 200
U 200
U
U
U
0
U
V
U
U
U 200
U 200
U 200
U 200
U 100
U 100
I) 100
U 1OO
pyrene
U 100
U 100
U 100
U 100
580
1800
570
1000
620
680
330
6400
0 100
U 100
0 100
U 100
U 40
U , 40
U 40
U 40
1100
3400
300
U 80
1400
550
670
300
88
120
U 40
U 40
2500
510
880
2100
1500
630
1600
200
620
1100
390
1300
380
230
180
180
chloro-
form
U 13
U 13
U 13
U 15
U 12
U 11
U 14
U 16
U 14
U 13
U 10
U 7.7
V 14
U 14
U 17
U 18
U 7.2
U 7.9
U 10
U 16
U 11
U 12
U 14
U 10
U 10
U 11
U 8
U 6.9
U 4.1
U 13
U 4.3
U 4.5
U 15
U 15
U 13
L 18
U 16
U 9.3
U 12
U 10
U 15
IJ 5.0
U 5.2
U 5
U 10
U 10
U 1O
U 10
ethyl-
benzene
U 13
U 13
U 13
U 15
U 12
U 11
U 14
U 16
U 14
U 13
U 10
U 7.7
U 14
U 14
U 17
U 18
U 7.2
U 7.9
U 10
U 16
U 11
U 12
U 14
U 10
U 10
U 11
U 8
U 6.9
U 4.1
IJ 13
U 4.3
U 4.5
U 15
U 15
U 13
L 18
U 16
U 9.3
U 12
V 10
U 15
L 5.0
U 5.2
U 5
U 10
U 10
U 10
0 1O
tetra-
chloro-
ethene
U 13
U 13
U 13
U 15
U 12
U 11
U 14
U 16
U 14
U 13
U 10
U 7.7
U 14
U 14
U 17
U 18
U 7.2
U 7.9
U 10
U 16
0 11
U 12
U 14
U 10
U 10
U 11
U 8
U 6.9
U 4.1
U 13
U 4.3
IJ 4.5
U 15
0 15
U 13
U 18
U 16
U 9.3
U 12
U 10
U 15
L 5.0
U 5.2
L 5
U 10
U 10
U 10
L 10
tri-
chloro-
ethylene
U 13
U 13
U 13
U 15
U 12
U 11
U 14
U 16
U 14
U 13
U 10
U 7.7
U 14
U 14
U 17
U 18
U 7.2
U 7.9
U 10
U 16
U 11
U 12
U 14
U 10
U 10
U 11
U 8
U 6.9
U 4.1
U 13
U 4.3
U 4.5
U 15
U 15
U 13
U 18
U 16
U 9.3
U 12
U 10
U 15
L 5.0
U 5.2
U 5
U 10
U 10
U 10
U 10
antimony arsenic
11 0 . 1 5.6
U 0. 1 7.3
U 0. 1 6.9
U 0. 1 6.9
0.2 9
2.0 67
U 0.1 14
0.2 14
1.4 39
0.1 15
2.0 25
0.3 14
U 0.
U 0.
U 0.
U 0.
U 0.
U 0
U 0.
U 0.
0.
0.
U 0.
U 0.
1 .
U 0.
U 0.
U 0.
U 0.
U 0.
U 0.
U 0.
U 0.
U 0.
U 0.
0.
0.
U 0.
U 0.
U 0.
U 0.
U 0.
U 0.
U 0.
8.2
6.9
6.5
8.1
1.9
2.1
3.4
5.6
12
11
7.2
14
31
9
6.6
5.7
4.5
8.0
4.6
5.5
12.
9.9
14.
18.
8.5
6.1
7.7
6.8
8.5
7.9
11.
8.9
0.2 10.
0.2 6.9
0.2 1O.
U 0. 1 8.5
beryl 1 ium
4.6
5.5
5.5
5.3
0.31
0.29
0.34
0.31
4 .8
4. 1
3.6
4.5
3.7
3.5
4.7
4.2
3.9
4.0
4.7
5.5
0.31
0.31
0.39
0.31
0.33
0.32
0.24
0.20
0.36
0.34
0.37
0.38
0.37
0.36
0.30
0.25
0.28
0.18
0.28
0.25
0.31
0.37
0.41
0.41
0.53
0.38
0.52
0.49
-------
STATION*
cadmlun
chromium copper
lead
mercury
nickel
selenium
3 SQ-14
3 SQ-17
3 SQ-18
3 SQ-20
3 SC-06
3 SC-07
3 SC-08
3 SC-14
3 SC-17
3 SC-18
3 SC-19
3 SC-20
3 CS-01
3 CS-11
3 CS-15
3 CS-17
3 DB-01
3 DB-05
3 DB-07
3 DB-15
3 EL -09
3 EL-10
3 EL-12
3 EL-17
3 EL-20
3 EL-22
3 EL-23
3 EL-24
3 SM-01
3 SM-03
3 SM-07
3 SM-20
3 EV-01
3 EV-02
3 EV-03
3 EV-04
3 EV-05
3 EV-06
3 EV-07
3 EV-11
3 BH-03
3 BH-04
3 BH-05
3 BH-07
3 BH-11
3 BH-12
3 BH-23
3 BH-24
0.9
0.9
1.1
0.9
3.6
1.1
0.9
1.2
1.8
1.2
2.3
2.0
0.7
0.6
1.1
1.9
0.1
0.1
0.2
0.4
1.0
2.0
0.4
0.7
0.7
0.5
0.2
0.2
0.14
0.21
0.17
0.14
1.9
0.83
1.5
1.1
3.1
1.1
1.9
0.9
0.98
1.2
0.55
0.88
0.31
0.50
0.33
0.36
60
67
66
65
93
79
71
65
129
73
80
86
38
41
57
57
49
51
60
76
41
41
43
40
49
38
25
22
32
43
35
40
50
54
48
67
62
34
53
48
68
81
86
82
63
57
66
69
43
48
48
44
205
807
231
299
240
170
293
198
56
30
59
52
28
33
50
74
112
165
106
101
152
89
39
20
33
40
33
40
85
81
82
111
101
49
79
70
400
72
69
72
79
61
62
67
6.8
9.0
8.5
9.0
132
233
151
175
194
131
360
163
23
9.3
19
13
0.4
U 0.1
5.6
9.9
245
607
35
100
176
51
29
23
5
5
6
6
38
25
47
82
40
25
34
15
46
37
13
18
13
11
10
8
0.04]
0.068
0.060
0.055
1.38
1.28
1.21
1.57
0.70
0.72
2.07
1.64
0.12
0.054
0.11
0.082
0.016
0.022
0.029
0.047
1.69
1.08
0.28
0.58
0.78
0.51
0.16
0.16
0.080
0.073
0.069
0.063
0.21
0.20
0.23
0.26
0.18
0.20
0.23
0.12
1.35
1.69
0.81
0.97
0.54
0.64
0.54
0.59
37
41
39
38
44
39
45
43
43
39
45
44
21
22
33
31
27
30
33
46
33
33
37
26
24
27
18
17
20.
27.
19.
21.
44
48
43
45
51
34
50
46
73.
89.
Ill
105
118
72
102
117
0.8
1 .0
0.7
0.4
0.4
0.3
0.2
0.3
0.6
0.7
0.1
0.7
0.4
0.3
0.7
0.7
0.3
0.1
0.6
1.0
0.1
0.3
0.3
0.1
U 0.1
0.2
U 0.1
0.1
0.4
0.3
U 0.1
U 0.1
0.2
U 0.1
0.2
0.2
0.4
U 0.1
0.2
U 0.1
0.4
0.2
0.1
0.1
0 0.1
0.3
0.2
U 0.1
0. 107
0.226
0.223
0.2
3.7
2.3
2.29
2.32
1 .29
1 .56
1.36
2.67
0.569
0.119
0.364
0.262
0.37
0.37
0.78
0.218
0.741
0 651
0.675
0.65
0.6
0.671
0.176
0.254
0.104
0.13
0.108
0.118
0.368
0.218
0.334
0.155
0.415
0.184
0.372
0.344
0.254
0.377
0.221
0.28
0.203
0.207
0.236
0.217
0.2
U 0. I
U 0. 1
U 0. 1
o,;
U 0.1
0. 1
U 0. 1
0.1
0. 1
o.;
U 0. !
U 0.1
0.1
U O.I
U 0. 1
U 0. 1
o.:
0. 1
o. :
U 0. 1
u o. :
u o. :
u o. :
o.:
oo.:
u o.:
u o.:
u o.:
U 0.
u o. :
U 0.
0.!
o.;
O.i
o.:
0.!
o.;
0.'
u o.:
o. :
o.;
o.;
0.
u o.:
o. :
o. ;
o. :
! 76
1 88
1 85
1 83
> 330
1 873
1 311
1 272
1 328
L 227
> 343
I 235
1 82
1 57
1 98
1 84
1 72
1 77
1 86
1 102
1 434
I 687
1 120
1 192
1 460
1 116
1 69
1 44
I 74
1 75
1 76
1 81
5 313
2 141
2 237
3 1074
5 249
2 78
I 132
1 88
1 102
2 135
2 111
1 117
1 113
I 97
1 114
1 115
-------
1
STATION*
3 SQ-14
3 SQ-17
3 SQ-18
3 SQ-20
3 SC-06
3 SC-07
3 SC-08
3 SC-14
3 SC-17
3 SC-18
3 SC-19
3 SC-20
3 CS-01
3 CS-11
3 CS-15
3 CS-17
3 DB-01
3 DB-05
3 DB-07
3 DB-15
3 EL-09
3 EL-10
3 EL-12
3 EL-17
3 EL-20
> 3 EL-22
,L 3 EL-23
0 3 EL-24
3 SH-01
3 SM-03
3 SH-07
3 SM-20
3 EV-01
3 EV 02
3 EV 03
3 EV-04
3 EV-05
3 EV-06
3 EV-07
3 EV-11
3 BH-03
3 BH-04
3 BH-05
3 BH-07
3 BH-11
3 BH-12
3 BH-23
3 BH-24
depth
(m)
23.8
25.6
24.7
18.9
6.7
7.6
13.4
14.9
14.6
16.8
15.8
18.3
40.5
21.3
28.3
24.1
112.8
88.4
97.5
109.7
173.1
184.1
189.0
170.7
173.7
179.2
121.9
137.2
13.4
10.1
23.5
30.5
16.8
16.8
13.4
13.7
10.1
11.9
16.5
97.5
5.5
11.9
11.9
10.1
7.0
6.1
17.4
11.0
% total
volatl le
solids
9. 19
10.44
11 .58
9.55
9.78
8.23
11.08
12.93
9.63
9.59
7.82
10.92
10.33
6.18
11.84
11.87
4.14
5.51
7.66
13.24
7.02
6.68
9.89
7.32
6.76
8.20
8.01
6.22
5.71
7.50
6.12
6.64
20.14
13.42
25.44
35.06
25.99
10.52
18.13
7.36
26.93
13.72
9.20
10.67
7.25
9.33
8.14
6.50
* SJlt
35.18
47.81
45.52
48.40
60.30
30.85
52.04
58.36
45.21
46.04
29.93
39.82
54.61
21.01
43.71
31.42
12.43
13.25
24.51
49.37
31.96
41.80
50.94
34.01
34.00
36.65
13.82
6.40
61.58
55.15
57.53
61.43
42.86
62.37
40.98
34.24
45.49
28.22
48.29
50.69
50.03
58.66
71.35
63.57
60.65
36.14
67.44
64.97
* clay
28.71
35.25
33.93
35.47
26.46
24.63
37.83
33.20
33.02
31.77
19.07
28.74
34.79
17.96
37.64
46.71
7.82
10.41
24.45
40.31
23.72
27.10
37.68
21.90
18.34
25.72
10.67
7.27
19.59
25.73
27.38
25.84
20.02
20.73
22.56
19.58
21.83
11.96
19.25
18.39
16.88
24.93
25.17
28.12
37.42
28.08
27.92
32.48
% total
organic
carbon
2.09
2.30
2.28
2.23
2.93
2.12
2.87
3.04
2.40
2.58
2.33
3.28
2.01
1.36
2.42
2.69
0.83
1.39
2.14
2.65
1.77
2.35
2.14
1.92
.39
.93
.11
.80
.32
.89
.32
.39
8.98
4. 17
11.00
15.42
9.22
4.51
6.67
2.29
12.15
4.83
2.34
3.15
2.07
3.69
2.01
2.09
BENTHIC
CODE
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
TOX
CODE
1
1
1
1
3
1
1
3
1
1
1
3
1
1
3
3
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
3
1
1
3
3
1
1
1
1
1
1
1
1
1
3
1
MICRO
CODE
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-------
a. Reference:
Battelle Marine Research Laboratory. 1985. Detailed chemical and biological analyses of selected sediments from Puget Sound. Draft Final
Report. U.S. EPA Region X, Seattle, WA. 300 pp.
b. Total PCBs are the sum of detected Aroclors.
c. U = undetected at detection limit shown. Detection limits were not available for all the chemicals for the "EV" stations.
d. L = less than the value shown. For purposes of this report, these were considered undetected.
e. Silver data were supplied by Eric Crecellus, Battelle Pacific NW Laboratories, personnel contnunication by H. Seller, November 22,1985.
O
IO
-------
SEQUIMBAY
Figure A-3. Sequim Bay Sampling Stations
Reference: Battelle 1985.
A-110
-------
Figure A-4. Sinclair Inlet Sampling Stations
Reference: Battelle 1985.
A-lll
-------
17
16
±
Nautical Miles
I
15
12
13
11
8
9
10
HARTSTENE ISLAND
Figure A-5. Case Inlet Sampling Stations
Reference: Battelle 1985.
A-112
-------
Figure A-6. Dabob Bay Sampling Stations
Reference: Battelle 1985- A-113
-------
47°-38'N
7
O
4
O
V2
Nautical Miles
Magnolia Bluff
47°-36'N
8
o2 o1
o11
16
0
Samplings
QI983
• I984
013
ELLIOTT BAY
122°-26'W
Figure A-7. Elliott Bay - Four-mile Rock Sampling Stations
Reference: Battelle 1985.
A-1-14
-------
°15 SAMISH BAY
Figure A-8. Samish Bay Sampling Stations
Reference: Battelle 1985.
A-115
-------
EVERETT HARBOR
Figure A-9. Everett Harbor - Port Gardner Sampling Stations
Reference: Battelle 1985.
A-116
-------
BELLINGHAM BAY
Figure A-10. Bellingham Bay Sampling Stations (Inner Harbor)
Reference: Battelle 1985.
A-117
-------
BELLINGHAM BAY
Samplings
• 1984
O19B3
Figure A-ll. Bellinghan Bay Sampling Stations (Outer Harbor)
Reference: Battelle 1985. A-118
-------
TABLE A-4. DUWAMISH RIVER Ia
STATION f
4 DR-01 Cl
4 DR-02 C2
DR-03 C3
DR-04 C4
DR-05 C5
DR-06 C6
DR-07 C7
DR 08 C8
4 SQ 09 C9
dimethyl
phtha-
late
8.5
3.1
11
6.4
8.2
11
12
4.8
11
dlethyl
phtha-
late
4.5
1.7
4.4
1.9
2.4
4.7
3.6
18
15
dl-n-
butyl
phtha-
late
13
7.2
36
13
24
130
31
60
17
hls(2
ethyl-
hexyl-
phtha-
late
280
120
1000
260
500
580
740
2800
100
hexa-
chloro-
buta-
dlene
Ub1.3
U 0.8
U 1.3
U 1 .3
U 1.3
U 1.3
U 1 .9
U 10
U 1 .3
2.6-dl~
nltro-
toluene'
U 3.0
U 2.6
U 5.3
U 3.3
U 2.9
U 4.7
U 5.1
U 5.3
U 3.2
1 ,3 dl
chloro-
benzene
U 40
U 40
U 40
U 40
U 40
U 40
U 40
U 40
U 40
1 ,4-dl-
chloro-
benzene
U 40
U 40
U 40
U 40
U 40
U 40
U 40
U 40
U 40
1 ,2-dl-
chloro-
benzene
U 40
U 94
U 40
U 40
U 40
U 40
U 40
U 40
U 40
Isophor-
one
U 2.0
U 2.0
U 2.7
U 1.9
U 3.0
U 2.7
U 3.3
U 3.5
U 3.3
hexa-
chloro-
benzene
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
1 .7
U 0.5
STATION *
4 DR-01 Cl
4 DR-02 C2
1 4 DR-03 C3
•-• 4 DR-04 C4
,o 4 DR-05 C5
4 DR-06 C6
4 DR-07 C7
4 DR-08 C8
4 SQ-09 C9
anthra-
cene
2.1
7.6
15
6.5
13
7.4
26
210
U 2.3
1-aethyl-
phenan-
threne
U 1.3
10
32
9.4
24
13
27
170
4.4
f luor-
anthene
37
64
220
110
160
170
300
1100
53
pyrene
31
52
230
120
160
160
270
1200
35
benzo(a)
anthra-
cene
7.4
17
100
33
140
50
180
590
U 2.7
chrysene
21
36
260
57
110
120
250
1400
16
total
benzo-
f luor-
anthenes
7.4
18
150
29
76
59
200
720
9.1
benzo(a )
pyrene
5.6
7.2
61
26
49
44
95
400
8.3
Indeno-
(1,2,
3-cd)
pyrene
U 3.0
4.4
44
21
29
19
59
170
6.6
dlbenzo-
(a , h (an-
thracene
U 3.0
U 2.9
U 7.0
U 3.0
U 3.3
U 5.9
U 4.3
42
U 3.3
benzo-
(ghl)
perylene
4.5
12
93
42
92
80
67
180
8.3
STATION f
4 DR-01 Cl
4 DR-02 C2
4 DR-03 C3
4 DR-04 C4
4 DR-05 C5
4 DR-06 C6
4 DR-07 C7
4 DR-08 C8
4 SQ-09 C9
4,4' -DDE
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
4,4' -ODD
0.6
0.9
3.9
0.6
2.6
3.2
5.6
71
U 0.5
4,4' -DDT
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
U 0.5
22
0.8
U 0.5
Total
PCBs
23
38
31
13
76
41.6
120
3900
2.7
naphtha-
lene
2.6
71
11
5.8
12
6.0
8.3
99
U 3.0
2-
methyl
naphth-
alene
5.5
110
13
2.8
14
9.2
9.7
140
9.1
1,1'
blphenyl
0 1.8
2.7
7.4
U 2.2
6.8
U 14
U 4.3
29
U 7.9
acenaph-
thylene
U 1.9
U 1.9
U 3.9
U 2.3
U 1 .7
U 3.6
1.7
2400
U 37
acenaph-
thene
U 1.9
U 1.9
U 3.9
U 2.3
9.4
3.6
23
100
U 2.8
f luorene
U 1 .7
2.6
U 3.5
U 2. 1
U 2.1
4.8
14
91
2.5
phenan
threne
23
54
150
80
120
97
190
560
U 40
-------
STATION *
4 DR-01 Cl
4 DR-02 C2
4 DR-03 C3
4 DR-04 C4
4 DR-05 C5
4 DR-06 C6
4 DR-07 C7
4 DR-08 C8
4 SQ-09 C9
% silt
0
0
2.6
2.9
3.4
5.0
4.6
2.8
4.5
% clay
13.1
9.9
34.5
13.7
42.3
39.9
49.2
84.8
68.8
* total
organic
carbon
0.36
0.63
3.5
2.8
2.1
2.3
1.8
2.2
1.4
* total
volatl le
solids
2.6
3.1
11
7.2
8.2
8.2
8.1
8.7
7.6
ARSENIC
8.6
12
17
14
18
17
20
34
22
CADMIUM
0. 13
0.15
0.45
0.26
0.42
0.35
0.40
3.1
0.64
COPPER
14
16
32
22
32
32
35
120
35
LEAD
9.4
8.9
20
14
17
17
24
160
12
ZINC
57
57
91
71
84
79
90
270
93
MERCURY
0.01
0.01
0.04
0.02
0.04
0.07
0.05
0.42
U0.03
TOX
CODE
3
1
1
1
1
1
3
3
1
BENTIIIC MICRO
CODE
0
0
0
0
0
0
0
0
0
CODE
0
0
0
0
0
0
0
0
0
a. Reference:
Chan, S.-L., M.H. Schiewe, O.W. Brown. 1985. Analyses of sediment samples for U.S. Army Corps of Engineers Seattle Harbor navigation project
operations and maintenance sampling and testing of Duwamish River sediments. Draft report. 15 pp. plus appendices.
b. U = undetected at detection limit shown.
ro
o
-------
X S E AJTT L E
/
\
Figure A-12. Sediment Samp)ling Station Locations for Dredged Material
Characterization.
Reference: Chan et al. 1985.
A-121
-------
TAIII,E A-5. A1.K1 EXTENSION3
STATION*
5 AP-01
5 AP-02
5 AP-03
5 AP-04
5 AP-05
5 AP-06
5 AP-07
5 PW-01
5 PW-02
5 PW-03
5 PW 04
LSKR04
LSKR05
LSKR06
LSJR02
LSLR02
LSLP02
LSKN02
LSOV01
LSOU01
LSU002
LS0003
STATION*
5 AP-01
5 AP-02
5 AP-03
, 5 AP-04
•-* 5 AP-05
K 5 AP-06
5 AP-07
5 PW-01
5 PW-02
5 PW-03
5 PW 04
LSKR04
LSKR05
LSKR06
LSJR02
LSLR02
LSLP02
LSKN02
LSOV01
LSU001
LSU002
LS0003
STATION*
5 AP-01
5 AP-02
5 AP-03
5 AP-04
5 AP-05
5 AP-06
5 AP-07
5 PW-01
5 PW-02
5 PW-03
5 PW-04
LSKR04
LSKR05
LSKR06
LSJR02
LSLR02
LSLP02
LSKN02
LSUV01
LS0001
LSUU02
LSU003
phenol
74.1
Ob4.3
5.3
74.2
1.7
0 4.3
04.3
0 4.3
31 .7
0 4.3
12.1
benzo(a)
anthra-
cene
13.0
0 18
0 18
74.2
26.4
10.4
11.9
2.6
5.3
5.5
6.7
4,4'-
DDD
U 0.08
0 0.08
0 0.08
0 0.08
0 0.08
0 0.08
0 0.08
U 0.08
U 0.08
0 0.08
0 0.08
1,2-di-
phenyl-
hydra-
zlne
U 5.8
0 5.8
0 5.8
0 5.8
0.2
0 5.8
U 5.8
0 5.8
0 5.8
0 5.8
0 5.8
benzo(a)
pyrene
24.7
0 12.5
U 12.5
100.0
21.3
10.4
13.2
0 12.5
13.2
4.1
21.6
4,4'-
DDT
U 0.10
0 0. 10
0 0.10
0 0.10
U 0.10
0 0.10
0 0.10
0 0.10
0 0.10
0 0. 10
0 0.10
1,4-dl
chloro-
benzene
27.3
2.7
0 3
82.3
13.8
10.4
6.6
0 3
0 3
4. 1
22.9
total
benzo-
f Juor-
naphtha- acenaph- acenaph- anthra- phenan- fjuor-
fluorene lene thene thylene cene threne anthene
3.9
0 3.4
U 3.4
25.8
U 3.4
U 3.4
U 3.4
U 3.4
2.6
U 3.4
0 3.4
3.9 U 3 0 2.7
01.6 03 U2.7 0
01.6 03 02.7 0
01.6 11.3 4.8
6.3 03 0 2.7
01.6 0 3 02.7
14.5 03 0 2.7
0 1.6 03 0 2.7 0
1.3 1.3 U 2.7
6.8 03 0 2.7
4.0 03 0 2.7
9. 1
2.9
2.9
95.2
10.0
9.1
4.0
2.9
5.3
2.7
4.0
Indeno-
(1.2.
3-cd)
dibenzo- benzo- dlnethyl
(a,h)an- (ghl) phtha-
anthenes pyrene thracene perylene late
48.1
012.4
2.6
012.4
57.7
33.8
60.7
012.4
33.1
27.3
51.3
tetra-
chloro-
ethane
0 5
0.04
0.01
0.03
0 5
0 5
0.03
0 5
U 5
0.06
O.01
032.8
032.8
032.8
1.6
18.8
9.1
032.8
032.8
032.8
032.8
032.8
ethyl-
benzene
0 5
0 5
0.04
0.05
0 5
0 5
0 5
0 5
0 5
0.03
0 5
034.4 6.5 0 3.7
034.4 031.7 0 3.7
034.4 031.7 0 3.7
3.2 33.9 0 3.7
2.5 13.8 0 3.7
034.4 10.4 0 3.7
034.4 10.6 0 3.7
034.4 031.7 0 3.7
034.4 4.0 0 3.7
034.4 6.8 0 3.7
034.4 9.4 1.4
hexa- butyl-
1,2-dl- 1,3-dl- chloro- benzyl
chloro- chloro- buta- phtha-
benzene benzene dlene late
03. 5 04 00.31 07. 5
0 3.5 04 00.31 0 7.5
03. 5 04 00.31 U7.5
U 3.5 0 4 00.31 0 7.5
03. 5 04 00.31 07. 5
0 3.5 0 4 00.31 0 7.5
03. 5 04 00.31 07. 5
03. 5 04 00.31 07. 5
U 3.5 0 4 00.31 0 7.5
03. 5 04 00.31 07. 5
03.5 04 00.31 U7.5
dlethyl
phtha-
late
5.2
2.7
0 3.6
4.8
21.3
6.5
5.2
0 3.6
6.6
6.8
4.0
2,4,6-
trl-
chloro-
phenol
0 23
0 23
0 23
0 23
0 23
0 23
0 23
0 23
0 23
U 23
U 23
35. 1
1 .3
1 .3
172.
10.0
7.8
14.5
2.6
15.9
5.5
12.1
dl-n-
butyl
phtha-
late
46.8
51.8
69.7
48.4
82.8
63.7
71 .2
29.6
44.0
61 .5
31.0
penta-
chloro-
phenol
0 209
0 209
0 209
0 209
0 209
0 209
0 209
0 209
0 209
0 209
U 209
42.9
2.6
2.7
206.
12.6
11.7
19.8
21 .9
26.4
9.6
25.6
dl-n
octyl
phtha-
late
0 7.5
14.6
U 7.5
4.8
0 7.5
2.6
0 7.5
0 7.5
0 7.5
0 7.5
0 7.5
2,6-dl-
nltro
toluene
0 230
0 230
0 230
0 230
U 230
0 230
0 230
0 230
U 230
0 230
U 23O
pyrene
49.4
5.3
2.6
338.
20. 1
20.8
36.9
21.9
33.0
15.0
35.0
total
PCBs
18
19
29
34
14
6.6
11
13
8.6
5.6
25 2
1,2,4-
trl-
chloro-
benzene
0 10
0 10
0 10
0 10
0 10
0 10
0 10
0 10
0 10
0 10
U 10
chrysene
20.8
0 18
1 .3
101 .
43.9
22. 1
17.2
3.9
9.3
8.2
16.2
4,4'
DDE
L°0.65
L0.66
L0.66
1.61
L0.63
L0.65
L0.66
L0.64
00.07
L0.68
L0.67
-------
STATION!
hexa-
chloi^o-
benzene
5 AP-01
5 AP-02
5 AP-03
5 AP-04
5 AP-05
5 AP-06
5 AP-07
5 PW-01
5 PW-02
5 PW 03
5 PW-04
LSKR04
I.SKR05
LSKR06
LSJR02
LSLR02
LSLP02
LSKN02
LSUV01
LSUU01
LSUU02
LSUU03
U
U
U
U
U
U
U
U
0
U
U
20
20
20
20
20
20
20
20
20
20
20
hexa
chlor-
ethane
U
U
U
U
U
U
U
0
U
U
U
12
12
12
12
12
12
12
12
12
12
12
chlor-
cyclo-
penta-
diene
U
U
U
U
U
U
U
U
U
0
U
110
110
110
110
110
110
110
110
110
110
110
ethyl-
hexyl )-
plitha-
late
U
U
U
U
U
U
U
U
U
1)
U
10
10
10
10
10
10
10
10
10
10
10
2,4 di
methyl -
phenol
U
U
U
U
U
U
U
U
U
U
U
5
5
5
5
5
5
5
5
5
5
5
N-ni-
troso -
dlphenyl-
amlne
U
U
U
U
U
U
U
U
U
U
0
4
4
4
4
4
4
4
4
4
4
4
.8
,8
.8
.8
.8
.8
.8
.8
.8
8
8
arsenic
4.9
9.3
4.7
11
14
31
15
2.0
19
20
si Iver
0
0.
0
0.
LO
LO.
LO
0.
LO
0
0.
.35
26
.28
48
21
23
.20
33
. 17
34
29
cadmium
LO
LO.
LO
LO
LO
LO
LO
LO
LO
LO.
LO.
.23
.21
18
.32
.21
,23
.20
22
.17
19
16
chromium copper
23
24
20
31
28
31
44
23
26
27
31
3.8
4.2
4.3
11
9.9
11 .6
18.4
3.6
U
13
16
ro
X total
STATION*
5 AP-01
5 AP-02
5 AP-03
5 AP-04
5 AP-05
5 AP-06
5 AP-07
5 PW-01
5 PW-02
5 PW-03
5 PW-04
LSKR04
LSKR05
LSKR06
LSJR02
LSLR02
LSLP02
LSKN02
LSUV01
LSOU01
LSUU02
LSUU03
•ercury
0.022
L0.012
0.020
0.05
0.056
0.053
0.053
L0.023
0.053
0.051
0.055
nickel
18
17
17
23
23
27
22
15
23
23
23
lead
4.7
5.8
5.8
16
14
10
13
5
20
11
13
antimony
L0.05
L0.05
L0.04
L0.08
L0.05
L0.05
L0.04
L0.05
L0.04
L0.04
L0.04
zinc
22
21
21
37
43
44
49
21
38
40
44
depth volatile
(n) solids
22
22
22
49
98
112
22
22
47
96
110
1
1
1
2
4
2
4
1
2
2
2
X total
organic
carbon"
0.57
0.57
0.57
0.74
1.07
0.74
1.07
0.57
0.74
0.74
0.74
fines6
0.54
0.60
0.60
4.1
5.4
3.8
1.9
0.89
2.1
3.0
2.4
BENTHIC TOX
CODE CODE
1 0
1 0
1 0
1 0
1 0
1 0
1 0
1 0
1 0
1 0
1 0
MICRO
CODE
0
0
0
0
0
0
0
0
0
0
0
a. References:
Osborn, J.G., O.E. Weitkamp, and T.H. Schadt. 1985. Alki wastewater treatment plant outfall improvements predesign study. Technical Report No.
6.0, Marine Biology. Municipality of Metropolitan Seattle. 50 pp.
Trial, W., and J. Michaud. 1985. Alki wastewater treatment plant outfall improvements predesign study. Technical Report No. 8.3, Water Quality.
Municipality of Metropolitan Seattle. 89 pp.
b. U = Undetected at detection limit shown. Detection limits are from:
Romberg, G.P., S.P. Pavlou, and E.A. Crecelius. 1984. Presence, distribution, and fate of toxicants in Puget Sound and Lake Washington. METRO
Toxicant Program Report No. 6A. Toxicant Pretreatment Planning Study Technical Report Cl. Municipality of Metropolitan Seattle, Seattle, WA.
231 pp.
c. L = Less than the value shown. For purposes of this report, these were considered undetected.
d. Total organic carbon values were estimated from a regression plot of total volatile solids with total organic carbon. See Figures A-13and A-14 .
The regression in figure A-13was used for the estimate and includes only data in the range of volatile solids found in the Alki Study {i.e., <10%).
e. Grain size data were unavailable for station AP-03. Because or its proximity to AP-02, an estimated value of 0.6% fine grained material was
assigned for use in calculations .
-------
z
o
o
IT
o
z
EC
O
UJ
O
DC
UJ
Q.
3.5
ID.5
17.5
24.5
31.5
36.5
7 14 21 28 35
PERCENT TOTAL VOLATILE SOLIDS
42
119 cases plotted. Regression statistics of TOC on VSOL1DS:
Correlation .9D7B4 R Squared .82417 S.E. o+ E«t 1.2B04& Sis. .DDDD
IntercePt(S.E.) -.5&853( .19400) Slope(S.E.) .42116< .01798)
Figure A-13. Plot of total organic carbon with total
volatile solids.
A-124
-------
z
o
m
cc
<
o
o
o
ec
o
UJ
O
oc
Ui
Q.
PERCENT TOTAL VOLATILE SOLIDS
47 cases plotted. Regression statistics of TOC on VSCL1DS'
Carrelatisn .75506 R Squared .57011 S.E. ot Est ".33882 Sig. .0000
Intercept^.E.) .40340< .10455) SloBe
-------
t
DEPTH CONTOURS IN
METERS AT MOW
100 200 300 400 500
500
1.000
1.500
Figure A-15.
Sediment collection stations offshore of
Point Williams, sampled May 26,1984.
Reference: Osborn et al. 1985
A-126
-------
f
I
I
•
183
•
LSKN02
\
\
/ I \
DEPTH CONTOURS IN /.—N
METERS AT MLLW ^ N
0 WO 200 300 400 SCO
SOLE IN METERS
SCALE IN FEET
500 1.000 1.SOO
Rgure A-16.
Sediment collection stations offshore of
AJkl Point, sampled May 25-26,1984.
Reference: Osborn et al. 1985.
A-127
-------
183
\
\
\
LSUU03\
\
BRACE
POINT
DEPTH CONTOURS IN
METERS AT MUW
500
1.000
1.500
A-128
Figure A-17.
Point Williams benthos reference
sampling station locations.
Reference: Osborn et al' 1985.
-------
LSKN02
183
\
DEPTH CONTOURS IN
METERS AT MLLW
0 100 200 300 400 500
N
500 1.000 1.500
SCALE IN METERS
SCALE IN FEET
Figure A-18.
Alki Point benthos sampling
station locations.
Reference: Osborn et al. 1985.
A-129
-------
TABLE A-6. TPPS PHASE 3 A * B3
STATION
• 6 EB-30
* 7 EB-30
* 6 EB-31
* 7 EB-31
* 6 EB-32
* 7 EB-32
6 EB-33
7 EB-33
* 6 EB-34
* 7 EB-34
6 EB-35
7 EB-35
6 EB-36
7 EB-36
* 6 EB-37
* 7 EB-37
6 EB 38
7 EB-38
» 6 EB-39
* 7 EB-39
* 6 WP-01
7 WP-01
• 6 WP-02
7 WP-02
* 6 WP-03
7 WP-03
* 6 WP-04
7 WP-04
* 6 WP-05
7 WP-05
* 6 WP-06
7 WP-06
• 6 WP-07
7 WP-07
* 6 WP-08
7 WP-08
* 6 WP-09
7 WP-09
• 6 WP-10
7 WP-10
« 6 WP-11
7 WP-11
6 WP-12
7 WP-12
6 WP-13
7 WP-13
6 WP 14
7 HP -14
6 WP-15
*
401230
401230
401630
401630
401830
401830
401406
401406
401512
401512
401603
401603
401606
401606
401612
401612
401706
401706
401810
401810
400330
400330
400430
400430
400510
400510
400530
400530
400621
400621
400712
400712
400730
400730
400810
400810
400830
400830
400210
400210
400310
400310
400275
400275
400375
400375
400575
40O575
4OO775
1824
2081
1818
2078
1825
2077
1779
2080
1780
2071
1775
2079
1776
2072
1777
2073
1778
2074
1814
2075
1806
2088
1807
2089
1788
2090
1809
2091
1810
2092
1811
2084
1812
2093
1813
2083
1815
2082
1787
2076
1789
2087
1786
2069
1784
2070
1785
2085
1817
phenol
Lc 52
l)b 4.3
U 4.3
U 4.3
U
U
0
U
U
U
U
U
U
L
U
U
U
U
L
U
u
u
u
u
u
u
u
u
u
u
u
u
u
u
0
u
u
u
u
u
u
u
u
u
u
u
u
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
236
4.3
4.3
4.3
4.3
40
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
29
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4.3
4 .3
4-3
2.4-di-
•ethyl-
phenol
U 5
U 5
L 24
U 5
L
U
0
U
U
U
U
U
U
U
U
U
U
U
U
U
0
u
u
u
u
u
0
0
u
0
u
u
u
u
0
u
u
u
u
0
u
u
u
u
u
u
u
u
30
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1 ,2 di-
phenyl -
hydra-
7. Ine
U 5.8
U 5.8
U 5.8
U 5.8
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
u
u
u
u
0
u
u
u
u
u
u
u
u
u
u
u
u
fj
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5 . &
n-nl troso
dlphenyl-
anlne
U 4.8
U 4.8
U 4.8
L 7
U
L
L
0
U
L
U
I
L
U
L
L
U
U
U
0
U
U
u
u
u
u
u
L
U
u
u
u
L
U
0
L
U
L
U
L
L
U
u
4.8
30
132
4.8
4.8
46
4.8
276
54
308
4.8
191
61
18
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4.8
486
4.8
542
4.8
4.8
4.8
4.8
3
4.8
4.8
14
4.8
196
4.8
75
22
4.8
18
63
4 . a
acenaph-
thene
L 37
25
L 68
L 34
U
L
L
U
L
L
U
U
U
L
L
L
L
L
L
U
U
U
L
U
U
L
U
L
L
I
U
L
L
U
U
U
U
I,
L
3
61
460
3
694
81
304
3
3
3
78
57-
293
85
804
43
177
3
142
29
3
3
3
10
3
3
64
4
3
12
104
37
488
3
35
12
1335
102
3
3
3
3
1O6
GO
acenaph-
thylene
L 34
U 2.7
L 63
25
U
L
L
U
U
L
U
L
U
L
L
L
U
U
L
U
L
U
U
L
L
I
L
L
L
U
L
U
L
U
U
L
u
2.7
56
431
2.7
2.7
46
2.7
42
4013
2.7
75
3
276
42
2.7
2.7
31
12
247
50
2.7
4
2.7
23
2.7
35
60
67
48
57
91
33
460
2.7
32
51
2278
643
2.7
14
2.7
2.7
95
Z . 7
anthra-
cene
79
86
149
64
I
L
U
L
U
L
U
U
L
L
L
L
U
U
174
82
49
65
53
217
3936
1636
67
2.9
98
1274
67
871
1080
2.9
136
16
277
360
2.9
4
124
106
2.9
63
203
34
78
189
26
22
21
4
517
126
9964
1273
29
2.9
2.9
26
32
74
f luor
ene
220
38
418
29
L
L
U
L
U
L
L
U
U
U
L
U
L
L
L
L
L
L
L
U
L
L
124
70
276
3.4
429
115
2683
118
3.4
97
490
86
173
218
704
3.4
150
4
324
79
3.4
3.4
106
14
3.4
44
38
15
48
31
65
45
293
10
21
63
4804
643
29
3.4
222
24
475
437
naphtha-
lene
241
93
444
172
L
L
U
L
U
U
L
U
U
u
u
u
u
u
L
U
L
U
U
U
U
U
U
u
u
u
u
I.
275
152
316
108
I .6
133
215
1.6
1.6
128
52
236
1.6
184
578
498
129
1 .6
308
139
1.6
1.6
1.6
33
1.6
1.6
42
27
1.6
65
65
1.6
1.6
1.6
1 .6
1.6
2669
328
1.6
1.6
1.6
1 .6
1 .6
45
phenan-
threne
241
303
313
184
367
220
316
203
220
681
4293
2574
157
U 2.9
261
3822
232
2970
1683
U 2.9
477
61
1032
1166
U 2.9
14
187
259
I) 2.9
298
285
343
397
392
104
61
84
135
401
383
33630
3150
56
U 2.9
U 2.9
132
124
298
-------
STATION
»' 6 RB 30
* 7 EH 30
* 6 EB-31
* 7 EB-31
* 6 EB-32
* 7 EB-32
6 EB-33
7 EB-33
* 6 EB-34
* 7 EB-34
6 EB-35
7 EB-35
6 EB-36
7 EB-36
* 6 EB-37
* 7 EB-37
6 EB 38
7 EB-38
* 6 EB-39
* 7 EB-39
* 6 WP-01
7 WP-01
* 6 WP-02
7 WP-02
* 6 WP-03
3> 7 WP-03
,L* 6 WP-04
Co 7 WP-04
•"** 6 WP-05
7 WP-05
* 6 WP-06
7 WP-06
* 6 WP-07
7 WP-07
* 6 WP-08
7 WP-08
* 6 WP-09
7 WP-09
* 6 WP-10
7 WP-10
* 6 WP-11
7 WP-11
6 WP-12
7 WP-12
6 WP-13
7 WP-13
6 WP-14
7 WP-14
6 WP-15
7 WP-15
6 WP-16
7 WP-16
*
401230
401230
401630
401630
401830
401830
401406
401406
401512
401512
401603
401603
401606
401606
401612
401612
401706
401706
401810
401810
400330
400330
400430
400430
400510
400510
400530
400530
400621
400621
400712
400712
400730
400730
400810
400810
400830
400830
400210
400210
400310
400310
400275
400275
400375
400375
400575
400575
400775
400775
400875
400875
1824
2081
1818
2078
1825
2077
1779
2080
1780
2071
1775
2079
1776
2072
1777
2073
1778
2074
1814
2075
1806
2088
1807
2089
1788
2090
1809
2091
1810
2092
1811
2084
1812
2093
1813
2083
1815
2082
1787
2076
1789
2087
1786
2069
1784
2070
1785
2085
1817
2094
1816
2086
(a,h)an-
thracene
U 34 4
U 34.4
392
U 34 4
U 34.4
U 34.4
4023
U 34.4
U 34.4
U 34.4
U 34.4
1048
U 34.4
U 34.4
850
U 34.4
471
U 34.4
U 34.4
U 34.4
L 10
U 34.4
U 34.4
172
U 34.4
U 34.4
U 34.4
U 34.4
U 34.4
U 34.4
0 34.4
U 34.4
780
U 34.4
U 34.4
0 34.4
U 34.4
U 34.4
L 26
71
L 203
1155
U 34.4
0 34.4
U 34.4
U 34.4
0 34.4
0 34.4
U 34.4
U 34.4
U 34.4
anthra-
cene
289
556
339
123
U 18
257
862
U 18
L 122
805
9481
3125
535
138
621
5414
573
851
905
844
341
81
740
1715
U 18
U 18
U 18
273
U 18
194
L 46
313
265
1757
298
95
209
162
1151
344
14947
4462
643
235
U 18
289
174
685
402
U 18
110
henzo(a )-
pyrene
814
758
1305
245
U 12.5
173
9770
U 12.5
9959
1022
U 12.5
5882
903
154
2092
2930
976
990
1432
1450
490
120
1002
3602
U 12.5
65
642
177
U 12.5
323
L 339
194
1852
1622
L 95
109
572
471
349
816
22598
6824
1877
447
L 1263
500
L 74
2173
402
651
121
f luor-
anthenes
1759
3283
2794
270
U 12.4
185
17529
U 12.4
18041
1084
U 12.4
11213
2475
338
3791
4140
3570
2574
3266
3463
1294
110
2640
4117
U 12.4
83
160
273
U 12.4
310
2534
463
3452
1757
1712
136
1032
412
1294
855
129359
8005
3405
782
I 2274
737
L 113
3928
1322
3077
161
chrysene
10 30
031
1201
179
U 18
350
1121
U 18
L 445
1362
10376
5147
1605
277
2255
7962
1552
1802
804
1385
886
96
1171
2058
U 18
82
227
532
V 18
220
L 33
522
1455
2297
L 106
115
725
250
4140
383
35409
6693
231
279
U 18
368
206
2440
575
U 18
144
f tuor -
anthene
440
732
522
294
528
374
1178
1703
1306
1022
5367
5882
468
369
65
11147
606
1624
3518
1450
858
116
1572
2744
U 7.6
76
428
614
U 7.6
401
488
746
622
1757
311
123
167
412
1203
689
71352
6299
260
U 7.6
U 7.6
342
2375
714
833
1302
203
( 1
,2.3-crt)
pyrene
U
U
U
U
U
U
L
U
U
U
U
U
U
I
U
I
U
U
U
U
L
V
I
341
303
522
64
32.8
68
2874
32.8
32.8
341
32.8
4412
32.8
308
32.8
105
32.8
257
32.8
2165
150
41
143
1012
32.8
32.8
32.8
191
32.8
142
136
313
32.8
2432
389
7
32.8
32.8
362
523
9093
5249
32.8
92
32.8
95
185
893
316
32.8
25
pyrene
r>5i
707
653
319
688
397
1494
1189
1878
1424
6440
6250
1137
718
95
11147
774
U 7.6
4774
2078
913
146
2712
3774
U 7.6
136
521
846
U 7.6
466
705
955
661
2838
441
176
321
588
1811
829
62811
7349
402
U 7.6
U 7.6
421
290
1042
1379
1509
243
(ghl)
pery lene
I
U
U
U
U
U
I
U
U
U
U
U
I
L
U
U
0
t
U
L
656
4T.5
1071
54
31.7
65
8046
31.7
31.7
341
31.7
5147
31.7
174
1699
115
808
192
31 .7
2597
163
32
200
1235
31.7
31 .7
31 .7
150
31.7
142
271
284
1587
2568
765
10
46
31.7
310
944
10658
5381
31 .7
109
31 .7
129
201
1756
270
31.7
25
chloro
benzene
tj
U
(I
V
U
U
U
U
U
L
U
L
U
U
U
L
U
U
U
U
U
U
I)
U
U
U
U
U
U
V
U
U
U
U
U
U
U
U
U
U
U
U
U
U
V
U
U
U
U
U
U
35
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
8978
3.5
18
3.5
3.5
3.5
51
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
3.5
-------
1,3-dl-
chloro-
STATION *
* 6 EB 30
* 7 EB 30
* 6 EB 31
* 7 EB 31
* 6 EB 32
* 7 EB-32
6 EB-33
7 EB-33
* 6 EB-34
* 7 EB-34
6 EB-35
7 EB-35
6 EB-36
7 EB-36
* 6 EB-37
* 7 EB-37
6 EB-38
7 EB-38
* 6 EB-39
* 7 EB-39
* 6 WP-01
7 WP-01
* 6 WP-02
7 WP-02
•f" * 6 WP-03
•— 7 WP-03
{£ » 6 WP-04
7 WP-04
* 6 WP-05
7 WP-05
* 6 WP-06
7 WP-06
* 6 WP-07
7 WP-07
* 6 WP-08
7 WP-08
* 6 WP-09
7 WP-09
* 6 WP-10
7 WP-10
* 6 WP-11
7 WP-11
6 WP-12
7 WP-12
6 WP-13
7 WP-13
6 WP-14
7 WP-14
6 WP-15
7 WP-15
6 WP 16
7 WP-16
401230
401230
401630
401630
401830
401830
401406
401406
401512
401512
401603
401603
401606
401606
401612
401612
401706
401706
401810
401810
400330
400330
400430
400430
400510
400510
400530
400530
400621
400621
400712
400712
400730
400730
400810
400810
400830
400830
400210
400210
400310
400310
400275
400275
400375
400375
400575
400575
400775
400775
4OO875
400875
1824
2081
1818
2078
1825
2077
1779
2080
1780
2071
1775
2079
1776
2072
1777
2073
1778
2074
1814
2075
1806
2088
1807
2089
1788
2090
1809
2091
1810
2092
1811
2084
1812
2093
1813
2083
1815
2082
1787
2076
1789
2087
1786
2069
1784
2070
1785
2085
1817
2094
1816
2O86
benzene
U
U
U
U
U
U
U
0
U
0
U
U
U
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
0
0
U
U
U
U
U
U
U
U
U
U
4
4
4
4
4
1
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
1.4-di
chloro-
benzene
U
U
U
U
U
U
U
U
0
L
U
U
U
U
0
L
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
0
U
U
U
U
U
U
U
U
U
U
0
U
0
0
U
U
0
U
U
3
3
3
3
3
3
3
3
3
7740
3
3
3
3
3
45
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
hexa-
chloro-
buta-
dlene
U 0.31
13
U 0.31
7
U 0.31
7
U 0.31
11
U 0.31
22
U 0.31
U 0.31
U 0.31
15
U 0.31
6
U 0.31
2
U 0.31
6
U 0.31
1
U 0.31
2
U 0.31
U 0.31
U 0.31
11
U 0.31
4
U 0.31
3
U 0.31
7
U 0.31
5
U 0.31
U 0.31
U 0.31
0 0.31
U 0.31
L 0.3
U 0.31
6
U 0.31
5
U 0.31
U 0.31
U 11
U 0.31
6
benzyl
butyl
phtha-
late
U
L
L
U
U
U
U
0
L
U
U
L
L
U
U
U
U
U
U
0
L
U
L
L
U
U
U
U
0
U
L
U
U
160
7.5
217
54
32
58
1724
7.5
7.5
124
7.5
1820
334
67
7.5
178
7.5
812
578
7.5
7.5
11
20
34
7.5
12
7.5
7.5
7.5
7.5
7.5
7.5
11
7.5
17
26
11
7.5
7.5
23
7.5
7.5
7.5
307
7.5
26
259
7.5
287
7.5
28
dlethyl
phtha-
late
L
U
U
L
U
U
L
U
L
L
L
L
L
U
U
U
L
U
U
U
L
L
L
t
L
t
U
U
L
U
U
U
L
U
11
36
3.6
51
25
42
3.6
3.6
90
43
3.6
83
23
318
29
45
88
20
45
3.6
3.6
9
3.6
17
3.6
11
3.6
97
3.6
23
50
22
11
8
10
47
8
12
3.6
17
3.6
9
3.6
50
3.6
18
3.6
27
57
3.6
51
di n
butyl-
phtha-
late
108
328
t, 13
515
t 32
888
L 106
405
531
310
2147
2941
211
4103
281
860
175
832
578
L 193
L 25
112
200
103
L 14
423
L 28
164
187
1423
L 45
194
198
1622
101
2035
73
162
L 43
94
U 2.4
262
U 2.4
475
U 2.4
4474
844
247
402
t 89
339
dimethyl
phtha-
late
L
U
U
I
U
L
U
U
U
L
U
L
U
U
U
U
U
U
U
U
I
L
t
U
U
U
U
U
U
U
U
U
U
U
U
L
U
L
U
U
U
U
U
U
U
L
U
I,
U
L
5
3.7
3.7
10
3.7
30
3.7
3.7
3.7
15
3.7
88
3.7
3.7
3.7
3.7
3.7
18
3.7
3.7
3.7
20
17
31
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
4
3.7
23
3.7
3.7
3.7
3.7
3.7
3.7
3.7
18
3.7
9
3.7
11
di-n
octyl -
phtha-
late
244
t 40
14386
539
482
678
2615
3243
L 2082
1269
9481
37868
27759
303
948
13376
1717
4158
6784
U 7.5
U 7.5
173
431
L 5
L 352
423
414
205
461
246
1165
254
489
500
L 259
258
181
235
2329
3571
1423
U 7.5
6166
249
L 530
2605
68602
625
1264
1331
367
4,4'- ODD
8
U 0.08
3
U 0.08
U 0.08
U 0.08
U 0.08
30
U 0.08
U 0.08
175
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
U 0.08
L 0.3
U 0.08
U 0.08
U 0.08
I 0.5
U 0.08
U 0.08
U 0.08
L 1
10
L 0.4
U 0.08
U 0.08
12
2
U 0.08
U 0.08
U 0.08
U 0.08
L 1
U 0.08
U 0.08
12
L 0.3
4.4' -DDE
2
3
3
4
1
5
11
30
6
22
47
U 0.07
37
10
9
14
7
4
8
6
5
L 0.3
1
t 3
0.1
L 0.4
0. 1
1
U 0.07
U 0.07
1
1
2
1
1
3
3
2
1
L 0.1
3
1
2
U 0.07
9
L 5
I 2
1
3
5
1
-------
STATION
* 6 EB-30
* 7 EB 30
* 6 EB-31
* 7 EB-31
* 6 EB-32
• 7 EB-32
6 EB 33
7 EB-33
* 6 EB-34
* 7 EB-34
6 EB-35
7 EB-35
6 EB-36
7 EB-36
* 6 EB-37
* 7 EB-37
6 EB-38
7 EB-38
* 6 EB-39
* 7 EB-39
* 6 WP-01
7 WP-01
* 6 WP-02
7 WP-02
-f * 6 WP-03
»-* 7 WP-03
£j * 6 WP-04
7 WP-04
* 6 WP 05
7 WP-05
* 6 WP-06
7 WP-06
* 6 WP-07
7 WP-07
* 6 WP-08
7 WP-08
* 6 WP-09
7 WP-09
« 6 WP-10
7 WP-10
* 6 WP-11
7 WP-11
6 WP-12
7 WP-12
6 WP-13
7 WP-13
6 WP-14
7 WP-14
6 WP-15
7 WP-15
6 WP-16
7 WP-16
*
401230
401230
401630
401630
401830
401830
401406
401406
401512
401512
401603
401603
401606
401606
401612
401612
401706
401706
401810
401810
400330
400330
400430
400430
400510
400510
400530
400530
400621
400621
400712
400712
400730
400730
400810
400810
400830
400830
400210
400210
400310
400310
400275
400275
400375
400375
400575
400575
400775
400775
400875
400875
1824
2081
1818
2078
1825
2077
1779
2080
1780
2071
1775
2079
1776
2072
1777
2073
1778
2074
1814
2075
1806
2088
1807
2089
1788
2090
1809
2091
1810
2092
1811
2084
1812
2093
1813
2083
1815
2082
1787
2076
1789
2087
1786
2069
1784
2070
1785
2085
1817
2094
1816
2086
4,4' -DDT
U 0.10
U 0. 10
U 0.10
U 0. 10
U 0.10
14
U 0 10
L 1
U 0.10
77
U 0.10
U 0.10
U 0. 10
15
U 0.10
67
U 0.10
28
U 0. 10
48
U 0.10
1
U 0.10
U 0.10
U 0.10
U 0.10
1
2
U 0.10
U 0.10
U 0.10
U 0.10
U 0.10
10
U 0. 10
V 0. 10
U 0.10
I 1
U 0. 10
U 0.10
U 0.10
U 0. 10
U 0. 10
U 0.10
U 0. 10
U 0.10
U 0. 10
U 0. 10
11
U 0.10
U 0.10
total
2,4,6-
ti i-
chloro-
PCBs" phenol
404
230
454
255
117
327
1060
2280
955
2070
3940
965
3970
487
915
1130
730
315
925
468
109
15
79
149
18
25
123
68
11
U 0.5
49
70
151
130
77
161
251
130
53
41
1080
131
102
106
480
87
145
146
221
275
76
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
V
U
U
U
U
U
U
U
U
U
U
U
U
U
V
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
penta
chjoro -
phenol
U
U
U
U
U
U
U
U
U
0
U
U
U
0
U
U
U
U
U
U
U
U
U
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
V
U
U
U
U
U
U
U
U
U
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
209
2.6-dl-
nl tro-
toluene
0
U
U
U
U
U
U
U
U
V
U
U
U
U
U
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
1,2,4-
tri-
chloro-
benzene
U
U
U
U
U
U
I)
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
V
U
U
U
U
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
hexa -
chloro-
benzene
U
U
U
U
U
U
U
tl
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
If
U
U
U
(1
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
liexa-
chloro-
ethane
U
U
U
U
U
U
(J
U
U
U
U
U
U
U
U
U
U
U
U
U
U
V
\]
V
U
U
U
0
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
I?
U
U
U
U
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
ch loro-
cyclo-
penta-
dlene
If
U
U
U
U
U
If
U
U
U
U
0
U
U
U
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
t)
U
U
U
U
U
U
U
U
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
no
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
110
ethyl-
hexyl )
phtha-
latp
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
0
U
U
U
U
U
U
U
U
U
U
U
U
U
U
0
U
U
U
U
U
U
U
U
U
U
V
U
U
U
U
U
U
U
U
U
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
-------
STATION *
antimony
arsenic
beryllium cadmium
chromium
copper
lead
mercury
nickel
selenium
* 6 EB-30
* 7 EB-30
* 6 EB-31
* 7 EB-31
* 6 EB-32
* 7 EB-32
6 EB-33
7 EB-33
* 6 EB-34
• 7 EB-34
6 EB-35
7 EB-35
6 EB-36
7 EB 36
* 6 EB-37
• 7 EB-37
6 EB-38
7 EB-38
* 6 EB-39
» 7 EB-39
» 6 WP-01
7 WP-01
* 6 WP-02
7 WP-02
"f" * 6 WP-03
>— 7 WP-03
j£ « 6 WP-04
7 WP-04
* 6 WP-05
7 WP-05
* 6 WP-06
7 WP-06
* 6 WP-07
7 WP-07
* 6 WP-08
7 WP-08
* 6 WP-09
7 WP-09
* 6 WP-10
7 WP-10
* 6 WP-11
7 WP-11
6 WP-12
7 WP-12
6 WP-13
7 WP-13
6 WP-14
7 WP-14
6 WP-15
7 WP-15
6 WP-16
•7 wp-ie
401230
401230
401630
401630
401830
401830
401406
401406
401512
401512
401603
401603
401606
401606
401612
401612
401706
401706
401810
401810
400330
400330
400430
400430
400510
400510
400530
400530
400621
400621
400712
400712
400730
400730
400810
400810
400830
400830
400210
400210
400310
400310
400275
400275
400375
400375
400575
400575
400775
4OO775
4OO875
400B-75
1824
2081
1818
2078
1825
2077
1779
2080
1780
2071
1775
2079
1776
2072
1777
2073
1778
2074
1814
2075
1806
2088
1807
2089
1788
2090
1809
2091
1810
2092
1811
2084
1812
2093
1813
2083
1815
2082
1787
2076
I7B9
2087
1786
2069
1784
2070
1785
2085
1817
2O94
1816
2O86
0.50
0.67
0.80
0.53
1.0
0.67
3.2
1.5
2.3
0.87
3.3
2.4
1 .3
0.46
3.7
1.0
3.2
0.60
1.0
0.94
0.80
L 0.30
0.30
0.33
1.3
L 0.30
0.80
0.33
L 0.30
0.60
0.40
0.40
0.30
0.33
0.70
0.33
L 0.30
0.33
0.60
0.33
1.3
0.33
0.40
0.33
3.0
0.40
1.9
0.33
0.80
O.33
O.5O
I. 0. 30
13
14
12
8.0
10
11
8.8
15
12
13
15
16
12
9.1
16
14
15
12
12
11
9.2
6.1
7.2
8.4
4.0
1.9
3.2
4.5
15
4.2
1.4
6.8
4.7
5.3
13
3.8
29
6.8
5.2
3.0
6.8
3.8
11
10
12
9.5
12
11
8.9
9.9
9.4
12
0.36
0.40
0.45
0.070
0.44
0.24
0.33
0.35
0.30
0.37
0.21
0.24
0.41
0. 16
0.30
0.11
0.40
0.20
0.37
0.18
0.26
0.32
0.51
0.26
0.12
0.16
0.19
0.24
0.11
0.16
0.23
0.24
0.41
0.34
0.22
0.21
0.28
0.33
0.15
0.090
0.20
0.21
0.47
0.49
0.49
0.34
0.50
0.36
0.47
0.47
O.42
O.4O
0.25
0.46
0.23
0.050
0.30
0.14
0.99
2.1
0.32
0.39
0.46
2.2
0. 15
0.34
0.70
0.36
0.86
0.63
0.47
0.33
0.33
0.10
0.43
0.10
0.080
0.080
0.060
0.070
0.070
t 0.010
0.14
0.080
0.23
0.030
0.10
0.030
0.18
0.14
0.070
0.15
0.40
0.10
0.35
0.20
0.31
0.40
0.33
0.11
0.34
0.11
O.47
o. 10
54
47
37
11
47
47
60
61
48
57
63
57
63
45
60
59
59
53
54
49
36
45
38
48
8.9
20
13
26
24
18
21
33
16
23
24
21
62
42
17
19
.37
29
40
50
43
45
48
53
39
52
63
51
62
50
55
13
50
46
130
87
52
55
120
120
57
34
62
54
61
54
82
58
32
15
37
25
13
7.6
17
14
12
6.3
34
14
20
13
33
10
28
21
29
7.2
27
11
36
35
39
34
41
40
46
40
74
38
55
81
68
30
62
72
89
210
54
94
670
430
88
49
90
98
95
100
82
120
51
22
43
27
13
10
18
28
14
13
42
24
12
23
17
13
19
21
19
13
31
17
35
39
33
40
42
44
35
43
29O
38
0.43
0.29
0.63
0.060
0.53
0.34
0.98
1.0
1.6
0.34
1 .6
1 .3
0.77
0.22
0.72
3.6
0.62
0.72
0.65
0.61
0.20
0.17
0.58
0.22
0.14
L 0.050
0.11
0.080
L 0.050
L 0.050
0.13
0.11
0.22
0.090
0.15
0.47
L 0.050
0. 11
0.15
t 0.050
0.37
0.27
0.36
0.11
0.34
0.11
0.39
0.88
0.33
0.20
0.33
O. 13
32
38
27
13
31
43
44
44
40
51
41
49
56
50
48
50
49
58
39
45
36
24
31
34
21
23
18
24
23
19
18
28
23
26
8.3
19
47
40
17
25
31
32
27
39
30
42
33
40
30
36
44
4O
I 0.20
0.30
I 0.33
L 0.20
0.20
0.34
0.48
L 0.20
0.56
L 0.20
L 0.20
L 0. 20
0.40
L 0.20
0.78
I 0.20
0.48
L 0.20
L 0.20
L 0.20
L 0.20
0.37
L 0.20
0.70
L 0.20
L 0.20
L 0.20
0.30
L 0.20
L 0.20
L 0.20
L 0.20
L 0.20
L 0.20
L 0.20
L 0.20
L 0.20
L 0.20
L 0.20
L 0.20
t 0.20
L 0.20
0.78
0.80
0.78
0.59
63
0.90
0.56
1 .7
0 56
O. 43
-------
en
STATION
* 6 EB-30
* 7 EB-30
* 6 EB-31
* 7 EB-31
* 6 EB-32
* 7 EB-32
6 EB 33
7 EB-33
» 6 EB-34
*27 EB-34
6 EB-35
7 EB-35
6 EB-36
7 EB-36
* 6 EB 37
« 7 EB 37
6 EB 38
7 EB 38
* 6 EB-39
* 7 EB-39
* 6 WP-01
7 WP-01
* 6 WP-02
7 WP-02
* 6 WP-03
7 WP-03
* 6 WP-04
7 WP-04
* 6 WP-05
7 WP-05
« 6 WP-06
7 WP-06
* 6 WP-07
7 WP-07
« 6 WP 08
7 WP-08
* 6 WP-09
7 WP-09
* 6 WP-10
7 WP-10
* 6 WP-11
7 WP-11
6 WP-12
7 WP-12
6 WP-13
7 WP-13
6 WP-14
7 WP-14
6 WP-15
7 WP-15
6 WP-16
7 WP-16
*
401230
401230
401630
401630
401830
401830
401406
401406
401512
401512
401603
401603
401606
401606
401612
401612
401706
401706
401810
401810
400330
400330
400430
400430
400510
400510
400530
400530
400621
400621
400712
400712
400730
400730
400810
400810
400830
400830
400210
400210
400310
400310
400275
400275
400375
400375
400575
400575
400775
400775
400875
400875
1824
2081
1818
2078
1825
2077
1779
2080
1780
2071
1775
2079
1776
2072
1777
2073
1778
2074
1814
2075
1806
2088
1807
2089
1788
2090
1809
2091
1810
2092
1811
2084
1812
2093
1813
2083
1815
2082
1787
2076
1789
2087
1786
2069
1784
2070
1785
2085
1817
2094
1816
2086
silver
3.9
0.24
3.6
0 . 050
2.3
0.74
4.6
1.1
3.4
2.1
4.1
0.94
5.4
1.3
3.9
1.5
5.2
2.2
2.1
2.4
2.8
0.24
4.2
0.45
1.0
0.030
1.0
0.21
0.050
L 0.020
1.3
0.20
2.9
0.060
1.1
0.030
0.21
0.28
1.0
0.030
1.6
0.24
3.7
0.51
3.7
0.56
3.7
0.61
3.3
0.58
5.0
0.58
tha 1 1 lum
I 0.10
L 0.10
L 0. 10
L 0 10
t 0.10
L 0. 10
L 0. 10
L 0. 10
I 0.10
L 0.10
L 0.10
L 0.10
L 0. 10
t 0. 10
L 0.10
t 0. 10
L 0.10
L 0. 10
L 0. 10
L 0.10
L 0.10
I 0.10
L 0.10
L 0. 10
L 0.10
L 0.10
L 0.10
L 0. 10
0.10
I 0.10
L 0.10
L 0. 10
L 0.10
L 0. 10
L 0.10
L 0.10
L 0.10
L 0.10
L 0. 10
L 0. 10
L 0.10
L 0.10
L 0.10
L 0.10
L 0.10
L 0.10
L 0.10
L 0. 10
L 0.10
L 0.10
I, 0.10
L 0 10
zinc
100
100
120
50
no
100
140
170
100
110
300
260
150
86
120
110
130
110
120
120
72
52
120
68
45
29
59
45
57
26
78
54
81
38
51
32
86
71
46
38
83
42
120
100
110
too
120
140
120
98
130
110
Iron
35000
30000
31000
14000
32000
30000
31000
30000
25000
25000
19000
19000
31000
19000
32000
25000
37000
26000
28000
24000
29000
23000
32000
18000
16000
10000
22000
15000
11000
12000
21000
17000
39000
16000
16000
13000
28000
26000
20000
12000
19000
13000
32000
32000
36000
29000
34000
30000
28000
30000
27000
30000
manganese
420
380
380
130
340
330
300
310
300
280
220
200
400
210
330
300
420
310
580
300
390
440
520
380
200
160
580
340
500
420
570
520
420
460
380
630
650
1000
340
360
280
190
580
460
520
380
630
380
430
450
410
500
* total
organic
carbon
1 .0
0.40
1 .7
0.04
1 .2
0.40
0.90
0.60
0.67
0.50
0.77
0.44
0.60
0.62
1 .3
0.50
1 .3
1 .2
0.92
0.14
0.53
0. 14
0.80
0.27
0.42
0.18
1 .2
0. 16
0.31
0.15
1 .4
0.28
0.18
0.27
0.50
0.06
0.48
0.20
0.38
0.08
0.60
0 10
0.95
0.70
0.90
0.26
0.75
0.59
0.76
0.45
1.3
0.75
* silt *
81 5
82.7
95.9
93.3
90.6
83. 1
82. 1
80.6
93.6
89.8
31 .9
38.8
86.6
80.0
72.2
80 3
86.6
62 1
78.7
69.6
18.9
16.3
29.4
24.0
10.5
4.7
16.4
9.0
7. 1
2.4
14.1
9.7
10.5
5.1
8.8
5.9
41 .5
18.6
12.6
4.8
23.4
6.7
91.2
96.6
91 .8
88.5
96.2
91 1
97.0
92.9
95.9
93.2
clay
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
TOX
CODE
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
BF.NTIHC
CODE
0
0
0
0
0
0
3
3
0
0
2
2
2
3
0
0
1
3
0
0
0
1
0
1
0
2
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
1
1
1
1
1
1
1
1
2
1
MICRO
CODE
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-------
* These stations were not used for developing sediment quality values as appropriate biological reference stations were not available.
a. Reference:
Romberg, G.P., S.P. Pavlou, and E.A. Crecelius. 1984. Presence, distribution, and fate of toxicants in Puget Sound and Lake Washington. METRO
Toxicant Program Report No. 6A. Toxicant Pretreatment Planning Study Technical Report Cl. Municipality of Metropolitan Seattle, Seattle, VIA.
231 pp.
b. U = Undetected at detection limit shown. Detection limits are from Romberg et al. 1984.
c. L = Less than the value shown. For purposes of this report, these were considered undetected.
d. Total PCBs are the sum of detected Aroclors.
I
!—>
00
-------
Figure A-19.
Note:
MAP SHOWING THE 26 STATIONS IN THE CENTRAL BASIN OF PUGET SOUND
AND ELLIOTT BAY SAMPLED DURING PHASE III OF THE TPPS PROGRAM.
These are "corrected" station locations based on a mini ranger
survey and some may vary from those illustrated in subsequent
figures. The corresponding station numbers listed in Table A-6
have 40 or 400 preceeding the station numbers shown on this map.
Reference: Romberg et al. 1984.
A-137
-------
1
STATION
8 EV-20
8 EV-21
8 EV-22
8 EV-23
8 EV 24
8 EV 25
f
EEW-1
EEW 2
EEW-3
EEW- 4
EEW 5
EEW 6
naphtha-
lene
1045
1762
94
17
76
345
1 . 1 '
blpheny I
44
8
b H
UD 6
16
18
acenaph-
thylene
U 6
U 6
18
6
25
43
acenaph-
thone
558
64
35
9
25
40
f luorone
315
33
41
16
42
61
dibenzo
thlo-
phene
150
9
44
22
U 6
39
phenan-
threne
779
94
192
92
229
222
anthra-
cene
168
12
80
26
61
90
1 methyl
phenan
throne
68
9
77
R2
U 6
90
f luor-
anthene
838
74
272
154
246
348
pyrene
562
110
299
464
1029
447
1
I -*
CO
STATION
8 EV-20
8 EV-21
8 EV-22
8 EV-23
8 EV-24
8 EV-25
STATION
8 EV-20
8 EV-21
8 EV-22
8 EV-23
8 EV 24
8 EV-25
*
EEW-1
EEW-2
EEW-3
EEW-4
EEW-5
EEW- 6
t
EEW-1
EEW-2
EEW-3
EEW-4
EEW-5
EEW-6
benzo(a)
anthra-
cene
371
15
117
124
205
320
mercury
0.08
0.06
0.09
0.06
0.10
0.08
chrysene
306
U 6
134
141
210
252
cadmlun
0.30
0.26
0.42
0.37
0.46
0.36
total
benzo-
fluor-
anthenes
274
9
395
111
818
546
copper
41.6
39.9
47.2
37.2
59.6
36.0
benzo(a)
pyrene
170
12
609
103
1030
829
zinc
80.5
72.5
81.0
71.2
80.2
69.4
- Indeno-
(1.2
3-cd)
pyrene
0
0
U
U
U
U
6
6
6
6
6
6
lead
14
14
14
16
21
18
.6
.7
.9
. 1
.5
.8
dlbenzo-
(a.h)an-
thracene
U 6
U 6
U 6
U 6
0 6
U 6
arsenic
7.9
8.6
12.5
10.0
9.1
9.2
benzo-
(ehl)
perylene '.
V 6
U 6
U 6
U 6
U 6
U 6
DEPTH
(m)
11.7
9.5
10.2
9. 1
10.1
9.7
TO
CO
1
1
1
1
1
1
X Silt
50.0
35.3
50.3
51 .3
48.7
30.3
X clay
20.1
10. 1
15.5
11 .3
14 0
12.5
BENTHIC MICRO
CODE CODE CODE
0
0
0
0
0
0
% total
organic
carbon
0.78
0.88
1.39
0.77
1.36
1.37
* total
volatile
solids
3.06
3.14
4.89
3.30
4.67
4.90
U S. Department of the Navy. 1985. Final environmental impact statement. Carrier Battle Group Puget Sound region ship homeporting project.
Technical Appendix. Vol. 2. Prepared for U.S. Department of the Navy, Western Division, Naval Facilities Engineering Command, San Bruno, CA.
b. U = Undetected at detection limit shown. Detection limits are estimates based on concentrations of detected chemicals. No detection limits are
provided in the Navy report.
-------
Figure A-20. Sediment sampling locations in the East Waterway.
Reference: U.S. Navy 1985.
A-139
-------
TABLE A H DUWAM1SH RIVER II
STATION *
9 DR-10
9 DR 11
9 DR-12
9 DR-13
9 DR-14
9 DR-15
9 DR 16
9 DR-17
9 DR-18
9 DR-19
9 DR-20
9 DR-21
9 DR-22
9 DR-23
9 DR-24
9 DR-25
9 DR-26
9 DR 27
9 DR-28
9 DR-29
9 DR-30
9 DR-31
9 DR-32
9 DR-33
9 DR-34
9 DR-35
9 DR-36
9 DR-37
9 DR-38
9 DR-39
9 SQ-21
CA1
CA2
CA3
CB1
CB2
CB3
CB4
CBS
CC1
CC2
CC3
CC4
CCS
CD1
CD2
CE1
CE2
CE3
CF1
CF2
CF3
CF4
CF5
CGI
CG2
CG3
CG4
COS
CHI
CW/A1
SEQUIH
total
PCBs
5400
530
24
HD 215
150
15
19
H 14.9
74
330
15
22
180
1800
16
790
170
40
2500
2200
650
560
88
1200
1300
620
1500
8.7
1400
H 124
37
naphtha-
lene
1400
200
7.4
H 201
660
5.7
UC 3.5
HU 3.1
69
520
U 2.8
32
58
120
9.5
190
160
75
360
80
180
130
8.9
46
46
44
100
U 1.7
40
M 75
7.0
2
mrthy 1
naphth-
a lene
390
49
6.1
H 73.4
200
U 2.9
U 3.7
HU 3.2
45
120
U 2.8
17
18
41
U 3.9
77
69
25
190
39
130
51
6.0
27
32
23
41
U 1.7
26
H 15
16
1 ,
1 '
biphenyl
11
8.9
2.8
HU16.5
U
U
HU
U
U
U
U
U
U
U
U
U
U
MU
U
78
2.5
3.1
2.7
6.5
29
2.5
2.4
1.3
4.1
3.3
11
9.0
4.5
54
6.4
23
7.4
2.7
1 .6
5.0
6.1
2.0
1.4
5.2
3.7
2.8
acenaph-
thene
H
U
U
71
60
7.7
32
480
2.5
3.1
HU2.7
U
U
U
U
U
H
U
18
140
2.5
2.4
19
67
3.3
150
79
40
400
47
26
41
2.7
25
29
34
45
1.3
34
35
2.8
f luorene
98
60
6.8
H 26.4
480
U 2.2
U 2.7
HU 2.4
26
120
U 2.2
V 2.1
12
26
U 2.9
150
75
32
320
29
27
33
U 2.4
17
15
22
30
U 1.2
17
H 36
3.9
phenan-
threne
970
300
32
H 128
1500
11
2.6
H 6.1
160
310
12
17
88
330
15
810
290
190
1000
240
130
220
21
170
200
100
170
U 1.0
160
H 355
48
anthra-
cene
170
130
2.0
H 61
170
2.8
U 2.2
HU 2.5
50
120
U 1.9
U 1.8
30
95
3.7
140
53
25
280
100
53
76
5.9
66
79
32
79
U 0.99
90
H 84
2.0
1 methyl
phenan-
threne
70
32
1 .9
M 7.4
68
2.0
2.3
HU 1 .9
37
14
U 1.7
U 1.7
7.7
14
U 2.3
51
24
13
8'2
72
33
33
U 1.8
26
28
18
24
U 0.93
29
H 42
U 1.9
f luor-
anthene
1400
620
9.2
H 125
850
13
U 2.0
HU 2.3
210
460
22
36
150
61
21
960
270
160
1900
680
200
380
38
430
440
190
290
U 0.89
380
H 540
63
-------
STATION *
9 DR-10
9 DR 11
9 DR-12
9 DR-13
9 DR 14
9 DR-15
9 DR-16
9 DR-17
9 DR 18
9 DR-19
9 DR-20
9 DR-21
9 DR-22
9 DR-23
9 DR-24
9 DR-25
9 DR-26
9 DR-27
9 DR-28
9 DR-29
9 DR-30
9 DR-31
9 DR-32
9 DR-33
9 DR-34
9 DR-35
9 DR-36
9 DR-37
9 DR-38
9 DR 39
9 SQ-21
CA1
CA2
CA3
CB1
CB2
CB3
CB4
CBS
CC1
CC2
CC3
CC4
CCS
CD1
CD2
CE1
CE2
CE3
CF1
CF2
CF3
CF4
CF5
CGI
CG2
CG3
CG4
CG5
CHI
CW/A1
SEQUIM
pyrene
1700
790
4.6
M 350
780
38
U 2.0
MU 1.8
770
830
40
36
250
1200
60
870
280
100
1700
880
340
600
56
580
580
310
830
U 0.87
470
M 830
45
benzol a )
anthra-
cene
590
280
3.2
M 177
220
4.2
U 1.9
MU2.8
92
190
U 2.8
2.0
180
500
5.7
250
87
25
920
450
120
240
12
340
230
320
170
U 1 .0
850
M 250
7.2
chrysene
1000
480
2.5
M 355
330
4.7
U 2.0
MU 3.1
180
370
U 3.1
3.0
200
810
8.1
510
200
20
860
700
230
550
15
530
420
260
460
U 1.1
440
M 410
7.9
benzofa )
pyrene
390
170
U 2.1
M 280
160
3.1
U 2.0
MU2.8
110
150
U 2.9
U 1 .9
54
470
5.8
150
54
3.7
320
240
69
150
8.2
230
210
69
160
U 1.1
130
M 370
4.1
dlbenzo -
{a ,h (an-
thracene
46
51
U 2.0
M 38
14
U 2.6
U 1.8
MU7.3
11
19
U 3.3
U 2.2
U 2.6
97
U 3.4
23
17
U 3.8
50
22
11
21
U 3.2
20
29
U 5.5
U 3.2
U 1.3
33
M 67
U 3.0
hexa-
chloro-
benzene
U
M
U 0
MUO
U
0.66
0. 15
0.66
0.26
0.21
.087
0.54
.092
0.15
0.14
U 0.089
U 0
U
U
U
U
U
U 0
U
U
MUO
U
.084
0.16
1.2
0.10
0.27
0.12
0.24
0.77
0.65
0. 14
0.14
.092
0.24
0.40
0.24
0.25
0.15
0.38
.094
0.28
4.4' DDE
41
4. 1
U 0.071
M 0.93
0.61
U 0.082
U 0.083
MUO . 087
0.26
1.4
U 0.081
U 0.075
0.94
11
U 0.095
4.0
1 . 1
2.3
15
15
3.3
3.6
0.30
10
7.2
5.6
9.9
U 0.12
0.83
M 0.72
0.26
4,4 ' -ODD
38
7.8
U 0.14
M 3.6
6. 1
U 0. 14
U 0. 16
MUO. 14
1 .9
4.3
U 0. 15
U 0.14
2.4
29
0.22
14
2.3
3.4
43
35
9.2
24
0.32
14
16
15
25
U 0.25
14
M 2.3
0.52
4, 4 '-DDT
U 1.2
U 0.53
U 0.15
MUO. 19
U 0.21
U 0.31
U 0. 17
MUO . 56
U 0.32
U 0.33
U 0. 12
U 0. 12
U 0.21
0.44
U 0. 14
U 0.41
U 0.30
U 0.21
0.96
0.69
U 0.85
0.49
U 0.14
U 0.23
U 0.35
2.4
U 0.33
U 0.20
U 0.38
M 0.67
U 0.38
-------
total
volati le
STATION * arsenic
9 DR-10
9 DR-11
9 DR-12
9 DR-13
9 DR-14
9 DR-15
9 DR-16
9 DR-17
9 DR-18
9 DR-19
9 DR-20
9 DR-21
9 DR-22
9 DR-23
9 DR-24
9 DR-25
9 DR-26
9 DR-27
9 DR-28
9 DR-29
9 DR-30
•f, 9 DR-31
1 9 DR-32
£ 9 DR-33
ro 9 DR-34
9 DR-35
9 DR-36
9 DR-37
9 DR-38
9 DR-39
9 SQ-21
CA1
CA2
CAS
CB1
CB2
CB3 M
CB4
CBS
CC1
CC2
CC3
CC4
CCS
CD1
CD2
CE1
CE2 H
CE3
CF1
CF2
CF3
CF4
CF5
CGI
CG2
CG3 M
CG4
CG5
CHI
CW/A1
SEQUIM
2.9
7.2
1 .2
4.3
3.6
3.1
3.5
2.7
3.0
4.6
1.7
2.5
3.1
24
1.7
29
47
57.
19
16
3.3
7.4
2.9
13
9.3
10
7.9
7.1
8.8
13
cadmium
2.4
0.97
U 0.05
0.18
0.13
M 0.07
U 0.05
0.05
0.09
0.30
0.11
U 0.05
U 0.05
1.36
U 0.05
1.73
N 2.64
10.4
1 .68
1.79
0.36
0.61
0.15
0.58
0.4
H 0.31
0.35
0.12
1.16
2.05
copper
83
45
8.7
28
39
M 17
20
18
19
38
13
15
15
115
16
76
H 63
66
74
96
37
63
18
96
79
N 48
59
32
65
55
lead
130
82
5.5
86
700
M 18
4.4
6.0
14
50
22
15
6.0
103
19
131
H 128
128
94
130
62
77
26
95
87
N 56
71
39
91
50
mercury
0.83
0.29
0.08
0.42
0.37
M 0.06
0.14
0.04
0.07
0.23
0.08
0.05
0.02
0.68
0.05
0.33
M 0.15
2.3
0.23
0.46
0.27
0.33
0.08
0.23
0.28
N 0.58
1.1
0. 11
0 11
0.85
zinc
202
78.2
18.4
58.4
63.9
M 30.7
34.2
26
30.7
66.1
24.6
27.8
21 .3
188
22.5
523
M 1211
2600
203
336
74.8
126
39
167
141
H 86.4
116
56.7
130
110
solids
4
3
3
H
2
2
H 1
2
6
1
4
M 3
3
5
6
3
N 5
2
6
5
H 5
5
3
M 7
2
67
94
41
49
63
53
71
81
50
12
50
.71
26
20
86
97
74
87
04
26
90
16
03
28
97
08
64
64
50
18
total
organic
carbon
1
0
0
0
0
M 0
0
0
0
2
0
N 0
0
1
M 0
0
H 0
0
2
1
1
H 1
0
1
1
M 1
1
M 1
1
1
42
99
59
55
83
35
35
34
38
10
39
45
61
36
45
72
57
52
00
66
06
34
70
25
20
20
36
04
62
11
TOX BENTHIC
% silt CODE COUE
50.
68.
4.
12.
21.
M 19.
37.
48.
15.
15.
26.
13.
31.
65.
14.
51.
38.
31 .
58.
79.
22.
75.
39.
68
79.
M 61.
69.
27 3 0
00 3 0
63 1 0
94 1 0
15
66
88
07
61
25
25
68
84
42
20
0
0
0
0
0
0
0
0
0
0
0
59 3 0
58 3 0
66 3 0
23
64
01
62
23
.2
85
04
49
55.6
78.
17.
74
77
0
0
0
0
0
0
0
0
0
0
0
0
0
MICRO
CODE
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
a. Reference: .
Chan, S.-L., M.H. Schiewe, D.W. Brown. 1985. Analyses of sediment samples for U.S. Army Corps of Engineers East, West and Duwamish Waterway
navigation improvement project operations and maintenance sampling and testing of Duwamish River sediments. Unpublished data.
b. M = mean of replicate measurements.
c. U = undetected at detection limit shown.
-------
APPENDIX B
STATION LISTINGS OF CHEMICALS EXCEEDING
SEDIMENT QUALITY VALUES
-------
APPENDIX B. STATION LISTINGS OF CHEMICALS
EXCEEDING SEDIMENT QUALITY VALUES
CONTENTS
Number Page
Table B-l Station listing of organic carbon normalized chemicals
exceeding equilibrium partitioning sediment quality
values B-2
Table B-2 Station listing of chemicals exceeding dry weight AET B-13
Table B-3 Station listing of chemicals exceeding organic carbon
normalized AET B-36
Table B-4 Station listing of chemicals exceeding fines normalized
AET B-48
Table B-5 Station listing of chemicals exceeding lowest dry
weight AET B-65
Table B-6 Station listing of chemicals exceeding Commencement
Bay dry weight normalized AET (non-Commencement Bay
stations) B-98
Table B-6A Commencement Bay AET sediment quality values B-119
-------
STATION LISTINGS OF CHEMICALS EXCEEDING
SEDIMENT QUALITY VALUES
The dry weight values are those found in the original data reports (see
Appendix A). Organic carbon and fines normalized values are calculated and
not adjusted for the proper number of significant figures (i.e., typically 2).
Toxicity, benthic, and microtox codes are indicated for all stations.
The toxicity code is defined as:
0 = No data available
1 = No significanta oyster larvae abnormality or amphipod mortality
2 = Significant3 oyster larvae abnormality
3 = Significant3 amphipod mortality
4 = Both significant3 oyster larvae abmnormality and amphipod
mortality.
The benthic code is defined as:
0 = No data available
1 = No significanta depressions in benthic infaunal abundances
2 = Significant3 depressions in benthic infaunal abundances of one
major taxonomic group
3 = Significant3 depressions in benthic infaunal abundances of
more than one major taxonomic group.
The microtox code is defined as:
0 = No data available
1 = No significanta decrease in bacterial luminescence
2 = Significant3 decrease in bacterial luminescence.
3 Significance implies statistically significant difference (P>0.05) from
reference conditions.
B-l
-------
TABLE B-l. STATION LISTING OF ORGANIC CARBON NORMALIZED CHEMICALS
EXCEEDING EQUILIBRIUM PARTITIONING SEDIMENT QUALITY VALUES
Organics expressed as ppb organic carbon, metals ppm organic carbon
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code: 3 Microtox code 2
Concentration OC
total PCBs 45045.0
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code: 3 Microtox code 2
Concentration OC
total PCBs 39682.5
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code: 2 Microtox code 2
.Concentration OC
total PCBs 16935.5
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code: 1 Microtox code 2
Concentration OC
total PCBs 46025.1
Group: 2 Station: B04
Toxicity code: 1 Benthic code: 1 Microtox code 0
Concentration OC
4,4'-DDT 234
Group: 2 Station: B15
Toxicity code: 3 Benthic code: 1 Microtox code 0
Concentration OC
4,4'-DDT 391.89
B-2
-------
Group: 3 Station: EB-09
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 18644.1
Group: 3 Station: EB-17
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 33645.8
Group: 3 Station: EB-20
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 46043.2
Group: 3 Station: EB-22
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 35595.9
Group: 3 Station: EB-23
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 13333.3
Group: 3 Station: SC-06
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 42764.5
B-3
-------
Group: 3 Station: SC-07
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 27735.8
Group: 3 Station: SC-08
Toxicity code: 3 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 22508.7
Group: 3 Station: SC-14
Toxicity code: 3 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 55000.0
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code: 0 Microtox code 0
Concentration OC
4,4'-DDT 1222.22
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 177272.7
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code: 3 Microtox code 0
Concentration OC
total PCBs 117777.8
B-4
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code;
fluoranthene
total PCBs
Microtox code 0
Concentration OC
697013.0
511688.3
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code:
total PCBs
2 Microtox code 0
Concentration OC
661666.7
Group: 6 Station: EB-38
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration OC
56153.8
Group: 6 Station: WP-13
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration OC
53333.3
Group: 6 Station: WP-15
Toxicity code: 0 Benthic code:
total PCBs
Microtox code 0
Concentration OC
19210.5
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
total PCBs
2 Microtox code 0
Concentration OC
21153.8
B-5
-------
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code;
total PCBs
Microtox code 0
Concentration OC
380000.0
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
fluoranthene
total PCBs
Microtox code 0
Concentration OC
1336818
219318.2
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code:
diethyl phthalate
total PCBs
4,4'-DDT
Microtox code 0
Concentration OC
51290.3
78548.4
2419.35
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
total PCBs
4,4'-ODT
Microtox code 0
Concentration OC
26250.0
2333.33
Group: 7 Station: WP-01
Toxicity code: 0 Benthic code:
4,4'-DDT
1 Microtox code 0
Concentration OC
714.29
B-6
-------
Group: 7 Station: WP-02
Toxlaity code: 0 Benthic code:
fluoranthene
total PCBs
Microtox code 0
Concentration OC
1016296
55185.2
Group: 7 Station: WP-03
Toxicity code: 0 Benthic code:
total PCBs
2 Microtox code 0
Concentration OC
13888.9
Group: 7 Station: WP-04
Toxicity code: 0 Benthic code:
diethyl phthalate
total PCBs
4,4'-DDT
Microtox code 0
Concentration OC
60625.0
42500.0
1250.00
Group: 7 Station: WP-06
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration OC
25000.0
Group: 7 Station: WP-07
Toxicity code: 0 Benthic code:
fluoranthene
total PCBs
4,4'-DDT
Microtox code 0
Concentration OC
650740
48148.1
3703.70
Group: 7 Station: WP-08
Toxicity code: 0 Benthic code;
diethyl phthalate
total PCBs
Microtox code 0
Concentration OC
78333.3
268333.3
B-7
-------
Group: 7 Station: WP-09
Toxicity code: 0 Benthic code:
total PCBs
4,4'-DDT
Microtox code 0
Concentration OC
65000.0
500.00
Group: 7 Station: WP-10
Toxicity code: 0 Benthic code:
fluoranthene
total PCBs
Microtox code 0
Concentration OC
861250.0
51250.0
Group: 7 Station: WP-11
Toxicity code: 0 Benthic code;
phenanthrene
fluoranthene
total .PCBs
.Microtox code 0
Concentration OC
3150000
6299000
131000.0
Group: 7 Station: WP-12
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration OC
15142.9
Group: 7 Station: WP-13
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration OC
33461.5
Group: 7 Station: WP-14
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration OC
24576.3
B-8
-------
Group: 7 Station: WP-15
Toxicity code: 0 Benthic code:
total PCBs
4,4'-DDT
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code:
total PCBs
1 Microtox code 0
Concentration OC
49111.1
2444.44
0 Microtox code 0
Concentration OC
380281.7
Group: 9 Station: DR-11
Toxicity code: 3 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
53535.4
Group: 9 Station: DR-13
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
39090.9
Group: 9 Station: DR-14
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 0
Concentration OC
18072.3
Group: 9 Station: DR-18
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
19473.7
B-9
-------
Group: 9 Station: DR-19
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 0
Concentration OC
15714.3
Group: 9 Station: DR-22
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
29508.2
Group: 9 Station: DR-23
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 0
Concentration OC
132352.9
Group: 9 Station: DR-25
Toxicity code: 3 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
109722.2
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
29824.6
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code:
dieldrin
heptachlor
1indane
Microtox code 0
Concentration OC
577
904
692
B-10
-------
Group: 9 Station: DR-28
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 125000.0
Group: 9 Station: DR-29
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 132530.1
Group: 9 Station: DR-30
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration X
total PCBs 61320.8
Group: 9 Station: DR-31
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 41791.0
Group: 9 Station: DR-32
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 12571.4
Group: 9 Station: DR-33
Toxicity code: 1 Benthic code: 0 Microtox code 0
Concentration OC
total PCBs 96000.0
B-ll
-------
Group: 9 Station: DR-34
Toxicity code: 1 Benthic code;
total PCBs
Microtox code 0
Concentration OC
108333.3
Group: 9 Station: DR-35
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
51666.7
Group: 9 Station: DR-36
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
110294.1
Group: 9 Station: DR-38
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
86419.8
B-12
-------
TABLE B-2. STATION LISTING OF CHEMICALS EXCEEDING
DRY WEIGHT NORMALIZED AET
Organics expressed as ppb dry weight, metals ppm dry weight
Group: 1 Station: BL-13
Toxicity code: 1 Benthic code: 1 Microtox code 2
Microtox
butyl benzyl phthalate
Concentration DW
83.0
Group: 1 Station: CI-11
Toxicity code: 4 Benthic code:
Amphipod
phenol
benzo(ghi)perylene
1,4-dichlorobenzene
benzyl alcohol
lead
Oyster
phenol
phenanthrene
benzo(ghi)perylene
1,4-dichlorobenzene
benzyl alcohol
lead
nickel
Benthic
1,4-dichlorobenzene
benzyl alcohol
lead
zinc
Microtox code 2
Concentration DW
1100
780.0
290.0
140
725
Concentration DW
1100
1800
780.0
290.0
140
725
40.0
Concentration DW
290.0
140
725
325.0
B-13
-------
Microtox
phenanthrene
fluoranthene
chrysene
benzo(ghi)perylene
1,2-dichlorobenzene
1,4-dlchlorobenzene
benzyl alcohol
lead
mercury
High molecular wt. PAH
Chlorinated benzenes
Concentration DW
1800
2400
1600.0
780.0
37.0
290.0
140
725
.53
13090.0
327.0
Group: 1 Station: CI-13
Toxicity code: 2 Benthic code:
Oyster
bis(2-ethylhexyl)phthalate
benzoic acid
mercury
Total phthalates
Benthic
bis(2-ethylhexyl)phthal ate
benzoic acid
cadmium
lead
mercury
Microtox
bis(2-ethylhexyl)phthalate
butyl benzyl phthalate
total PCBs
benzoic acid
mercury
Total phthalates
Microtox code 2
Concentration DW
3100.0
690
1.10
3548.0
Concentration DW
3100.0
690
6.70
450
1.10
Concentration DW
3100.0
210.0
140.0
690
1.10
3548.0
B-14
-------
Group: 1 Station: CI-16
Toxicity code: 2 Benthic code: 3 Microtox code 2
Oyster Concentration DW
N-nitrosodiphenylamine 220.0
1,2-dichlorobenzene 350.0
1,4-dichlorobenzene 260.0
di-n-butyl phthalate 1600.0
4-methylphenol 1200
Chlorinated benzenes 667.0
Benthic Concentration DW
2,4-dimethylphenol 50.0
N-nitrosodiphenylamine 220.0
1,2-dichlorobenzene 350.0
1,4-dichlorobenzene 260.0
4-methylphenol 1200
Chlorinated benzenes 667.0
Microtox Concentration DW
N-nitrosodiphenylamine 220.0
1,2-dichlorobenzene 350.0
1,4-dichlorobenzene 260.0
di-n-butyl phthalate 1600.0
4-methylphenol 1200
Chlorinated benzenes 667.0
Group: 1 Station: CI-17
Toxicity code: 1 Benthic code: 1 Microtox code 2
Microtox Concentration DW
fluoranthene 1900
1,4-dichlorobenzene 119.0
Group: 1 Station: CI-20
Toxicity code: 4 Benthic code: 1 Microtox code 1
Amphipod Concentration DW
phenol 1200
l.l'-biphenyl 270.0
dibenzothiophene 250.0
B-15
-------
Oyster Concentration DW
phenol 1200
l,l'-biphenyl 270.0
dibenzothiophene 250.0
Group: 1 Station: HY-12
Toxicity code: 2 Benthic code: 1 Microtox code 2
Oyster Concentration DW
phenol 500
benzo(ghi)perylene 740.0
dibenzo(a,h)anthracene 260.0
di-n-butyl phthalate 5100.0
Total phthalates 5100.0
Microtox Concentration DW
chrysene 1800.0
benzo(ghi)perylene 740.0
di-n-butyl phthalate 5100.0
mercury .46
Total phthalates 5100.0
Group: 1 Station: HY-14
Toxicity code: 1 Benthic code: 2 Microtox code 2
Microtox Concentration DW
fluoranthene 2500
chrysene 2800.0
benzo(ghi)perylene 720.0
High molecular wt. PAH 16650.0
Group: 1 Station: HY-17
Toxicity code: 2 Benthic code: 3 Microtox code 2
Oyster Concentration DW
benzo(a)pyrene 2400.0
Total benzofluoranthenes 3700
pyrene 4300
tetrachloroethene 210
ethylbenzene 50
total xylenes 160
High molecular wt. PAH 18402.0
B-16
-------
Benthic Concentration DW
tetrachloroethene 210
ethylbenzene 50
total xylenes 160
arsenic 86.0
zinc 268.0
Microtox Concentration DW
fluoranthene 3900
benzo(a)pyrene 2400.0
chrysene 2700.0
ethylbenzene 50
total PCBs 170.0
total xylenes 160
High molecular wt. PAH 18402.0
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code: 3 Microtox code 2
Amphipod Concentration DW
benzo(a)anthracene 2300.0
benzo(a)pyrene 6100.0
Total benzofluoranthenes 8500
dibenzo(a,h)anthracene 1500
indeno(l,2,3-cd)pyrene 2700.0
1,2,4-trichlorobenzene 260.0
hexachlorobenzene 730.0
hexachlorobutadiene 730.0
dibenzothiophene 320.0
1-methylphenanthrene 530.0
High molecular wt. PAH 30000.0
Chlorinated benzenes 1328.0
B-17
-------
Oyster
phenol
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
total PCBs
nickel
dibenzothiophene
1-methylphenanthrene
High molecular wt. PAH
Total phthalates
Chlorinated benzenes
Benthic
Total benzofluoranthenes
dibenzo(a,h)anthracene
1,2,4-trichlorobenzene
hexachlorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
total PCBs
arsenic
dibenzothiophene
1-methylphenanthrene
Chlorinated benzenes
Concentration DW
530
2300.0
6100.0
8500
1500
2700.0
260.0
73.0
180.0
730.0
3000.0
2000.0
52.0
320.0
530.0
30000.0
3560.0
1328.0
Concentration DW
8500
1500
260.0
730.0
73.0
180.0
730.0
3000.0
2000.0
90.0
320.0
530.0
1328.0
B-18
-------
Microtox Concentration DW
fluoranthene 3600
benzo(a)pyrene 6100.0
chrysene 2700.0
1,2,4-trichlorobenzene 260.0
hexachlorobenzene 730.0
1,2-dichlorobenzene 73.0
1,4-dichlorobenzene 180.0
hexachlorobutadiene 730.0
bis(2-ethylhexyl)phthalate 3000.0
total PCBs 2000.0
mercury .50
dibenzothiophene 320.0
1-methylphenanthrene 530.0
High molecular wt. PAH 30000.0
Total phthalates 3560.0
Chlorinated benzenes 1328.0
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code: 3 Microtox code 2
Amphipod Concentration DW
phenanthrene 2300
benzo(ghi)perylene 1100.0
dibenzo(a,h)anthracene 440.0
dimethyl phthalate 350.0
Oyster Concentration DW
phenanthrene 2300
benzo(a)pyrene 2000.0
benzo(ghi)perylene 1100.0
dibenzo(a,h)anthracene 440.0
dimethyl phthalate 350.0
tetrachloroethene 170
total PCBs 1500.0
nickel 56.0
Benthic Concentration DW
dimethyl phthalate 350.0
tetrachloroethene 170
total PCBs 1500.0
B-19
-------
Microtox Concentration DW
phenanthrene 2300
fluoranthene 2500
benzo(a)pyrene 2000.0
chrysene 2300.0
benzo(ghi)perylene 1100.0
hexachlorobutadiene 170.0
butyl benzyl phthalate 110.0
dimethyl phthalate 350.0
total PCBs 1500.0
total xylenes 110
High molecular wt. PAH 13790.0
Group: 1 Station: HY-24
Toxicity code: 1 Benthic code: 1 Microtox code 2
Microtox Concentration DW
chrysene 2300.0
hexachlorobutadiene 140.0
butyl benzyl phthalate 470.0
dimethyl phthalate 120.0
total PCBs 250.0
mercury .49
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code: 2 Microtox code 2
M,icrotox Concentration DW
1,2,4-trichlorobenzene 34.0
hexachlorobenzene 96.0
hexachlorobutadiene 130.0
total PCBs 420.0
Chlorinated benzenes 203.0
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code: 1 Microtox code 2
Amphipod Concentration DW
1,2,4-trichlorobenzene 64.0
hexachlorobenzene 230.0
B-20
-------
Microtox Concentration DW
chrysene 1800.0
1,2,4-trichlorobenzene 64.0
hexachlorobenzene 230.0
hexachlorobutadiene 270.0
total PCBs 1100.0
Chlorinated benzenes 395.0
Group: 1 Station: HY-43
Toxicity code: 1 Benthic code: 1 Microtox code 2
Microtox Concentration DW
1,2,4-trichlorobenzene 51.0
hexachlorobenzene 130.0
hexachlorobutadiene 180.0
ethylbenzene 37
total xylenes 120
Chlorinated benzenes 274.2
Group: 1 Station: HY-47
Toxicity code: 2 Benthic code: 2 Microtox code 2
Oyster Concentration DW
1,4-dichlorobenzene 120.0
hexachlorobutadiene 290.0
di-n-butyl phthalate 1500.0
Benthic Concentration DW
1,4-dichlorobenzene 120.0
hexachlorobutadiene 290.0
Microtox Concentration DW
1,2,4-trichlorobenzene 51.0
hexachlorobenzene 100.0
1,4-dichlorobenzene 120.0
hexachlorobutadiene 290.0
di-n-butyl phthalate 1500.0
Chlorinated benzenes 312.0
B-21
-------
Group: 1 Station: HY-50
Toxicity code: 1 Benthic code:
Microtox
benzyl alcohol
Group: 1 Station: MI-13
Toxicity code: 1 Benthic code:
Microtox
1 Microtox code 2
Concentration DW
73
1 Microtox code 2
Concentration DW
dimethyl phthalate
FINES
110.0
.89
Group: 1 Station: RS-13
Toxicity code: 4 Benthic code:
Amphipod
2-methylphenol
Oyster
2-methylphenol
Microtox code 1
Concentration DW
72
Concentration DW
72
B-22
-------
Group: 1 Station: RS-18
Toxicity code: 4 Benthic code: 3 Microtox code 2
Amphipod Concentration DW
N-nitrosodiphenylamine 610.0
acenaphthene 2500.0
anthracene 1400.0
phenanthrene 11000
fluorene 3100.0
fluoranthene 8100
benzo(a)anthracene 3200.0
benzo(a)pyrene 4000.0
Total benzofluoranthenes 4200
chrysene 4700.0
dibenzo(a,h)anthracene 320.0
indeno(l,2,3-cd)pyrene 770.0
pyrene 5600
2-methylphenol 71
dibenzofuran 2000
2-methylnaphthalene 1200
antimony 420.0
arsenic 9700.0
cadmium 184.00
copper 11400
iron 52900
lead 6250
manganes 748
thallium 3.20
zinc 3320.0
mercury 52.00
l.l'-biphenyl 1100.0
dibenzothiophene 1100.0
1-methylphenanthrene 1300.0
Low molecular wt. PAH 20190.0
High molecular wt. PAH 30890.0
B-23
-------
Oyster
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
1,4-dichlorobenzene
2-methylphenol
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
iron
lead
manganes
nickel
thallium
zinc
mercury
l,r-biphenyl
dibenzothiophene
1-methylphenanthrene
Low molecular wt. PAH
High molecular wt. PAH
Concentration DW
610.0
2500.0
1400.0
11000
3100.0
3200.0
4000.0
4200
4700.0
320.0
770.0
5600
250.0
71
2000
1200
420.0
9700.0
184.00
11400
52900
6250
748
93.0
3.20
3320.0
52.00
1100.0
1100.0
1300.0
20190.0
30890.0
B-24
-------
Benthic Concentration DW
N-nitrosodiphenylamine 610.0
acenaphthene 2500.0
anthracene 1400.0
phenanthrene 11000
fluorene 3100.0
fluoranthene 8100
1,4-dichlorobenzene 250.0
dibenzofuran 2000
2-methylnaphthalene 1200
antimony 420.0
arsenic 9700.0
cadmium 184.00
copper 11400
iron 52900
lead 6250
thallium 3.20
zinc 3320.0
mercury 52.00
l.l'-biphenyl 1100.0
dibenzothiophene 1100.0
1-methylphenanthrene 1300.0
Low molecular wt. PAH 20190.0
B-25
-------
Microtox
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)pyrene
chrysene
1,4-dichlorobenzene
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
iron
lead
manganes
thallium
zinc
mercury
l,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Low molecular wt. PAH
High molecular wt. PAH
Chlorinated benzenes
Concentration DW
610.0
2500.0
1400.0
11000
3100.0
8100
4000.0
4700.0
250.0
2000
1200
420.0
9700.0
184.00
11400
52900
6250
748
3.20
3320.0
52.00
1100.0
1100.0
1300.0
20190.0
30890.0
268.0
Group: 1 Station: RS-19
Toxicity code: 4 Benthic code:
Amphipod
antimony
arsenic
cadmium
copper
lead
thallium
zinc
mercury
Microtox code 2
Concentration DW
36.00
1550.0
16.00
2240
1020
.46
906.0
3.20
B-26
-------
Oyster
di-n-butyl phthalate
antimony
arsenic
cadmium
copper
lead
thallium
mercury
Benthic
arsenic
cadmium
copper
lead
thallium
zinc
mercury
Microtox
di-n-butyl
antimony
arsenic
cadmium
copper
lead
thai!ium
mercury
phthalate
Concentration DW
1600.0
36.00
1550.0
16.00
2240
1020
.46
3.20
Concentration DW
1550.0
16.00
2240
1020
.46
906.0
3.20
Concentration DW
1600.0
36.00
1550.0
16.00
2240
1020
.46
3.20
Group: 1 Station: RS-20
Toxicity code: 1 Benthic code:
Benthic
arsenic
Microtox
mercury
Microtox code 2
Concentration DW
90.0
Concentration DW
.59
B-27
-------
Group: 1 Station: RS-24
Toxicity code: 3 Benthic code:
Amph i pod
antimony
arsenic
cadmium
iron
manganes
zinc
Microtox code 1
Concentration DW
26.00
700.0
9.60
37100
484
1620.0
Group: 1 Station: SI-11
Toxicity code: 1 Benthic code:
Benthic
arsenic
lead
zinc
Microtox
lead
Microtox code 2
Concentration DW
93.0
661
491.0
Concentration DW
661
Group: 1 Station: SI-12
Toxicity code: 3 Benthic code:
Benthic
N-nitrosodiphenylamine
lead
zinc
Microtox
N-nitrosodiphenylamine
Microtox code 2
Concentration DW
130.0
496
337.0
Concentration DW
130.0
Group: 1 Station: SI-15
Toxicity code: 3 Benthic code: 1 Microtox code 1
Amph i pod
1-methylphenanthrene
Concentration DW
370.0
B-28
-------
Group: 1 Station: SP-12
Toxicity code: 2 Benthic code:
Microtox
benzyl alcohol
Microtox code 2
Concentration DW
61
Group: 1 Station: SP-14
Toxicity code: 4 Benthic code:
Amphipod
phenol
naphthalene
4-methylphenol
2-methylnaphtha 1ene
manganes
total volatile solids
total organic carbon
l,l'-biphenyl
Low molecular wt. PAH
Oyster
phenol
naphthalene
4-methylphenol
2-methylnaphthalene
manganes
nickel
total volatile solids
total organic carbon
l,l'-biphenyl
Low molecular wt. PAH
Benthic
phenol
naphthalene
4-methylphenol
2-methylnaphtha!ene
total volatile solids
total organic carbon
l,l'-biphenyl
Microtox code 2
Concentration DW
1700
4400.0
96000
810
556
44.70
16.00
310.0
6065.0
Concentration DW
1700
4400.0
96000
810
556
40.0
44.70
16.00
310.0
6065.0
Concentration DW
1700
4400.0
96000
810
44.70
16.00
310.0
B-29
-------
Mlcrotox
phenol
naphthalene
4-methylphenol
2-methylnaphthalene
manganes
total volatile solids
total organic carbon
1,1'-biphenyl
Low molecular wt. PAH
Concentration DW
1700
4400.0
96000
810
556
44.70
16.00
310.0
6065.0
Group: 1 Station: SP-15
Toxicity code: 4 Benthic code:
Amphipod
4-methylphenol
Oyster
4-methylphenol
Benthic
4-methylphenol
Microtox
4-methylphenol
Microtox code 2
Concentration DW
2600
Concentration DW
2600
Concentration DW
2600
Concentration DW
2600
Group: 1 Station: SP-16
Toxicity code: 4 Benthic code:
Amphipod
benzyl alcohol
Oyster
4-methylphenol
benzyl alcohol
Benthic
4-methylphenol
benzyl alcohol
Microtox code 2
Concentration DW
130
Concentration DW
890
130
Concentration DW
890
130
B-30
-------
Microtox Concentration DW
4-methylphenol 890
benzyl alcohol 130
Group: 2 Station: B15
Toxicity code: 3 Benthic code: 1 Microtox code 0
Amphipod Concentration DW
4,4'-DDT 5.8
Group: 3 Station: EV-01
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
fluoranthene 4800
thallium .50
Group: 3 Station: EV-04
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
phenol 1400
acenaphthene 3300.0
naphthalene 5900.0
acenaphthylene 770.0
phenanthrene 4700
fluorene 2100.0
fluoranthene 4100
zinc 1074.0
total volatile solids 35.06
total organic carbon 15.42
Low molecular wt. PAH 17180.0
B-31
-------
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Amphipod
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(ghi)perylene
pyrene
Low molecular wt. PAH
High molecular wt. PAH
Microtox code 0
Concentration DW
8900
980.0
5200
1900.0
1000.0
6400
11080.0
20140.0
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code:
Amphipod
4,4'-DDT
0 Microtox code 0
Concentration DW
22.0
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code:
Amphipod
acenaphthylene
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
2400
3900.0
71.00
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
benzo(a)pyrene
Total benzofluoranthenes
benzo(ghi)perylene
dibenzo(a,h)anthracene
butyl benzyl phthalate
mercury
4,4'-DDE
Microtox code 0
Concentration DW
132.0
9770.0
17529
8046.0
4023
1724
.98
11.00
B-32
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code:
Benthic
anthracene
phenanthrene
fluorene
benzo(a)anthracene
chrysene
total PCBs
lead
zinc
mercury
4,4'-DDE
4,4'-DDD
Low molecular wt. PAH
Microtox code 0
Concentration DW
3936.0
4293
2683.0
9481.0
10376
3940.0
670
300.0
1.60
47.00
175.00
11431.0
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code:
Benthic
acenaphthylene
total PCBs
silver
4,4'-DDE
Microtox code 0
Concentration DW
4013
3970.0
5.40
37.00
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
Benthic
4,4'-DDD
2 Microtox code 0
Concentration DW
12.00
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
total PCBs
mercury
4,4'-DDE
4,4'-DDD
Microtox code 0
Concentration DW
2280.0
1.00
30.00
30.00
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
Microtox code 0
B-33
-------
Benthic
N-nitrosodiphenylamine
anthracene
Total benzofluoranthenes
butyl benzyl phthalate
lead
zinc
mercury
Concentration DW
276.0
1636.0
11213
1820
430
260.0
1.30
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
diethyl phthalate
4,4'-DDT
4,4'-DDE
Microtox code 0
Concentration DW
308.0
318.0
15.0
10.00
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
Benthic
butyl benzyl phthalate
4,4'-DDT
Microtox code 0
Concentration DW
812.0
28.0
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code:
Amphipod
total PCBs
4,4'-DDE
Microtox code 0
Concentration DW
5400.0
41.00
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code:
Amphipod
zinc
0 Microtox code 0
Concentration DW
1211.0
B-34
-------
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
cadmium 10.40
zinc 2600.0
mercury 2.30
B-35
-------
TABLE B-3. STATION LISTING OF CHEMICALS EXCEEDING
ORGANIC CARBON NORMALIZED AET
Organics expressed as ppb organic carbon, metals ppm organic carbon
Group: 1 Station: BL-31
Toxicity code: 1 Benthic code: 1
Microtox
phenol
Group: 1 Station: CI-11
Toxicity code: 4 Benthic code:
Oyster
1,4-dichlorobenzene
Group: 1 Station: CI-13
Toxicity code: 2 Benthic code:
Microtox
bis(2-ethylhexyl)phtha1ate
Group: 1 Station: CI-16
Toxicity code: 2 Benthic code:
Oyster
1,2-dichlorobenzene
Benthic
1,2-dichlorobenzene
Microtox
1,2-dichlorobenzene
Microtox code 2
Concentration OC
38532.1
2 Microtox code 2
Concentration OC
3273.1
3 Microtox code 2
Concentration OC
47692.3
3 Microtox code 2
Concentration OC
3211.0
Concentration OC
3211.0
Concentration OC
3211.0
B-36
-------
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code:
Amphi pod
benzo(a)pyrene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
hexachlorobenzene
hexachlorobutadiene
Chlorinated benzenes
Oyster
benzo(a)pyrene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
Chlorinated benzenes
Benthic
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
Chlorinated benzenes
1,2,4-trichlorobenzene
hexachlorobenzene
Microtox
dibenzo(a,h)anthracene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
total PCBs
Chlorinated benzenes
Microtox code 2
Concentration OC
137387.4
33783.8
60810.8
16441.4
16441.4
29909.9
Concentration OC
137387.4
33783.8
60810.8
5855.9
16441.4
4054.1
16441.4
67567.6
29909.9
Concentration OC
16441.4
67567.6
29909.9
5855.9
16441.4
Concentration OC
33783.8
5855.9
16441.4
16441.4
67567.6
45045.0
29909.9
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code:
Microtox
hexachlorobutadiene
total PCBs
Microtox code 2
Concentration OC
4497.4
39682.5
B-37
-------
Group: 1 Station: HY-24
Toxicity code: 1 Benthic code:
Microtox
butyl benzyl phthalate
Microtox code 2
Concentration OC
9179.7
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code:
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
total PCBs
Microtox code 2
Concentration OC
1371.0
3871.0
5241.9
16935.5
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code:
Amphipod
hexach1orobenzene
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
total PCBs
Microtox code 2
Concentration OC
9623.4
Concentration OC
2677.8
9623.4
11297.1
46025.1
Group: 1 Station: HY-43
Toxicity code: 1 Benthic code:
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
Microtox code 2
Concentration OC
1764.7
4498.3
6228.4
Group: 1 Station: HY-47
Toxicity code: 2 Benthic code:
Oyster
1,2,4-trichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
Microtox code 2
Concentration OC
2771.7
6521.7
15760.9
B-38
-------
Benthlc Concentration OC
hexachlorobutadiene 15760.9
1,2,4-trichlorobenzene 2771.7
Microtox Concentration OC
1,2,4-trichlorobenzene 2771.7
hexachlorobenzene 5434.8
hexachlorobutadiene 15760.9
Group: 1 Station: MI-13
Toxicity code: 1 Benthic code: 1 Microtox code 2
Microtox Concentration OC
2,4-dimethylphenol 1336.4
Group: 1 Station: RS-13
Toxicity code: 4 Benthic code: 1 Microtox code 1
Amphipod Concentration OC
fluorene 71014.5
fluoranthene 188405.8
benzo(a)anthracene 159420.3
benzo(a)pyrene 142029.0
Total benzofluoranthenes 434782.6
chrysene 202898.6
dibenzo(a,h)anthracene 33333.3
indeno(l,2,3-cd)pyrene 86956.5
1,4-dichlorobenzene 15942.0
2-methylphenol 10434.8
4-methylphenol 81159.4
dibenzofuran 57971.0
2-methylnaphthalene 63768.1
Low molecular wt. PAH 528985.5
High molecular wt. PAH 1488406
l.l'-biphenyl 11594.2
1-methylphenanthrene 28985.5
B-39
-------
Oyster Concentration OC
acenaphthene 56521.7
naphthalene 173913.0
phenanthrene 159420.3
fluorene 71014.5
fluoranthene 188405.8
benzo(a)anthracene 159420.3
benzo(a)pyrene 142029.0
Total benzofluoranthenes 434782.6
chrysene 202898.6
benzo(ghi)perylene 66666.7
dibenzo(a,h)anthracene 33333.3
indeno(l,2,3-cd)pyrene 86956.5
1,2-dichlorobenzene 2318.8
1,4-dichlorobenzene 15942.0
2-methylphenol 10434.8
4-methylphenol 81159.4
dibenzofuran 57971.0
2-methylnaphthalene 63768.1
Low molecular wt. PAH 528985.5
High molecular wt. PAH 1488406
l.l'-biphenyl 11594.2
dibenzothiophene 14202.9
1-methylphenanthrene 28985.5
Group: 1 Station: RS-18
Toxicity code: 4 Benthic code: 3 Microtox code 2
Amphipod Concentration OC
dibenzofuran 22650.1
antimony 4756.5
arsenic 109852.8
cadmium 2083.8
copper 129105.3
selenium 271.8
mercury 588.9
l.l'-biphenyl 12457.5
B-40
-------
Oyster
acenaphthene
phenanthrene
fluorene
dibenzofuran
antimony
arsenic
cadmium
copper
lead
mercury
1,1'-biphenyl
dibenzothiophene
Benthic
antimony
arsenic
cadmium
copper
lead
1,1'-biphenyl
Microtox
antimony
arsenic
cadmium
copper
lead
mercury
1,1'-biphenyl
Concentration OC
28312.6
124575.3
35107.6
22650.1
4756.5
109852.8
2083.8
129105.3
70781.4
588.9
12457.5
12457.5
Concentration OC
4756.5
109852.8
2083.8
129105.3
70781.4
12457.5
Concentration OC
4756.5
109852.8
2083.8
129105.3
70781.4
588.9
12457.5
Group: 1 Station: RS-19
Toxicity code: 4 Benthic code:
Amphipod
di-n-butyl phthalate
dibenzofuran
antimony
arsenic
cadmium
copper
lead
selenium
zinc
mercury
1-methyIphenanthrene
Microtox code 2
Concentration OC
275862.1
18965.5
6206.9
267241.4
2758.6
386206.9
175862.1
241.4
156206.9
551.7
24137.9
B-41
-------
Oyster
fluorene
di-n-butyl phthalate
dibenzofuran
antimony
arsenic
cadmium
copper
lead
mercury
dibenzothiophene
1-methylphenanthrene
Benthic
antimony
arsenic
cadmium
copper
lead
zinc
dibenzothiophene
Microtox
di-n-butyl phthalate
antimony
arsenic
cadmium
copper
lead
mercury
Total phthalates
dibenzothiophene
Concentration OC
24137.9
275862.1
18965.5
6206.9
267241.4
2758.6
386206.9
175862.1
551.7
16724.1
24137.9
Concentration OC
6206.9
267241.4
2758.6
386206.9
175862.1
156206.9
16724.1
Concentration OC
275862.1
6206.9
267241.4
2758.6
386206.9
175862.1
551.7
275862.1
16724.1
Group: 1 Station: RS-20
Toxicity code: 1 Benthic code:
Benthic
antimony
arsenic
cadmium
copper
lead
Microtox code 2
Concentration OC
642.9
32142.9
1071.4
48928.6
27857.1
B-42
-------
Microtox
di-n-butyl phthalate
copper
iron
manganese
nickel
mercury
Total phthalates
Concentration OC
264285.7
48928.6
5678571
82857.1
6785.7
210.7
264285.7
Group: 1 Station: RS-24
Toxicity code: 3 Benthic code:
Amphipod
antimony
arsenic
cadmium
zinc
Microtox code 1
Concentration OC
3250.0
87500.0
1200.0
202500.0
Group: 1 Station: SI-11
Toxicity code: 1 Benthic code:
Benthic
lead
2 Microtox code 2
Concentration OC
31476.2
Group: 1 Station: SI-12
Toxicity code: 3 Benthic code:
Benthic
lead
2 Microtox code 2
Concentration OC
31794.9
Group: 1 Station: SP-14
Toxicity code: 4 Benthic code:
Amphipod
4-methylphenol
Oyster
4-methylphenol
Microtox code 2
Concentration OC
600000.0
Concentration OC
600000.0
B-43
-------
Benthic
4-methylphenol
Microtox
4-methylphenol
Group: 1 Station: SP-15
Toxicity code: 4 Benthic code:
Amphipod
4-methylphenol
Oyster
4-methylphenol
Benthic
4-methylphenol
Microtox
4-methylphenol
Group: 1 Station: SP-16
Toxicity code: 4 Benthic code:
Amphipod
4-methylphenol
benzyl alcohol
Oyster
4-methylphenol
benzyl alcohol
Benthic
benzyl alcohol
Microtox
benzyl alcohol
Concentration OC
600000.0
Concentration OC
600000.0
Microtox code 2
Concentration OC
126213.6
Concentration OC
126213.6
Concentration OC
126213.6
Concentration OC
126213.6
Microtox code 2
Concentration OC
60544.2
8843.5
Concentration OC
60544.2
8843.5
Concentration OC
8843.5
Concentration OC
8843.5
B-44
-------
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Amphi pod
phenanthrene
Microtox code 0
Concentration OC
271341.5
Group: 3 Station: SM-01
Toxicity code: 3 Benthic code:
Amphipod
bis(2-ethylhexyl)phthalate
Microtox code 0
Concentration OC
212121.2
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code:
Amphipod
4,4'-DDT
Microtox code 0
Concentration OC
1222.22 *
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code:
Amphipod
acenaphthylene
bis(2-ethy1hexy1)phtha1 ate
total PCBs
4,4'-DDD
Microtox code 0
Concentration OC
109090.9
127272.7
177272.7
3227.27
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
butyl benzyl phthalate
silver
Microtox code 0
Concentration OC
14666.7
191555.6
511.1
B-45
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code:
Benthic
total PCBs
lead
silver
4,4'-DDD
Microtox code 0
Concentration OC
511688.3
87013.0
532.5
22727.27
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code:
Benthic
acenaphthylene
total PCBs
silver
Microtox code 0
Concentration OC
668833.3
661666.7
900.0
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
Benthic
lead
2 Microtox code 0
Concentration OC
22307.7
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
total PCBs
lead
4,4'-ODD
Microtox code 0
Concentration OC
380000.0
35000.0
5000.00
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
1,2-dichlorobenzene
butyl benzyl phthalate
copper
lead
zinc
Microtox code 0
Concentration OC
62727.3
4090.9
413636.4
27272.7
97727.3
59090.9
B-46
-------
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
Microtox code 0
Concentration OC
49677.4
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
Benthic
butyl benzyl phthalate
Microtox code 0
Concentration OC
67666.7
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code:
Amphi pod
total PCBs
4,4'-DDD
4,4'-DDE
Microtox code 0
Concentration OC
380281.7
2676.06
2887.32
Group: 9 Station: DR-25
Toxicity code: 3 Benthic code:
Amphipod
zinc
Microtox code 0
Concentration OC
72638.9
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code:
Amphipod
zinc
Microtox code 0
Concentration OC
212456.1
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code:
Amphipod
cadmium
zinc
mercury
Microtox code 0
Concentration OC
2000.0
500000.0
442.3
B-47
-------
TABLE B-4. STATION LISTING OF CHEMICALS EXCEEDING
FINES NORMALIZED AET
Organics expressed as ppb fine grained material,
metals ppm fine grained material
Group: 1 Station: CI-11
Toxicity code: 4 Benthic code: 2
Amphipod
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
1,4-dichlorobenzene
benzyl alcohol
dibenzothiophene
Oyster
acenaphthene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
1,2-dichlorobenzene
1,4-dichlorobenzene
benzyl alcohol
Chlorinated benzenes
dibenzothiophene
Benthic
benzyl alcohol
lead
Microtox
benzyl alcohol
Microtox code 2
Concentration Fines
1981.7
1600.6
736.8
355.7
482.7
Concentration Fines
1168.7
1981.7
1600.6
94.0
736.8
355.7
830.8
482.7
Concentration Fines
355.7
1842.0
Concentration Fines
355.7
Group: 1 Station: CI-13
Toxicity code: 2 Benthic code:
Oyster
bis(2-ethylhexyl)phthalate
Benthic
bis(2-ethylhexyl)phthalate
B-48
Microtox code 2
Concentration Fines
3959.1
Concentration Fines
3959.1
-------
Microtox
bis(2-ethylhexyl)phthalate
butyl benzyl phthalate
Concentration Fines
3959.1
268.2
Group: 1 Station: CI-16
Toxicity code: 2 Benthic code:
Oyster
2,4-dimethylphenol
1,2-dichlorobenzene
1,4-dichlorobenzene
4-methylphenol
Chlorinated benzenes
Benthic
2,4-dimethylphenol
1,2-dichlorobenzene
Microtox
2,4-dimethylphenol
1,2-dichlorobenzene
Microtox code 2
Concentration Fines
67.9
475.2
353.0
1629.3
905.6
Concentration Fines
67.9
475.2
Concentration Fines
67.9
475.2
Group: 1 Station: CI-20
Toxicity code: 4 Benthic code:
Oyster
2,4-dimethylphenol
Microtox code 1
Concentration Fines
36.4
Group: 1 Station: HY-17
Toxicity code: 2 Benthic code:
Microtox
total PCBs
Microtox code 2
Concentration Fines
254.0
B-49
-------
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code:
Amphi pod
benzo(a)pyrene
Total benzofluoranthenes
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
Chlorinated benzenes
Oyster
benzo(a)pyrene
Total benzofluoranthenes
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
hexach1orobutadi ene
bis(2-ethylhexyl)phthalate
total PCBs
Chlorinated benzenes
Benthic
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
Microtox
benzo(a)pyrene
dibenzo(a,h)anthracene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
total PCBs
Chlorinated benzenes
Microtox code 2
Concentration Fines
8077.3
11255.3
1986.2
3575.2
344.3
966.6
966.6
3972.5
1758.5
Concentration Fines
8077.3
11255.3
1986.2
3575.2
344.3
966.6
96.7
238.3
966.6
3972.5
2648.3
1758.5
Concentration Fines
344.3
966.6
966.6
3972.5
Concentration Fines
8077.3
1986.2
344.3
966.6
966.6
3972.5
2648.3
1758.5
B-50
-------
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code:
Oyster
total PCBs
Microtox
hexach1orobutadi ene
butyl benzyl phthalate
total PCBs
Microtox code 2
Concentration Fines
1735.1
Concentration Fines
196.6
127.2
1735.1
Group: 1 Station: HY-24
Toxicity code: 1 Benthic code:
Microtox
butyl benzyl phthalate
total PCBs
Microtox code 2
Concentration Fines
576.6
306.7
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code:
Microtox
hexachlorobenzene
total PCBs
Microtox code 2
Concentration Fines
123.9
542.0
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code:
Amphipod
hexachlorobenzene
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexach1orobutadi ene
total PCBs
Microtox code 2
Concentration Fines
294.6
Concentration Fines
82.0
294.6
345.8
1409.0
B-51
-------
Group: 1 Station: HY-43
Toxicity code: 1 Benthic code:
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
Group: 1 Station: HY-47
Toxicity code: 2 Benthic code:
Oyster
hexachlorobutadiene
Benthic
hexachlorobutadiene
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
Microtox code 2
Concentration Fines
89.2
227.4
314.9
Microtox code 2
Concentration Fines
370.4
Concentration Fines
370.4
Concentration Fines
65.1
127.7
370.4
B-52
-------
Group: 1 Station: RS-13
Toxicity code: 4 Benthic code: 1 Microtox code 1
Amphipod Concentration Fines
acenaphthene 3102.6
phenanthrene 8751.0
fluorene 3898.2
fluoranthene 10342.1
benzo(a)anthracene 8751.0
benzo(a)pyrene 7796.3
Total benzofluoranthenes 23866.3
chrysene 11137.6
benzo(ghi)perylene 3659.5
dibenzo(a,h)anthracene 1829.8
indeno(l,2,3-cd)pyrene 4773.3
pyrene 9546.5
naphthalene 9546.5
1,4-dichlorobenzene 875.1
2-methylphenol 572.8
4-methylphenol 4455.1
dibenzofuran 3182.2
benzyl alcohol 167.1
2-methylnaphthalene 3500.4
Low molecular wt. PAH 29037.4
High molecular wt. PAH 81702.5
Chlorinated benzenes 1002.4
l.l'-biphenyl 636.4
dibenzothiophene 779.6
1-methylphenanthrene 1591.1
B-53
-------
Oyster Concentration Fines
acenaphthene 3102.6
phenanthrene 8751.0
fluorene 3898.2
fluoranthene 10342.1
benzo(a)anthracene 8751.0
benzo(a)pyrene 7796.3
Total benzofluoranthen'es 23866.3
chrysene 11137.6
benzo(ghi)perylene 3659.5
dibenzo(a,h)anthracene 1829.8
indeno(l,2,3-cd)pyrene 4773.3
pyrene 9546.5
naphthalene 9546.5
1,2-dichlorobenzene 127.3
1,4-dichlorobenzene 875.1
2-methylphenol 572.8
4-methylphenol 4455.1
dibenzofuran 3182.2
benzyl alcohol 167.1
2-methylnaphthalene 3500.4
Low molecular wt. PAH 29037.4
High molecular wt. PAH 81702.5
Chlorinated benzenes 1002.4
l.l'-biphenyl 636.4
dibenzothiophene 779.6
1-methylphenanthrene 1591.1
B-54
-------
Group: 1 Station: RS-18
Toxicity code: 4 Benthic code:
Amphi pod
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
f "luorene
fluoranthene
benzo(a)anthracene
benzojajpyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
1,4-dichlorobenzene
2-methylphenol
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
mercury
Low molecular wt. PAH
High molecular wt. PAH
1 ,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Microtox code 2
Concentration Fines
1829.6
7498.5
4199.2
32993.4
9298.1
24295.1
9598.1
11997.6
12597.5
14097.2
959.8
2309.5
16796.6
5698.9
749.9
213.0
5998.8
3599.3
1259.75
29094.2
551.9
34193.2
18746.3
71.99
9.60
156.0
60557.9
92651.5
3299.3
3299.3
3899.2
B-55
-------
Oyster
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
1,4-dichlorobenzene
2-methylphenol
dibenzofuran
2-methy1 naphtha 1ene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
mercury
Low molecular wt. PAH
High molecular wt. PAH
Chlorinated benzenes
l,l'*biphenyl
dibenzothiophene
1-methylphenanthrene
Concentration Fines
1829.6
7498.5
4199.2
32993.4
9298.1
24295.1
9598.1
11997.6
12597.5
14097.2
959.8
2309.5
16796.6
5698.9
749.9
213.0
5998.8
3599.3
1259.75
29094.2
551.9
34193.2
18746.3
71.99
9.60
156.0
60557.9
92651.5
803.8
3299.3
3299.3
3899.2
B-56
-------
Benthic
N-nitrosodiphenylamine
acenaphthene
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
zinc
mercury
1 ,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Microtox
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
chrysene
pyrene
dibenzofuran
2-methy1 naphtha 1ene
antimony
arsenic
cadmium
copper
lead
thallium
mercury
Low molecular wt. PAH
High molecular wt. PAH
l,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Concentration Fines
1829.6
7498.5
5998.8
3599.3
1259.75
29094.2
551.9
34193.2
18746.3
71.99
9.60
9958.0
156.0
3299.3
3299.3
3899.2
Concentration Fines
1829.6
7498.5
4199.2
32993.4
9298.1
24295.1
9598.1
11997.6
14097.2
16796.6
5998.8
3599.3
1259.75
29094.2
551.9
34193.2
18746.3
9.60
156.0
60557.9
92651.5
3299.3
3299.3
3899.2
B-57
-------
Group: 1 Station: RS-19
Toxicity code: 4 Benthic code:
Amphipod
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
di-n-butyl phthalate
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
zinc
mercury
Low molecular wt. PAH
High molecular wt. PAH
Total phthalates
l.l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Microtox code 2
Concentration Fines
12539.2
17868.3
4388.7
26645.8
10971.8
8463.9
12539.2
12539.2
2382.4
658.3
2821.3
21316.6
4702.2
50156.7
3448.3
1912.2
1128.53
48589.3
501.6
70219.4
31974.9
43.89
14.42
28401.3
100.3
40188.1
98338.6
50156.7
721.0
3040.8
4388.7
B-58
-------
Oyster
anthracene
phenanthrene
fluorene
f luoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
1,4-dichlorobenzene
di-n-butyl phthalate
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
zinc
mercury
Low molecular wt.
High molecular wt
Total phthalates
1,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
PAH
PAH
Benthic
dibenzofuran
antimony
arsenic
cadmium
copper
iron
lead
thallium
zinc
mercury
1,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Concentration Fines
12539.2
17868.3
4388.7
26645.8
10971.8
8463.9
12539.2
12539.2
2382.4
658.3
2821.3
21316.6
4702.2
313.5
50156.7
3448.3
1912.2
1128.53
48589.3
501.6
70219.4
31974.9
43.89
14.42
28401.3
100.3
40188.1
98338.6
50156.7
721.0
3040.8
4388.7
Concentration Fines
3448.3
1128.53
48589.3
501.6
70219.4
752351.1
31974.9
14.42
28401.3
100.3
721.0
3040.8
4388.7
B-59
-------
Microtox Concentration Fines
anthracene 12539.2
phenanthrene 17868.3
fluorene 4388.7
fluoranthene 26645.8
benzo(a)anthracene 10971.8
benzo(a)pyrene 8463.9
chrysene 12539.2
pyrene 21316.6
di-n-butyl phthalate 50156.7
total PCBs 438.9
dibenzofuran 3448.3
antimony 1128.53
arsenic 48589.3
cadmium 501.6
copper 70219.4
lead 31974.9
thallium 14.42
mercury 100.3
Low molecular wt. PAH 40188.1
High molecular wt. PAH 98338.6
Total phthalates 50156.7
l.l'-biphenyl 721.0
dibenzothiophene 3040.8
1-methylphenanthrene 4388.7
Group: 1 Station: RS-20
Toxicity code: 1 Benthic code: 2 Microtox code 2
Benthic Concentration Fines
antimony 30.98
cadmium 51.6
copper 2358.0
lead 1342.5
mercury 10.2
Group: 1 Station: SP-12
Toxicity code: 2 Benthic code: 1 Microtox code 2
Oyster Concentration Fines
benzyl alcohol 123.6
B-60
-------
Group: 1 Station: SP-14
Toxicity code: 4 Benthic code:
Amphipod
naphthalene
4-methylphenol
1,l'-biphenyl
Oyster
naphthalene
4-methylphenol
1,l'-biphenyl
Benthic
4-methylphenol
Microtox
4-methylphenol
Group: 1 Station: SP-15
Toxicity code: 4 Benthic code:
Amphipod
4-methylphenol
Oyster
4-methylphenol
Benthic
4-methylphenol
Microtox
4-methylphenol
Microtox code 2
Concentration Fines
6606.6
144144.1
465.5
Concentration Fines
6606.6
144144.1
465.5
Concentration Fines
144144.1
Concentration Fines
144144.1
Microtox code 2
Concentration Fines
10038.6
Concentration Fines
10038.6
Concentration Fines
10038.6
Concentration Fines
10038.6
B-61
-------
Group: 1 Station: SP-16
Toxicity code: 4 Benthic code:
Amphipod
benzyl alcohol
Oyster
4-methylphenol
benzyl alcohol
Benthic
benzyl alcohol
Microtox
benzyl alcohol
Group: 2 Station: B15
Toxicity code: 3 Benthic code:
Amphipod
4,4'-DDT
Microtox code 2
Concentration Fines
237.0
Concentration Fines
1622.6
237.0
Concentration Fines
237.0
Concentration Fines
237.0
Microtox code 0
Concentration Fines
8.17
Group: 3 Station: EV-01
Toxicity code: 3 Benthic code:
Amphipod
fluoranthene
0 Microtox code 0
Concentration Fines
7633.6
Group: 3 Station: EV-04
Toxicity code: 3 Benthic code:
Amphipod
acenaphthene
acenaphthylene
phenanthrene
fluorene
fluoranthene
naphthalene
Low molecular wt. PAH
Microtox code 0
Concentration Fines
6131.5
1430.7
8732.8
3901.9
7618.0
10962.5
31921.2
B-62
-------
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Amphipod
phenanthrene
fluoranthene
pyrene
Low molecular wt. PAH
Microtox code 0
Concentration Fines
12981.3
7584.6
9334.9
16161.0
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code:
Amphipod
4,4'-DDT
Microtox code 0
Concentration Fines
40.89
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code:
Amphipod
acenaphthylene
total PCBs
4,4'-ODD
Microtox code 0
Concentration Fines
2739.7
4452.1
81.05
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
butyl benzyl phthalate
Microtox code 0
Concentration Fines
2099.9
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code:
Benthic
total PCBs
lead
4,4'-DDD
4,4'-DDE
Microtox code 0
Concentration Fines
12351.1
2100.3
548.6
147.3
B-63
-------
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
4,4'-ODD
Microtox code 0
Concentration Fines
37.22
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
butyl benzyl phthalate
di-n-octyl phthalate
lead
Total phthalates
Microtox code 0
Concentration Fines
711.3
4690.7
97597.9
1108.2
110309.3
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
Benthic
butyl benzyl phthalate
Microtox code 0
Concentration Fines
1307.6
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code:
Amphipod
total PCBs
4,4'-ODD
4,4'-DDE
Microtox code 0
Concentration Fines
10742.0
75.59
81.56
B-64
-------
TABLE B-5. STATION LISTING OF CHEMICALS EXCEEDING
LOWEST DRY WEIGHT NORMALIZED AET
Organics expressed as ppb dry weight, metals ppm dry weight
Group: 1 Station: BL-13
Toxicity code: 1 Benthic code: 1 Microtox code 2
Lowest AET
butyl benzyl phthalate
Concentration DW
83.0
Group: 1 Station: CI-11
Toxicity code: 4 Benthic code:
Lowest AET
phenol
phenanthrene
fluoranthene
chrysene
benzo(ghi)perylene
1,2-dichlorobenzene
1,4-dichlorobenzene
benzyl alcohol
lead
nickel
zinc
mercury
High molecular wt. PAH
Chlorinated benzenes
Microtox code 2
Concentration DW
1100
1800
2400
1600.0
780.0
37.0
290.0
140
725
40.0
325.0
.53
13090.0
327.0
Group: 1 Station: CI-13
Toxicity code: 2 Benthic code:
Lowest AET
bis(2-ethylhexyl)phthalate
butyl benzyl phthalate
total PCBs
benzoic acid
cadmium
lead
mercury
Total phthalates
Microtox code 2
Concentration DW
3100.0
210.0
140.0
690
6.70
450
1.10
3548.0
B-65
-------
Group: 1 Station: CI-16
Toxicity code: 2 Benthic code:
Lowest AET
2,4-dimethylphenol
N-nitrosodipheny1 amine
1,2-dichlorobenzene
1,4-dichlorobenzene
di-n-butyl phthalate
4-methylphenol
Chlorinated benzenes
Microtox code 2
Concentration DW
50.0
220.0
350.0
260.0
1600.0
1200
667.0
Group: 1 Station: CI-17
Toxicity code: 1 Benthic code:
Lowest AET
fluoranthene
1,4-dichlorobenzene
Microtox code 2
Concentration DW
1900
119.0
Group: 1 Station: CI-20
Toxicity code: 4 Benthic code:
Lowest AET
phenol
l,l'-biphenyl
dibenzothiophene
Microtox code 1
Concentration DW
1200
270.0
250.0
Group: 1 Station: HY-12
Toxicity code: 2 Benthic code:
Lowest AET
phenol
chrysene
benzo(ghi)perylene
dibenzo(a,h)anthracene
di-n-butyl phthalate
mercury
Total phthalates
Microtox code 2
Concentration DW
500
1800.0
740.0
260.0
5100.0
.46
5100.0
B-66
-------
Group: 1 Station: HY-14
Toxicity code: 1 Benthic code: 2 Microtox code 2
Lowest AET Concentration DW
fluoranthene 2500
chrysene 2800.0
benzo(ghi)perylene 720.0
High molecular wt. PAH 16650.0
Group: 1 Station: HY-17
Toxicity code: 2 Benthic code: 3 Microtox code 2
Lowest AET Concentration DW
fluoranthene 3900
benzo(a)pyrene 2400.0
Total benzofluoranthenes 3700
chrysene 2700.0
pyrene 4300
tetrachloroethene 210
ethyl benzene 50
total PCBs 170.0
total xylenes 160
arsenic 86.0
zinc 268.0
High molecular wt. PAH 18402.0
B-67
-------
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code:
Lowest AET
phenol
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
total PCBs
arsenic
nickel
mercury
dibenzothiophene
1-methylphenanthrene
High molecular wt. PAH
Total phthalates
Chlorinated benzenes
Microtox code 2
Concentration DW
530
3600
2300.0
6100.0
8500
2700.0
1500
2700.0
260.0
730.0
73.0
180.0
730.0
3000.0
2000.0
90.0
52.0
.50
320.0
530.0
30000.0
3560.0
1328.0
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code:
Lowest AET
phenanthrene
fluoranthene
benzo(a)pyrene
chrysene
benzo(ghi)perylene
dibenzo(a,h)anthracene
hexachlorobutadiene
butyl benzyl phthalate
dimethyl phthalate
tetrachloroethene
total PCBs
total xylenes
nickel
High molecular wt. PAH
Microtox code 2
Concentration DW
2300
2500
2000.0
2300.0
1100.0
440.0
170.0
110.0
350.0
170
1500.0
110
56.0
13790.0
B-68
-------
Group: 1 Station: HY-24
Toxicity code: 1 Benthic code:
Lowest AET
chrysene
hexachlorobutadiene
butyl benzyl phthalate
dimethyl phthalate
total PCBs
mercury
Microtox code 2
Concentration DW
2300.0
140.0
470.0
120.0
250.0
.49
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code:
Lowest AET
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
total PCBs
Chlorinated benzenes
Microtox code 2
Concentration DW
34.0
96.0
130.0
420.0
203.0
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code:
Lowest AET
chrysene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
total PCBs
Chlorinated benzenes
Microtox code 2
Concentration DW
1800.0
64.0
230.0
270.0
1100.0
395.0
Group: 1 Station: HY-43
Toxicity code: 1 Benthic code:
Lowest AET
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
ethyl benzene
total xylenes
Chlorinated benzenes
Microtox code 2
Concentration DW
51.0
130.0
180.0
37
120
274.2
B-69
-------
Group: 1 Station: HY-47
Toxicity code: 2 Benthic code:
Lowest AET
1,2,4-trichlorobenzene
hexachlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
di-n-butyl phthalate
Chlorinated benzenes
Microtox code 2
Concentration DW
51.0
100.0
120.0
290.0
1500.0
312.0
Group: 1 Station: HY-50
Toxicity code: 1 Benthic code:
Lowest AET
benzyl alcohol
1 Microtox code 2
Concentration DW
73
Group: 1 Station: MI-13
Toxicity code: 1 Benthic code:
Lowest AET
dimethyl phthalate
FINES
Microtox code 2
Concentration DW
110.0
.89
Group: 1 Station: RS-13
Toxicity code: 4 Benthic code:
Lowest AET
2-methylphenol
Microtox code 1
Concentration DW
72
B-70
-------
Group: 1 Station: RS-18
Toxicity code: 4 Benthic code:
Lowest AET
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
1,4-dichlorobenzene
2-methylphenol
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
iron
lead
manganes
nickel
thallium
zinc
mercury
l,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Low molecular wt. PAH
High molecular wt. PAH
Chlorinated benzenes
Microtox code 2
Concentration DW
610.0
2500.0
1400.0
11000
3100.0
8100
3200.0
4000.0
4200
4700.0
320.0
770.0
5600
250.0
71
2000
1200
420.0
9700.0
184.00
11400
52900
6250
748
93.0
3.20
3320.0
52.00
1100.0
1100.0
1300.0
20190.0
30890.0
268.0
B-71
-------
Group: 1 Station: RS-19
Toxicity code: 4 Benthic code:
Lowest AET
di-n-butyl phthalate
antimony
arsenic
cadmium
copper
lead
thallium
zinc
mercury
Microtox code 2
Concentration DW
1600.0
36.00
1550.0
16.00
2240
1020
.46
906.0
3.20
Group: 1 Station: RS-20
Toxicity code: 1 Benthic code:
Lowest AET
arsenic
mercury
Microtox code 2
Concentration DW
90.0
.59
Group: 1 Station: RS-24
Toxicity code: 3 Benthic code;
Lowest AET
antimony
arsenic
cadmium
copper
iron
lead
manganes
zinc
Microtox code 1
Concentration DW
26.00
700.0
9.60
385
37100
531
484
1620.0
Group: 1 Station: SI-11
Toxicity code: 1 Benthic code:
Lowest AET
arsenic
lead
zinc
Microtox code 2
Concentration DW
93.0
661
491.0
B-72
-------
Group: 1 Station: SI-12
Toxlcity code: 3 Benthic code:
Lowest AET
N-nitrosodiphenylamine
lead
zinc
Microtox code 2
Concentration DW
130.0
496
337.0
Group: 1 Station: SI-15
Toxicity code: 3 Benthic code:
Lowest AET
1-methylphenanthrene
1 Microtox code 1
Concentration DW
370.0
Group: 1 Station: SP-12
Toxicity code: 2 Benthic code:
Lowest AET
benzyl alcohol
Microtox code 2
Concentration DW
61
Group: 1 Station: SP-14
Toxicity code: 4 Benthic code:
Lowest AET
phenol
naphthalene
4-methylphenol
2-methylnaphtha 1ene
manganes
nickel
total volatile solids
total organic carbon
l,l'-biphenyl
Low molecular wt. PAH
Microtox code 2
Concentration DW
1700
4400.0
96000
810
556
40.0
44.70
16.00
310.0
6065.0
Group: 1 Station: SP-15
Toxicity code: 4 Benthic code:
Lowest AET
4-methylphenol
Microtox code 2
Concentration DW
2600
B-73
-------
Group: 1 Station: SP-16
Toxicity code: 4 Benthic code:
Lowest AET
4-methylphenol
benzyl alcohol
Microtox code 2
Concentration DW
890
130
Group: 2 Station: B03
Toxicity code: 1 Benthic code:
Lowest AET
1,2-dichlorobenzene
1 Microtox code 0
Concentration DW
50.0
Group: 2 Station: B04
Toxicity code: 1 Benthic code:
Lowest AET
fluoranthene
butyl benzyl phthalate
mercury
Microtox code 0
Concentration DW
2140
125.0
.52
Group: 2 Station: B09
Toxicity code: 1 Benthic code:
Lowest AET
dimethyl phthalate
Microtox code 0
Concentration DW
160.0
Group: 2 Station: B15
Toxicity code: 3 Benthic code:
Lowest AET
4,4'-DDT
1 Microtox code 0
Concentration DW
5.8
B-74
-------
Group: 3 Station: BH-03
Toxicity code: 1 Benthic code:
Lowest AET
copper
nickel
mercury
total volatile solids
Microtox code 0
Concentration DW
400
73.5
1.35
26.93
Group: 3 Station: BH-04
Toxicity code: 1 Benthic code:
Lowest AET
nickel
mercury
Microtox code 0
Concentration DW
89.6
1.69
Group: 3 Station: BH-05
Toxicity code: 3 Benthic code:
Lowest AET
nickel
mercury
FINES
Microtox code 0
Concentration DW
111.0
.81
.97
Group: 3 Station: BH-07
Toxicity code: 1 Benthic code:
Lowest AET
nickel
mercury
FINES
Microtox code 0
Concentration DW
105.0
.97
.92
Group: 3 Station: BH-11
Toxicity code: 1 Benthic code:
Lowest AET
nickel
mercury
FINES
Microtox code 0
Concentration DW
118.0
.54
.98
B-75
-------
Group: 3 Station: BH-12
Toxicity code: 1 Benthic code:
Lowest AET
nickel
mercury
Microtox code 0
Concentration DW
72.0
.64
Group: 3 Station: BH-23
Toxicity code: 3 Benthic code:
Lowest AET
nickel
mercury
FINES
Microtox code 0
Concentration DW
102.0
.54
.95
Group: 3 Station: BH-24
Toxicity code: 1 Benthic code:
Lowest AET
nickel
mercury
FINES
Microtox code 0
Concentration DW
117.0
.59
.97
Group: 3 Station: CS-01
Toxicity code: 1 Benthic code:
Lowest AET
phenol
silver
FINES
Microtox code 0
Concentration DW
560
.57
.89
Group: 3 Station: DB-07
Toxicity code: 1 Benthic code:
Lowest AET
silver
0 Microtox code 0
Concentration DW
.78
B-76
-------
Group: 3 Station: DB-15
Toxicity code: 3 Benthic code:
Lowest AET
nickel
FINES
Microtox code 0
Concentration DW
46.0
.90
Group: 3 Station: EB-09
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
silver
zinc
mercury
Microtox code 0
Concentration DW
330.0
.74
434.0
1.69
Group: 3 Station: EB-10
Toxicity code: 1 Benthic code:
Lowest AET
acenaphthene
phenanthrene
fluoranthene
chrysene
pyrene
total PCBs
lead
silver
zinc
mercury
High molecular wt. PAH
Microtox code 0
Concentration DW
630.0
2100
2300
1500.0
3400
279.0
607
.65
687.0
1.08
12200.0
Group: 3 Station: EB-12
Toxicity code: 1 Benthic code:
Lowest AET
silver
FINES
Microtox code 0
Concentration DW
.68
.89
B-77
-------
Group: 3 Station: EB-17
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
silver
mercury
Microtox code 0
Concentration DW
646.0
.65
.58
Group: 3 Station: EB-20
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
silver
zinc
mercury
Microtox code 0
Concentration DW
640.0
.60
460.0
.78
Group: 3 Station: EB-22
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
silver
mercury
Microtox code 0
Concentration DW
687.0
.67
.51
Group: 3 Station: EB-23
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
0 Microtox code 0
Concentration DW
148.0
Group: 3 Station: EV-01
Toxicity code: 3 Benthic code:
Lowest AET
phenanthrene
fluoranthene
total PCBs
nickel
thallium
zinc
Microtox code 0
Concentration DW
1900
4800
445.0
44.0
.50
313.0
B-78
-------
Group: 3 Station: EV-02
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
nickel 48.0
Group: 3 Station: EV-03
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 516.0
nickel 43.0
total volatile solids 25.44
B-79
-------
Group: 3 Station: EV-04
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
phenol 1400
acenaphthene 3300.0
naphthalene 5900.0
acenaphthylene 770.0
phenanthrene 4700
fluorene 2100.0
fluoranthene 4100
butyl benzyl phthalate 440.0
total PCBs 965.0
nickel 45.0
thallium .30
zinc 1074.0
total volatile solids 35.06
total organic carbon 15.42
Low molecular wt. PAH 17180.0
Group: 3 Station: EV-05
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
fluoranthene 1800
total PCBs 394.0
nickel 51.0
thallium .50
total volatile solids 25.99
Group: 3 Station: EV-07
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
phenanthrene 1600
total PCBs 155.0
nickel 50.0
thallium .40
B-80
-------
Group: 3 Station: EV-11
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
nickel
Microtox code 0
Concentration DW
171.0
46.0
Group: 3 Station: SC-06
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
nickel
silver
zinc
mercury
Microtox code 0
Concentration DW
1253.0
44.0
3.70
330.0
1.38
Group: 3 Station: SC-07
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
copper
silver
zinc
mercury
Microtox code 0
Concentration DW
588.0
807
2.30
873.0
1.28
Group: 3 Station: SC-08
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
nickel
silver
zinc
mercury
FINES
Microtox code 0
Concentration DW
646.0
45.0
2.29
311.0
1.21
.90
B-81
-------
Group: 3 Station: SC-14
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
nickel
silver
zinc
mercury
FINES
Microtox code 0
Concentration DW
1672.0
43.0
2.32
272.0
1.57
.92
Group: 3 Station: SC-17
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
nickel
silver
zinc
mercury
Microtox code 0
Concentration DW
231.0
43.0
1.29
328.0
.70
Group: 3 Station: SC-18
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
silver
mercury
Microtox code 0
Concentration DW
229.0
1.56
.72
Group: 3 Station: SC-19
Toxicity code: 1 Benthic code:
Lowest AET
lead
nickel
silver
zinc
mercury
Microtox code 0
Concentration DW
360
45.0
1.36
343.0
2.07
B-82
-------
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Lowest AET
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
chrysene
benzo(ghi)perylene
pyrene
total PCBs
nickel
silver
mercury
Low molecular wt. PAH
High molecular wt. PAH
Microtox code 0
Concentration DW
8900
980.0
5200
1900.0
2000.0
1000.0
6400
384.0
44.0
2.67
1.64
11080.0
20140.0
Group: 3 Station: SM-01
Toxicity code: 3 Benthic code:
Lowest AET
bis(2-ethylhexyl)phthalate
0 Microtox code 0
Concentration DW
2800.0
Group: 3 Station: SQ-17
Toxicity code: 1 Benthic code:
Lowest AET
nickel
0 Microtox code 0
Concentration DW
41.0
Group: 4 Station: DR-03
Toxicity code: 1 Benthic code:
Lowest AET
4,4'-DDD
0 Microtox code 0
Concentration DW
3.90
Lowest AET
4,4'-DDD
Concentration DW
2.60
B-83
-------
Group: 4 Station: DR-06
Toxicity code: 1 Benthic code:
Lowest AET
4,4'-DDD
0 Microtox code 0
Concentration DW
3.20
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code:
Lowest AET
4,4'-DDT
4,4'-DDD
Microtox code 0
Concentration DW
22.0
5.60
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code:
Lowest AET
acenaphthylene
bis(2-ethylhexyl)phthalate
total PCBs
zinc
mercury
4,4'-DDD
Microtox code 0
Concentration DW
2400
2800.0
3900.0
270.0
.42
71.00
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code:
Lowest AET
N-nitrosodiphenylamine
benzo(a)pyrene
Total benzofluoranthenes
benzo(ghi)perylene
dibenzo(a,h)anthracene
indeno(1,2,3-cd)pyrene
butyl benzyl phthalate
total PCBs
iron
manganes
nickel
silver
mercury
4,4'-DDE
High molecular wt. PAH
Total phthalates
Microtox code 0
Concentration DW
132.0
9770.0
17529
8046.0
4023
2874.0
1724
1060.0
31000
300
44.0
4.60
.98
11.00
46897.0
4445.0
B-84
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code: 2 Microtox code 0
Lowest AET Concentration DW
anthracene 3936.0
phenanthrene 4293
fluorene 2683.0
fluoranthene 5367
benzo(a)anthracene 9481.0
chrysene 10376
pyrene 6440
di-n-butyl phthalate 2147.0
total PCBs 3940.0
lead 670
nickel 41.0
silver 4.10
zinc 300.0
mercury 1.60
4,4'-DDE 47.00
4,4'-DDD 175.00
Low molecular wt. PAH 11431.0
High molecular wt. PAH 31664.0
Total phthalates 11628.0
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code: 2 Microtox code 0
Lowest AET Concentration DW
N-nitrosodiphenylamine 54.0
acenaphthylene 4013
chrysene 1605.0
butyl benzyl phthalate 334.0
total PCBs 3970.0
iron 31000
manganes 400
nickel 56.0
silver 5.40
mercury .77
4,4'-DDE 37.00
Total phthalates 28327.0
B-85
-------
Group: 6 Station: EB-38
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET Concentration DW
N-nitrosodiphenylamine 61.0
chrysene 1552.0
benzo(ghi)perylene 808.0
dibenzo(a,h)anthracene 471.0
total PCBs 730.0
Iron 37000
manganes 420
nickel 49.0
silver 5.20
mercury .62
Group: 6 Station: WP-12
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET Concentration DW
N-nitrosodiphenylamine 75.0
benzo(a)pyrene 1877.0
iron 32000
manganes 580
silver 3.70
Total phthalates 6166.0
FINES .91
Group: 6 Station: WP-13
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET Concentration DW
total PCBs 480.0
iron 36000
manganes 520
silver 3.70
FINES .92
Group: 6 Station: WP-14
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET Concentration DW
iron 34000
manganes 630
silver 3.70
FINES .96
B-86
-------
Group: 6 Station: WP-15
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET Concentration DW
benzo(a)pyrene 2173.0
Total benzofluoranthenes 3928
chrysene 2440.0
benzo(ghi)perylene 1756.0
indeno(l,2,3-cd)pyrene 893.0
total PCBs 146.0
iron 28000
manganes 430
silver 3.30
High molecular wt. PAH 13631.0
FINES .97
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code: 2 Microtox code 0
Lowest AET Concentration DW
total PCBs 275.0
iron 27000
manganes 410
nickel 44.0
silver 5.00
4,4'-DDD 12.00
FINES .96
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code: 3 Microtox code 0
Lowest AET Concentration DW
fluoranthene 1703
total PCBs 2280.0
iron 30000
manganes 310
nickel 44.0
silver 1.10
mercury 1.00
4,4'-DDE 30.00
4,4'-DDD 30.00
Total phthalates 3648.0
B-87
-------
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code: 2 Microtox code 0
Lowest AET Concentration DW
N-nitrosodiphenylamine 276.0
anthracene 1636.0
phenanthrene 2574
fluoranthene 5882
benzo(a)anthracene 3125.0
benzo(a)pyrene 5882.0
Total benzofluoranthenes 11213
chrysene 5147.0
benzo(ghi)perylene 5147.0
dibenzo(a,h)anthracene 1048
indeno(l,2,3-cd)pyrene 4412.0
pyrene 6250
butyl benzyl phthalate 1820
di-n-butyl phthalate 2941.0
dimethyl phthalate 88.0
total PCBs 965.0
lead 430
nickel 49.0
silver .94
zinc 260.0
mercury 1.30
High molecular wt. PAH 48106.0
Total phthalates 42800.0
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code: 3 Microtox code 0
Lowest AET Concentration DW
N-nitrosodiphenylamine 308.0
butyl benzyl phthalate 67.0
di-n-butyl phthalate 4103.0
diethyl phthalate 318.0
4,4'-DDT 15.0
total PCBs 487.0
nickel 50.0
silver 1.30
4,4'-DDE 10.00
Total phthalates 4791.0
B-88
-------
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
Lowest AET
phenanthrene
chrysene
butyl benzyl phthalate
4,4'-DDT
total PCBs
manganes
nickel
silver
mercury
Total phthalates
Microtox code 0
Concentration DW
2970
1802.0
812.0
28.0
315.0
310
58.0
2.20
.72
5840.0
Group: 7 Station: WP-01
Toxicity code: 0 Benthic code:
Lowest AET
manganes
1 Microtox code 0
Concentration DW
440
Group: 7 Station: WP-02
Toxicity code: 0 Benthic code:
Lowest AET
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
pyrene
total PCBs
manganes
High molecular wt. PAH
Lowest AET
manganes
Microtox code 0
Concentration DW
2744
1715.0
3602.0
4117
2058.0
1235.0
1012.0
3774
149.0
380
20429.0
Concentration DW
340
B-89
-------
Group: 7 Station: WP-05
Toxicity code: 0 Benthic code:
Lowest AET
di-n-butyl phthalate
manganes
Microtox code 0
Concentration DW
1423.0
420
Group: 7 Station: WP-06
Toxicity code: 0 Benthic code:
Lowest AET
manganes
Microtox code 0
Concentration DW
520
B-90
-------
Group: 7 Station: WP-07
Toxicity code: 0 Benthic code:
Lowest AET
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
chrysene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
di-n-butyl phthalate
4,4'-DDT
manganes
High molecular wt. PAH
Microtox code 0
Concentration DW
1757
1757.0
1622.0
2297.0
2568.0
2432.0
1622.0
10.0
460
17028.0
Group: 7 Station: WP-08
Toxicity code: 0 Benthic code:
Lowest AET
di-n-butyl phthalate
total PCBs
manganes
mercury
Microtox code 0
Concentration DW
2035.0
161.0
630
.47
Group: 7 Station: WP-09
Toxicity code: 0 Benthic code:
Lowest AET
manganes
nickel
Microtox code 0
Concentration DW
1000
40.0
Group: 7 Station: WP-10
Toxicity code: 0 Benthic code:
Lowest AET
benzo(ghi)perylene
manganes
Total phthalates
Microtox code 0
Concentration DW
944.0
360
3705.0
B-91
-------
Group: 7 Station: WP-11
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET Concentration DW
acenaphthylene 643.0
anthracene 1273.0
phenanthrene 3150
fluorene 643.0'
fluoranthene 6299
benzo(a)anthracene 4462.0
benzo(a.)pyrene 6824.0
Total benzofluoranthenes 8005
chrysene 6693.0
benzo(ghi)perylene 5381.0
dibenzo(a,h)anthracene 1155
indeno(l,2,3-cd)pyrene 5249.0
pyrene 7349
total PCBs 131.0
Low molecular wt. PAH 6139.0
High molecular wt. PAH 51417.0
Group: 7 Station: WP-12
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET Concentration DW
butyl benzyl phthalate 307.0
iron 32000
manganes 460
FINES .97
Group: 7 Station: WP-13
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET Concentration DW
di-n-butyl phthalate 4474.0
iron 29000
manganes 380
nickel 42.0
Total phthalates 7123.0
FINES .89
B-92
-------
Group: 7 Station: WP-14
Toxicity code: 0 Benthic code
Lowest AET
N-nitrosodiphenylamine
fluoranthene
butyl benzyl phthalate
total PCBs
iron
manganes
nickel
silver
mercury
Total phthalates
FINES
Microtox code 0
Concentration DW
63.0
2375
259.0
145.0
30000
380
40.0
.61
.88
69723.0
.91
Group: 7 Station: WP-15
Toxicity code: 0 Benthic code:
Lowest AET
butyl benzyl phthalate
4,4'-DDT
total PCBs
iron
manganes
silver
FINES
Microtox code 0
Concentration DW
287.0
11.0
221.0
30000
450
.58
.93
Group: 7 Station: WP-16
Toxicity code: 0 Benthic code:
Lowest AET
iron
manganes
nickel
silver
FINES
Microtox code 0
Concentration DW
30000
500
40.0
.58
.93
Group: 8 Station: EV-20
Toxicity code: 1 Benthic code
Lowest AET
acenaphthene
0 Microtox code 0
Concentration DW
558.0
B-93
-------
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
mercury
4,4'-DDE
4,4'-DDD
Microtox code 0
Concentration DW
5400.0
.83
41.00
38.00
Group: 9 Station: DR-11
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
4,4'-ODD
Microtox code 0
Concentration DW
530.0
7.80
Group: 9 Station: DR-13
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
mercury
4,4'-DDD
Microtox code 0
Concentration DW
215.0
.42
3.60
Group: 9 Station: DR-14
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
lead
4,4'-ODD
Microtox code 0
Concentration DW
150.0
700
6.10
Group: 9 Station: DR-19
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
330.0
4.30
B-94
-------
Group: 9 Station: DR-22
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
180.0
2.40
Group: 9 Station: DR-23
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
mercury
4,4'-DDE
4,4'-DDD
Microtox code 0
Concentration DW
1800.0
.68
11.00
29.00
Group: 9 Station: DR-25
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
zinc
4,4'-DDD
Microtox code 0
Concentration DW
790.0
523.0
14.00
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
zinc
4,4'-DDD
Microtox code 0
Concentration DW
170.0
1211.0
2.30
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code:
Lowest AET
cadmium
zinc
mercury
4,4'-DDD
Microtox code 0
Concentration DW
10.40
2600.0
2.30
3.40
B-95
-------
Group: 9 Station: DR-28
Toxicity code: 1 Benthic code:
Lowest AET
fluoranthene
total PCBs
4,4'-DDE
4,4'-DDD
Microtox code 0
Concentration DW
1900
2500.0
15.00
43.00
Group: 9 Station: DR-29
Toxicity code: 1 Benthic code:
Lowest AET
total
zinc
mercury
4,4'-DDE
4,4'-DDD
PCBs
Microtox code 0
Concentration DW
2200.0
336.0
.46
15.00
35.00
Group: 9 Station: DR-30
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
650.0
9.20
Group: 9 Station: DR-31
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
560.0
24.00
Group: 9 Station: DR-33
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
4,4'-DDE
4,4'-DDD
Microtox code 0
Concentration DW
1200.0
10.00
14.00
B-96
-------
Group: 9 Station: DR-34
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
1300.0
16.00
Group: 9 Station: DR-35
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
mercury
4,4'-ODD
Microtox code 0
Concentration DW
620.0
.58
15.00
Group: 9 Station: DR-36
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
mercury
4,4'-DDE
4,4'-DDD
Microtox code 0
Concentration DW
1500.0
1.10
9.90
25.00
Group: 9 Station: DR-38
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
1400.0
14.00
Group: 9 Station: DR-39
Toxicity code: 1 Benthic code:
Lowest AET
mercury
4,4'-DDD
Microtox code 0
Concentration DW
.85
2.30
B-97
-------
TABLE B-6. STATION LISTING OF CHEMICALS EXCEEDING COMMENCEMENT BAY DRY
WEIGHT NORMALIZED AET (NON-COMMENCEMENT BAY STATIONS)
Organic compounds expressed as ppb dry weight, metals as ppm dry weight
Group: 3 Station: BH-03
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
copper
nickel
mercury
total volatile solids
Concentration DW
400
73.5
1.35
26.93
Group: 3. Station: BH-04
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
nickel
mercury
Concentration DW
89.6
1.69
Group: 3 Station: BH-05
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
nickel
Concentration DW
111.0
Group: 3 Station: BH-07
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
nickel
Concentration DW
105.0
B-98
-------
Group: 3 Station: BH-11
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 118.0
Group: 3 Station: BH-12
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 72.0
Group: 3 Station: BH-23
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 102.0
Group: 3 Station: BH-24
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 117.0
Group: 3 Station: CS-01
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
phenol 560
B-99
-------
Group: 3 Station: DB-15
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 46.0
Group: 3 Station: EB-09
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
mercury 1.69
Group: 3 Station: EB-10
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
acenaphthene 630.0
phenanthrene 2100
zinc 687.0
Group: 3 Station: EB-17
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 646.0
Group: 3 Station: EB-20
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 640.0
B-100
-------
Group: 3 Station: EB-22
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
Group: 3 Station: EV-01
Toxicity code: 3 Benthic code:
Concentration DW
687.0
Microtox code 0
Amphipod
phenanthrene
fluoranthene
total PCBs
nickel
thallium
Concentration DW
1900
4800
445.0
44.0
.50
Group: 3 Station: EV-02
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
nickel
Concentration DW
48.0
Group: 3 Station: EV-03
Toxicity code: 3 Benthic code:
Microtox code 0
Amphipod
total PCBs
nickel
total volatile solids
Concentration DW
516.0
43.0
25.44
B-101
-------
Group: 3 Station: EV-04
Toxicity code: 3 Benthic code:
0 Microtox code 0
Amphi pod
phenol
acenaphthene
naphthalene
phenanthrene
fluorene
fluoranthene
total PCBs
nickel
thallium
zinc
total volatile solids
total organic carbon
Low molecular wt. PAH
Concentration DW
1400
3300.0
5900.0
4700
2100.0
4100
965.0
45.0
.30
1074.0
35.06
15.42
17180.0
Group: 3 Station: EV-05
Toxicity code: 3 Benthic code:
0 Microtox code 0
Amphipod
nickel
thallium
total volatile solids
Concentration DW
51.0
.50
25.99
Group: 3 Station: EV-07
Toxicity code: 1 Benthic code:
0 Microtox code 0
Amphipod
phenanthrene
nickel
thallium
Concentration DW
1600
50.0
.40
B-102
-------
Group: 3 Station: EV-11
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
nickel
Concentration DW
46.0
Group: 3 Station: SC-06
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
nickel
mercury
Concentration DW
1253.0
44.0
1.38
Group: 3 Station: SC-07
Toxicity code: 1 Benthic code:
Microtox code 0
Amphipod
total PCBs
copper
zinc
mercury
Concentration DW
588.0
807
873.0
1.28
Group: 3 Station: SC-08
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
nickel
mercury
Concentration DW
646.0
45.0
1.21
Group: 3 Station: SC-14
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
nickel
mercury
Concentration DW
1672.0
43.0
1.57
B-103
-------
Group: 3 Station: SC-17
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
nickel
Concentration DW
43.0
Group: 3 Station: SC-19
Toxicity code: 1 Benthic code:
0 Microtox code 0
Amphipod
nickel
mercury
Concentration DW
45.0
2.07
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Microtox code 0
Amphipod
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(ghi)perylene
pyrene
nickel
mercury
Low molecular wt. PAH
High molecular wt. PAH
Concentration DW
8900
980.0
5200
1900.0
1000.0
6400
44.0
1.64
11080.0
20140.0
Group: 3 Station: SQ-17
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
nickel
Concentration DW
41.0
B-104
-------
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
4,4'-DDT
Concentration DW
22.0
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
Concentration DW
3900.0
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code;
Microtox code 0
Benthic
N-nitrosodiphenylamine
benzo(a)pyrene
Total benzofluoranthenes
benzo(ghi)perylene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
butyl benzyl phthalate
di-n-octyl phthalate
antimony
iron
manganes
nickel
mercury
High molecular wt. PAH
Concentration DW
132.0
9770.0
17529
8046.0
4023
2874.0
1724
2615
3.20
31000
300
44.0 *
.98
46897.0
B-105
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code: 2 Microtox code 0
Benthic Concentration DW
anthracene 3936.0
phenanthrene 4293
fluorene 2683.0
fluoranthene 5367
benzo(a)anthracene 9481.0
chrysene 10376
pyrene 6440
di-n-octyl phthalate 9481
total PCBs 3940.0
antimony 3.30
lead 670
manganes 220
nickel 41.0
zinc 300.0
-mercury 1.60
Low molecular wt. PAH 11431.0
High molecular wt. PAH 31664.0
Total phthalates 11628.0
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code: 2 Microtox code 0
Benthic Concentration DW
N-nitrosodiphenylamine 54.0
di-n-octyl phthalate 27759
total PCBs 3970.0
iron 31000
manganes 400
nickel 56.0
mercury .77
Total phthalates 28327.0
B-106
-------
Group: 6 Station: EB-38
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
N-nitrosodiphenylamine 61.0
Total benzofluoranthenes 3570
benzo(ghi)perylene 808.0
dibenzo(a,h)anthracene 471.0
di-n-octyl phthalate 1717
antimony 3.20
iron 37000
manganes 420
nickel 49.0
mercury .62
Group: 6 Station: WP-12
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
N-nitrosodiphenylamine 75.0
benzo(a)pyrene 1877.0
Total benzofluoranthenes 3405
di-n-octyl phthalate 6166
iron 32000
manganes 580
Total phthalates 6166.0
Group: 6 Station: WP-13
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
di-n-octyl phthalate 530.0
iron 36000
manganes 520
B-107
-------
Group: 6 Station: WP-14
Toxiclty code: 0 Benthic code: 1 Microtox code 0
Benthic
iron
manganes
Concentration DW
34000
630
Group: 6 Station: WP-15
Toxicity code: 0 Benthic code:
1 Microtox code 0
Benthic
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
di-n-octyl phthalate
iron
manganes
High molecular wt. PAH
Concentration DW
2173.0
3928
2440.0
1756.0
893.0
625.0
28000
430
13631.0
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
Benthic
di-n-octyl phthalate
iron
manganes
nickel
Microtox code 0
Concentration DW
1331
27000
410
44.0 *
B-108
-------
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code:
Microtox code 0
Benthic
di-n-octyl phthalate
total PCBs
iron
manganes
nickel
mercury
Concentration DW
3243
2280.0
30000
310
44.0 *
1.00
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
Microtox code 0
Benthic
N-nitrosodiphenylamine
anthracene
phenanthrene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(gh i)perylene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
butyl benzyl phthalate
di-n-octyl phthalate
lead
nickel
zinc
mercury
High molecular wt. PAH
Total phthalates
Concentration DW
276.0
1636.0
2574
5882
3125.0
5882.0
11213
5147.0
5147.0
1048
4412.0
6250
1820
37868
430
49.0 *
260.0
1.30
48106.0
42800.0
B-109
-------
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code: 3 Microtox code 0
Benthic
N-nitrosodiphenylamine
4,4'-DDT
manganes
nickel
Concentration DW
308.0
15.0
210
50.0 *
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code: 3 Microtox code 0
Benthic
phenanthrene
butyl benzyl phthalate
di-n-octyl phthalate
4,4'-DDT
manganes
nickel
mercury
Total phthalates
Concentration DW
2970
812.0
4158
28.0
310
58.0 *
.72
5840.0
Group: 7 Station: WP-01
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
Concentration DW
440
B-110
-------
Group: 7 Station: WP-02
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
benzo(ghi)pery!ene
indeno(l,2,3-cd)pyrene
pyrene
manganes
High molecular wt. PAH
Concentration DW
2744
1715.0
3602.0
4117
1235.0
1012.0
3774
380
20429.0
Group: 7 Station: WP-03
Toxicity code: 0 Benthic code:
Microtox code 0
Benthic
di-n-octyl phthalate
Concentration DW
423.0
Group: 7 Station: WP-04
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
Concentration DW
340
Group: 7 Station: WP-05
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
Concentration DW
420
B-lll
-------
Group: 7 Station: WP-06
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
Concentration DW
520
Group: 7 Station: WP-07
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
benzo(a)anthracene
benzo(a)pyrene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
pyrene
di-n-octyl phthalate
4,4'-DDT
manganes
High molecular wt. PAH
Concentration DW
1757.0
1622.0
2568.0
2432.0
2838
500.0
10.0
460
17028.0
Group: 7 Station: WP-08
Toxicity code: 0 Benthic code:
1 Microtox code 0
Benthic
manganes
Concentration DW
630
Group: 7 Station: WP-09
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
nickel
Concentration DW
1000
40.0
B-112
-------
Group: 7 Station: WP-10
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
benzo(ghi)perylene 944.0
di-n-octyl phthalate 3571
manganes 360
Group: 7 Station: WP-11
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
acenaphthylene 643.0
anthracene 1273.0
phenanthrene 3150
fluorene 643.0
fluoranthene 6299
benzo(a)anthracene 4462.0
benzo(a)pyrene 6824.0
Total benzofluoranthenes 8005
chrysene 6693.0
benzo(ghi)perylene 5381.0
dibenzo(a,h)anthracene 1155
indeno(l,2,3-cd)pyrene 5249.0
pyrene 7349
Low molecular wt. PAH 6139.0
High molecular wt. PAH 51417.0
Group: 7 Station: WP-12
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
iron 32000
manganes 460
FINES .97
B-113
-------
Group: 7 Station: WP-13
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
di-n-octyl phthalate 2605
iron 29000
manganes 380
nickel 42.0
Total phthalates 7123.0
Group: 7 Station: WP-14
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
N-nitrosodiphenylamine 63.0
fluoranthene 2375
di-n-octyl phthalate 68602
iron 30000
manganes 380
nickel 40.0
silver .61
mercury .88
Total phthalates 69723.0
FINES .91
Group: 7 Station: WP-15
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic Concentration DW
di-n-octyl phthalate 1264
4,4'-DDT 11.0
iron 30000
manganes 450
silver .58
FINES .93
B-114
-------
Group: 7 Station: WP-16
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
iron
manganes
nickel
silver
FINES
Concentration DW
30000
500
40.0 *
.58
.93
Group: 8 Station: EV-20
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
acenaphthene
Concentration DW
558.0
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
Concentration DW
5400.0
Group: 9 Station: DR-11
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
Concentration DW
530.0
Group: 9 Station: DR-14
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
lead
Concentration DW
700
B-115
-------
Group: 9 Station: DR-23
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
Concentration DW
1800.0
Group: 9 Station: DR-25
Toxicity code: 3 Benthic code;
Amphipod
total PCBs
zinc
Microtox code 0
Concentration DW
790.0
523.0
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
zinc
Concentration DW
1211.0
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code:
Microtox code 0
Amphipod
cadmium
zinc
mercury
Concentration DW
10.40
2600.0
2.30
Group: 9 Station: DR-28
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
Concentration DW
2500.0
B-116
-------
Group: 9 Station: DR-29
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 2200.0
Group: 9 Station: DR-30
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 650.0
Group: 9 Station: DR-31
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 560.0
Group: 9 Station: DR-33
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1200.0
Group: 9 Station: DR-34
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1300.0
B-117
-------
Group: 9 Station: DR-35
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 620.0
Group: 9 Station: DR-36
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1500.0
Group: 9 Station: DR-38
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1400.0
B-118
-------
TABLE B-6A. COMMENCEMENT BAY AET SEDIMENT QUALITY VALUES3
(ug/kg dry weight for organics; mg/kg dry weight for metals)
Chemical
Low molecular weight PAH
naphthalene
acenaphthylene
acenaphthene
fluorene
phenanthrene
anthracene
High molecular weight PAH
fluoranthene
pyrene
benzo( a) anthracene
chrysene
benzof 1 uoranthenes
benzo(a)pyrene
i ndeno(l, 2, 3-c,d) pyrene
d ibenzo( a, h) anthracene
benzo(g,h,i )perylene
Total PCBs
Total chlorinated benzenes
1 ,3-di chl orobenzene
1 ,4-di chl orobenzene
1 ,2-di chl orobenzene
1, 2, 4-t rich! orobenzene
hexachl orobenzene (HCB)
Total phthalates
dimethyl phthalate
di ethyl phthalate
di-n-butyl phthalate
butyl benzyl phthalate
bis(2-ethylhexyl )phthalate
di-n-octyl phthalate
Benthic
AET
5,200
2,100
>560
500
540
1,500
960
18,000
3,900
4,300
1,600
2,800
3,700
2,400
690
260
740
420
670
>170
260
>350
51
130
>5,100
160
>73
>5,100
>470
>3,100
>420
Amphipod
AET
5,200
2,100
>560
500
540
1,500
960
12,000
1,900
2,600
1,300
2,300
3,000
1,600
690
260
740
1,100
400
>170
120
50
64
230
>5,100
160
>73
>5,100
>470
1,900
>420
B-H9
-------
TABLE B-6A. (Continued)
Amphipod Benthic
Chemical AET AET
Pesticides
p,p'-DDE
p,p'-DDD
p,p'-DDT 3.9 >5.8
aldrin
chlordane
dieldrin
heptachlor
gamma-HCH (lindane)
Phenol s
Phenol
2-methyl phenol
4-methyl phenol
2,4-dimethyl phenol
pentachlorophenol
Miscellaneous extractables
hexachloroethane
hexachlorobutadiene
1-methyl phenanthrene
1-methyl naphtha! ene
biphenyl
dibenzothiophene
dibenzofuran
benzyl alcohol
benzoic acid
N-ni trosodi phenyl ami ne
Volatile organics
trichloroethene
tetrachl oroethene
ethyl benzene
total xylenes
Metals (mg/kg dry weight)
antimony
arsenic
beryl 1 ium
cadmium
chromium
copper
iron
lead
manganese
mercury
500
63
1,200
>50
>140
—
290
310
670
260
240
540
73
>690
220
—
>210
>50
>160
5.3
93
—
6.7
—
310
27,000
660
230
1.1
1,200
>72
670
29
>140
—
270
370
670
270
250
540
73
650
28
—
140
37
120
3.1
85
—
5.8
—
310
26,000
300
200
0.52
B-120
-------
TABLE B-6A. (Continued)
nickel
selenium
si 1 ver
thai 1 ium
zinc
39
—
>0.56
0.24
490
39
—
>0.56
0.24
260
Conventional variables
total organic carbon 15% 15%
total volatile solids 22% 22%
percent fine-grained >89% >89%
a ">" in AET columns indicate that a definite AET could not be established because
there were no impacted stations with chemical concentrations above the highest con-
centration among nonimpacted stations.
B-121
-------
APPENDIX C
SUMMARY OF REVIEW OF DATA FOR INCLUSION
IN PUGET SOUND DATABASE
-------
TABLE C-l. SUMMARY OF REVIEW OF PUGET SOUND DATA
Analytical
Study (References)" Techniques
Alki Extension
(9,15)
Commencement Bay (14)
Duwaml sh
Duwamt sh
Head
River
(12)
I, II (2)
Eight Bay (1)
o
I Everett
OMPA Z,
Seahurst
TPPS (3,
Harbor
19 (6,
(4,5,
(16) cores
(16) grabs
7)
8,11,13,17)''
10) Phase IIIA
Phase 1 1 IB
OK
OK
OK
OK
OK
c
c
OK
OK
OK
Chemistry
Detection
Limits
OK
high for
pesticides
OK
OK
high for
many organics
OK
OK
OK
OK
OK
Benthlc Infauna
Scope of
Chemicals
OK
limited
volatlles
OK
no volatlles,
acids
OK
mostly PAH
mostly PAH
no volatlles,
polars
no volatlles
no volatiles
Synoptic?
Yes
Yes
Yes
N.A.d
Yes
N.A.
Yes
Yes
Yes
Yes
Sampling/
Subsampl ing
OK
OK
OK
N.A.
E«cludede
N.A.
OK
Excluded6
OK
OK
Replication
OK (5 rep)
OK (4 rep)
E«cludedf
N.A.
— g
N.A.
OK (5 rep)
g
OK (4 rep)
OK (4 rep)
Reference
sites available?
Yes
Yes
— g
N.A.
— g
N.A.
Excluded"
— g
No for 17/26 sta.
No for 6/26 sta.
Synoptic?
N.A.
Yes
Yes
Yes
Yes
Yes
N.A.
N.A.
Yes .
Excluded1
Toxicology
Accepted
Frozen? Technique?
N.A. N.A.
No Yes
ExcludedJ — g
No Yes
No Yes
No Yes
N.A. N.A.
N.A. N.A.
ExcludedJ — 9,k
—9 — g
-------
a References:
1. Battelle (1985a)
2. Chan et al. (1985a,b)
3. Comiskey et al. (1984)
4. Dinnell et al. (1984)
5. Landolt et al. (1984)
6. Mai ins et al. (1980)
7. Malins et al. (1982)
8. Nevissi et al. (1984)
9. Osborn et al. (1985)
10. Romberg et al. (1984)
11. Stober et al. (1983)
12. Stober et al. 1984a)
13. Stober et al. 1984b)
14. Tetra Tech, Inc. (1985)
15. Trial et al. (1985)
16. U.S. Department of the Navy (1985)
17. Word et al. (1984).
b The chemical and biological data for the Seahurst study are not currently
in a form that allows for easy compilation, although infaunal data are
of good quality. Sampling efforts were not always focused on the same
stations for biological and chemical tasks of the seven sampling phases
of this study. A station-by-station correlation of all data is not currently
available. Furthermore, sediments used for benthic infaunal analyses were
collected up to 6 months before sediments collected for chemical analyses
(Matsuda, B., 20 September 1985, personal communication). This time lag
may not be critical for stations resampled at the identical location, but
it adds uncertainty. Because of the intractability of the data and uncertain-
ties regarding how many of the data are synoptic, Seahurst infaunal data
are not recommended for use in this project. Sediments used for amphipod
bioassays were not collected synoptically with sediments used for chemical
analyses.
c The selected organic priority pollutants reported in this study (almost
exclusively PAH) were analyzed by procedures subject to interferences by
fatty acid methyl esters and related compounds. The relatively nonspecific
GC detection system used for PAH (flame ionization detection, as opposed
to mass spectrometry) and the co-elution of interfering substances with
PAH resulted in an inability to confirm all compound identities and their
quantification. In addition, a confirmation column was not used to verify
peaks identified as PCBs. However, the data were accepted after a careful
review.
d Not applicable (i.e., this biological indicator was not tested).
e These infaunal data were excluded from the database because of the subsampling
procedure used. Grab samples were subsampled with cores after retrieval.
As noted in "Recommended protocols for sampling and analyzing subtidal
benthic macroinvertebrate assemblages in Puget Sound" [Puget Sound Estuary
Program; Tetra Tech (1986) draft]: "Subsamples are not recommended for
benthic infaunal analyses because it is unknown what effect the sampling
C-2
-------
process has on the spatial distribution of motile organisms. For example,
surface-dwelling organisms may move to the edges of the sample as the grab
is being retrieved. If the sampling process disrupts the natural spatial
patterns of the organisms, collection of a representative subsample for
infaunal analysis may not be possible."
f Only one or two replicates were available for infaunal stations in this
study. Such levels of replication could not provide an estimate of variance
that would be comparable to infaunal data from the other studies considered
(these studies had either four or five replicates).
9 Dashes indicate that the data from this study have already been excluded
because of another review criterion.
h These infaunal samples were not sieved prior to analysis. No appropriate
unsieved reference samples were available for statistical comparisons.
"" Bioassay and chemistry samples were not taken from the same sediment
homogenate. They were collected at different times.
J Freezing may alter sediment properties (e.g., effective particle size)
and is therefore not recommended (Swartz et al . 1984). Samples that were
stored at 4° C are not excluded. Sediments used for microtox bioassays
in the Commencement Bay Remedial Investigation were stored for less than
3 wk at 4° C in test tubes that were flushed with nitrogen and then sealed.
Under these inert atmospheric conditions, the storage time is not expected
to have an effect on the results (see Appendix H).
k Uncertainties about the reliability of the Phase IIIA bioassays are described
in TPPS documents (Comiskey et al. 1984). The bioassay method used in
Phase IIIA was not consistent with methods used in the other studies considered.
C-3
-------
APPENDIX D
EVALUATION OF STATISTICAL RELATIONSHIPS AMONG CHEMICAL AND
BIOLOGICAL VARIABLES USING PATTERN RECOGNITION TECHNIQUES
by
Tetra Tech, Inc. / G.A. Erickson & Associates
prepared for
Resource Planning Associates
for
Puget Sound Dredged Disposal Analysis
and
Puget Sound Estuary Program
-------
CONTENTS
Page
TABLE OF CONTENTS D-ii
LIST OF FIGURES D-iv
LIST OF TABLES D-v
INTRODUCTION D-l
OBJECTIVES D-l
APPROACH TO EXPLORATORY MULTIVARIATE DATA ANALYSIS D-l
ANALYTICAL SCOPE D-2
DISCUSSION OF MAJOR RESULTS D-7
GENERAL APPROACH D-7
CHEMICAL FACTORS D-10
BIOLOGICAL FACTORS D-10
BIOLOGICAL-CHEMICAL RELATIONSHIPS D-ll
UTILITY OF ORGANIC CARBON OR GRAIN-SIZE NORMALIZATIONS D-27
EFFECTS OF STATION LOCATION ON FACTOR LOADINGS D-28
SUMMARY D-32
MAJOR STATISTICAL RELATIONSHIPS D-32
RECOMMENDATIONS FOR DEVELOPING SEDIMENT QUALITY VALUES D-35
ADDITIONAL ANALYSES RECOMMENDED TO REFINE OR VERIFY RESULTS D-35
EXHIBIT D-l - TECHNICAL BACKGROUND ON PATTERN RECOGNITION TECHNIQUES D-38
D-ii
-------
CONTENTS - EXHIBIT D-l
OVERVIEW OF MULTIVARIATE DATA ANALYSIS METHODS D-39
PREPROCESSING D-39
DISPLAY D-39
UNSUPERVISED LEARNING D-40
SUPERVISED LEARNING D-42
TYPICAL SEQUENCE OF METHODS D-43
PATTERN RECOGNITION TECHNIQUES APPLIED TO COMMENCEMENT BAY DATA D-46
DATA SETS EVALUATED D-46
DATA ENTRY AND VALIDATION D-55
CHEMICAL EVALUATIONS D-55
BIOLOGICAL EVALUATIONS D-56
DOCUMENTATION D-56
METHODS D-57
DATA PREPARATION AND DATA SET CREATION D-57
COMPUTER RUNS D-58
SPECIFIC RESULTS FROM INDIVIDUAL AND COMPARED COMPUTER RUNS D-58
INTERACTIVE SCREEN LOG FOR RUN #17 D-68
D-iii
-------
FIGURES
Number Page
1 Area map of Commencement Bay and Cam Inlet D-3
2 Scatterplot of abundance of Praxillei la gracilis and
sediment concentration of pyrene D-19
3 Scatterplot of a major "biological" factor and a
"bioassay/conventional chemical" factor D-21
4 Scatterplot of a major "biological" factor and a
"chlorinated butadiene" factor D-23
5 Scatterplot of a "biological" factor and an
"organic enrichment/sediment toxicity" factor D-24
6 Hierarchial classification analysis using 5 factors
from factor analysis of 64 numerically dominant
infaunal species D-26
7 Scatterplot of a "chlorinated organics" factor and a
"PAH" factor D-30
8 Scatterplot of a "chlorinated organics" factor and a
"metals" factor D-31
D-iv
-------
TABLES
Number Page
1 Frequent significant correlations among chemical or
conventional variables and biological variables D-15
2 Number of occurrences of anti-correlated variables in a
mixed chemical-biological factor from factor analyses of
data subsets D-17
3 Critical concentrations of chemicals indicated by
sensitive species D-20
D-l Variables in CHEMBIO data set, combined chemical and
biological data for 56 stations in the Commencement
Bay area D-47
D-2 Variables in MSQSGVAL data set (data set contains 144
sediment samples in the Commencement Bay area) D-50
D-3 Variables contained in CB2.DAT extended benthic data set D-53
D-4 Pattern recognition analysis - computer runs D-59
D-5 Comparison of means and standard deviations for variables
in MSQSGVAL (144 stations) and CHEMBIO (56 stations) D-61
D-6 Outlier variables from pattern recognition analysis D-62
D-7 Chemical factor differences, Run #5-IMSQ2 vs Run #10-IMSQ7 D-65
D-8 Chemical variables with >l significant (P<0.05) correla-
tions with biological variables D-67
D-v
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INTRODUCTION
OBJECTIVES
The main findings of the application of a pattern recognition software
system (ARTHUR) to sediment chemistry and biological data from 56 Commencement
Bay and Carr Inlet stations are presented in this appendix. As specified
in the Sediment Quality Values Work Plan (August 1985), the objectives
of the pattern recognition analysis task were:
• To identify statistical relationships among sediment contaminants
and biological effects
• To identify relationships that may be useful in developing
sediment quality values
• To summarize additional analyses that may be needed in the
future to refine or verify the apparent relationships.
These analyses were not intended to be used to establish sediment quality
values, but apparent associations identified by the analyses provided guidance
for the appropriate application of approaches to develop chemical-specific
sediment quality values. All objectives were realized. The pattern recognition
analyses were successful in:
• Providing corroboration of trends among chemical variables
in a Commencement Bay data set and an independent Puget Sound
chemical data set that had been previously analyzed using ARTHUR
(Quinlin et al. 1984; more limited site-specific bioeffects
information was available for this earlier study)
t Confirming chemical-biological trends that had been previously
identified in the Commencement Bay data set using alternative
data analysis techniques (Tetra Tech 1985a)
• Identifying new relationships among chemical and biological
indicators (e.g., apparent "sensitive species" to certain
chemical contaminants) that warrant additional investigation
• Providing evidence that dry-weight as well as organic carbon
normalization of chemical data resulted in interpretable trends
with respect to biological effects.
APPROACH TO EXPLORATORY MULTIVARIATE DATA ANALYSIS
The problem represented by the Commencement Bay data is the potential
complexity of relationships expected among toxic materials, physical/chemi-
cal parameters of the system, and biological communities. The purpose
of exploratory analysis is to use multivariate techniques to quickly determine
D-l
-------
unbiased relationships among the samples or among the 64 benthic abundance
variables, 15 taxonomic group variables, 3 bioassay variables, 1 total
species count variable, 10 conventional chemistry and grain size variables,
and 100 chemical variables.
Exploratory analyses do not require any assumptions concerning the distri-
bution of variables because no statistical hypothesis testing is conducted.
Two principal techniques, factor analysis and cluster analysis, were used
in these analyses. Factor analysis helps to define linear relationships
among the measured variables that may reveal more fundamental physical,
chemical, or biological forces and processes affecting the samples. Cluster
analysis helps to define relationships among the samples that may reveal
natural grouping or categorization that can be interpreted based on factor
analysis results or other fundamental influences on the samples. Additional
discussion of these techniques is presented in the Methods section (see
Exhibit D-l).
In defining relationships among samples and variables, factor analysis
and cluster analysis were also useful for identifying potential "anomalies."
The term "anomalies" indicates data values identified in an early stage
of statistical analysis as exhibiting unusual characteristics relative
to other data values. A stepwise analysis was important in evaluating
the effects of these anomalies on interpretations of the data. For the
most part, these data were associated with samples collected adjacent to
major pollution sources. Later analyses that excluded these values were
used to evaluate trends observed in initial analyses that included anomalies,
and to explore underlying trends that may have been masked by the anomalies.
ANALYTICAL SCOPE
The data analyzed are a subset of stations in the database compiled for
development of chemical-specific sediment quality values for Puget Sound (see
Commencement Bay Figure A-l in Appendix A). An additional 88 Commencement
Bay stations analyzed for chemical concentrations only were included in the
evaluation of chemical-chemical relationships (i.e., a total of 144 chemistry
stations; most are shown in Figure 1). The Work Plan limited the scope of
analyses to the Commencement Bay/Carr Inlet stations because of three factors:
1. Considerable data manipulation would have been required to
compile and enter biological data from other data sets (e.g.,
benthic species-level abundance data, percent toxicity response)
in addition to the chemical data already being entered, which
would have delayed the overall project schedule. The only
biological data required to determine sediment quality values
(see Section 5 of the main report) were codes in the database
that specify whether each biological indicator was or was
not significant at each station.
2. A combination of all appropriate data sets is not recommended
until the large chemical and biological data set generated for
the Elliott Bay Toxics Action Plan is available. This combined
D-2
-------
• HY-49
HY-50 •
HY-51 •
• CB-14
• CB 13
COMMENCEMENT
BAY
i
GJ
HYLEBOS
WATERWAY HY-47
HY-48
HY-45
CITY
WATERWAY
Figure
1. Locations of Commencement Bay stations sampled
for surficial sediment chemistry.
-------
O
I
• RS-24
RUSTON
N
• RS-20
COMMENCEMENT
BAY
RS-13
TACOMA
O
1 1
40(
1 I 1
1
0
1000
RS-12
FEET
Figure 1. (Continued).
-------
data set may be subjected to the most efficient data reduction
and analysis techniques determined in this task.
3. Of the data sets identified for the sediment quality values
project (see Appendix C), only the Commencement Bay/Carr Inlet
data set contained a full complement of paired chemistry,
toxicity (amphipod, oyster larvae, Microtox bioassays), and
benthic infaunal data for analysis of the possible relation-
ship of sediment chemistry to site-specific biological effects.
The pattern recognition analyses enabled a more detailed investiga-
tion of these data than was possible under the constraints
of the earlier Superfund investigation (e.g., use of species
abundances rather than total taxa abundances in multivariate
analyses of chemical-biological relationships; Tetra Tech
1985a).
Four caveats were recognized in the application of pattern recognition
techniques to this project. First, the pattern recognition results were
used only to identify potential relationships among variables as supple-
mental information for the development of sediment quality values, not
to derive quantitative equations such as may be required for specifying
a predictive model for sediment quality. The relationships discussed in
this appendix can be investigated further by a suitable experimental design,
further sampling and analysis, and appropriate statistical tests. As directed
in the Work Plan, suggested analyses to refine or verify the relationships
are summarized at the end of this report.
Second, no statistical confirmation (hypothesis) tests were required
for the exploratory tests conducted on the data, and data uncertainties
were not employed. These analyses have produced findings that suggest
possible chemical-biological relationships. Confirmation that these relation-
ships occur with a defined statistical confidence would require hypothesis
testing. As discussed in the previous section, pattern recognition techniques
do not require any statistical assumptions about variable distributions
when used in an exploratory mode.
For example, the chemical data used in the analysis were first autoscaled
(i.e., by a transformation similar to a z-score transformation). Autoscaling
is a one-to-one mapping of the values of a variable from one reference
system to another. The mapping preserves the shape of the variable distri-
bution, zero-centers the distribution, and uniformly scales the variance.
Also, factor analysis (used as part of the pattern recognition procedure)
does not require variables to be normally distributed, especially where
the approach is being applied for information compression (i.e., a Karhunen-
Loeve transformation; see for example Watanabe 1973) as was done in this work.
Hence, data transformations and knowledge of the data distributions become
important only if multivariate hypothesis testing were to be conducted.
The ARTHUR program can incorporate data uncertainties in its statistical
analyses, but these capabilities were not applied in this study because
of resource constraints. It was assumed that, for the trends observed,
D-5
-------
environmental variability exceeded the expected analytical variability
of the measurements. As a test of this assumption, additional pattern
recognition tests were performed with subsets of the data to verify the
repeatability of some findings (e.g., the identification of potentially
sensitive benthic species). The conclusions reached in this report are
based on consideration of these additional tests.
Third, the data set is biased in the greater number of stations from
an area that is generally recognized as polluted. Only 5 of the 56 biological
stations were located outside the defined Commencement Bay Remedial Investi-
gation area: four in Carr Inlet and a fifth in deep water outside Hylebos
Waterway. This constraint is not considered severe because sediments sampled
within the Commencement Bay system showed a substantial range in concentra-
tions for the chemicals measured, and many chemicals were undetected at
several of the stations. Also, statistically significant bioeffects (e.g.,
toxicity or depressed benthic infaunal abundances) occurred at only 29
of the 52 Commencement Bay stations.
Therefore, although there are substantially more stations from Commencement
Bay than from a defined reference area, conditions within Commencement
Bay range from low pollution and effects to high pollution and effects.
Therefore, a potential for identification of effects on pollution-sensitive
species remains. The data are especially useful for identifying potential
relationships among chemicals and species that are not extremely sensitive
to pollutants.
Fourth, a complete discovery of relationships in the data set is not
guaranteed with pattern recognition analyses. Although the data analyses
techniques used in this project provide a reasonably comprehensive multivariate
analysis of the data, it is not expected that all relationships have been
identified. Nonlinear relationships among variables are hard to determine.
The results indicate some additional experiments and analyses that should
be performed. Although the findings may be used as guidelines for the
appropriate development of sediment quality values or for regulatory actions,
such actions must be consistent with the general biogeochemical understand-
ing of the environmental systems under study.
D-6
-------
DISCUSSION OF MAJOR RESULTS
GENERAL APPROACH
The combination of pattern recognition techniques in ARTHUR were used
to confirm appropriate groupings of chemicals and normalizations of chemical
data, identify sensitive benthic species and system-wide relationships among
chemicals and bioeffects, and determine the importance of area-specific contami-
nation on the ability to discern potential chemical-bioeffects relationships.
The approach used in this study combined statistical methods in sequences that
are typical of exploratory evaluation of complex data sets. The sequence of
statistical manipulations (including treatment of undetected chemicals) are
described in detail in Exhibit D-l. The general sequence of steps was:
1. Autoscale the data to put the variables on a common footing
of "unit variance", and list univariate statistical parameters
2. Apply factor analysis techniques to calculate factors represent-
ing more efficient dimensions of variation in the data
3. Interpret the factors for underlying chemical or biological
meaning
4. Project the scaled measurement values onto the new factor
axes and plot the data according to these "scores"
5. Evaluate the factor plots for indications of anomalies (i.e.,
unusual values) or structure and relationships among the stations
6. Apply cluster analysis techniques to explore for intrinsic
group association between the stations
7. Apply other selected techniques, as appropriate, to calculate
interfeature correlations, variable variance weights between
groups of samples, or variable selection to look for important
variables.
Data Description
Data analyzed from Commencement Bay/Carr Inlet included:
• Chemical data for 144 stations (0-2 cm sediment samples; most
stations are shown in Figure 1)
• Chemical, bioassay, and benthic infaunal data for a subset
of 56 stations (Figure A-l in Appendix A; benthic data were
missing for two of these stations).
D-7
-------
The latter chemical-biological data set incorporated 192 variables, including:
• Bioeffects data for 64 benthic abundance variables, 15 taxonomic
group abundance variables, 3 bioassay variables, and a "species
richness" variable representing the total number of unique benthic
infauna species at each station
• Chemical concentration data for 100 organic compounds, metals,
and metalloids
0 Conventional chemical data for 10 variables (e.g., grain size,
total organic carbon, total sulfides).
Separate pattern recognition analyses were conducted using chemical variables
normalized to (1) sediment dry weight, (2) total organic carbon content
of the sediment, and (3) total percent fine-grained material in each sample.
These different data normalizations were used to determine chemical-chemical
relationships (i.e., groups of chemicals with covarying distributions in
the environment), and chemical-biological effects relationships. The importance
of each kind of data normalization is briefly summarized in the following
sections.
Dry Weight Normalization—
Most sedimentary contaminants are associated predominantly with the
solid material in bulk sediments, not with the interstitial water. Thus,
dry-weight contaminant concentrations are preferred to wet-weight concentra-
tions. Use of dry-weight concentrations precludes the possibility that
variations in sedimentary moisture content will obscure informative trends
in chemical data. Pattern recognition analyses were also conducted using
biological effects data and chemical concentration data normalized to sediment
dry weight to determine whether a relationship existed between biological
effects and the total mass of chemical in a given volume of sample (i.e.,
represented by the dry weight concentration).
Total Organic Carbon Normalization—
Chemical concentration gradients, particularly of nonpolar, nonionic
organic compounds, have been observed to correlate positively with sedimentary
organic carbon content (e.g., Choi and Chen 1976). This observation is
commonly interpreted in one of two ways: (1) organic matter is the "active
fraction" of sediment and serves as a sorptive sink for neutral, and possibly
polar or metallic, compounds, or (2) carbon-rich particles may be an important
transport medium for contaminants [e.g., PAH may tend to be associated
with soot particles (Prahl and Carpenter 1983)]. Also, if organic matter
is a sorptive sink for contaminants, toxic biological effects from exposure
to contaminated sediments should decrease with increasing organic carbon
content (see Appendix H for more detailed discussion). Hence, pattern
recognition analyses were conducted with biological effects data and chemical
concentrations normalized to organic carbon content to examine whether
increases in toxicity or biological effects correspond to increased contaminant
D-8
-------
concentrations relative to total organic carbon content. Total organic
carbon was also used as a variable in the analysis of dry-weight normalized
chemical data.
Normalization to Percent Fine-Grained (<63um) Particles—
On a limited spatial basis, contaminant concentrations are often inversely
correlated with particle size. Thus, contaminants (especially metals)
may be concentrated in the fine-grained particles of bulk sediments. This
observation is often explained in terms of surface area, in that finer
particles have greater specific surface area, and thus greater sorption
capacity, than larger particles. Because organic carbon content also tends
to vary inversely with particles size, normalizing to percent fines may
be effectively equivalent to normalizing to organic carbon content. The
percent fine-grained material in each sample was also included as a variable
in dry-weight and TOC normalized analyses.
Statistical Procedures
Statistical procedures applied to these data included factor analysis,
cluster analysis, and category classification. Major chemical and biological
factors derived from the total data set are briefly described in the next
two sections, followed by a presentation of biological-chemical relationships,
the influences of different normalizations of sediment chemistry, and the
importance of small-scale geographic effects. Most of the discussion of
benthic infauna-chemistry results focuses on individual species, which
were not examined in detail in previous studies. Preliminary analysis
of the statistical results by G.A. Erickson and Associates was performed
without specific knowledge of the results of the Commencement Bay Remedial
Investigation (Tetra Tech 1985a).
Factors derived in factor analysis are mathematical (usually linear)
combinations of individual variables, and represent different aspects of
the data set. The goal of factor analysis is to explain as much of the
variation in the data with as few dimensions (factors) as possible. In
the current study, up to 10 factors were extracted from the data set and
documented. The 5 factors that accounted for the greatest amount of variability
in the data set were examined in detail. Each of these 5 factors accounted
for at least 5 percent of the total variability and, in combination, accounted
for approximately 65-75 percent of the total variability in the data set.
The variables making up a factor may have either a positive or negative
loading (i.e., influence) on the total value of the factor. When several vari-
ables load strongly in either the positive or negative direction onto a factor,
the factor may serve as a replacement for this combination of variables,
thus reducing the number of variables in the system. If these factors
are interpretable as physical, chemical, or biological influences on a
system, they provide additional insight on the relationships between the
variables making up the factor.
D-9
-------
CHEMICAL FACTORS
Each of the following four chemical factors were extracted from the
chemical data set as linear combinations of the concentrations of related
chemicals. Chemicals for each factor are listed in decreasing order of
the strength of their loading:
1. A phenols and light aromatic hydrocarbon factor: 4-methylphenol,
isopimaradiene (a diterpene), 2-methoxyphenol (guaiacol), an alkyl-
ated benzene isomer (tentatively identified as a cymene isomer),
and phenol.
2. A metals factor: nickel, iron, barium, zinc, total metals (the
sum of U.S. EPA priority pollutant metals), selenium, arsenic,
manganese, beryllium, lead, antimony, copper, cadmium.
3. A chlorinated compound factor: hexachloroethane, 1,2,4-trichloro-
benzene, hexachlorobenzene, hexachlorobutadiene, other tri-, tetra-,
and pentachlorinated butadienes, total PCBs [often joined by some
polynuclear aromatic hydrocarbon (PAH), e.g., dibenzo(a,h)anthra-
cene or indeno(l,2,3-cd)pyrene].
4. A high molecular weight PAH (HPAH) factor: total HPAH, dibenzo-
(a,h)anthracene, indeno(l,2,3-cd)pyrene, total benzofluoranthenes,
methylpyrenes, benzo(a)pyrene, chrysene, benzo(a)anthracene.
These factors indicated groups of chemicals with similar geographic
distributions. For example, the metals factor was strongly influenced
by the concentrations of metals at stations near the ASARCO smelter along
the Ruston-Pt. Defiance Shoreline (Figure 1); the chlorinated compounds
factor was strongly influenced by concentrations of chlorinated compounds
at stations toward the mouth of Hylebos Waterway (Figure 1). Concentrations
of HPAH near the Kaiser Ditch toward the head of Hylebos Waterway strongly
influenced the HPAH factor.
Similarly interpretable factors were derived from several NOAA data
sets on other areas of Puget Sound (Quinlin et al . 1984). These previous
factors included HPAH, low molecular weight PAH (LPAH), metals, and DDTs
and high molecular weight chlorinated hydrocarbons (e.g., PCBs). The similarity
of these derived factors in different data sets from around Puget Sound
suggest similar chemical-chemical relationships in each area (at least
for these select chemicals). Because of this covariance, it is recommended
that sediment quality values be derived for the sum of these variables
as well as for the individual chemicals.
BIOLOGICAL FACTORS
In a manner similar to the derivation of chemical factors, several
factors dominated by biological variables emerged from an evaluation of
the benthic infauna and conventional chemical data set (e.g., grain size,
organic carbon). These data were evaluated separately from the toxic chemical
D-10
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data to examine features that might suggest community relationships among
benthic infauna. The resultant factors sometimes included significant
contributions from grain size variables (e.g., percent sand and silt content),
but not from other conventional chemical variables (e.g., TOC, sul fides,
total volatile solids, oil and grease). The inclusion of conventional
chemistry variables in this analysis enabled some interpretation of habitat
type as an important characteristic of benthic communities. For example,
the following 3 factors contained combinations of biological variables
that could be interpreted from an environmental perspective (run #17, Table
D-4 in Exhibit D-l; variables listed for each factor are in decreasing
order of their loading on varimax rotated factors; benthic infauna are
in terms of abundance):
1. A factor composed primarily of molluscs, ostracods, and decapods,
some species of which are pollution-tolerant and most of which
are abundant in Commencement Bay waterways): This factor includes
Total molluscs, Axinopsida spp., Axinopsida serricata, Nucula tenuis,
Euphi lomedes spp., Euphi lomedes producta, Total abundance, Macoma
spp., Macoma carlottensis, Eteone Tonga. Pinni xa spp., Lumbri neri s
spp.
2. A factor composed primarily of benthic infauna associated with
fine-grained sediment types: includes Tharyx multifi1i s, Tharyx
spp., Total polychaetes, Total abundance, [-Sand], Lumbrineris
spp., [+ Silt], Lumbrineris sp. group 1, [+ Clay], Glycera capitata,
Leitoscoloplos pugettensis, Macoma elimata
3. A factor composed primarily of benthic infauna associated with
sandy sediment types: includes Prionospio steenstrupi, Prionos-
pio spp., Odostomia spp., Mysel1 a tumida, Mitrella gouldi, Total
crustaceans, Mediomastus spp., Leptochelia dubia. [+ Sand], [- Silt],
- Lumbrineris sp. group 1.
Variables preceded by a minus sign (-) in these lists have a negative
loading on the factor (i.e., are inversely correlated with variables having
a positive loading). Non-biological variables (i.e., sand and silt content)
are shown in brackets. Sand, silt, and clay were the only non-biological
variables included in the analysis that loaded strongly on these 3 factors
(e.g., organic carbon content did not load strongly on these factors).
The first two factors showed a high degree of correlation in most of
the Commencement Bay study areas. The third factor had a stronger influence
from Carr Inlet stations as well as selected Commencement Bay stations
(e.g., St. Paul Waterway stations near a pulp and paper discharge).
BIOLOGICAL-CHEMICAL RELATIONSHIPS
Statistical relationships among biological and chemical variables were
examined to ensure that the prediction of sediment quality values would
reflect known empirical trends. Many studies have documented that the
presence of toxic substances can result in decreased abundances of, or
sublethal effects on, affected organisms (e.g., Gary 1979; Boesch and Rosenberg
D-ll
-------
1981; Eagle 1981; Gray 1982; Wolfe et al. 1982). In cases where opportunistic
or pollution-tolerant species have shown an initial increase in abundance
after an exposure to toxic chemicals (e.g., Capitella capitata at the West
Falmouth oil spill site), high abundances of those taxa have usually been
attributed to their abilities to become established in a disturbed or polluted
environment and in the absence of competition for resources. Thus, there
is little or no documentation of a significant enhancement of benthic organisms
as a direct response to a toxic chemical, although such a response is theoreti-
cally possible. There is also no evidence that enhancement occurs for
one species or taxonomic group in the presence of toxic chemicals without
a significant depression being observed in the abundance of another species
or group.
Hence, the development of sediment quality values has generally assumed
that increasing concentration of certain toxic chemicals results in an
increase in biological effects. In the current pattern recognition study,
no a priori assumption was made by the statistician concerning the direction
of population change in response to a toxic chemical effect. All significant
correlations among chemical and biological variables, whether positive
or negative, were examined using scatterplots of the data distributions.
Examination of scatterplots was also used to prevent blind acceptance of
apparent positive or negative trends between two variables based on summary
statistical results.
Approach
A series of analyses was used to identify apparent biological-chemical
relationships. Results of these analyses are provided in the following
sections. Briefly, the steps followed are:
1. Examine correlations among variables as a preliminary check
for linear relationships
2. Identify sensitive species using factor analysis:
Conduct factor analyses using all chemical-biological
stations in the data set to identify factors to which
chemical and biological variables contributed in opposite
directions (i.e., are inversely correlated)
Check the stability (i.e., reproducibi1ity) of these
factors, and the possible existence of additional mixed
biological-chemical factors by re-running factor analyses
on subsets of the whole data sets (i.e., subsets were
defined geographically according to the known distribu-
tion of different types of major chemical sources in
the study area)
Using data from all chemical-biological stations, examine
scatterplots of the individual chemical and biological
variables contributing to the mixed biological-chemical
factors to verify their implied inverse relationships.
D-12
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3. Interpret scatterplots of the primarily chemical and primarily
biological factors (discussed in previous sections) to identify
possible relationships between combinations of chemicals and
combinations of biological variables (note: because factor
analysis attempts to derive independent factors character-
istic of the data set, a correspondence between these factors
is not necessarily expected; there is an implied correspondence
only among variables that load onto a common factor).
4. Perform cluster analyses using the factors to define groups
of stations according to similarities in the species compositions
and abundances of infaunal organisms; check to see if differences
among these clusters can be attributed to differences in the
associated sediment chemistry or sediment toxicity.
Significant Correlations Among Variables
To derive chemical-chemical variable coefficients, biological-biological
variable coefficients, and biological-chemical coefficients, correlation
coefficients were calculated for the 192 variables of the various data
sets. Correlation coefficients give an indication of the degree to which
two variables are related linearly. In multivariate data sets, care must
be taken in interpreting correlation coefficients where multiple correlations
are present among variables. For example, copper may correlate strongly
with the amount of clay present in samples, and the mere presence of a
large percentage of clay may also be unsuitable for some benthic organism.
A negative correlation between copper concentrations and the abundance
of the benthic organism could be misinterpreted unless the effects of the
covarying clay have been removed (e.g., by normalizing copper concentrations
to percent clay content).
Correlation matrices were scanned for coefficients that were significant
for pairs of variables (i.e., at least at the 95 percent confidence interval;
P<0.05). At this stage of the analysis, data for all stations were included
to determine if there were system-wide correlations among variables. Very
strong correlations were observed in the 144 station chemical data set
for several chemical-chemical variable pairs (e.g., significant coefficients
of r>0.8 were found for a number of variables associated with LPAH and HPAH).
Only a few of the linear chemical-biological correlations exceeded a
coefficient of r=0.7 (r2=0.5; i.e., a linear relationship between the variables
accounts for approximately 50 percent of the variability):
• "Other" taxa versus aniline (r=+0.986), 2,4,5-trichlorophenol
(r=+0.780), and isophorone (r=+0.697)
t Nematoda versus aniline (r=+0.985), 2,4,5-trichlorophenol
(r=+0.781), and isophorone (r=+0.700).
In both cases, these results appeared to be driven by a single unusual
data value. The "Other" taxa are dominated by Nematoda, which are present
D-13
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in very high abundance at Station CI-11 at the head of City Waterway.
Aniline, 2,4,5-trichlorophenol, and isophorone were detected at this station,
but were either undetected or present at lower concentrations at all other
stations.
At smaller correlation coefficients (e.g., r>0.4 to 0.7), some additional
biological variables exhibited significant correlations (P<0.001) with
chemical variables. Of these biological variables, bioassay responses
(i.e., percent amphipod mortality and oyster abnormality) were significantly
correlated with the largest number of chemicals (P<0.001; r>0.4). These
bioassay response variables were also negatively correlated with the numbers
of unique species at each station [i.e., amphipod mortality (r= -0.468)
and oyster abnormality (r= -0.537)]. This inverse relationship between
numbers of unique species and bioassay responses suggests that this measure
of species richness may be sensitive to toxicological responses of indicator
organisms.
Of 51 biological variables retained for the combined chemical-biologi-
cal evaluations, 37 had correlations significant at the 95 percent confidence
level (P<0.05) with at least one organic/inorganic chemical or conventional
(e.g., grain size) variable. Ten chemical or conventional variables that
were significantly correlated with >10 percent (i.e., >4) of these bio-
logical variables are listed in Table 1. The number of times each variable
was positively and negatively correlated with biological variables is also
indicated.
All of the individual chemicals in Table 1 that were negatively correlated
with more than one biological variables are either crustal elements that
derive predominantly from natural sources (e.g., nickel, beryllium, and
chromium) or compounds that could derive as natural biological products [e.g.,
9-hexadecenoic acid methyl ester; fatty acid methyl esters are possibly derived
from microbial methylation of naturally occurring fatty acids (Ehrhardt et al.
1980)]. The suggested interrelationships between metals and biological vari-
ables (Table 1) may simply reflect silt/sand correlations with biological
variables because the metals also have significant positive correlations
with percent silt content and negative correlations with percent sand content.
A similar grain size dependency was not observed for organic compounds.
Overall, when data from all 56 chemical-biological stations in Commence-
ment Bay/Carr Inlet were combined in a simple correlation analysis, no
strong evidence was found for a chemical-benthic species relationship that
could be interpreted as an adverse effect of a pollution source common
to the entire area. This lack of obvious linear relationships between
pairs of chemical-biological variables suggested the need for more complex
factor analysis involving combinations of several variables.
Identification of Potentially Sensitive Species
Factor analysis and factor plots (i.e., scatter plots where at least
one of the axes is a derived factor from factor analysis) were performed using
the combined chemical-biological data set (56 stations). As a test of
D-14
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TABLE 1. FREQUENT SIGNIFICANT CORRELATIONS AMONG CHEMICAL
OR CONVENTIONAL VARIABLES AND BIOLOGICAL VARIABLES a
Number of Significant Correlations
Conventional/Chemical Variable Negative Positive Total
Silt
Sand
Nickel
9-Hexadecenoic acid methyl ester
Total organic carbon
Benzo(ghi )perylene
Beryllium
Chromium
Isophorone
Benzyl alcohol
9
10
6
3
3
0
2
2
1
0
8
7
0
2
2
4
2
2
3
4
17
17
6
5
5
4
4
4
4
4
Significant correlations (P<0.05) based on all 56 biological stations
throughout Commencment Bay/Carr Inlet.
D-15
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the stability of the relationships indicated by factors derived with the
entire chemical-biological data set, stations were assigned to one of the
following five groups and subjected to additional factor analysis. These
subset analyses were used to separate the influence of grossly different
known sources of chemicals, because similar toxicity and benthic infaunal
responses could be expected from completely different chemical exposures:
1 - All biological stations in the relatively low contaminated Blair
and Milwaukee Waterways, and reference stations in Carr Inlet (Figure
A-l in Appendix A)
2 - All biological stations in the adjacent Middle and City Waterways
(Figure A-l) (with a mixture of contaminants)
3 - All biological stations in upper Hylebos Waterway (Figure A-l;
exposed to major sources discharging some chlorinated compounds
but mainly metals and PAH)
4 - All biological stations in lower Hylebos Waterway (Figure A-l;
exposed to major sources discharging primarily chlorinated compounds)
5 - All biological stations in Sitcum Waterway and along the Ruston-Pt.
Defiance Shoreline (Figure A-l; evidence of significant metals
contamination in both areas)
6 - All biological stations in St. Paul Waterway (Figure A-l; exposed
to the discharge of a major pulp and paper facility including high
concentrations of phenolic substances).
In the five subset analyses conducted, stations in groups 2 through 6
were tested in combination with group 1 stations, believed to be the least
contaminated. This pairing of groups ensured a contrast between stations
with low contamination and stations with higher concentrations of chemicals
in each subset analysis. The factor analysis results were then examined
to determine which chemical and biological variables loaded onto the same
factor. Variables chosen for further study were those with the highest
(absolute value) loading on each factor, down to a level that explained
over 50 percent of the total variance contained by the factor, or to where
the loadings decreased noticeably in magnitude.
Most of the factors in these analyses appeared to be primarily chemical
or primarily biological factors. A single mixed chemical-biological factor
appeared frequently in the different subset analyses. Biological and chemical
variables on this mixed factor were loaded with opposite signs. The frequencies
with which variables were prominent on this factor are listed in Table 2.
Frequently appearing taxa were Praxillella gracilis (Polychaeta), Euclyme-
ninae (Polychaeta), Euphi lomedes producta (Ostracoda), and Nucula tenuis
(Pelecypoda). Frequently appearing chemicals were naphthalene, anthracene,
benzo(ghi)perylene, pyrene, 2-methylnaphthalene, total organic carbon,
9-hexadecenoic acid methyl ester (tentative identification), and retene
(probable identification). This factor was present in the factor analysis
of all 56 stations, and its persistence and the repeated appearance of
D-16
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TABLE 2. NUMBER OF OCCURRENCES OF ANTI-CORRELATED VARIABLES
IN A MIXED CHEMICAL-BIOLOGICAL FACTOR FROM
FACTOR ANALYSES OF DATA SUBSETS
Chemical
Variable
Number of
Occurrences^
Biological
Variable
Number of
Occurrences^
9-Hexadecenoic
acid methyl ester 5
Benzo(ghi)perylene 4
Beryllium 4
Acenaphthalene 3
Total organic carbon 3
Retene 3
Unidentified diterpeneb 3
2-Methylnaphthalene 2
Anthracene 2
Pyrene 2
Naphthalene 2
Euclymeninae 6
Praxillella gracilis 5
Nucula tenuis 4
Euphilomedes producta 3
Nemocardium centifilosum 4
Mitrel1a~gouldi2
Phy11ochaetropterus
prolifica 2
Axinppsida serricata 2
Total molluscs 2
Macoma elimata 2
Euphilomedes 2
Callianassa spp. 2
The variables listed were present in a mixed biological-chemical factor
in repeated factor analyses of the entire data set and subsets of the
data (separated geographically as discussed in text). The number of
occurrences indicates the number of factor analyses in which the variable
appeared in the factor; these chemical and biological variables were
loaded on the factor in opposite directions (i.e., were anti-correlated).
Tentatively identified (with low confidence) as kaur-16-ene.
D-17
-------
the variables in Table 2 in different subset analyses (including analyses
normalized to organic carbon or fine-grained material) strongly suggests
that the factor is not an artifact.
These results led to the direct examination of potential relationships
between the biological and chemical variables that frequently appeared
in this mixed chemical-biological factor. Scatterplots of these variables
demonstrated several instances where selected benthic infaunal taxa showed
strong tendencies for lower abundances at higher chemical concentrations.
An example of this inverse correlation behavior is shown in Figure 2.
Praxillella gracilis (Polychaeta) ranged in abundance up to 387 individuals/m2
at stations where pyrene concentrations were less than about 1900 ug/kg
dry weight. At higher pyrene concentrations found at 8 stations, the abundance
of this polychaete never exceeded 16 individuals/m^. Approximate chemical
concentrations above which abundances of benthic taxa were consistently
low in these scatterplots are summarized in Table 3.
These taxa were not the most abundant species identified, but were
moderately abundant. As a result, their possible chemical sensitivity
was not readily apparent using traditional statistical techniques. With
the exception of Euphilomedes producta, none of these species have been
identified previously as possible indicator organisms. Their potential
use as potential sensitive indicators of chemical contamination should
be explored further. Euphi lomedes spp. often exhibit enhanced abundances
in response to moderate organic enrichment of the sediments (Word 1978,
1980).
Inverse Relationships Among Chemical Factors and Biological Factors
Several factor plots indicated an inverse relationship between predomi-
nantly taxonomic factors and predominantly chemical factors, or factors
that combined chemicals or conventional sediment characteristics (e.g.,
organic carbon content) with measures of sediment toxicity. As discussed
previously, an inverse relationship may be most characteristic of a direct
toxic response of an organism to chemical contamination. For example,
in pattern recognition analyses run with only the biological effects data
and conventional sediment variables (i.e., excluding toxic chemicals),
higher species abundances appear to be associated with areas low in organic
enrichment and sediment toxicity as shown in Figure 3.
Figure 3 represents a factor projection scatter plot of the stations
as they relate to a biological factor (vertical axis; composed primarily
of taxa ) and a conventional-bioassay factor influenced primarily by volatile
solids content, TOC and toxicity response. The upper right portion of
the plot contains no stations, indicating that high infaunal abundances
are not coincident with organically-enriched sediments that are toxic in
laboratory tests.
Similar, but sometimes less obvious relationships were observed for
individual chemicals. This is not surprising because the composition of
equally toxic sediments can vary widely over the entire study area. Hence,
general variables such as organic carbon content or bioassay response may
D-18
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387 -i
UJ
o
<
o
z
CD
<
JO
ai*r
It
3
UJ
!
•TRANSITION POINT
- 1900 ppb
11
1000
2000
I
3000
4000
5000
MOO
PYRENE
(ppb, dry wt.)
Figure 2. Scatterplot of the abundance of Praxillella gracilis
and sediment concentration of pyrene.
D-19
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TABLE 3. CRITICAL CONCENTRATIONS OF CHEMICALS
INDICATED BY SENSITIVE SPECIES
Low Abundance Threshold Concentration by
Praxillel la
Chemical
Naphthalene
2-Methyl naphthalene
Anthracene
Pyrene
Benzo(ghi )pery lene
Retene
9-Hexadecenoic acid
Methyl Ester
Total organic carbon
gracilis
1100
380
560
1900
400
270
560
3 %
Euclymeninae Euphilomedes
1100
390
560
1900
450
510
560
3 %
producta
1300
380
560
1900
—
270
1800
7 %
Species a
Nucula
tenuis
1400
565
—
3300
570
--
1700
4 %
a Above these concentrations, abundances of the species indicated were
uniformly low; concentrations in ug/kg dry weight (ppb) unless otherwise
indicated.
D-20
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8
x g
z
o
2
O
o
o
CO
LU
CO
u.
o
CO
UJ
o
<
o
I>
m
LOW VALUES
HIGH VALUES •
BIOASSAY AND CONVENTIONAL CHEMICAL
LOADING FACTOR
Figure 3. Scatterplot of a major "biological" factor and a
"bioassay/conventional chemical" factor.
D-21
-------
show stronger relationships with the distribution of benthic effects in
the overall data set than any one or group of chemicals.
In several computer runs, it was clear that higher population abundances
indicated by biological factors occurred only within a restricted, low
value range of the chemical factor (e.g., see Figure 4). This behavior
was also observed in factor plots based only on conventional, bioassay,
and benthic variables (e.g., see Figure 5). The relationships observed
indicate that abundances of benthic organisms do respond to chemical contami-
nation and in a manner that parallels (but does not perfectly duplicate)
the responses observed in bioassays.
Infaunal Classification Analyses
Numerical classification analyses were performed on the benthic infaunal
data to define groups of stations (i.e., clusters) based on the similarity
of taxonomic composition of infaunal organisms. The resulting station
clusters were evaluated to determine if they could be distinguished on
the basis of observed differences in biological/toxicity effects or chemical
measurements. Euclidean distance (standardized to the maximum similarity)
was used as a similarity measure, and a hierarchial clustering strategy
was used to generate the station groups (see Appendix A).
The similarity measure used is sensitive to the absence of species as
well as to the number of individuals of species that are present. The
Euclidean distance can result in high resemblance between stations that
do not have many attributes in common, but whose attribute scores are low
(Boesch 1977). Such resemblances are expected among heavily impacted stations
although different taxa may be present at the stations. This similarity
would not be expected to be found using the Bray-Curtis similarity measure
as previously applied with these data (Tetra Tech 1985a).
Clusters of stations were determined based on their similarity in species
composition and abundance as defined by five discrete factors generated
in a factor analysis of the 64 numerically dominant infaunal species.
Several factor analyses were conducted during a stepwise series of tests
with intervening technical review. Preliminary factor analysis indicated
five "anomalous" stations that were removed prior to the final factor and
cluster analyses. As previously discussed, were primarily associated with
samples collected adjacent to major pollutant sources. These data were
excluded from subsequent analyses for three reasons:
• The effect of these particular data was clear from the preliminary
analyses
• The preliminary trends observed required reexamination without
the effects of the anomalies
• Any underlying trends that may have been "masked" by the anomalous
data points needed to be understood.
D-22
-------
3
Q.
Sii
U-ST
m . >• !
0 o o
§111
"i
•
• •„
LOW CC3NCENTRAT10N
HIGH CONCENTRATION
CHLORINATED BUTADIENE FACTOR
FACTOR • + PENCBD + PENTACHL » TETCBD + TRICED _
Figure 4. Scatterplot of a major "biological" factor and a
"chlorinated butadiene" factor.
D-23
-------
t-S
d2 !
^•i-s
gill
o3o§
T
UJ c> b b
CD i ' '
I
LOW VALUES HIGH VALUES
FACTOR LOADING (INTERPRETED AS ORGANIC
ENRICHMENT AND SEDIMENT TOXICITY)
FACTOR • + 0.398 VSOUDS + 0.383 ABNORM * 0.352 TOC - 0.339 SOLIDS
+ 0309 MORT + 0^72 NITROGEN + 0.194 CHANGE - 0.176 SAND -
Figure 5. Scatterplot of a "biological" factor and an "organic
enrichment/sediment toxicity factor.
D-24
-------
Because the Commencement Bay study design was intentionally focused around
potential sources to determine concentration gradients, such anomalies
were expected. Concern regarding treatment of anomalies is higher and
more critical in experimental designs based on random sampling (either
spatially or temporally). Thus, the treatment of anomalies in the staged
analysis conducted for this project was considered appropriate.
A dendogram showing the grouping of stations at different dissimilarity
values is shown in Figure 6. Stations with statistically significant sediment
toxicity or statistically significant depressed abundances of major taxonomic
groups (P<0.05; Tetra Tech 1985a) are also indicated.
Seven groups of stations were defined at inter-group dissimilarities
ranging generally from 30 to 40 percent (Figure 6). Group VII displayed
the highest intra-group dissimilarity as well as the highest dissimilarity
to other groups. The station clusters are characterized by no or few bioeffects
in Clusters V, VI, and VII (i.e., effects indicated only by a single toxicity
bioassay or, even less frequently, by a single major taxonomic infaunal
indicator) to a group almost entirely composed of stations with multiple
indications of biological effects (i.e., Cluster II). Cluster II contained
all of the stations where sediments exhibited the most severe effects (e.g.,
>50 percent mortality or abnormality in toxicity tests, and almost complete
absence of benthic infaunal organisms).
Factor projection plots (i.e., scatterplots of pairs of factors) were
examined to determine the major factors contributing to the definition
of these clusters. The factor that clearly distinguished the low biological
effects clusters from Cluster II (high biological effects) had the following
composition of variables (in order of the loading by each variable):
Factor = -0.354 Praxi1 lei la graci1 is - 0.320 Axinopsida serricata -
0.318 Nucula tenuis - 0.283 Euphilomedes producta
- 0.270 Eteone longa - 0.263 Pholoe minuta - 0.238 Euchone
incolor - 0.234 Euclymeninae - 0.208 Lumrineris sp. gr. 1...
Stations in Cluster II had uniformly high values for this factor (i.e.,
low abundances of the indicated species). Several of these species are
the same as reported in Table 2, which listed apparently sensitive species
that appeared to be inversely correlated with selected chemicals in factor
analyses of the complete chemical-toxicity-infauna data set. Axinopsida
serricata was the dominant mollusc found in the waterways, and Euphi lomedes
spp. was the dominant crustacean taxon. The other four factors from factor
analysis either did not contribute to the separation of these particular
clusters or had a minimal effect. Hence, stations that appear to be strongly
impacted on the basis of statistically significant (P<0.05) bioassay responses
and depressions in major taxonomic groups, have similar low abundances
of individual species that may be sensitive to chemical contamination.
Sediments from three stations in Cluster II did not exhibit statistically
significant effects in previous studies (Tetra Tech 1985a). Of these stations,
sediments from Station MD-12 had high concentrations of several
D-25
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PERCENT DISSIMILARITY
100.0 90.0
O 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0'
100.0 90.0 80.0 70.0 60.0 SO.O 40.0 30.0 20.0 10.0 0.00
PERCENT DISSIMILARITY
' COOeS FOR SIGNIFICANT EFFECT OBSERVED ARE:
0: SIGNIFICANT OYSTER LARVAE BIOASSAY
A: SIGNIFICANT AMPHIPOD BIOASSAY
H: SIGNIFICANT DEPRESSION IN TOTAL MOLLUSC ABUNDANCE
C: SIGNIFICANT DEPRESSION IN TOTAL CRUSTACEAN ABUNDANCE
P: SIGNIFICANT DEPRESSION IN TOTAL POLYCHAETE ABUNDANCE
T: SIGNIFICANT DEPRESSION IN TOTAL ABUNDANCE
Figure 6. Hierarchial classification analysis using 5 factors
from factor analysis of 64 numerically dominant
infaunal species.
D-26
-------
chemicals at or near the level above which effects were always found at
other stations, and Stations CR-14 and HY-44 had coarse sediments (i.e.,
>75 percent rocks and sand) that are not expected to support high abundances
of organisms. Hence, when only benthic species data comprise the clustering
factors, the factors do not necessarily distinguish between sediments with
potential major chemical impacts and all sediments that may have low abundances
of sensitive species because of presumed natural factors.
The most frequent significant indicator in the no-effects to low-effects
groups of stations was a significant amphipod bioassay response. A significant
amphipod bioassay in the absence of other indicators of bioeffects occurred
only at stations with sediments containing greater than 80 percent fine-grained
material. This response has been interpreted as possibly indicating a
grain size effect in the bioassay rather than necessarily a toxic effect,
although not all sediments with high percentages of fine-grained material
are significantly toxic by this bioassay (Tetra Tech 1985a). A toxic effect
cannot be ruled out; it is also possible that the amphipod bioassay may
be more sensitive to some forms of contamination than are other indicators.
In general, the cluster results suggest that laboratory bioassay results
and determinations of significant depressions in major taxonomic groups
are reasonably sensitive to changes in the structure of benthic communities.
They further suggest that a high degree of concordance may be expected
among these indicator variables.
Preliminary analyses were conducted to determine if the sediment concen-
tration of some toxic chemical factor was higher in a group of stations
having high toxicity compared with a group of stations with lower toxicity,
but similar benthic cluster assignments. No clear relationships were found
with specific chemicals, likely because the stations composing each group
had diverse chemical sources. Additional analyses are warranted after
controlling for gross differences in chemical composition within a particular
benthic cluster. These analyses were not conducted because the current
data set contains only a few stations that have closely related benthic
assemblages and similar chemical sources, which limits the confidence with
which a statistical analysis can be made. Solutions to this problem are
discussed in the summary (see Additional Analyses Recommended to Refine
or Verify Results).
UTILITY OF ORGANIC CARBON OR GRAIN-SIZE NORMALIZATIONS
The grain-size dependency suggested for metals in a previous section
(i.e., Significant Correlations Among Variables) may indicate a need to
normaYize chemical concentrations for the content of fine-grained materials
in the sediments to distinguish any independent effect of the chemicals.
Likewise, a normalization of organic compounds to total organic carbon
may be appropriate because of the known correlation between these variables.
Exploratory runs were made with both of these normalizations. In terms
of the biological relationships, few new observations emerged. The results
tend to corroborate the findings determined with the dry-weight normalized
data, especially the general patterns of decreased abundances of sensitive
speci'es with selected combined chemical factors.
D-27
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Normalization to percent fine-grained material seemed to produce results
in factor analysis that were nearly identical to dry-weight calculations.
Normalization to TOC was also consistent in indicating potentially sensitive
species, with the possible suggestion of additional species that may decline
in abundance with increased concentration of certain chemicals. The apparently
sensitive species indicated in all three types of runs were:
Axinopsida serricata
Total mollusc abundance
Nucula tenuis
Euphilomedes producta
Euclymeninae
Praxillella gracilis.
Additional sensitive species suggested by the TOC normalized analyses were:
Macoma elimata
Nemocardium centifilosum
Lumbrineris spp.
Euchone sp. A
Nephtys cornuta.
Generally, it appears that analyzing the variables without normalizing
to fines or organic carbon content provides the majority of interpretable
information. This fact may indicate that the primary influence of chemicals
in sediments on the biological systems and individual species is related
to the total volume concentration (mass) that is present for a chemical.
Second order effects may be related to the relative content to fines and
organic carbon. However, until the actual mechanisms of chemical-organism
interactions are determined (e.g., in laboratory studies), all data sets
should be analyzed with and without normalization to these "master" variables,
to confirm the results seen in these calculations.
EFFECTS OF STATION LOCATION ON FACTOR LOADINGS
A more distinctive effect than chemical concentration normalizations,
and hence of more general concern, were the substantial differences observed
between individual study areas (e.g., waterways) represented in the data
set. These differences indicate the importance of intensively sampling
small regions, in addition to larger-scale sampling to yield an integrated
picture of Puget Sound sediment systems.
Distinctive Basin Behavior in Chemical Factors
The most extreme chemical variations observed were in the Hylebos
Waterway (chlorinated organics and HPAH) and in the Ruston area (metals).
These major chemical differences are not unlike those that may be observed
throughout Puget Sound (e.g., Eagle Harbor creosote contamination, or lead
contamination around Harbor Island).
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A factor plot based on analysis of chemistry data for 144 Commencement
Bay/Carr Inlet stations is shown in Figure 7. The results clearly identify
the well-known geographic variations in chemical concentrations in this
system. The factor plotted on the horizontal axis is strongly influenced
by HPAH compounds. The factor plotted on the vertical axis is primarily
composed of chlorinated organic compounds. In both factors, the highest
concentrations are represented by stations from Hylebos Waterway. Within
Hylebos Waterway, sediments near the head of the waterway have the highest
PAH concentrations. Sediments toward the mouth of the waterway have the
highest chlorinated organic compound concentrations. A similar plot for
a metals factor and a chlorinated organics factor demonstrates the relative
differences between Hylebos Waterway and the Ruston-Pt. Defiance Shoreline
(Figure 8). Analyses were performed with and without these "anomalous"
samples to verify the stability of statistical trends.
Because of these substantial chemical differences, the relatively
small size of the biological data set (56 stations), and the large number
of variables in the problem, relationships between sensitive species and
chemical concentration were substantially blurred when all of the data
were analyzed together. By analyzing subsets of the data (e.g., two water-
ways at a time), combined chemical-biological factors emerged (see section
on Identification of Sensitive Species).
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STATIONS FROM
MOUTH OF HYLEBOS
ALL ENCLOSED STATIONS ARE
FROM HYLEBOS WATERWAYS
STATIONS FROM HEAD
OF HYLEBOS
• LOW CONCENTRATION
HIGH MOLECULAR WEIGHT POLYCYCLIC
AROMATIC HYDROCARBONS FACTOR
FACTOR • 0.299 PB79 + 0.289 PB76 + 0.280 TBFLANTH + 0.274 METH2PYR
* 0.258 HMWPAH + 0.236 HEXADECS + 0.216 PBB4 + 0.215 PB83
+ 0.207 PB39 + 0.199 PB72 + 0.188 PB71 + 0.177 PB73.-
HIGH CONCENTRATION
Figure 7. Scatterplot of a "chlorinated organics" factor and
a "PAH" factor.
D-30
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Q
m
rr
O
r—
O
+ N
O
UJ ._i
01
*"
DC
O
O .
E
|
O O
* »
• •
•
STATIONS FROM
MOUTH OF
HYLEBOS
• RUSTON
STATIONS
• LOW CONCENTRATIONS
HIGH CONCENTRATIONS
METALS FACTOR
FACTOR * • 0.328 NICKEL • 0.318 IRON • 0.263 ZINC - 0.258 COPPER
- 0.253 BARIUM - 0.247 TOTMET - 0.244 ICOGRP1 • 0.243 SELENIUM
• 0.242 ARSENIC • 0.231 MANGANESE - 0.195 BERYLLIUM- 0.1S6 LEAD
- 0.146 CADMIUM - 0.136 ANTIMONY - 0.132 CHROMIUM- 0.105 MRCURY _.
Figure 8. ScatterpTot of a "chlorinated organics
a "metals" factor.
factor and
D-31
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SUMMARY
Many of the pattern recognition results have corroborated previous
results obtained with traditional statistical techniques (Tetra Tech 1985a)
and have not been reported in detail here. This corroboration indicates
that traditional techniques have been useful in obtaining the majority
of interpretable information contained in the data set, and satisfies one
of the major objectives of conducting the pattern recognition analyses
(i.e., to validate historical findings). The results of this study are
also generally supportive of other previous Puget Sound analyses of chemical-
chemical interrelationships using ARTHUR (e.g., Quinlin et al. 1984; Chapman
et al. 1984).
MAJOR STATISTICAL RELATIONSHIPS
Major statistical relationships among sediment contaminants and biological
effects that were identified using pattern recognition techniques include:
Chemical Factors
Four chemical factors were interpretable from analysis of the complete
set of 144 chemistry stations: (1) a phenols and light aromatic hydrocarbon
factor; (2) a metals factor; (3) a chlorinated compound factor; and (4)
a high molecular weight PAH factor. Previous studies identified similar
groups of significantly correlated chemicals (e.g., groups of hydrocarbons,
metals, and chlorinated compounds). These results suggest that these major
pollutant groups have approximately similar compositions throughout Puget
Sound, although local variations certainly exist. Assuming that the composition
of chemicals is important in the type and magnitude of bioeffects produced,
this similarity implies that sediment quality values for these chemicals
may be applicable to much of Puget Sound. Given that different combinations
of chemical sources occur in different areas of Puget Sound, there is still
a concern that different synergistic effects may occur in these different
areas. This concern cannot be resolved by extrapolation of the results
in this report, except to note that a multitude of different chemical sources
found over a reasonably large geographic area is already represented in
the Commencement Bay/Carr Inlet data set.
Biological Factors
Three biological factors were interpretable from analysis of the 54
benthic infauna stations:
1) A factor composed primarily of molluscs, ostracods, and decapods,
some species of which are pollution-tolerant and most of which
are abundant in Commencement Bay waterways
2) A factor composed primarily of benthic infauna associated
with fine-grained sediment types
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3) A factor composed primarily of benthic infauna associated
with sandy sediment types.
These factors showed little correlation with chemical factors, and appeared
to be most strongly influenced by natural conditions (e.g., variations
in sand and silt content).
Biological-Chemical Relationships
Factor analysis provided a means to calculate factors that contained
contributions from both chemical and biological variables (i.e., there
was an apparent relationship among these variables). One factor identified
in the overall data set, and verified in reanalyses with subsets of the
data, contained chemical variables that were inversely correlated with
benthic infauna variables on the same factor. These results were interpreted
to indicate potential sensitive species to chemical contamination in Commence-
ment Bay. These relationships were further analyzed using individual scatter
plots of the species abundance for the chemicals of interest. Several
of these species had not previously been reported as potentially sensitive
indicators of contaminated sediments, including two polychaetes (Praxillella
gracilis, and Euclymeninae), and a clam (Nucula tenuis). A fourth species,
Euphilomedes producta, has been recognized as a potential indicator organisms
for moderate organic enrichment (showing enhanced abundances), but has
not previously been shown to exhibit a negative correlation with increasing
pollutant concentrations.
Some of the factor plots displayed a general reduction in infaunal abundances
where higher values of selected chemicals (e.g., metals, HPAH), bioassay
responses, or organic enrichment were observed. Three important points
were apparent after review of the results:
• No one chemical or chemical group accounted for all toxicity or
benthic effects on a system-wide basis
• When analyzed at the system-wide level, the most apparent species-chemi-
cal relationships did not always predict the most severe bioeffects
observed in localized "hotspots" in Commencement Bay (i.e., off
major discharges; however, these stations were distinguished in
a cluster analysis on infaunal data)
• Associations between bioeffects and chemical concentrations can
be observed in the most biologically impacted areas, but generally
only when the data set has been geographically segmented.
Factor and cluster analyses on the benthic infaunal species data separated
a cluster that was almost entirely comprised of stations with sediments
exhibiting both statistically significant sediment bioassay responses and
depressions in the abundance of several major taxonomic groups. Sediments
from stations in three other clusters typically had none of these significant
effects, or less frequently, exhibited only a single significant effect
(e g often an amphipod bioassay response only). The factor that distinguished
the low- to no-effect clusters from the highly impacted cluster was strongly
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influenced by potentially sensitive species summarized above. Overall,
these analyses indicated that laboratory bioassay results and determina-
tions of significant depressions in major taxonomic groups are reasonably
sensitive to changes in the structure of benthic communities. The results
also suggest that in Commencement Bay and Carr Inlet, a high degree of
concordance may be expected among those indicator variables (especially
the oyster larvae bioassay and benthic depressions). This concordance
was indicated by the following items in this study and the previous remedial
investigation (Tetra Tech 1985a):
• "Impact" versus "no impact" designations made by benthic and
bioassay indicators agreed at 67-79 percent of the 48 stations
in the Commencement Bay Remedial Investigation (and at 83-
100 percent of the 6 stations in a separate dredging study
conducted concurrently with identical methods in Blair Waterway
and included in the ARTHUR analyses)
• A significant depression in the abundance of at least one
major taxonomic group was observed in 6 of 7 cases (86 percent)
that also exhibited significant toxicity in both the amphipod
and oyster larvae bioassays
• Eighty-nine percent of the cases exhibiting a significant
depression in the abundance of at least two major taxonomic
groups occurred in a similarity cluster (assigned on the basis
of species-level benthic data) that contained 75 percent of
the cases exhibiting significant toxicity in both amphipod
and oyster larvae toxicity
• All 6 cases exhibiting a significant depression in the abundance
of at least three major taxonomic groups occurred in this
same similarity cluster; 83 percent of these cases exhibited
toxicity in the oyster larvae bioassay, 50 percent exhibited
toxicity in the amphipod bioassay.
The amphipod bioassay results for Commencement Bay showed the least agreement
in comparison with the benthic infauna results. This lower degree of con-
cordance may result from a sensitivity of the amphipod bioassay to fine-
grained sediments. However, in sediments containing <70 percent fine-grained
material, significant benthic depressions were observed in 100 percent
(6 cases) of the sediments exhibiting significant amphipod mortality (no
benthic data were available for a seventh station).
Appropriate Normalization of Chemical Data
There does not appear to be any justification based on pattern recognition
analyses to recommend one normalization technique over others for exploring
potential biological-chemical relationship; normalization of chemical data
to either organic carbon or a grain-size variable (i.e., percent fine-grained
material) tended to produce the same results in factor analysis as chemical
data normalized to dry weight of sediment. The abundance of a few additional
species not apparent using dry-weight normalized data may decrease with
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increased concentrations of chemicals relative to organic carbon content.
These species include: Macoma elimata, Nemocardium centifilosum, Lumbrineris
spp., Euchone sp. A, and Nephtys cornuta.
RECOMMENDATIONS FOR DEVELOPING SEDIMENT QUALITY VALUES
Results of the pattern recognition analyses recommended for consideration
in developing sediment quality values include:
1. Given that the total concentration for some chemical groups
(e.g., HPAH) correlates well with the concentrations of all
individual components of that group, it may not be necessary
to set sediment quality values for each individual chemical
in the group. Correlations established by pattern recognition
analyses were used to support the definition of appropriate
chemical groups for the derivation of sediment quality values.
2. Benthic species that appear to have a predictable and significant
response to contaminants or conventional sediment variables
(e.g., apparent PAH-sensitive species, or species sensitive
to organic enrichment) may be useful as indicators for assessing
benthic effects. The agreement between benthic effects assessed
according to major taxonomic groups (e.g., Polychaeta, Crustacea,
and Mollusca) and those assessed by potential sensitive species
(e.g., Praxillella gracilis) identified by pattern recognition
techniques was examined in the development of sediment quality
values.
3. Hyperbolic, or inverse, relationships between the response
of selected biological indicators and contaminant concentrations
were observed. This evidence is supportive of a critical
assumption in most sediment quality approaches that a threshold
concentration exists, above which a chemical can be expected
to elicit a negative biological response.
ADDITIONAL ANALYSES RECOMMENDED TO REFINE OR VERIFY RESULTS
An attempt to include associated fish histopathology data in previous
ARTHUR analyses (Quinlin et al . 1984) was inconclusive with respect to
chemical influence on the biological systems. This lack of correspondence
may result from the mobile nature of fish, their integrative feeding patterns,
and their higher positions in the food chain. Because of these reasons and
resource constraints, a multivariate testing of Commencement Bay histopathology
data with sediment chemistry data was not conducted. Unless additional evidence
becomes available that would prompt an analysis, it is recommended that further
analyses focus on the influence of chemicals on biological systems in intimate
contact with the sediments. Additional analyses are recommended below.
Data Set Expansion
The data sets for which chemical and associated biological effects
data are available for the same sediment sample, or identical sampling
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location are still too small. This is especially true given the number
of variables that are potentially of interest and the apparent chemical
complexity of the Puget Sound system (typically requiring some form of
segmentation to derive interpretable results). Data acquisition is recommended
in two directions.
More Intensive Sampling—
First, given the extensive variation in urban embayments such as Comnence-
ment Bay, further data gathering should intensively sample small geographic
regions to supplement more dispersed sampling over the entire Puget Sound.
Specifically, one-third or more sampling stations should have 2-4 additional
sampling stations chosen in close proximity (e.g., 50-100 yd). This sampling
strategy will help evaluate the microscale variation that seems to be present
in chemical concentrations and biological effects. A major problem with
multivariate techniques apparent in this application is the difficulty
in distinguishing a particular localized chemical effect when the biological
effect produced by one chemical is similar to the effect produced by another
chemical in a different part of the system under study. Hence, large data
sets combining multiple areas with completely different chemical influences
will likely be difficult to interpret, but large data sets covering a small
area (exposed to some combination of sources) will make the most efficient
use of a multivariate approach.
More "Pristine" Sampling—
Second, more samples from less affected (reference) areas must be
included. All data sets analyzed to date have been biased toward polluted
areas. Experimental designs containing 20-33 percent of the sampling stations
(and samples) in areas believed to be less affected by anthropogenic influences
are recommended.
Data Analysis
The suggestions above are for further data gathering, regardless of
which data analysis methods are used. The exploratory approach applied
in this project viewed the chemical, toxicity, and biological variables
as descriptors of the sediment stations. This approach could be supplemented
by some other analytical approaches and methods to resolve some of the
difficulties in multivariate analysis encountered in this study. These
approaches include path modeling (canonical correlation) and classification
methods.
Path Modeling (Canonical Correlation) Analysis--
Path modeling is a new technique that is an advance of regression
analysis. The technique allows variables to be assigned to sectors of
influence in a manner that approaches modeling the system. Canonical correla-
tion is a method that calculates the degree of correlation between factors
in the independent variables with factors in the dependent variables.
A simple two-block path modeling calculation is similar to a canonical
correlation analysis.
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Both techniques could be applied to sediment chemical and biological
data to partially overcome the complexity of separate influences from different
chemical groups (or sources). In a combined data set, such as Commencement
Bay or Elliott Bay, this approach may reveal distinct factor relationships
without having to break the data set into appropriate subsets (e.g., geographic
units). Although analysis of data subsets (as conducted in this project)
get to the same relationships, this approach requires substantial insight
or intuition on the part of the research team.
Classification Modeling for Screening—
Separate from the above recommendation, classification techniques such
as available in the ARTHUR system could be used as an interim decision
system, based on the existing data sets. For example, embayment data can
be grouped into stations that are above or below some accepted sediment
quality value for biological effects. Once these designations have been
made, classification methods can be "trained" to decide which group status
to assign to new sediment samples based on values of selected chemical
variables. Part of the classification analysis would pinpoint which variables
are key to the decision-making process.
An initial approach would look at the classification accuracy achieved
when a few of the most influential factor variables are used. Other techniques
can be used to evaluate the importance of each chemical variable. This
approach could complement sediment quality values for specific chemical
concentrations and may allow screening analysis of sediments based on a
few simple measurements (e.g., bioassays with selected chemical measure-
ments).
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EXHIBIT D-l
TECHNICAL BACKGROUND ON PATTERN RECOGNITION TECHNIQUES
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OVERVIEW OF MULTIVARIATE DATA ANALYSIS METHODS
A general overview of multivariate methods is presented in this section
followed by a description of specific tasks performed in the exploratory
analysis performed for this project. ARTHUR is a system consisting of
about 70 separate routines for data preprocessing, display, unsupervised
learning, and supervised learning operations for general pattern recognition.
It is used for exploratory data analysis (factor and cluster analysis),
anomaly evaluation, selected feature-time plots, feature distribution statis-
tics and interfeature correlations, and classification/ prediction model
development.
PREPROCESSING
Preprocessing includes transformation of variables and manipulation
of samples. Transformation of variables can include scaling (this operation
preserves the shape of the original variable distribution), mathematical
functional operations (eg., logarithmic), and linear combinations of variables.
Manipulation of samples can include changing property/category value (re-
stratifying), random deletion, and specific deletion and assignment to
test sets.
The types and combinations of preprocessing steps that can be applied
to data are possibly infinite in number. The most common preprocessing
steps can be applied using either a combination of or individual methods
from 16 routines in the ARTHUR system. These steps include feature scaling,
linear combinations of features, feature ratios, feature selection, weighting,
category changes, merge sets, split sets, random subsets, and missing data
filling.
Feature/variable scaling is commonly employed to eliminate dependence
on units of measure and to put all variables on a common footing of relative
(within feature) variance. The autoscaling option in ARTHUR method SCALE
was used in this project. This transformation subtracts the mean value
of the variable from each measured value and divides by a term proportional
to the variance. This transformation is similar to the standard normal
variable transformation (z-transform), and is a one-to-one mapping of the
values of a variable from one reference system to another.
DISPLAY
Display routines are provided in the ARTHUR system to allow plots of
data versus selected axes of information, and to allow other graphical
information representations. Several of the methods have line-printer
graphical output to portray performance results (eg., regression methods
plots of residual errors, hierarchical cluster analysis dendograms).
Plots of the data versus selected pairs of variables, eigenvectors or
factors can be done on line-printer, Calcomp, or Tektronix terminal output.
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The line-printer routine is the method VARVAR (Variable by Variable plot-
ting). A method for nonlinear mapping of higher dimensional information
to 2-space is also provided (NLM).
UNSUPERVISED LEARNING
Unsupervised learning consists of applying exploratory techniques,
in an unbiased manner, to search for relationships among the samples (stations)
and among the variables measured for the samples. The main techniques
include factor analysis, cluster analysis, and data plots.
Factor Analysis
Factor analysis is a mathematical data analysis approach that is aimed
at representing the variation (or information) in the data in the fewest
dimensions. The data are initially represented by their measured values
for the original variables. This is usually described as the distribution
of samples in the measurement space (or NVAR-space, where NVAR = the number
of original variables). The first step in factor analysis is usually calcula-
tion of principal component (pc) axes which fit the distribution of samples
in an ordered manner; the first axis lies along the direction of the greatest
variation in the data, the next lies orthogonal (perpendicular) to the
first, along the next greatest variation in the data, etc. The samples
can be projected onto these axes and plotted. The next steps in factor
analysis are usually retention of a subset of the pc-axes, rotation by
small amounts and reorthogonalization of the axes, and interpretation according
to the variables that have the highest loadings onto the axes (or factors).
Factor analysis was typically done using the following sequence of methods
in the ARTHUR system: KAPRIN, KAVECT, KAVARI, KAVECT, KAORTH, and KAVECT
(optional CHSUB followed by the above sequence again). KAPRIN performs
principal component extraction. KAVARI performs Varimax rotation on the
various pc. KAORTH performs reorthogonalization of the rotated vectors.
KAVECT prints out detailed information on the pc/factors, including variance
retained and factor loadings of each variable. CHSUB was used to randomly
keep 80 percent of the samples in each category before recalculating pc's
and factors.
The linear principal component extraction step in KAPRIN is invariant
with respect to the number of pc's calculated (up to the limit of the original
number of variables) as long as the samples and variables in the data remain
unchanged. Deletion of a few anomalous samples can have major effects
on the calculated pc's. Varimax rotation and reorthogonalization are steps
that can be useful for interpretation of principal components such as physical,
chemical, or other factors. These steps, along with randomly calculated
subsets, were used to help identify, interpret, and ascertain which were
the major factors and the most strongly contributing variables in the data.
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Cluster Analysis
Cluster analysis was done on the samples using the hierarchical clus-
tering method (HIER) in the ARTHUR system. Both single-link and complete-link
connection dendograms were calculated and printed on line-printer plots.
Single-link connection similarity calculations start with the two
samples in the data that lie closest in the n-dimensional space of the
measurements, where n is equal to the number of measurements made on each
sample (or station). This distance is the simple Euclidean distance, and
corresponds to the calculated similarity between the two samples. The
similarity measure for each sample X-j and Xj is defined as:
Sij = 1 - dij/MAX(dij)
where
M n ,
dij = C £ (X1k - Xjk)2 ]°'5
k=l
and
MAX(dij) is the largest interpoint distance.
The most unlike samples give S-M=0, and identical objects give S-ji=l.
The initial two samples selected with this algorithm are defined as a cluster.
The method then looks for the next smallest distance in the data set.
If this involves one of the previous samples, the new sample is connected
to the first cluster with a similarity value representing its distance
from the nearest sample already in the cluster. If the next smallest distance
involves two samples, neither of which is already in a cluster, then a
new cluster is begun using these two samples, and the method moves on to
look for the next smallest distance. These steps are repeated until all
samples are connected.
Complete-link similarity calculations start the same way as single-link
for the first two samples. Other clusters are built up by linking samples
previously not in a cluster by distance-similarity and by graphing samples
to clusters by maximum distance to the samples in the cluster.
Interpretation of cluster analysis results is similar for both single-
and complete-link methods. Groups of samples linked at higher similarity
values must be examined using external knowledge to identify whether some
common basis is obvious for explaining why the samples appear to form a
cluster. If a basis is not obvious, classification methods can be used
to determine if classification accuracies into the apparent groups are
high and which are the important variables. The variables that are important
can point toward a possible explanation for the observed clusters.
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Anomaly Evaluation
Potential sample anomalies were evaluated using a combination of methods
contained in the ARTHUR system. The two approaches most frequently used
in this study employed factor plots and cluster analysis.
The factor analysis approach uses the following sequence of methods
in the ARTHUR system: KAPRIN,KATRAN,VARVAR,KAVARI,KATRAN,VARVAR. This
sequence calculates the principal component or factor axes (KAPRIN followed
by KAVARI), projects the data onto the new axes (KATRAN), and plots the
data versus selected factor pairs as axes (VARVAR). Any bad samples in
the data tend to adversely influence the calculation of the factors. As
a result, they show up as wel1-separated points (anomalies) on the data
plots, even when only the most significant factor pairs are used as axes.
The cluster analysis approach uses the standard output information
from hierarchical cluster analysis to identify potential anomalies. The
method sequence in the ARTHUR system is DIST,HIER. The output information
is in the form of a connection dendogram. Potential anomalous samples
appear with very low connection similarity values in the dendogram, indicating
that they are quite different from all of the other samples in the data
set.
SUPERVISED LEARNING
Supervised Learning consists of methods to develop classification
or prediction models, developed on a "training set" of samples that have
known category/property values, testing of the models on the training set
and any available "test-set" samples, evaluation of the variables important
to the models, and the physical/chemical implications of the models.
Category Classification
Category classification is aimed at developing models that accurately
classify samples into discreet groups or into continuous categories (eg.,
low, middle, and high concentration groups). The methods include K-Nearest
Neighbor (KNN), principal component modeling (SIMCA), discriminant analysis
(LEDISC and REGRESS), and Bayesian Probability (BAYES).
KNN uses a committee vote of nearest neighbors to classify. The under-
lying philosophy is that the closer two samples are in the multidimensional
measurement space, the more similar they should be. SIMCA (SIPRIN + SICLAS)
uses principal components (eigenvectors) for each group to define the posi-
tion and distribution of the group in the measurement space. Classifi-
cation of a sample is based on the smallest distance between the sample
and the pc-models for the categories. Discriminant analysis uses a linear
function to separate two categories, based on positioning the separation
by a minimization of squared-error. BAYES classification is most suited
to large data sets with many samples, where the distribution histograms
for each variable can be accurately characterized for each category. Classifi-
cation is made on the basis of a summation of probabilities that the sample
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belongs to each category weighted by the variable-category probabilities
for the measurement values of the sample.
TYPICAL SEQUENCE OF METHODS
Appendix A-l is the interactive dialogue from computer run #17 (output
code INMC9). It illustrates the typical sequence of methods applied to
the data for exploratory analysis.
Dnivariate Distribution Parameters
Autoscaling of the variables was one of the first methods applied to
the data sets, under almost all circumstances. Using the method SCALE
in ARTHUR, each variable was transformed to a new feature with zero mean
and unit variance. It removes the units of measure from the variables,
and puts them all on a common footing of variance (this transformation
does not change the original shape of the variable distribution). In addition,
the method SCALE calculates distribution parameters for each variable over
the data set, consisting of mean, standard deviation, normalized standard
deviation, minimum value, maximum, range, and coefficients of skewness
and kurtosis.
Feature Correlations
The correlation matrix of all interfeature correlations was calculated
once on the autoscaled data for each data set. This step provides complemen-
tary information to the principal component and factor analysis calculations.
The method CORREL in ARTHUR calculates the correlation coefficient, the
low and high values of the 95 percent confidence interval about the coefficient,
and the probability that the correlation would be calculated if the sample
were drawn from a random parent population. The correlation matrix was
scanned after each run for values with a probability <0.001 and coefficients
above r=0.500.
Basic Exploratory Analysis
Below is a listing of methods and option parameters that would be typi-
cal of those used to do initial exploratory work with the data.
ARTHUR VAX level command to start ARTHUR program
NMQ9 four-character code to identify run output
(title) unique title to identify the run
INPUT,!,... read data from file named in response to
ARTHUR prompt, according to parameters
specified to define number of variables, data
format, etc.
INFILL,1,1$ missing data fill by means for categories
SCALE 1 2$ autoscale variables, list univariate statistics
CORREL 2$ calculate interfeature correlations
KAPRIN'2,4,0,0,10$ principal component (pc) extraction of 10 pc's
KAVECT$ ' lists detailed eigenvector information
KATRAN 2 3,4,5 data projection onto five pc axes
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VARVAR,3,0,-1,1,0,1$ plots of data versus pairs of pc's
STATUS,-! lists status of files in use by ARTHUR
SAVE,3 saves eigenvector projected data
SAVE,7 saves ARTHUR run lineprinter output file
EXIT end ARTHUR run
Infill shows how much data is missing and what feature values are mis-
sing for each sample. SCALE autoscales the data and prints out univariate
statistical information on the distribution parameters of each variable
(feature) over the entire data set. CORREL calculates and prints the inter-
feature correlation matrix. The correlation matrix lists the 95 percent
confidence interval values about the correlation coefficient and the probability
(in parentheses) that the correlation could have come from a parent population
with zero interfeature correlation for the two variables.
Methods KAPRIN and KAVECT calculate the linear principal components
(eigenvectors) and list the detailed information on the eigenvectors, includ-
ing variance retained by each, feature loadings and communalities. Methods
KATRAN and VARVAR project the data onto principal component axes and plot
the data in two dimensions versus pairs of the first five pc's in the above
menu. Sample index and category plots are all generated. Structure (grouping
in the plots) and anomalies should be examined.
After applying the above menu of methods to get an initial view of
data structure and relationships between variables, a more extended series
of methods, below, can be applied to give more information on relationships
between the samples and between the variables:
Additional Exploratory Analysis
The following menu of methods is a list of typical methods that would
provide a more detailed look at the relationships between variables, data
plots, and cluster analysis relationships. The sequence starts after several
initial steps have been applied, including autoscaling the data and putting
the scaled features into file 2.
KAPRIN,2,4,0,0,10$ extract and print 10 eigenvectors
KAVECT$ list information on all 10 pc's
KAVARI,4,4,0,0,5$ Varimax rotation of 5 pc's
KAVECT$ 'list info on 5 rotated pc's
KAORTH,4,4$ orthogonalize rotated pc's
KATRAN,2,3,4,5$ project data onto rotated axes
VARVAR,3,0,-1,1,0,1$ plot data for rotated axes
DIST,2,3$ calculate intersample distance matrix
HIER,3,0,1$ cluster analysis (complete link)
TUTRAN,2,3$ transpose the scaled data matrix
DIST,3,3$ add distance (over sample space)
HIER,3,0,1$ cluster analysis on features
The above sequence starts with extraction of linear principal components
(eigenvectors) in KAPRIN, and listing detailed information about the pc's
and the variables that contribute to each, in KAVECT. Additional information
D-44
-------
on underlying relationships between the variables is obtained by applying
Varimax rotation and reorthogonalization to the pc's to calculate factors
that may be more easily interpreted, chemically or biologically. Interpretation
is based on each variable's contribution (and loading) on each pc given
in KAVECT and the HIER dendogram to see which features/variables are most
similar over the samples. The interpretation will need the knowledge and
intuition of the scientists/analysts working on the problem as to whether
the variable associations (factors) make sense biologically, chemically,
or physically. The data plots for the rotated eigenvectors can supplement
the previous plots.
D-45
-------
PATTERN RECOGNITION TECHNIQUES APPLIED TO COMMENCEMENT BAY DATA
The pattern recognition tasks used in this project were divided into
four areas: (1) data entry and validation, (2) analysis of chemical information
for the sampling stations, (3) analysis of relationships among biological
and chemical variables, and (4) documentation. These tasks are described
in the following sections.
DATA SETS EVALUATED
Three data sets were studied. The data sets are briefly described
in the following paragraphs. The variables in each data set are listed
in Tables D-l, D-2, and D-3. All data sets were received as ASCII files
on IBM-PC compatible floppy diskettes, accompanied by descriptive material
and hard-copy printouts for verification.
Set 1 -- CHEMBIO
This data set consisted of 56 station samples characterized by 116
variables and parameters listed in Table D-l. The stations were located
in nine waterways/areas: Blair (12 stations), Carr Inlet (4), Milwaukee (3),
City (6), Middle (1), Hylebos (14), Ruston (8), Sitcom (3), and St. Paul (5).
The variables included chemical and biological measures.
Set 2 -- MSQSGVAL
This set consisted of 144 stations in the above waterways/areas charac-
terized by 114 chemical variables and parameters. The data set did not
include any biological/benthic variables and was only used for detailed
chemical factor analysis to complement factor analysis on the data sets
that had fewer stations.
Set 3 -- CB2
This set consisted of 54 of the 56 CHEMBIO stations characterized
by abundance measures for 64 'benthic infauna variables. A measure of total
individual species counted at each station was later included with this
data set. After exploratory data analysis, a subset of the variables from
this set were added to the CHEMBIO set for later analyses on chemical-biological
relationships.
The pattern recognition tasks applied in this project are divided
into four areas: 1) data entry and validation, 2) analysis of chemical
information for the sampling stations, 3) analysis of relationships between
biological variables and chemical information, and 4) documentation. These
tasks are described in the following sections.
D-46
-------
TABLE D-l. VARIABLES IN CHEMBIO DATA SET, COMBINED CHEMICAL
AND BIOLOGICAL DATA FOR 56 STATIONS IN THE COMMENCEMENT BAY AREA
Variable
PA65
PA34
PA21
PA64
PB36
PB37
PB62
PB01
PB55
PB77
PB78
PB81
PB80
PB39
PB72
PB73
TBFLANTH
PB74
PB75
PB76
PB79
PB82
PB83
PB84
PB08
PB09
PB25
PB26
PB27
PB52
PB12
PB53
PB66
PB67
PB68
PB69
PB70
PB71
PB54
PP92
PV23
PV85
PV87
PV38
Number
,1
,2
,3
,4
,5
,6
,7
,8
,9
,10
,11
,12
,13
,14
,15
,16
,17
,18
,19
,20
,21
,22
,23
,24
,25
,26
,27
,28
,29
,30
,31
,32
,33
,34
,35
,36
,37
,38
,39
,40
,41
,42
,43
,44
Complete Name
phenol
2,4-dimethylphenol
2,4,6-trichlorophenol
pentachlorophenol
2 , 6-di ni trotol uene
1,2-diphenylhydrazine
N-nitrosodiphenylamine
acenaphthene
naphthalene
acenaphthalene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
benzo(b) fluoranthene
benzo(k) fluoranthene
chrysene
benzo(gh1 )perylene
dibenzo (a, h) anthracene
indeno(l,2,3-cd)pyrene
pyrene
1 ,2,4-trichlorobenzene
hexachlorobenzene
1,2-dichlorobenzene
1,3-dichlorobenzene
1,4-dichlorobenzene
hexachlorobutadlene
hexachloroethane
hexachlorocycl open tad iene
bis(2-ethylhexyl )phthalate
butyl benzyl phthalate
di-n-butyl phthalate
di-n-octyl phthalate
diethyl phthalate
dimethyl phthalate
isophorone
4,4'-DDT
chloroform
tetrachloroethene
trichloroethylene
ethylbenzene
D-47
-------
TABIE D-l. (Continued1
TPCBS ,45
A65850 ,46
A95487 ,47
A108394 ,48
A95954 ,49
B132649 ,50
B62533 ,51
B100516 ,52
B91576 ,53
TRICBD ,54
TETCBD ,55
PENCBD ,56
TXYLENES.57
ANTIMONY, 58
ARSENIC ,59
BARIUM ,60
BERYLLIU.61
CADMIUM ,62
CHROMIUM, 63
COPPER ,64
IRON ,65
LEAD ,66
MANGANES.67
NICKEL ,68
SELENIUM, 69
SILVER ,70
THALLIUM, 71
ZINC ,72
MERCURY ,73
SEG ,74
SOLIDS ,75
VSOLIDS ,76
TOC ,77
NITROGEN, 78
SULFIDE ,79
GREASE ,80
ROCKS ,81
SAND ,82
SILT ,83
CLAY ,84
POLYCH ,85
OLIGO ,86
MOLLUSC ,87
CRUSTA ,88
ECHINO ,89
OTHER ,90
TOTAL ,91
THARYX ,92
PRIONOSP.93
LUMBRI ,94
Total PCB's
benzole acid
2-methylphenol
4-methylphenol
2,4,5-trichlorophenol
dibenzofuran
ani line
benzyl alcohol
2-methyl naphthalene
Total trichlorinated butadienes
Total tetrachlorinated butadienes
Total pentachlorinated butadienes
Total xylenes
antimony
arsenic
barium
beryl lium
cadmium
chromium
copper
iron
lead
manganese
nickel
selenium
silver
thallium
zinc
mercury
station waterway segment (location
% dry weight (dry wt/wet wt)
code)
volatile solids (total organic content)
total organic carbon
organic nitrogen
free sulfide
oil and grease (freon extractable
coarse fraction of sediment size
sand fraction of sediment size
fine fraction of sediment size
portion)
very fine fraction of sediment size
Total polychaete abundance
Total oligochaete abundance
Total mollusc abundance
Total Crustacea abundance
Total echinoderm abundance
Misc species abundance
Total abundance
Tharyx multifilis abundance
Prionospio spp. abundance
Lumbrineris spp. abundance
D-48
-------
TABLE D-l. (Continued)
AXINOPS ,
MACOMA ,
PSEPHID ,
AMPHIPOD,
EUPHILO ,
MORT
ABNORM ,
CHANGE .
METHYLET,
MOXYPHEN,
PENTACHL.
BIPHENYL.
DIBENZOT,
METHYL2P,
METHYL1P,
HEXADEC9,
ISOPIMAR,
KAUR16EN,
METHYLPY,
RETENE ,
METH2PYR,
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
COPROSTA,116
Axlnopsida spp. abundance
Macoma carlottensis abundance
Psephidia lourdi abundance
Total amphipod abundance
Euphilomedes spp. abundance
% Amphipod mortality
% Oyster larvae abnormality
% Change in luminescence (microtox)
1-methyl-2-(1-methylethyl)benzene
2-methoxyphenol
pentachlorocyclopentane
1,1'-biphenyl
dibenzothiophene
2-methylphenanthrene
1-methylphenanthrene
9-hexadecenoic acid methyl ester
isopimaradiene
unidentified diterpenoid hydrocarbon
1-methylpyrene
retene
2-methylpyrene
coprostanol
D-49
-------
TABLE D-2. VARIABLES IN MSQSGVAL DATA SET (DATA SET
CONTAINS 144 SEDIMENT SAMPLES IN THE COMMENCEMENT BAY AREA)
Var #
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
-" , —
Var Name
PA65
PA34
PA21
PA64
PB36
PB37
PB62
PB01
PB55
PB77
PB78
PB81
PB80
PB39
PB72
PB73
TBFLANTH
PB74
PB75
PB76
PB79
PB82
PB83
PB84
PB08
PB09
PB25
PB26
PB27
PB52
PB12
PB53
PB66
PB67
PB68
PB69
PB70
PB71
PB54
PP92
PV23
PV85
PV87
PV38
PP106
Complete Variable Name
phenol
2,4-dimethylphenol
2,4,6-trichlorophenol
pentachlorophenol
2,6-dinitrotoluene
1,2-diphenylhydrazine
N-nitrosodiphenylamine
acenaphthene
naphthalene
acenaphthalene
anthracene
phenanthrene
fluorene
fluoranthene
benzo (a) anthracene
benzo(a)pyrene
Total benzofluoranthenes
benzo (b)f luoranthene
benzo (k) fluoranthene
chrysene
benzo(ghi jperylene
d i ben zo( a, h) anthracene
indeno(l,2,3-cd)pyrene
pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
1,2-dichlorobenzene
1,3-dichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
hexachloroethane
hexach lorocyclopentadiene
bis(2-ethylhexyl)ph thai ate
butyl benzyl phthalate
di-n-butyl phthalate
di-n-octyl phthalate
diethyl phthalate
dimethyl phthalate
isophorone
4,4'-DDT
chloroform
tetrachloroethene
trichloroethylene
ethylbenzene
PCB-1242
D-50
-------
TABLE D-2. (Continued)
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
PP107
PP110
PP111
TPCBS
A65850
A95487
A108394
A95954
B132649
B62533
B100516
B91576
TRICBD
TETCBD
PENCBD
TXYLENES
BA
ANTIMONY
ARSENIC
BARIUM
BERYLLIU
CADMIUM
CHROMIUM
COPPER
IRON
LEAD
MANGANES
NICKEL
SELENIUM
SILVER
THALLIUM
ZINC
MERCURY
SEG
SOLIDS
VSOLIDS
TOC
NITROGEN
SULFIDE
GREASE
ROCKS
SAND
SILT
CLAY
CODE
TIO
METHYLET
MOXYPHEN
PENTACHL
BIPHENYL
PCB-1254
PCB-1248
PCB-1260
Total PCB's
benzole acid
2-methylphenol
4-methylphenol
2,4,5-trichlorophenol
dibenzofuran
aniline
benzyl alcohol
2-methyl naphthalene
Total trichlorinated butadienes
Total tetrachlorinated butadienes
Total pentachlorinated butadienes
Total xylenes
Basin Code
antimony
arsenic
barium
beryl lium
cadmium
chromium
copper
iron
lead
manganese
nickel
selenium
silver
thai lium
zinc
mercury
station waterway segment (location
% dry weight (dry wt/wet wt)
code)
volatile solids (total organic content)
total organic carbon
organic nitrogen
free sulfide
oil and grease (freon extractable
coarse fraction of sediment size
sand fraction of sediment size
fine fraction of sediment size
portion)
very fine fraction of sediment size
station type designation
1-methy 1-2- (1 -methyl ethyl ) benzene
2-methoxyphenol
pentachlorocyclopentane
1,1' -biphenyl
D-51
-------
TABLE D-2. (Continued)
96. DIBENZOT dibenzothiophene
97. METHYL2P 2-methyIphenanthrene
98. METHYL1P 1-methylphenanthrene
99. HEXADEC9 9-hexadecenoic acid methyl ester
100. ISOPIMAR isopimaradiene
101. KAUR16EN unidentified diterpenoid hydrocarbon
102. METHYLPY 1-methylpyrene
103. RETENE retene
104. METH2PYR 2-methylpyrene
105. COPROSTA coprostanol
106. LMWPAH Light Molecular Weight Hydrocarbons
107. HMWPAH Heavy Molecular Weight Hydrocarbons
108. ICOGRP1 elemental metals group 1
109. TOTCBD Total chlorinated butadienes
110. PHTHAL Total phthalates
111. CLBENZ Total chlorinated benzenes
112. FINES Silt + Clay sediment content
113. TOTMET Total elemental metals
114. TOTORG Total organics
D-52
-------
TABLE D-3. VARIABLES CONTAINED IN CB2.DAT EXTENDED BENTHIC DATA SET
Var#
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
Name
16Nemert
20Nemato
37Pholoe
53Eteone
72Microp
98Platyn
lOlNepht
102Nepht
106Nepht
IHGlyce
129Lumbr
135Lumbr
136Lumbr
138Lumbr
151Schis
154Leito
172Po1yd
185Prion
186Prion
187Prion
197Spiop
206Phyl1
207Spioc
210Cirra
214Thary
215Chaet
216Cossu
221Scal1
223Arman
231Capit
236Notom
238Medio
252Praxi
256Euc1y
297Pista
323Eucho
324Eucho
338Tubif
348M1tre
3520dost
353Turbo
373Nucul
386Megac
391Parvi
394Axino
399Mysel
407Nemoc
Taxonomic Name
Nemertea
Nematoda
Pholoe minuta
Eteone Tonga
Micropodarke dubia
Platynereis bicanaliculata
Nephtys cornuta
Nephtys cornuta franciscana
Nephtys ferruginea
Glycera capitata
Lumbrineris spp
Lumbrinerls sp. gr. 4
Lumbrineris sp. gr. 3
Lumbrineris sp. gr. 1
Schistomeringos rudolphi
Leitoscoloplos pugettensis
Polydora spp
Prionospio cirrifera
Prionospio steenstrupi (= P.
Prionospio (Minuspio) multi
Spiophanes berkelyorum
NODC Taxonomic Code
43
47
5001060101
5001130205
5001210801
5001240501
5001250104
500125010498
5001250111
5001270101
50013101
5001310194
5001310195
5001310197
5001360598
5001400102
50014304
5001430502
malmgreni) 5001430506
branchiata 5001430599
5001431004
Phyllochaetopterus prolifica 5001490202
Spiochaetopterus costarum
Cirratulus cirratus
Tharyx multif i lis
Chaetozone spp
Cossura spp
Scalibregma inflatum
Armandia brevis
Capital "la capitata
Notomastus tenuis
Mediomastus spp.
Praxillella gracilis
Euclymeninae
Pista cristata
Euchone incolor
Euchone sp. A
Tubificidae
Mitrella gouldi
Odostomia spp
Turbonilla spp
Nucula tenuis
Megacrenella columbiana
Parvilucina tenuisculpta
Axinopsida serricata
Mysella tumida
Nemocardium centifilosum
5001490302
5001500101
5001500302
50015004
50015201
5001570101
5001580202
5001600101
5001600302
50016004
5001630901
5001631
5001680701
5001700204
5001700299
500902
5105030204
51080101
51080102
5502020201
5507010301
551501010
5515020201
5515100102
5515220301
D-53
-------
TABLE D-3. (Continued)
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
411Macom
412Macom
415Macom
416Macom
421Telli
429Pseph
455Euphi
456Euphi
472Balan
479Eudor
496Lepto
544Photi
599Capre
619Calli
639P1nn1
6 63 Amp hi
665Amphi
Macoma elimata
Macoma obliqua
Macoma carlottensis
Macoma nasuta
Tellina modesta
Psephidia lordi
Euphilomedes carcharodonta
Euphilomedes producta
Balanus crenatus
Eudorella pacifica
Leptochelia dubia
Photis brevipes
Caprel lidae
Callianassa spp.
Pinnixa spp
Amphiuridae
Amphiodia urtica
5515310102
5515310106
5515310112
5515310114
5515310204
5515470501
6111070301
6111070303
6134020104
6154040202
6157020103
6169260201
617101
61830402
61890604
812903
8129030104
Variable Added to Benthic Data After Exploratory Results
65. DIVERSTY Total Different Species Counted at Station
D-54
-------
DATA ENTRY AND VALIDATION
Data receipt, entry and validation was a more time-consuming process
than originally estimated. Three data sets were incorporated into the
data analysis.
Receive Data
Data were obtained on PC floppy diskettes. The data were in ASCII formatted
files, with explanatory material and hard copy printouts for verification.
Read and Load Diskettes—
The ASCII files were read from the diskettes into SYMPHONY database
or Microsoft WORD wordprocessing programs. Within these programs, the
files were converted to ARTHUR format and rewritten to ASCII output files.
These files were then uploaded on a mainframe computer system.
Data Validation
The data were printed using ARTHUR UTILIT after loading and were scanned
for correspondence with the hard copy information at randomly selected
data points throughout the set. ASCII files on diskettes were also scanned
during wordprocessing or database conversion to ARTHUR format.
The remaining validation steps utilized the ability of various combina-
tions of methods to pinpoint atypical values or samples. Potential sample
anomalies were most frequently identified using a combination of factor
analysis, plus data plots, and cluster analysis. Constant or redundant
variables were identified using the method INDUMP in ARTHUR. Missing data
were filled with the mean value for the variable over the appropriate waterway
category to which the station was assigned.
CHEMICAL EVALUATIONS
After the data were entered into ARTHUR, they were analyzed by the combina-
tion of methods previously described to search for relationships among
the variables, among the samples, and between the variables and character-
istics of the samples. The primary aim was to identify fundamental chemical
factors based on relationship's among the chemical variables and to determine
distinctions between the sampling stations in their assigned category,
if present. Steps involved in chemical analysis of the data included:
1. Calculation and interpretation of factors
2. Evaluation of interfeature correlations
3. Examination of data plots versus selected factors
4. Examination of cluster analysis results for indications of
natural groups, based on interpretable associations
D-55
-------
5. Stratification of samples into test groups to determine the
degree of differentiation and the relevance between the groups.
BIOLOGICAL EVALUATIONS
The relationships between chemical variables and variables characteriz-
ing biological aspects of the samples were evaluated using the same explora-
tory data analysis techniques employed in the evaluation of the chemical
variables. Two types of biological variables considered were bioassay
results that characterized stations as "active" or "inactive" in creating
a significant effect on test tissue or organisms, and benthic infauna abundances
for selected individual species and summary taxonomic groups.
The bioassay data were also used as the basis for sample stratification
into subsets. The aim was to determine if chemical variables could distinguish
between the bioassay groups and if so, were logically interpretable by
the scientists on the team. The data were stratified into active and inactive
groups, according to the bioassay results and accounting for major chemical
differences between various waterways. These sets were then analyzed with
variance weighting of variables, k-nearest neighbor classification, and
principal component and factor plots.
The benthic abundance data were evaluated separately to examine biologi-
cal factors that might suggest community relationships. The benthic data
were then analyzed in combination with the chemical data to look for sugges-
tions of influence by chemistry variations. The results of these evaluations
are presented in the summary report.
DOCUMENTATION
Documentation for the study included an interim outline report covering
initial exploratory results and presented to U.S. COE and U.S. EPA project
review staff in mid-November. Results of interest, upon completion of
all computer runs and interpretation of results, were documented in a separate
summary report.
The specific detailed results are not reported for each computer run.
The increase in the number of data sets included in the study and the larger
number of preliminary exploratory runs that evolved precluded detailed
reporting of all results. The focus of the reporting of results is on
those runs that culminate a series of exploratory and preceding steps.
D-56
-------
METHODS
DATA PREPARATION AND DATA SET CREATION
The following discussion is a description of the steps involved in trans-
forming the three data sets for use in the ARTHUR system.
CHEMBIO Data
The CHEMBIO data set was received on floppy diskette on 15 October,
1985. Several conversion steps were applied to the CHEMBIO data to prepare
it for exploratory analysis. Data coded as below detection limit were
replaced by a random value between zero and the specified detection limit.
A conversion program in dBaselll changed all missing data to the missing
data flag specific to ARTHUR.
The computer runs done with the CHEMBIO data are coded with NMC charac-
ters included in the Print Code. At later stages in the study, these codes
also include benthic infauna abundance data (from CB2). Runs were also
made with chemical variables normalized to fines and total organic carbon.
MSQSGVAL Data
After initial exploratory runs on CHEMBIO, concern was raised over
whether the chemical factors would be representative of the data contained
in a larger set of stations. To address this concern the MSQSGVAL data
was received on 25 October. The data were comprised of 144 samples (stations)
and 110 chemical variables. Two files were contained on the diskette:
MSQSGVAL.LIS containing the values and MSQSGVAL.POR containing the variable
names. The data were ARTHUR formatted, with the category number corres-
ponding to station waterway groups comparable to CHEMBIO.
An initial exploratory data analysis was performed on the full MSQSGVAL
data. Several anomalies were spotted; after interpretation of their effect
on the entire data set those samples were removed, and the exploratory
data analysis was repeated. Also, 12 variables had extensively missing
data and the set was reduced to 98 variables for subsequent analysis.
CB2 Benthic Data
After initial exploratory results using CHEMBIO and MSQSGVAL were
evaluated, it was decided to include an extended benthic infauna data set
containing abundance values for 64 taxa at 54 of the 56 CHEMBIO stations.
The CB2 data, in two files, were obtained on floppy diskette and uploaded
to the mainframe computer on 10 December. Values for an additional variable,
total individual species counted at each station, were added from hardcopy
obtained from Tetra Tech.
D-57
-------
The CB2 data were analyzed separately and then joined to the CHEMBIO
set for analysis combined with conventional and previous biological variables.
Following analysis of these results, the extended data set was trimmed
to 142 variables and analyzed to provide chemical-biological results.
COMPUTER RUNS
Although 8 to 10 computer runs were anticipated at the start of the
study, the analysis of the three data sets prompted a greater number of
exploratory runs. Twenty-seven computer runs were made for the study.
The runs and a quick description of each, in the order in which they
were done are listed in Table D-4. Reporting of results from these runs
concentrates on #5-IMSQ2, #10-IMSQ7ZD, #12-IMSQ8, #15-INMC7, #16-INMC8,
#17-INMC9, #18a-INC10ZD,and #23-IABC4.
SPECIFIC RESULTS FROM INDIVIDUAL AND COMPARED COMPUTER RUNS
In the following paragraphs, specific results from the individual compu-
ter runs or comparison between a set of runs are listed.
INMC2 and IMSQ2 Univariate Distribution Statistics and Differences
Statistics for mean, standard deviation, range, maximum and minimum
values, and higher moments were obtained for each variable over the CHEMBIO
and MSQSGVAL data sets. Examination of these statistics indicated substantial
differences between the two data sets in several of the chemical variables.
These differences, listed in Table D-5, indicate that some caution should
be observed in accepting the results that suggest chemical-biological relation-
ships may be present, based on CHEMBIO calculations. Because of its smaller
sample size (56 versus 144 stations), CHEMBIO is less representative of
Commencement Bay chemical variation than MSQSGVAL.
Valid "Anomalies"
During the Commencement Bay Superfund project, considerable effort
was taken to validate the data in the data sets for this study. This quality
assurance/quality control effort was aimed at correcting gross analytical
errors in the data set prior to data interpretation.
Analysis using pattern recognition techniques indicated that apparent
anomalies remained in the data sets. These anomalies were highly unusual,
but nonetheless real and valid data (see Table D-6). In most cases, the
unusual values derived from highly contaminated samples collected at stations
closest to pollution outfalls. Interpretation of pattern recognition results
was performed with and without these anomalies to ensure that their effect
on the data set was understood.
D-58
-------
TABLE D-4. PATTERN RECOGNITION ANALYSIS -- COMPUTER RUNS
Run #
1
2
3
4
5
6
7
8
9
10
11
12
13
14a
14b
Print Code
IGAEN
IGAEM
INMC2
INMC3
IMSQ2
IMSQ3
IMSQ4
IMSQ5
CIS51
IMSQ7ZD
INMC4ZT
INM42ZD
IMSQ8
INM32
INMC5
INMC6
Data Set
CHEMBIO/
NOMISSCR
MSQSGVL2
CHEMBIO/
NOMISSCR
CHEMBIO/
NOMISSCR
MSQSGVL2
MSQSGVL2/
OMSQ2
MSQSGVL2/
OMSQ2
MSQSGVL2/
OMSQ2
MSQSGVL2
MSQSGVL2
ONMC3 +
OCB22
OMSQ4
VNMC3 +
VNMC2
NMC +
OCB22
CNMC5
Description
initial exploratory run on combined
chem & bio data -- 56 station set
initial exploratory run on 144 station
chemistry variable set
2nd exploratory run on 56 station set
chemistry variables only
3rd exploratory run on 56 station set
biological variables only
2nd exploratory run on 144 station set
98 chemistry variables retained
attempted replot run of KAORTH V2-V3 w/o
major trend samples
exploratory run using TOC normalized
chemical variables — 144 station set
exploratory run using FINES normalized
chemical variables -- 144 station set
outliers removed, 139 station chem vars,
exploratory run
outlier removed, 98 chem vars, short
exploratory run
combined benthic/bio vars exploratory
run (miscombined data)
generate chem factor scores for 144 station
set (PMSQ8ZD & VMSQ8ZD data saved)
listing of factor scores
CHEMBIO + BENTHIC exploratory analysis --
created CNMC5.DAT
BIO-BENTH-CONVENTIALS Vars, 56 stations,
restratif ied, exploratory run
D-59
-------
TABLE D-4. (Continued)
15 INMC7 CNMC5
16 INMC8 CNMC7
17 INMC9 CNMC7
18a INC10 CNMC5
18b
19
20
21
22
23
24
25
INM11
& DVRS
IACB1 &
IACB2
IABC1
IABC2
IABC3 &
IAB32
IABC4
IABC5
IABC6
DVRS +
CNMC5
ACBBB
BCBBB +
ACBBB
ABCD
ABCD
ABCD
ABCD
ABCD
26
27
IABC7 ABCD
IUTI1
ABCD
reduced to 66 BIO-BENTH-CONVENTIALS Vars,
56 stations, created CNMC7.DAT
66 Vars, 51 stations, exploratory run
66 Vars, outlier removed (51 stations),
exploratory run (comp. INMC7 and INMC8)
reduced combined set to 142 variables,
56 stations, chem-bio-benth exploratory
input of diversity data and joining to
CNM5, saved as ACBBB.DAT
subset factor analysis, saved 136 Var data
set as BCBBB.DAT (Var 115 wrong)
created BioBenth Cluster Stratification &
analyzed (WEIG,SELE,DIST/KNN), saved ABCD
created Impact Category Stratification &
analyzed similar to previous run
outliers removed, subset factor analysis
recalculated for Waterway Stratification
combined bioassay, waterway, benthcluster
stratification analysis
Variable by Variable scatter plots
Waterway stratified subset analysis with
TOC normalization
Waterway stratified subset analysis with
SILT normalization
Utility listing of values for selected
variables and species abundance plots
versus SILT and SAND
D-60
-------
TABLE D-5. COMPARISON OF MEANS AND STANDARD DEVIATIONS FOR
VARIABLES IN MSQSGVAL (144 STATIONS) AND CHEMBIO (56 STATIONS)a
» ui i au i c
Abbrev.
PA34
PA21
PA64
PB37
PB26
PB52
PB12
PB67
PB71
PP92
A65850
A108394
A95954
B100516
SULFIDE
METHYLET
MOXYPHEN
PENTACHL
COPROSTA
no
Mean
13.96
10.25
62.24
14.84
23.73
64.48
65.00
63.21
82.46
31.18
203.2
870.6
12.44
35.78
25.56
290.1
96.66
11.46
133.0
L)OUVHL
Std.Dev.
19.77
13.24
107.4
102.1
39.21
130.0
231.1
124.3
149.2
15.34
686.7
7989.
12.68
53.68
86.06
691.2
362.4
31.37
285.3
Mean
7.17
5.88
28.91
3.34
14.39
48.16
75.36
34.09
43.39
16.47
96.97
1957.
6.43
22.28
15.48
409.8
176.9
6.41
85.84
LlltMDlU
Std.Dev.
8.18
5.83
27.79
4.83
27.43
112.6
371.3
69.71
51.20
12.35
161.5
12,800.
7.29
26.73
43.59
996.7
566.3
16.72
379.1
These variables had substantially different means and standard deviations
between the two data sets; abbreviations are defined in Tables A-l
through A-3.
D-61
-------
TABLE D-6. OUTLIER VARIABLES FROM PATTERN RECOGNITION ANALYSIS
Run # 3 INMC2 CHEMBIO 2nd exploratory run on 56 station set
chemistry variables only
* KAPRIN Plots
Index* StationID Outlier in Eigenvector/Factor...
38 RS-18 Vl(high), V2(low)
20 HY-22 Vl(mod-high), V2(high), V3(mod-high), V5(high)
48 SP-14 V3(low), V4(mod-high)
7 CI-11 V4(low)
* KAORTH Plots
Index* StationID Outlier in Eigenvector/Factor...
38 RS-18 Vl(high)
20 HY-22 V2(high)
48 SP-14 V3(low)
7 CI-11 V4(low)
Run # 5 IMSQ2 MSQSGVL2 2nd exploratory run on 144 station set
98 chemistry variables retained
* KAPRIN Plots
Index* StationID Outlier in Eigenvector/Factor...
113 SP-14 V4(low), VB(mod-high)
* KAORTH Plots
Index* StationID Outlier in Eigenvector/Factor...
(48) HY-16 Vl(mod-high); end of trend in stations 45-58
99 RS-18 V4(high)
102 RS-21 V4(high)
113 SP-14 V5(low)
Run # 7 IMSQ4 MSQSGVL2/ exploratory run using TOC normalized
OMSQ2 chemical variables -- 144 station set
* KAPRIN Plots
Index* StationID Outlier in Eigenvector/Factor...
78 HY-46 Vl(low), V3(high), V4(high)
131 RS-03 V5(high)
* KAORTH Plots
Index* StationID Outlier in Eigenvector/Factor...
78 HY-46 V2(high)
131 RS-03 V5(high)
D-62
-------
TABLE D-6. (Continued)
Run # 8 IMSQ5 MSQSGVL2/ exploratory run using FINES normalized
OMSQ2 chemical variables -- 144 station set
* KAPRIN Plots
Index* StationID Outlier in Eigenvector/Factor...
103 RS-22 V2(low - opposite of 102 & 99)
* KAORTH Plots
Index* StationID Outlier in Eigenvector/Factor...
103 RS-22 VI(low)
113 SP-14 V5(low - slight hint of trend)
D-63
-------
Chemical Factor Changes with Anomaly Removal
After initial interpretation using all data was complete, five anomalous
values were removed from the MSQSGVAL data set and exploratory steps were
recalculated on the remaining 139 stations. The five stations left out
in this stage of the analysis were #48-HY-6, #78HY-46, #99RS-18, #102RS-21,
and #113SP-14. Factor analysis results were compared for the run (#10-IMSQ7ZD)
versus the original analysis containing all 144 stations (#5-IMSQ2). Three
of the first five factors showed some correspondence in the variables having
the greatest loadings. The correspondence list is shown in Table D-7,
with common variables underlined.
Combined Chemical-Biological Results. Run #18a-INC10
After exploratory runs were made on the chemical and biological varia-
bles separately, they were combined in an analysis of potential chemical-
biological variable relationships. The CHEMBIO data set was used and 38
benthic abundance variables were added from the CB2 data set after explora-
tory runs on those data alone. The resultant analysis focused on 142 chemical
and biological variables.
Results from the principal component analysis are briefly summarized
below, including the highest loading variables in order of their contribution:
#1. Heavy organics and metals (strongly influenced by station RS-18):
PB81, B132649, PB01, NICKEL, PB39, DIBENZOT, PB80, METHYL2P, LEAD,
PB72, CADMIUM, COPPER, MERCURY, SELENIUM, ANTIMONY, ARSENIC, THALLIUM,
PB76, PB84, ZINC, PB62, B91576, PB78, METH2PYR, PB73, MANGANESE,
#2. Benthic infauna associated with sandy sediment types: SAND, -CLAY,
-SILT, 154Leito, AMPHIPOD, 98Platyn, lOGNepht, 238Medio, -THARYX,
412Macoma, 207Spioc, -214Tharyx, 816Prion, 421Telli, -BERYLLIUM,
CRUSTA, -138Lumbri, 496Lepto, PRIONOSP, LUMBRI, ...
#3. Mixed chemical-biological factor: MOLLUSC, 394Axino, AXINOPS,
-PA21, -SULFIDE, -KAUR16EN, 456Euphi, -TOC, ANTIMONY, ARSENIC,
373Nucul, MERCURY, -PA65, -MOXYPHEN, SELENIUM, CADMIUM, -ZSlCapit,
THALLIUM, COPPER, -VSOLIDS, -OTHER, -20Nemato,...
#4. Chlorinated organics and HPAH: PB82, PB52, PB09, PB08, PB83, TPCBS,
-MOXYPHEN, PB12, TBFLANTH, -ISOPIMAR, -A108394, PB73, ...
#5. Benthic infauna associated with silty sediment types (and including
some organic compounds): 238Medio, TOTAL, EUPHILO, POLYCH, 207Spioc,
D-64
-------
TABLE D-7. CHEMICAL FACTOR DIFFERENCES, Run #5-IMSQ2 vs Run #10-IMSQ7
IMSQ2 (144 stations)
Factor & Variables
IMSQ7 (139 stations;
Factor & Variables
1. PAH and some metals
LMWPAH, PB80. PB81, PB01,
B132649, PB39, LEAD,
DIBENZOT, CADMIUM, PB78.
METHYL2P, BIPHENYL, PB72,
ANTIMONY, HMWPAH, ICOGRP1
2. Metals
ANTIMONY, ARSENIC, TOTMET,
COPPER, CADMIUM, MERCURY,
SELENIUM, ICOGRP1, -PB83,
SOLIDS, LEAD. -PB79,
1. PAH and other organics
TOTORG, HPAH, PB84, LPAH.
PB39, PB72, PB81, PB73, PB78,
TBFLANTH, PB83, PB76, PB82,
-SOLIDS, VSOLIDS, PB80, B91576,
PB01, PB79, TOC, B132649, PB55,
DIBENZOT
2. Metals
ANTIMONY, TOTMET, ICOGRP1, COPPER,
ARSENIC. CADMIUM. MERCURY.
SELENIUM. LEAD. ZINC, IRON,
NICKEL, BARIUM, MANGANESE,
3. Chlorinated Organics 3. Chlorinated Organics
PB52, PB08, PENCBD, TETCBD, PB52, PENCBD. PB09, TETCBD.
TOTCBD. TRICBD. PB09. PENTACHL TOTCBD. PB08, TRICBD, PENTACHL,
CLBENZ, ... BERYLLIUM, -A95487, TPCBS, ...
4. Other Organics
MOXYPHEN, A108394, PB55,
KAUR16EN, METHYLET, -PB76,
-TBFLANTH, RETENE, -HMWPAH,
PB77, -PB83, -PB79, -PB82,
ISOPIMAR, B91576, ...
5. Pulp Organics/ & Mn
ISOPIMAR, KAUR16EN, A108394,
METHYLET, MOXYPHEN, MANGANESE,
A95487
4. Mixture
-CHROMIUM, -IRON, PB08, PB52,
PENCBD, B132649, TOTCBD, -SULFIDE
TETCBD, -MANGANESE, PB92, -CLAY,
TRICBD, RETENE, CLBENZ, PB36,
-PB76, ...
5. Grain Size Related
SILT, FINES, -SAND, CLAY,
-METHYLPY, -PB12, -PB82, -SOLIDS
D-65
-------
CRUSTA, 455Euphi, A95487, 16Nemert, MOLLUSC, VSOLIDS, PB55, 421Telli
PB77, -SOLIDS, B91576,...
A scatter plot of the stations versus factors 2 and 5 showed inversely
correlated behavior for the two factors with the exception of stations
RS-13 and RS-14, which were high in scores for both factors.
Conventional Chemical and Biological Results — Run #17-INMC9
Prior to the complete chemical-biological run (#18a, above), the 56
CHEMBIO stations were evaluated for only conventional chemical, bioassay
score, and biological/benthic variables. Factors mainly reflected biologi-
cal loadings, with the exception of one factor loaded negatively with TOC,
VSOLIDS and bioassay score variables, and positively with large grain size
and biological variables. Results of the principal components analysis
included the following factors:
#1. TOTAL, AXINOPS, MOLLUSC, 394Axino, LUMBRI, 138Lumbri, THARYX, SILT,
456Euphi, -SAND, POLYCH, 214Tharyx, 114Glyce, MACOMA, 415Macoma,
#2. -TOC, -ABNORM, SOLIDS, -VSOLIDS, CRUSTA, -MORT, 186Prionosp, 106Nepht,
-CLAY, SAND, -CHANGE, PRIONOSP, 455Euphi, 238Medio, ...
#3. 102Nepht, -SOLIDS, 223Arman, -236Notom, 399Mysel, 421Telli, 3520dost,
-411Macoma, SULFIDE, -20Nemato, 348Mitre, -CHANGE
#4. -342Eucho, 214Tharyx, THARYX, ISNemert, 102Nepht, POLYCH, 207Spioc,
-348Mitre, 411Macoma, -373Nucul, 353Turbo, -53Eteone, ...
Scatterplots of the stations versus the first two factors indicated
that Sitcom and Pt. Defiance-Ruston stations had different behavior, even
though they had been grouped in the same waterway category for analysis
because of metals contamination in both areas. Also, Cam Inlet stations
behaved considerably different than the Blair and Milwaukee stations with
which they had been grouped. The upper Hylebos stations were in a tight
group and the lower Hylebos stations were in a moderately tight group except
for #28HY-44 and #30HY-50.
Scatter plots for factor 2 versus factor 4 indicated the possibility
that the two benthic compositions represented species of different habitat
orientations. Where one group was high, the other was low.
Correlations Among Variables
Linear Pearson correlation coefficients were calculated for the chemical
and biological data. The biological variables were searched for chemical
variable correlation coefficients that had a calculated probability of less
than 0.05 of being drawn from a parent population with a truly random rela-
tionship (zero correlation). These were listed and the frequencies for
which the chemical variables were observed were tabulated. These are listed
in Table D-8, for both positive and negative r-values, with the total
D-66
-------
TABLE D-8. CHEMICAL VARIABLES WITH >.! SIGNIFICANT (P<0.05)
CORRELATIONS WITH BIOLOGICAL VARIABLES
Chemical a
MORT
ABNORM
NICKEL
MANGANESE
RETENE
PB77
A95487
SILT
SAND
PB27
HEXADEC9
TOC
CHANGE
PB36
PB68
METHYLPY
PB79
PB76
PB84
BERYLLIUM
PB26
PENCBD
PENTACHL
PB72
SOLIDS
PB67
CHROMIUM
PB66
PA64
PP92
COPROSTA
B62533
A95954
PB54
PA21
B100516
PB37
PA65
PB70
Number of Observances (@ P
Negative r's Positive r's
1
2
6
2
-
-
-
9
10
1
3
3
1
3
-
-
-
-
-
2
-
-
-
-
-
-
2
1
1
-
-
-
-
1
-
-
-
-
»
1
-
-
2
3
3
8
7
2
2
2
-
-
3
1
4
2
1
2
2
1
1
1
1
1
2
1
1
1
3
3
3
3
2
4
3
2
1
< 0.05)
Total Obs
1
3
6
2
2
3
3
17
17
3
5
5
1
3
3
1
4
2
1
4
2
1
1
1
1
1
4
2
2
1
3
3
3
4
2
4
3
2
1
Codes for variables are explained in Tables A-l through A-3
D-67
-------
number of observances out of 51 biological variables (13 taxonomic groups
and 38 benthic species).
INTERACTIVE SCREEN LOG FOR RUN # 17 — INMC9 OUTPUT CODE
An example of the screen log for a typical run on ARTHUR is recreated
in this section. The run consisted of a second stage exploratory analysis
of 66 conventional chemical, biological and benthic variables after removing
five anomalous stations.
(Comments in quotation marks added to explain intent of following steps)
Version 4.0 (Released 1 January 1985).
AAA RRRR TTTTT H H U U RRR
AARR T HHUURR
A-jmw-A RRR T HHHH U U RRR
A ARR T HHUURR
A ARR T HHUURR
General Pattern Recognition
***************************
Multivariate Data Analysis System
* **********************************
**************************************************
* Copyright (c) Infometrix, Inc. 15 August 1984 *
* All rights reserved. *
**************************************************
Use of this program implies that the individual
has read and agrees to the terms and conditions of
the Infometrix Software Program License Agreement
for ARTHUR and the Limited Warranty contained
therein.
Set terminal to UPPER case and enter to continue:
Please enter 4 character identification code:
NMC9
Enter session title:
WEIGHT/FACTOR ANALYSIS OF CNMC7 W/ 5 ANOMALIES REMOVED
Option? "Note - following steps brought data set CNMC7.DAT into file
1"
LET
Option LET called at 21:22:44 on 17-DEC-85.
Enter destination file# for copy of data (1 thru 6):
1
Let file #1 contain data from file named?
CNMC7
D-68
-------
Option? "Following sequence with CHDATA deleted 5 anomalous stations'
CHDATA,1,1,-1
Option CHDA called at 21:23:05 on 17-DEC-85.
Enter the pattern-changes in form
TRAIN/TEST, 1st inc. sam. indx, last sam. indx, cat# (end 0$)
TRAIN,1,16,1
TRAIN,18,19,1
TRAIN,21,22,2
TRAIN,24,26,2
TRAIN,27,32,3
TRAIN,33,40,4
TRAIN,41,41,5
TRAIN,44,51,5
TRAIN,52,56,6
Option?
CHCAT,1,1,0
Option CHCA called at 21:24:51 on 17-DEC-85.
Number Data Vectors In Training Set 51
Number Data Vectors In Test/evaluation Set... 0
Number of Categories... 6
Category Members
1
2
3
4
5
6
Option?
UTILIT,1,-1
18
5
6
8
9
5
Option UTIL called at 21:25:15 on 17-DEC-85.
Option? "Next step autoscales the data and prints univariate statistics"
SCALE,1,2
Option SCAL called at 21:25:24 on 17-DEC-85.
Option? "Next step calculates variance weights between categories"
WEIGHT,2,3,-1,-1,3
Option WEIG called at 21:25:39 on 17-DEC-85.
D-69
-------
Option? "Next 8 steps generate factor analysis and factor plots"
KAPR,2,4,0,0,10
Option KAPR called at 21:26:08 on 17-DEC-85.
Eigenvalue Var. Preserved
Each Total
1 1.205E+01 25.8 25.8
2 8.365E+00 17.9 43.8
3 5.221E+00 11.2 55.0
4 4.437E+00 9.5 64.5
5 3.435E+00 7.4 71.9
Option?
KAVE,0,0
Option KAVE called at 21:26:35 on 17-DEC-85.
Option?
KATR,2,3,4,5
Option KATR called at 21:26:53 on 17-DEC-85.
Option?
VARV,3,0,-1,1,0,1
Option VARV called at 21:27:15 on 17-DEC-85.
Option?
KAVA,4,4,0,0,8
Option KAVA called at 21:28:08 on 17-DEC-85.
Eigenvalue Var. Preserved
Each Total
1 8.366E+00 19.9 19.9
2 6.511E+00 15.5 35.4
3 6.296E+00 15.0 50.4
4 5.015E+00 11.9 62.4
5 4.776E+00 11.4 73.7
Option?
KAVE,0,0
Option KAVE called at 21:28:30 on 17-DEC-85.
Option?
KATR,2,3,4,6
D-70
-------
Option KATR called at 21:28:44 on 17-DEC-85.
Option?
VARV,3,0,-1,1,0,1
Option VARV called at 21:29:04 on 17-DEC-85.
Option? "Next step calculates the distance matrix using 6 factor scores"
DIST,3,4
Option DIST called at 21:29:48 on 17-DEC-85.
Option? "Next step calculates hierarchical cluster analysis ..."
HIER,4,0,1 "... dendogram using the factor distance matrix"
Option HIER called at 21:29:59 on 17-DEC-85.
Option?
STATUS,-!
Option STAT called at 21:30:27 on 17-DEC-85.
File # File Specification File Purpose
1 ONMC9ZD a data file
2 CNMC9ZD a data file
3 VNMC9ZD a data file
4 FNMC9ZD a data file
5 PNMC9ZD a data file
6 RNMC9ZD a data file
7 INMC9ZD the printer file
8 ANMC9ZD the interactive output file
9 BNMC9ZD the ASCII data output file
Option? "Run completed, the next step saves the printer file on disk"
SAVE,7
Option SAVE called at 21:30:57 on 17-DEC-85.
Option? "next step saves the factor projection data to disk"
SAVE,3
Option SAVE called at 21:33:34 on 17-DEC-85.
Option? "All steps complete, so quit ARTHUR"
EXIT
FORTRAN STOP
$ LO
COUSE2 logged out at 17-DEC-1985 21:34:29.85}
D-71
-------
APPENDIX E
RECOMMENDED CONTAMINANTS OF CONCERN
FOR MANAGEMENT OF DREDGED MATERIAL
-------
RECOMMENDED CONTAMINANTS OF CONCERN
Several previous reports (e.g., Konasewich et al. 1982) have discussed
criteria for the selection of contaminants of concern present in Puget
Sound. In general, chemicals of concern have the following attributes:
• A demonstrated or suspected effect on ecology or human health
(i.e., the focus of chemical concerns is on ultimate biological
effects); toxic effects of chemicals are typically characterized
by laboratory tests of acute or chronic toxicity to marine
benthic organisms or tests of carcinogenicity or mutagenicity
• Environmental persistence of the parent compounds or of toxic
metabolites
• A potential for remaining in a toxic form for a long time
in the environment
• One or more present or historical sources of sufficient magnitude
to be of concern (i.e., widespread distribution and high concentra-
tion relative to Puget Sound reference sediments).
Contaminants of concern recommended in this section were selected based on
consideration of the above attributes, existing lists of contaminants of
concern in Puget Sound [i.e., Konasewich et al. 1982; Quinlan et al. 1985;
Jones and Stokes 1983], results of PSDDA/PSEP workshops held to establish
procedures for environmental analysis of metals and organic contaminants (e.g.,
Tetra Tech 1986b, 1986c), and chemical data from a wide range of Puget
Sound studies and sampling areas [e.g., Metro TPPS study (Romberg et al. 1984),
Commencement Bay Remedial Investigation (Tetra Tech 1985), NOAA Technical
Memorandum OMPA-19 (Mai ins et al., 1982), Eight Bay study (Battelle 1985b)].
Inorganic and organic contaminants of concern in dredged materials are
listed in Tables E-l and E-2, respectively. The lists comprise many U.S. EPA
priority pollutants with several noteworthy additions and deletions summarized
in the tables. Although the total number of contaminants in Tables E-l
and E-2 is fairly large, it is not recommended that different subsets of
this list be used for different geographic areas of Puget Sound. Such
a recommendation would have to be based on the assumption that certain
chemicals are unlikely to occur in certain regions of Puget Sound. The
a priori exclusion of chemicals is not advised. Potential pollution sources
"("e.g., highly populated and industrialized areas, isolated industrial facili-
ties, agricultural runoff) are located in many regions of Puget Sound,
and estuarine and atmospheric circulation can transport contaminants throughout
most of Puget Sound. Justification for deletion of a particular chemical
class (e.g., priority pollutant acid compounds) from the list of contaminants
of concern in a particular geographic area should always be based on representa-
tive field analyses that confirm the absence (or acceptably low concentration)
of the chemical class.
E-2
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TABLE E-l. INORGANIC CONTAMINANTS OF POTENTIAL CONCERN
IN PUGET SOUND SEDIMENTS3
Antimony
Arsenic"
Cadmium
Copper^
Chromium
Leadb
Mercury^
Nickel
Silverb
Zinc
Cyanide
The first group of elements consists of 10 of the 13 U.S. EPA priority
pollutant metals and cyanide. The remaining 3 priority pollutant
metals not recommended are beryllium, thallium, and selenium. Chromium
may only be of local concern in areas where chromium-rich wastes are
being discharged (e.g., chrome plating industries)
Beryllium and thallium are toxic but have not been found at concentrations
that exceed reference levels in Puget Sound (see Tetra Tech 1986c).
High selenium concentrations have been reported in a single Puget
Sound study; these values are considered to be elevated most likely
because of spectral interferences during the particular instrumental
analysis used (see Appendix A in Tetra Tech 1986c). Other studies
using alternative techniques have not found levels of selenium in
excess of reference conditions.
These elements have been suggested previously as contaminants of concern
in Puget Sound (see Konasewich et al. 1982; Jones and Stokes 1983).
Note: Three non-priority pollutant metals (aluminum, iron, and manganese)
are not of toxicological concern, but are useful for normalization of other
metals data and as tracers of natural terrigenous material (see Appendix A
in Tetra Tech 1986c).
E-3
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TABLE E-2. ORGANIC CONTAMINANTS OF POTENTIAL CONCERN
IN PUGET SOUND SEDIMENTS3
Phenols and Substituted Phenols (organic acids)
Phenol 2,4-dimethylphenol
2-methylphenol J,c pentachlorophenold
4-methylphenolb»c
Miscellaneous Organic Acids (selected samples only)6
2-methoxyphenol
3,4,5-trichloroguaiacol
4,5,6-trichloroguaiacol
tetrachloroguai acol
mono- and di- chlorodehydroabietic acids
Low Molecular Weight Aromatic Hydrocarbons (neutrals)d
naphthalene fluorene
acenaphthylene phenanthrene
acenaphthene anthracene
Alkylated Low Molecular Weight Aromatic Hydrocarbons (neutrals)d»f
2-methylnaphthalene 1-methylnaphthalene
1-, 2-, and 3-methyl phenanthrenes
High Molecular Weight PAH (neutrals)
f 1 uoranthene benzo(k)f1uoranthene
pyrene benzo(a)pyrene
benzo(a)anthracene indeno(l,2,3-c,d)pyrene
chrysene dibenzo(a,h)anthracene
benzo(b)fluranthene benzo(g,h,i)perylene
Chlorinated Aromatic Hydrocarbons (neutrals)
1,3-di chlorobenzene 1,2,4-tri chlorobenzene
1,4-dichlorobenzene hexachlorobenzene (HCB)
1,2-dichlorobenzene
Total PCBs (mono- through decachlorobiphenyls)
E-4
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TABLE E-2 (continued).
Chlorinated Aliphatic Hydrocarbons (neutrals)
trichlorobutadiene isomersd»9 hexachlorobutadiened»9
tetrachlorobutadiene isomersd»9
pentachlorobutadiene isomersd»9
Phthalate Esters (neutrals)d
dimethyl phthalate butyl benzyl phthalate
diethyl phthalate bis(2-ethylhexyl)phthalate
di-n-butyl phthalate di-n-octyl phthalate
Miscellaneous oxygenated compounds (neutrals)
i sophorone polychlorodi benzofuransd»i
benzyl alcoholb»h polychlorodibenzodioxins^
benzoic acidb»n coprostanolJ
dibenzofuranb»h
Organonitrogen Compounds (organic bases)*5
N-ni trosodi phenylami ne
nitrogen heterocycles [e.g., 9(H)-carbazole]1
Pesticides (neutrals)"1
p,p'-DDEd endrind
p,p'-DDDd heptachlor
p,p'-DDTd alpha-HCH
aldrind beta-HCH
dieldrind delta-HCH
alpha-chlordane gamma-HCH
Volatile Halogenated Alkenes (neutrals)
trichloroethene tetrachloroethene
Volatile Aromatic and Chlorinated Aromatic Hydrocarbons (neutrals)
benzene styrene (ethenylbenzene)
toluene total xylenes
ethyl benzene chlorobenzene
E-5
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TABLE E-2 (continued).
NOTE: Compounds not recommended from the priority pollutant list include:
• Halogenated ethers (two volatile and five semi volatile compounds)
are rarely reported in Puget Sound and are not expected
to persist in sediments.
• Hexachlorocyclopentadiene has not been confirmed to be present
in Puget Sound sediments, is easily degraded during laboratory
analysis, and has no suspected sources in Puget Sound.
t Acrolein and acrylonitri le have not been detected in Puget
Sound sediments and are difficult to analyze for in routine
volatile analyses.
t Other priority pollutants not recommended are indicated
in the following footnotes and in Table E-3.
a Additional compounds are listed in Table E-3. These additional compounds
have been rarely or never detected in Puget Sound, but can be analyzed
relatively easily with the compounds on this list.
b Indicates U.S. Hazardous Substance List (HSL) compound that is not
also on the U.S. EPA priority pollutant list.
c 2-Methyl phenol is an HSL compound and is a known component of krafft
pulp effluents. 4-Methylphenol is an HSL compound that was reported
at high concentration in numerous areas of Commencement Bay. There
are few data available for this compound but it has been found in
pulp mill effluent, and could derive from degradation of other substances.
The occurrence of 4-methylphenol was highly correlated with sediment
toxicity and effects on benthic biota in a problem area near a pulp
and paper operation in Commencement Bay. The compound may also be
derived as a groundwater contaminant in other areas.
d Compound or group of compounds has been designated previously as a
contaminant of concern in Puget Sound (Jones and Stokes 1983; Konasewich
et al. 1982; Quinlan et al. 1985).
e These compounds are recommended only for areas adjacent to pulp mills.
Guaiacol was repored in Commencement Bay and is useful as an indicator
of pulp mill effluent (both kraft and sulfite mills). Chlorinated
guaiacols are toxic, persistent, and are excellent indicators of chlori-
nated pulp mill effluents (e.g., bleached kraft mills). Analytical
recoveries of chlorinated guaiacols will probably be low (as is the
case of chlorinated phenols) unless analytical procedures are modified
to stabilize the compounds (e.g., by derivitization techniques). Chlori-
nated dehydroabietic acids are also good indicators of chlorinated pulp
E-6
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TABLE E-2 (continued).
effluent and are toxic and persistent (based on studies of unchlorinated
dehydroabietic acid). Modified analytical procedures (e.g., derivitiza-
tion) will also be needed to recover resin acids.
f These non-priority pollutant compounds are often detected in Puget
Sound sediments. Although this is not an exhaustive list of alkylated
aromatic compounds, the compounds shown are accessible as analytical
standards and are useful for determining alkylated/non-alkylated ratios
for indicating PAH sources.
9 Tri-, tetra-, and pentachl orobutadienes are non-priority pollutants
that have been detected at highly elevated levels in certain areas
of Puget Sound (e.g., Hylebos Waterway in Commencement Bay). Because
standards are generally unavailable for these compounds, they are
recommended for analysis only where chlorinated butadienes are suspected
to have a major source (standards are available for hexachlorobutadiene).
h Di benzofuran, benzyl alcohol, and benzoic acid are HSL compounds and
have been detected frequently in Commencement Bay.
^ Chlorinated dibenzofurans and dioxins are recommended as special analyses
only, as determined by specific project goals. Both classes of compounds
are of concern because of their severe toxic effects on higher organisms
[only 2,3,7,8-tetrachlorodibenzodioxin (TCDD) is a U.S. EPA priority
pollutant]. Few analyses have been conducted for these compounds
in Puget Sound in the past. Recent data suggest that higher molecular
weight isomers of the dibenzofurans and dioxins are relatively common
(e.g., the much less toxic hexa- and octachlorinated forms), but 2,3,7,8-
TCDD has not been detected in Puget Sound samples.
J Not a U.S. EPA priority pollutant, and not known to be toxic but is
useful as a source indicator of sewage and agricultural wastes.
k The remaining 7 priority pollutant organic bases are seldom detecteed
in Puget Sound and often present analytical problems (e.g., benzidine
and 3,3-dichlorobenzidine). N-ni trosodiphenylamine can probably be
recovered well with the PSEP organics full-scan procedure even though
the procedure is not designed for high recovery of organic bases (i.e.,
by back-extracting rinse waters at pH>12)
1 9(H)-carbazole is a component of creosote and coal tar and has been
reported in Puget Sound regions with these sources (although not exclus-
ively with these sources)
m Toxaphene, a priority pollutant pesticide mixture, has not been recommended
because it has not been reported in Puget Sound and requires some
additional analytical work.
E-7
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Detection of the chemicals in Tables E-l and E-2 will require at least
three different analytical procedures (for metals, volatile organics, and
semivolatile organics). The lists in these tables are appropriate for
dredged materials; a reduced list of contaminants may be appropriate for
other matrices (e.g., biological tissues).
Organic priority pollutants that are not strongly recommended as contaminants
of concern are listed in Table E-3. These compounds have been detected
infrequently (or not at all) in Puget Sound, but are relatively easy to
analyze along with chemically similar compounds listed in Table E-2 (e.g.,
the chlorinated phenols from Table E-3 could be analyzed along with pentachloro-
phenol listed in Table E-2). The analysis of the entire set of chemicals
will not necessarily be more costly than analysis of a reduced set of chemicals
because "full scan" analyses for metals, volatile organics, and semivolatile
organics can be used to detect a wide range of chemicals with a single
analysis for each of the three chemical groups.
In the evaluation of dredged material, contaminants of concern must
be identified relative to their proposed disposal environment (i.e., aquatic,
nearshore, or upland disposal). The specific contaminants of concern in
a given sediment may be different at the dredged site and the disposal
site because of differences in their environmental mobility and route of
exposure to biological organisms. For example, physicochemical conditions
of sediments are substantially altered in the transfer of material from
the subtidal environment to an upland or nearshore environment. These
changes can favor the leaching of acid soluble metals from otherwise stable
solid matrices, or volatilization of some organic compounds (including
various PCB congeners). As a result, substantially greater metals bioaccumula-
tion or other loss from a terrestrial site could occur relative to that
expected at the original marine site.
The general criteria listed above do not explicitly take into account
these differences in disposal environments. An evaluation of dredged material
should include consideration of the following additional factors (in order
of decreasing importance):
• Available regulatory limits or other guidelines relating contami-
nant concentrations to biological effects relevant to the
disposal site
0 Concentrations of contaminants relative to reference conditions
appropriate to the disposal site.
Although attention is usually focused on contaminant effects at the
disposal site, contaminant release can occur at the dredging site during
dredging operations. Dredged material destined for upland and nearshore
disposal may require evaluation for contaminants of concern in both aquatic
and terrestrial environments. Finally, contaminants of concern may vary
geographically within one type of environment. Some contaminants may be
of general concern, but such a list should not be used as the sole guide.
Data required for evaluation of contaminants of concern in a dredged material
E-8
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TABLE E-3. ADDITIONAL ORGANIC CONTAMINANTS OF LIMITED CONCERN3
Substituted Phenols (acids)
2-chlorophenol 2,4,5-tri chlorophenol
2,4-dichlorophenol pentachlorophenol
4-chloro-3-methylphenol 2-ni trophenol
2,4,6-trichlorophenol 2,4-dintrophenol
4,6-dinitro-o-cresol
Chlorinated Hydrocarbons (neutrals)
2-chloronaphthalene hexachloroethane
Pesticides (neutrals)
alpha-endosulfan endrin aldehyde
beta-endosulfan heptachlor epoxide
endosulfan sulfate
Volatile Halogenated Alkanes (neutrals)
chloromethane carbon tetrachloride
bromomethane bromodichloromethane
chloroethane 1,2-dichloropropane
di chl oromethane chlorodi bromomethane
1,1'-di chloroethane 1,1,1-tri chloroethane
chloroform bromoform
1,2-di chl oroethane 1,1,2,2-tetrachloroethane
1,1,1-tri chloroethane
Volatile Halogenated Alkenes (neutrals)
vinyl chloride cis-l,3-dichloropropene
1,1'-di chloroethene trans-1,3-di chloropropene
trans-1,2-di chloroethene
Volatile Aromatic and Chlorinated Aromatic Hydrocarbons (neutrals)
styrene (ethenylbenzene)b chlorobenzene
a These priority pollutant compounds (except as noted) are not strongly
recommended because they are seldom, if ever, detected in Puget Sound.
However, these compounds can be analyzed relatively easily with the
other chemicals in their class listed in Table E-2.
b Indicates U.S. EPA Hazardous Substance List (HSL) compound that is
not also on the U.S. EPA priority pollutant list.
E-9
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should include at least one analysis for a broad range of contaminants
(subject to waiver for small dredging projects in areas away from suspected
sources).
CONTAMINANTS OF CONCERN FOR AQUATIC DISPOSAL
Most Puget Sound studies have focused attention on contaminants of concern
relative to the aquatic (marine) environment. Two NOAA studies have summarized
evaluations for 463 contaminants in Puget Sound sediments: (1) Konasewich
et al. (1982) presented a rationale for the inclusion of 15 contaminants
or groups of contaminants based on a review of evidence for 230 inorganic
and organic pollutants; (2) Quinlan et al. (1985) updated the previous
NOAA report and reviewed data for an additional 233 pollutants. The latter
report did not identify any new contaminants of concern, and argued for
the deemphasis of one contaminant (i.e., mercury) on the basis of recent
bioaccumulation data. Data suggesting the importance of some contaminants
not addressed in the NOAA studies (e.g., alkylated phenols) have recently
been released (Tetra Tech 1985). These more recent data also suggested
that elevated mercury concentrations in some surface sediments may be associated
with observed sediment toxicity and depressed abundances of benthic infauna.
CONTAMINANTS OF CONCERN FOR NEARSHORE AND UPLAND DISPOSAL
For nearshore and upland disposal, chemical concentrations measured
in marine sediments should be comparable with those measured in terrestrial
soils. Hence, contaminants of concern for terrestrial disposal sites may
be identified through a review of available U.S. FDA limits for cropland
soils, or by comparison with the overall crustal abundance (i.e., average
content in soils of all types) of trace constituents. The concern for
a given level of contamination relative to biological effects in terrestrial
environments should be evaluated using terrestrial biological indicators.
E-10
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APPENDIX F
RECOMMENDED ANALYTICAL DETECTION LIMITS
-------
RECOMMENDED ANALYTICAL DETECTION LIMITS
Appropriate detection limits are a critical consideration for sediment
quality management. For example:
• Sediment quality approaches that rely on observations of biological
effects require chemical analyses that can detect low concentra-
tions of potent toxic contaminants.
• Detection limits for samples must be considerably lower than
the established sediment quality values against which they
are tested.
• The reference approach requires sensitive chemical analyses
for relatively uncontaminated reference sites. Analyses with
high detection limits will give regulators very limited data
upon which to base sediment quality values.
APPROACHES TO DETECTION LIMITS
Environmental analytical chemists have not universally agreed upon a
convention for determining and reporting the lower detection limits of
analytical procedures. Values reported as lower detection limits are commonly
based on instrumental sensitivity^ levels of blank contamination, and/or
matrix interferences and have various levels of statistical significance.
The American Chemical Society's Committee on Environmental Improvement
(CEI) defined the following types of detection limits in an effort to standard-
ize the reporting procedures of environmental laboratories (Keith et al. 1983):
• Instrument Detection Limit (IDL) — the smallest signal above
background noise that an instrument can detect reliably.
• Limit of Detection (LOD) — the lowest concentration level
that can be determined to be statistically different from
the blank. The recommended value for LOD is 3 , where is
the standard deviation of the blank in replicate analyses.
• Limit of Quantitation (LOQ) — the level above which quantitative
results may be obtained with a specified degree of confidence.
The recommended value for LOQ is 10 , where is the standard
deviation of blanks in replicate analyses.
0 Method Detection Limit (MDL) — the minimum concentration
of a substance that can be identified, measured, and reported
with 99 percent confidence that the analyte concentration
is greater than zero. The MDL is determined from seven replicate
analyses of a sample of a given matrix containing the analyte
(Glaser et al. 1981).
F-2
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The CEI recommended that results below 3 should be reported as "not detected"
(ND) and that the detection limit (or LOD) be given in parentheses. In
addition, if the results are near the detection limit (3 to 10 , which
is the "region of less-certain quantitation"), the results should be reported
as detections with the limit of detection given in parentheses.
The CEI definitions are useful for establishing a conceptual framework
for detection limits, but are somewhat limited in a practical sense. The
IDL does not address possible blank contaminants or matrix interferences
and is not a good standard for complex environmental matrices. The LOQ
accounts for blank contamination, but not specifically for matrix complexity
and interferences. The high 10 level specified for LOQ helps to preclude
false positive findings, but may also necessitate the rejection of valid
data. The MDL is the only operationally defined detection limit and provides
a high statistical confidence level but, like the LOQ, may be too stringent
and necessitate the rejection of valid data.
Metals
The LOD (3 ) detection limit is appropriate for metals data. Inter-
ferences are a major determinant of attainable detection limits and are
not accounted for in the LOD calculation. However, the LOD is appropriate
because matrix and interelement interferences can be corrected for during
instrumental analysis (e.g., by matrix matching and background corrections.)
Organic Analytes
Interferences are also a major determinant of detection limits of organic
analytes, but cannot be corrected for easily during instrumental analysis.
An alternative detection limit, the lower limit of detection (LLD), is
recommended for data generated by gas chromatography-mass spectrometry
(GC/MS). LLD are established by analysts based on their experience with
the instrumentation and with interferences in the sample matrix being analyzed.
LLD are greater than instrumental detection limits because they take into
account sample interferences. To estimate LLD, the noise level should
be determined in the retention window for the quantitation mass of repre-
sentative analytes. These determinations should be made for at least three
field samples in the sample set under analysis. The signal required to
exceed the average noise level by at least a factor of two should then
be estimated. This signal is the minimum response required to identify
a potential signal for quantification. The LLD is the concentration corre-
sponding to the level of th-is signal based on calibrated response factors.
Based on best professional judgment, this LLD would then be applied to
samples in the set with comparable or lower interference. Samples with
much higher interferences (e.g., at least a factor of two higher) should
be assigned LLD at a multiple of the original LLD.
FACTORS AFFECTING DETECTION LIMITS
The following factors influence the attainable detection limits for
metal and organic analytes:
F-3
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• Physical sample size available - In most cases, the more sample
that is available for analysis, the better the detection levels
that can be achieved. Thus, for a given method, larger samples
will have lower detection limits than smaller samples.
t Presence of interfering substances - For example, the presence
of elemental sulfur in a solvent extract to be analyzed by
gas chromatography-electron capture detection (GC/ECD) will
potentially obscure analyte peaks and raise the amount of
analyte required for detection.
• Range of pollutants to be analyzed - For example, if only
one compound is of interest, a method can be optimized for
that compound without regard to potential effects on other
compounds. Dedicated protocols can yield far lower detection
limits than full-scan protocols.
• Instrumental sensitivity - For detection of most priority
pollutant metals, inductively coupled plasma (ICP) emission
spectrometry is less sensitive than graphite furnace atomic
absorption (6FAA) spectrophotometry. Thus, GFAA detection
allows for lower detection limits. However, simultaneous
multi-element analyses are possible for ICP but not GFAA.
• Level of confirmation of results - For example, GC/ECD is
more sensitive than GC/MS for chlorinated pesticide analysis.
However, a single GC/ECD analysis does not provide positive
identification of a compound, whereas GC/MS provides more
information for molecular confirmation.
• Level of pollutant found in the field and in analytical blanks
- For example, due to bottle preparation procedures, analytical
blanks are often contaminated with varying concentrations
of methylene chloride. This variation in contaminant level
often precludes sensitive detection levels in tissue.
Organic Compounds
The choice of analytical procedures can affect the attainable detection
limits of semivolatile organic compounds. Removal of interferences from
extracts (e.g., removal of elemental sulfur by treatment with metallic
mercury, removal of biological macromolecules by gel permeation chromatography)
can significantly reduce the detection limits of many organic analytes.
The separation and dedicated analysis of chemically distinct fractions
can also reduce detection limits, but requires greater laboratory effort
and expense than a full-scan analysis.
RECOMMENDED DETECTION LIMITS - LOW LEVEL
Certain program goals will require sensitive detection limits. For
example, low detection limits will be required to assess pollutant concen-
trations in relatively uncontaminated reference areas. High detection
F-4
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limits could yield undetected values, which would be of little use in estab-
lishing sediment quality values. Low detection limits would also be appropriate
for programs that must determine pollutant concentrations corresponding
to biological effects. In such cases, the LOD (for metals) and LLD (for
organic compounds) would be more likely than LOQ to allow for reports of
detected values. Whereas LOQ would provide more statistical confidence
than LOD or LLD, they would be far more likely to result in undetected
values. For samples that are to be evaluated with sediment quality values,
detection limits should be less than half, preferably less than one tenth,
the sediment quality values.
The detection limits recommended in this report are considered to be
typically attainable values based on the best professional judgment and
experience of analytical chemists who considered the instrumental sensi-
tivity of affordable equipment, common problems with blank contamination
and matrix interferences, and reasonable levels of laboratory analytical
effort. The recommended values are not absolute, as analytical procedures
and laboratory precision can affect attainable detection levels. State-
of-the-art instrumentation and dedicated analytical procedures will enable
laboratories to attain lower detection limits.
Metals
The following recommended limits are based on a 5-g (wet) sediment sample
in a 100-mL digestate:
0.01 ppm (dry weight) Hg
0.02 ppm (dry weight) Be
0.1 ppm (dry weight) Sb, As, Cd, Cr, Cu, Pb, Se, Ni, Ag, Tl
0.2 ppm (dry weight) Zn
Instrumental techniques are major determinants of the attainable detection
limits for metals. With the exception of mercury, all of the above metals
can be detected at the specified levels with graphite furnace atomic absorp-
tion. Mercury should be detected by cold vapor atomic absorption. Other
instrumental techniques can also be used to attain the specified levels
(e.g., hydride generation atomic absorption can be used for arsenic, antimony,
and selenium). Several metals (e.g., zinc) may occur at relatively high
levels even in uncontaminated sediments. If instrumentation is available,
it could be cost-effective to screen samples by ICP and then analyze undetected
compounds with the more sensitive GFAA.
Organic Compounds
Attainable detection limits for organic analytes can vary considerably
depending on extraction, extract cleanup, and instrumental detection techniques
used. For a sample size of approximately 5 g (wet) and GC/MS detection,
detection limits for most volatile organic analytes of interest should
be in the range of 1-15 ppb (dry weight).
The detection limits of most semivolatile analytes should be in the
range of 1-50 ppb (dry weight), assuming a 100-g (wet weight) sediment
F-5
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sample size and GC/MS detection. If pesticides and PCBs are analyzed by
a more sensitive instrumental technique, GC/ECD, detection limits for single-
component pesticides should fall in the 0.1-5 ppb range and PCBs should
be detectable at 5-20 ppb. To attain these detection limits for typically
complex sedimentary extracts, extract cleanup steps will be required (e.g.,
adsorption chromatography and sulfur cleanup are necessary for extracts
to be analyzed by GC/ECD).
RECOMMENDED DETECTION LIMITS - MODERATE LEVEL
Some projects involving dredged materials may not require very sensitive
detection limits. Dredged materials to be disposed of at established disposal
sites may be tested against fairly high sediment quality values. However,
disposal in relatively uncontaminated areas may require analyses with low
detection limits.
Metals
When samples are being evaluated in comparison to high sediment quality
values, it is recommended that detection limits be at least 10 times lower
than than the sediment quality values. In such cases, multielement ICP
analyses may be sufficient and cost-effective (except for mercury, which
requires cold vapor atomic absorption). Detection limits consistent with
ICP analyses could also be appropriate when sediments are being screened
for gross contamination.
Organic Compounds
The Contract Laboratory Program (CLP) has designed analyses to meet
approximately 1,000 ppb (dry weight) or approximately 300-600 ppb (wet
weight) detection limits. CLP detection limits could be appropriate for
screening of samples for gross contamination. Lower detection limits (100
ppb dry weight) have been recommended for evaluation of dredged materials
for the Fourmile Rock disposal site (U.S. Environmental Protection Agency/
Washington Department of Ecology 1984). In a recent roundtable discussion
among Puget Sound chemists sponsored by PSEP/PSDDA, LLD in the range of
1-50 ppb dry weight were considered appropriate for all uses except screening-
level analyses.
F-6
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APPENDIX G
RECOMMENDATIONS FOR
ANCILLARY SEDIMENT VARIABLES
-------
RECOMMENDATIONS FOR ANCILLARY SEDIMENT VARIABLES
Variables other than organic and metal contaminants can provide useful
data for assessing sediment quality or for facilitating development of
chemical-specific sediment quality values. The physical and chemical ancillary
variables considered to be of greatest use are:
• Total solids (dry wt/wet wt expressed as a percent)
• Total organic carbon content (as percent of dry wt sediment)
• Percent fine-grained (<63 urn) particles (as percent of dry
wt sediment)
• Iron and manganese content (ppm dry wt).
Recommendations for the use of these variables are discussed in the following
sections. Other variables (e.g., sulfides, chemical oxygen demand, oil
and grease) have use as gross indicators of sediment quality, but have
limited use in the development of chemical-specific sediment quality values.
These latter variables should not be used as replacements for such sediment
quality values because substantial chemical contamination can be found
in sediments that appear "acceptable" according to conventional measurements.
TOTAL SOLIDS
Total solids data are essential as they allow for calculation of contam-
inant concentrations on a dry-weight basis. Most sedimentary contaminants
are associated predominantly with the solid material in bulk sediments,
not with the interstitial water. Thus, dry-weight contaminant concentrations
are preferred to wet-weight concentrations. Use of dry-weight concentrations
precludes the possibility that variations in sedimentary moisture content
will obscure informative trends in chemical data.
TOTAL ORGANIC CARBON
Chemical concentration gradients, particularly of nonpolar, nonionic
organic compounds, have been observed to correlate positively with sedimentary
organic carbon content. This observation is commonly interpreted in one
of two ways: (1) organic matter is the "active fraction" of sediment and
serves as a sorptive sink for neutral, and possibly polar or metallic,
compounds (see Section 2.3 of the main report and Appendix H), or (2) carbon-
rich particles may be an important transport medium for contaminants (e.g.,
PAH may tend to be associated with soot particles; Prahl and Carpenter
1983). Laboratory and field investigations suggesting a strong relationship
between organic carbon content and nonionic organic compounds (e.g., geochemical
studies and bioaccumul ation studies) have led to the preferential use of
organic carbon normalization for the equilibrium partitioning approach
(JRB Associates 1984b) and the Screening Level Concentration approach (Battelle
G-2
-------
1986; as discussed in Section 8.6 of the main report, this approach does
not require use of organic carbon normalization).
Total volatile solids (TVS) is sometimes measured instead of total organic
carbon (TOC). TVS only crudely approximates TOC because (1) organic matter
can be volatilized during the total solids determination (which precedes
TVS determination), (2) inorganic substances (e.g., carbonates) can be
lost during high-temperature combustion (e.g., 550° C), and (3) TVS is
a rough measure of organic matter content, not organic carbon content.
Thus, a conversion constant for organic matter to organic carbon must be
empirically derived and applied to TVS data.
PERCENT FINE-GRAINED (<63 urn) PARTICLES
On a limited spatial basis, contaminant concentrations are often inversely
correlated with particle size. Thus, contaminants may be concentrated
in the fine-grained particles of bulk sediments. This observation is often
explained in terms of surface area, in that finer particles have greater
specific surface area, and thus greater sorption capacity, than larger
particles. However, organic carbon content also tends to vary inversely
with particle size in natural sediments (Choi and Chen 1976; JRB Associates
1984b). Thus, normalizing to percent fines may be effectively equivalent
to organic carbon normalization.
Grain size, independent of its correlation with contaminant concentration,.
is an environmentally significant variable. It may play a role in sediment
toxicity during bioassays (Tetra Tech 1985; Ott 1985) and affects benthic
ecological structure. Thus, grouping of biological data according to sediment
grain size could be a useful way to factor out natural environmental effects
from contaminant-related effects. Grain size distribution should be an
important factor when choosing reference samples for bioassays or benthic
infaunal abundance assessments.
MANGANESE OR IRON CONTENT
Trace metals can be selectively enriched in iron and manganese oxides
and hydrous oxides under oxidizing conditions (e.g., Jenne and Luoma 1975;
Brannon et al. 1976). In such cases, normalization of metal concentrations
to manganese and/or iron can reduce the effect of dilution of chemically
enriched oxide and hydrous oxide phases with variable amounts of unrelated
material in sediments. This normalization is not highly reliable, as site-
specific and compound-specific differences affect the significance of oxide-
and hydrous oxide-metal associations.
USE OF CONVENTIONAL VARIABLES IN SEDIMENT MANAGEMENT
In the present project, conventional variables were evaluated as tools
for sediment management in two ways (1) conventional variables were used
to normalize chemical concentrations when generating AET and SLC values
(see Sections 5.3 and 5.4 of the main report), and (2) conventional variables
were themselves used as indicators of sediment quality (e.g., a total organic
carbon AET was developed, potentially as an indicator of organic enrichment).
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Other uses (e.g., as geochemical indicators or as normalizing variables
for analyses of chemical gradients) are valuable, but were not considered
in this project.
For both the AET and SLC approaches, use of chemicals normalized to
dry weight was consistently more accurate (by measures defined in Section 7.1)
than use of chemicals normalized to organic carbon content or percent fine-
grained sediment (see Tables 13 and 17 in Sections 7.1.1 and 7.1.4, respec-
tively). AET values for organic carbon content, total volatile solids,
and percent fine-grained material did not identify biologically impacted
stations that were not identified by chemical variables (see Section 8.7).
Thus, dry weight normalization is recommended for field-based approaches
(i.e., AET, SLC) based on its greater predictive success relative to normaliza-
tion to organic carbon content or percent fine-grained material.
The lower predictive success of sediment quality values for chemicals
normalized to organic carbon content or percent fine-grained material is
not considered to be a good basis for precluding further use of these vari-
ables. The available results suggest the need for further testing of the
toxicological aspects of organic carbon normalization theory (e.g., how
does organic carbon affect sediment toxicity and is this effect consistent
with different kinds of organic matter and different test organisms).
Such testing is currently being conducted by Battelle (1985b) and U.S. EPA
(Newport).
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APPENDIX H
RESPONSE TO COMMENTS ON DRAFT REPORTS
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INTRODUCTION
(Prepared by the sponsoring agencies)
The preliminary result of the PSDDA/PSEP study on sediment quality
values were initially presented in a series of four separate draft reports
(Table H-l). These reports were distributed to a variety of technical
and management experts at a number of agencies and consulting firms for
their review. The draft reports generated significant interest and controversy,
and numerous comments were received.
The comments submitted covered a variety of topics, ranging from correction
of typographical errors to complete report reorganization. It is apparent
that many of the technical and editorial comments could have been avoided
had the reader been provided the entire series of draft reports at one
time. With only partial information, it was difficult to put the different
parts of the sediment quality study into perspective. The final sediment
quality values report represents the revision and synthesis of the four
draft reports into a single document. We hope that this reorganization
will help put the overall Puget Sound sediment quality values effort into
better focus.
The agencies sponsoring this study regard all comments received on
the draft reports as important, useful, and constructive, and have seriously
considered all concerns in preparation of the final report. In addition,
in order to facilitate review and a better understanding of how the final
report responds to the issues and concerns raised by reviewers, the agencies,
with the assistance of Tetra Tech, Inc., have prepared the following response
to comments section. This section generally addresses the majority of
the technical comments received. Because specific comments were submitted
based on review of separate draft reports, the response to comments section
is divided accordingly. Where possible, however, an attempt has been made
to identify the location in the final report where the reader may find
additional information if desired. We hope that this section, combined
with the specific changes made in the report itself, will be useful to
you.
The development of sediment quality values for use in Puget Sound
is an ongoing effort, with interim values currently being identified for
use in Puget Sound by PSDDA and PSEP technical committees. If you have
additional comments on the final sediment quality values report, or questions
about companion efforts, please contact either Keith Phillips (U.S. Army
Corps of Engineers, 764-3624) or Catherine Krueger (U.S. EPA, 442-1287).
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TABLE H-l. LIST OF RELEVANT DRAFT REPORTS AND PREPARATION DATES
TASK 1: Evaluation of Puget Sound data sets for the development
of sediment quality values (October 1985)
TASK 2: Evaluation of statistical relationships among chemical
and biological variables using pattern recognition
techniques (February 1986)
TASK 3: Evaluation of approaches for the development of sediment
quality values for Puget Sound (October 1985)
TASK 4 and 5a: Application of selected sediment quality values approaches
to Puget Sound data (March 1986)
[A fifth draft report (Task 5b) concerning an approach to risk exposure
assessment has been finalized as a separate report (Tetra Tech 1986a)]
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RESPONSE TO COMMENTS ON DRAFT SEDIMENT QUALITY VALUES REPORTS
TASK 1 REPORT: EVALUATION OF PUGET SOUND DATA SETS FOR THE DEVELOPMENT
OF SEDIMENT QUALITY VALUES
1. It was unclear what criteria were used for review/select ion of data.
Clearly define the criteria for data selection and the rationale for
using these criteria (e.g., why were only synoptic data sets considered
appropriate for use?).
Selection of site-specific chemical/biological data for the compiled
Puget Sound database was carried out in three basic steps:
• Identify synoptic data sets (see Appendix C of the final
report): Available data sets were reviewed for synoptic
collection of data and only synoptically collected chemical
and biological data were considered further. (Note: a
synoptic data set is one for which toxicity data are col-
lected on the same sediment homogenate used for sediment
chemistry and benthic infaunal samples are collected at
the identical station locations and at the same time, or
at nearly the same time, as sediment chemistry samples.)
0 Review quality assurance information (see Section 5.1.1
of the final report): Potential data sets were reviewed
for documentation of quality assurance (QA) methods and
summaries of QA review (such documentation was typically
provided in the reports in which the data were presented).
0 Review data comparability (see Section 5.1.1 and Appendix C
of the final report): Available data were also subjected
to a more detailed review that focused on issues related
to data comparability.
Synoptically collected data were used to maximize the probability
of detecting trends among biological and chemical variables. Differences
between independent chemical and biological samples collected at a "station"
could hamper attempts at establishing correlations between chemical concentra-
tions and biological effects. For this reason, it was considered essential
that chemical and biological data be collected from nearly identical subsamples
from a given location. For example, acceptable toxicity measurements were
only those made on a subsample of the same sediment homogenate used for
chemical analysis. Because such homogenization and subsampling may compromise
the integrity of benthic samples (e.g., through loss of motile benthic
organisms), benthic and chemistry samples could not be taken from the same
homogenized sample. Instead, acceptable benthic infaunal analyses were
those conducted on replicate sediment samples from the same station sampled
for chemical analyses. Acceptable replicate samples were those from studies
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with clear documentation of multiple positioning techniques used for station
location. Reliable positioning is important for correcting for vessel
drift between collections of replicate grab samples or for returning to
the same station within a few days to complete chemical and biological
components of a survey.
A detailed QA review of all data that were considered for inclusion in
the database was beyond the scope of this project. However, the chemical
and biological methods were reviewed for every data set considered in an
attempt to ensure comparability of chemical, bioassay, and benthic infaunal
data from all studies. Items reviewed for chemical data were analytical
techniques, detection limits, and the chemical scope of pollutants analyzed
(e.g., polar and nonpolar semivolatile organic compounds, metals, volatile
organic compounds). The latter item was considered important to document
for sediment quality value approaches that are based on field data (e.g.,
the toxicity endpoint and AET approaches), because the availability of
a wide diversity of chemical data enhances the probability that toxic agents
(or chemicals that covary with toxic agents) can be identified in sediments
with observed biological impacts. No data sets were excluded from the
database as a result of the review of chemical data.
The QA review of benthic infaunal data focused on sampling methods,
and in particular, on subsampling techniques (e.g., cores taken from grab
samples), and on the level of replication. The QA review of toxicological
data focused on sediment storage (fresh vs. frozen) and on the general
acceptance of bioassay methods used.
[see Appendix C and Section 5.1.1 for discussion relevant to comment #1]
2. Reviewers were concerned that this effort apparently focused on U.S. EPA
priority pollutants and did not address other chlorinated compounds
such as those present in Commencement Bay samples.
Data for several compounds that are not U.S. EPA priority pollutants
were used in the sediment quality values project. The most comprehensive
data set made available for this effort was from the Commencement Bay Remedial
Investigation. Some of these samples did, indeed, contain a large number
of chlorinated compounds that were only identified qualitatively. It was
not within the economic scope of the investigation to quantify the several
hundred compounds present in each sample. However, attempts were made
to quantify chlorinated compounds considered to be representative of the
major components.
The complex mixture of unidentified chlorinated compounds observed
in Hylebos Waterway by several investigators tended to follow the distribution
of one or more of the following identified chemicals. Chlorinated compounds
detected and quantified included tri-, tetra-, and pentachlorobutadienes,
hexachlorobutadiene, six different chlorinated phenols, five different
chlorinated benzenes (including hexachlorobenzene, a major chlorinated
compound in Hylebos Waterway), hexachloroethane, three chlorinated ethers
(low concentrations only), PCBs, chloroform, three chlorinated ethenes,
and a pentachlorocyclopentane isomer (tentative identification). While
H-5
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these compounds were not all of the chlorinated compounds quantitatively
noted in the extracts, they were considered to be representative of the
major components.
In addition, Commencement Bay data made available for the sediment
quality values effort included blank-corrected analyses for the 13 U.S. EPA
priority pollutant metals, 3 additional metals (including iron and manganese
used as natural indicators), 78 extractable U.S. EPA priority pollutant
compounds, 12 additional U.S. EPA Hazardous Substance List compounds, and
selected tentatively identified compounds for which all samples were analyzed.
Twenty two of the samples were also analyzed for the 31 U.S. EPA volatile
priority pollutants and/or 2,3,7,8-tetrachlorodibenzodioxin. Over 550
tentatively identified compounds were identified in a preliminary survey
of 17 stations, from which 14 compounds were selected for scanning and
quantification in the remainder of the Commencement Bay project based on
their use as potential source markers, routinely high concentrations, or
occasional extreme concentration.
As noted in the final Commencement Bay report (Tetra Tech 1985a; p.
3.71), there were no chemicals detected [and quantified] in historical
Commencement Bay studies that were not also found in the study conducted
for the remedial investigation.
[comment #2 applies to data presented in Appendix A]
3. Several reviewers were concerned that data sets that did not include
volatiles and polar compounds were excluded from analyses. If so,
what was the rationale for their exclusion?
Data sets were not included or excluded based on whether these compounds
had been analyzed for in a particular project. For the final recommended
data sets listed in the Task 1 draft report, and in Appendix C of the final
report, the absence of data for these compounds was simply documented.
This documentation served only to identify a limitation in the use of the
data set for generating or validating sediment quality values for those
particular compounds (including volatiles for a portion of the Commencement
Bay data set). In one case, a data set that did not include either volatiles
or polar compounds, and also did not include bioassay or benthic infauna
data, was still recommended for use with associated fish hi stopathological
data (should sediment quality values for this type of data be developed).
[see Appendix C for discussion relevant to comment #3]
TASK 2 REPORT- EVALUATION OF STATISTICAL RELATIONSHIPS AMONG CHEMICAL
' AND BIOLOGICAL VARIABLES USING PATTERN RECOGNITION TECHNIQUES
4 It was not clear to some reviewers what the objective of the ARTHUR
analysis was, whether the objective was realized, and how the results
of the analyses influenced development/recommendation of specific
sediment quality values.
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The ARTHUR statistical routine was used to perform exploratory analyses
on a data subset to be included in the sediment quality values database.
ARTHUR is a system comprised of approximately 70 different procedures that
can be used for data processing, display, and pattern recognition analysis.
These pattern recognition techniques can be used to quickly extract important
information from complex data sets (192 chemical and biological variables
were considered in this project). The statistical techniques incorporated
into the pattern recognition system can also reveal relationships among
variables that might be obscured in a preliminary nonstatistical analysis.
Therefore, the objective of the this pattern recognition task was to investigate
underlying trends among variables that might be useful in later development
of sediment quality values. This objective was realized. The analysis
was successful in:
• Providing corroboration of trends among chemical variables
in the Commencement Bay data set and an independent Puget
Sound chemical data set that had been previously analyzed
using ARTHUR (e.g., see discussion on p. 34 of the draft
report concerning groups of significantly correlated chemicals)
• Confirming trends that had been previously identified in
the data set using alternative data analysis techniques
(e.g., confirming the need for subset analysis by geographic
region to establish chemical-biological relationships, and
providing supportive evidence of a critical assumption in
most sediment quality value approaches that a threshold
concentration exists, above which a chemical can be expected
to elicit a negative biological response)
• Identifying new relationships among chemical and biological
indicators (e.g., apparent "sensitive species" to certain
chemical contaminants; these preliminary results were then
subjected to evaluation during the application of sediment
quality value approaches)
• Providing evidence that normalization of chemical concentrations
to total organic carbon or percent fine-grained material
produces results in factor analysis that are nearly identical
to those based on dry-weight normalized data. Hence, these
results demonstrated that nearly all of the interpretable
trends with respect to the chemical-biological effects data
from Commencement Bay/Carr Inlet can be derived using dry-weight
concentrations.
This latter result, in consideration of accuracy results for different
normalization techniques from the Task 4 application of sediment quality
value approaches (see Section 7 of the final report), suggests that dry-weight
normalized data may be the most useful for identifying stations with known
biological impacts (see additional discussion in comment #19/23).
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These and other results described on p. 37 of the draft report were used
to guide development of sediment quality values.
[see Appendix D for discussion relevant to comment #4]
5. Although outliers are often considered to carry as much or more information
than data which fit a given model, it appears that in some cases data
were ignored or eliminated to gain refinement or to investigate subset
trends. What procedures were applied to data that appeared to be
outliers and why? How might the practice employed have affected the
conclusions drawn?
Use of the term "outlier" in the discussion of the results may have
caused some confusion. In the final report, the term "anomaly" has been
substituted. There was no attempt or plan to summarily throw out or ignore
any data in the data set. The conclusions drawn in the study were made
in consideration of analyses conducted with and without anomalous values
because the ARTHUR analysis was conducted as a stepwise series of statistical
tests with intervening technical review. All data were analyzed initially.
The term "outlier" was intended to indicate data values identified in the
early stage of statistical analysis as exhibiting unusual characteristics
relative to other data values. For the most part, these data were associated
with samples collected adjacent to major pollution sources. Further analyses
were then applied to examine the behavior of the remainder of the data
set without these unusual values, so that the initial trends might be checked
and other trends might be more apparent (e.g., less likely to be masked
by samples collected close to particular sources). This staged analysis
was important in enabling a more complete interpretation of the available
data to be incorporated into the final recommendations and conclusions
(e.g., see discussion on p. 12; 16; 30-32 of the draft report).
For statistical analysis, the concern regarding treatment of anomalies
is higher and more critical in experimental designs based on random sampling
(either spatially or temporally). The Commencement Bay study was spatially
biased because there were more samples concentrated around potential sources
to determine concentration gradients. Thus, the treatment of anomalies
described above was considered appropriate.
[see Appendix D for discussio'n relevant to comment #5]
6. Assumptions were made throughout the report that the demonstration
of chemical effects requires decreases in animal abundance with increasing
contamination concentrations. Several reviewers commented that some
species may initially show increased abundance with increased contamination
(i.e., if contamination eliminates more pollution-sensitive species
thereby allowing more pollution-tolerant species to increase in abundance
as a result of decreased competition). How much uncertainty might
this consideration add to the conclusion drawn?
There was no a priori assumption made by the statistician that the
only change expected to occur was "population decrease = chemical effect".
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Changes in the species composition of benthic populations were also considered,
although they were not directly observed in the analyses that were run.
Many studies have documented that the presence of toxic chemicals can result
in decreased abundances of, or sublethal effects on, affected organisms
(see Gray 1979; Boesch and Rosenberg 1981; Eagle 1981; Gray 1982; Wolfe
et al. 1982). In cases reported in the literature where opportunistic
or pollution-tolerant species have shown an initial increase in abundance
after an exposure to toxic chemicals (e.g., Capitella capitata at the West
Falmouth oil spill site), high abundances of those taxa have usually been
attributed to their abilities to become established in a disturbed environment
and in the absence of competition for resources.
To our knowledge, a significant enhancement of benthic organisms as
a direct response to a toxic chemical has never been demonstrated, although
such a response is theoretically possible. There is also no evidence that
enhancement occurs for one species or taxonomic group in the presence of
toxic chemicals unless a significant depression occurs in another species
or group. Any station exhibiting a significant enhancement of one taxonomic
group in association with a significant depression for another taxonomic
group was always defined as "impacted". Hence, the conclusions drawn from
the ARTHUR analysis are not expected to be affected by the phenomena described
in the comment.
It should also be noted that as a further check on the ARTHUR results
reported, scatterplots of data distributions were produced and evaluated
to prevent blind acceptance of apparent positive or negative trends between
two variables based on summary statistical results.
7. If half of the stations with significant amphipod bioassay responses
showed no benthic depression in Commencement Bay, how can the report
claim a high degree of concordance among bioassay results and benthic
depressions? (see p. 28, para. 2-3 of draft report).
The amphipod bioassay results for Commencement Bay did show the least
agreement in comparison with the benthic infaunal results. There was neither
a significant depression in the abundance of a major taxonomic group nor
a significant response in the oyster larvae bioassay at 7 of the 16 stations
that exhibited a significant amphipod bioassay response. As discussed
in the report, a possible (but not conclusive) explanation may be that
the high percentages of fins-grained material at these stations contributes
to the amphipod bioassay response (e.g., Ott 1985; Tetra Tech 1985a).
However, a high concordance between combined bioassay results (i.e., oyster
larvae and amphipod discussed in the report) and benthic infaunal results
in general was indicated by the following items:
• "Impact" vs. "no impact" designations made by benthic and
bioassay indicators agreed at 67-79 percent of the 48 stations
in the Commencement Bay Remedial Investigation (and at 83-100
percent of the 6 stations in a separate dredging study conducted
concurrently with identical methods in Blair Waterway and
included in the ARTHUR analyses)
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• A significant depression in the abundance of at least one
major taxonomic group was observed in 6 of 7 cases (86 percent)
that also exhibited significant toxicity in both the amphipod
and oyster larvae bioassays
• Eighty-nine percent of the cases exhibiting a significant
depression in the abundance of at least two major taxonomic
groups occurred in a single similarity cluster (assigned
on the basis of species-level benthic data). This similarity
cluster also contained 75 percent of the cases exhibiting
significant toxicity in both amphipod and oyster larvae
bioassays
t All six of the cases exhibiting a significant depression
in the abundance of at least three major taxonomic groups
occurred in this same similarity cluster; 83 percent of
these cases exhibited toxicity in oyster larvae bioassay,
50 percent exhibited toxicity in the amphipod bioassay.
The final report stresses that the high concordance between bioassay
and benthic infauna results is best supported by the oyster larvae bioassay.
Overall, a significant depression in the abundance of at least one major
taxonomic group was observed in 79 percent of the cases (11 of 14) that
exhibited significant toxicity in the oyster larvae bioassay. However,
in sediments containing <70 percent fine-grained material, significant
benthic depressions were also observed in 100 percent of the cases (6 of 6)
of the sediments exhibiting a toxic response in the amphipod bioassay (no
benthic data were available for a seventh station). As discussed in the
response for comment #24/19, impacted or nonimpacted designations made
by benthic and either of the bioassay indicators agreed at 67-79 percent
of the 48 Commencement Bay stations evaluated. This level of agreement
is significant (P<0.05, binomial test), and suggests that benthic comparisons
based on higher taxa were as sensitive in the Commencement Bay study as
the bioassays in identifying problem sediments, although different organisms
may differ widely in their sensitivity to individual chemicals present
as a complex mixture of chemicals in contaminated sediments.
[see Appendix D for discussion relevant to comment #7]
8 The report was confusing as to whether the interpretation of biological/
chemical relationships was based on data that included habitats in
which certain taxa are not commonly found.
The data set comprised the 64 numerically dominant species in the
Commencement Bay and Carr Inlet samples. Sediments that exhibited low
abundances of organisms and high concentrations of certain chemicals exhibited
a range of grain size. Because the abundance of organisms depends on grain
size and water depth, these samples could not all be compared with the
same reference samples (a similar problem does not exist for dry-weight
chemical^measurements see Tetra Tech 1985a), Major habitat features were
taken into account before determining the significance of benthic effects
in these samples relative to reference conditions by matching groups of
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reference and study site stations of similar depth and grain size. Hence,
the relationships observed between certain chemicals and benthic effects
did not appear to be explained solely by habitat. These procedures were
also used in the development of sediment quality values from benthic data
in Task 4 of the project so that the effect of natural habitat factors
would be minimized.
9. Several reviewers requested clarification regarding the statistical
assumptions used in analysis (e.g., normality of variables distributions).
Was data transformed to induce normality and if so, in what way?
It is true that the choice of data transformations and knowledge of
the data distributions are critically important to multivariate hypothesis
testing. However, multivariate hypothesis testing was not the purpose
of the analyses conducted. The exploratory analyses conducted using ARTHUR
required no statistical assumptions about variable distributions (i.e.,
pattern recognition analysis as applied is useful regardless of the underlying
distribution of the data). Specifically, the factor analyses conducted
as part of the ARTHUR analysis does not require variables to be normally
distributed, especially where the approach is being applied for information
compression (i.e., a Karhunen-Loeve transformation; see for example Watanabe
1973) as was done in this project.
In the pattern recognition analysis, the chemical data were first
autoscaled (i.e., by a transformation similar to the z-score transformation).
Autoscaling is a one-to-one mapping of the values of a variable from one
reference system to another. The mapping preserves the shape of the variable
distribution (regardless of the specific distribution), by simply zero-centering
the distribution, and uniformly scaling the variance. This transformation
makes some aspects of the analysis (e.g., data display) easier, but results
in a data set that yields the same information as before the transformation.
[see Appendix D and comments #11 and #12 for discussion relevant to comment #9]
10. It is unclear as to why and how the data were "normalized" with respect
to total organic carbon (TOC) content, and why TOC may not have been
used as a factor in the analysis.
To normalize chemical data to total organic carbon (TOC) content,
the dry-weight concentration in a sample is divided by the decimal percent
TOC. As one means of interpreting environmental trends, concentrations
are normalized in this manner because many chemicals tend to be concen-
trated in organically-enriched fractions of bulk sediments. Hence, normaliza-
tion of dry-weight concentrations to TOC content can help to dampen variations
caused by patchiness or other depositional factors that could mask trends
among samples of dissimilar texture. Theoretical and some experimental
evidence also suggests that the chemical-TOC association may affect the
bioavailability of certain chemicals (see discussion for comment #22).
Total organic carbon (TOC) was used as an independent variable in
the analysis of dry-weight normalized chemical data (see last paragraph
on p. 7; Tables 2 and 3, and Figure 5 of the draft report). Statistical
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analyses using TOC-normalized data did not include TOC as a variable because
its contribution had already been analyzed in the dry-weight analyses.
TOC normalization of the data resulted in few additional trends that
were not already evident based upon dry-weight normalized data. As noted
in the draft report (p. 30), all data sets should continue to be analyzed
with and without normalization to "master" variables such as TOC to confirm
the results seen in this project, because the actual mechanism of chemical-
organism interactions have not been firmly established in laboratory studies.
[see Appendix G and Section 8.8 for discussion relevant to comment #10]
11. When one autoscales data, one is essentially rendering all variables
equally important (i.e., very rare taxa have equal importance to those
that numerically dominate the community). To what extent could the
influence of autoscaling the ARTHUR analyses have affected the inter-
pretation of analyses results or led to misinterpretation of results?
The process indicated in the comment is incorrect. Autoscaling does
not affect the shape of the variable distribution, and retains all of the
variable-variable relationships represented by the original data (Green
1979). Therefore, this transformation has no effect on the interpretation
of results. See additional discussion in comment #9.
[see Appendix D for discussion relevant to comment #11]
1Z. In autoscaling the data, the authors have used a standard z-score
transformation. This transformation is only appropriate when the
data are normally distributed. Most of the biological data used was
in the form of abundance estimates, which are notoriously Poisson-
distributed. What effect could the skewed distribution of many of
the biological, and probably some of the chemical, data have on the
results of the factor analysis?
The autoscaling technique is similar to a standard Z-score transformation
because it generates a zero-centered distribution with uniformly scaled
variance. This kind of transformation simplifies data processing and review
(e.g., see SPSS 1975), but does not by itself affect the data distribution
(see comment #11). When this tranformation is applied to data that are
normally distributed, the resulting value (i.e., the Z-score) can then,
and only then, also be interpreted according to certain statistical criteria
(i.e., the distribution defines a characteristic probability density function
of the normal distribution; see for example Crow et al. 1960). Such interpreta-
tions were not made and were not necessary for pattern recognition analyses.
As noted in the response to comment #9, the exploratory analyses conducted
for this project required no assumptions or corrections for data distributions
in order to yield useful results.
[see Appendix D for discussion relevant to comment #12]
13 At least one reviewer was concerned that canonical correlation analysis
is the only multivariate technique appropriate for comparing major
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trends in several different data sets, and that factor analysis is
a technique that is only appropriate for deriving trends in a single
data set. What is the rationale for selecting factor analysis as
an appropriate technique for use in this study? To what extent did
the use of multiple databases collected at different locations and
times and the use of the analytical procedure employed influence the
results of the factor analysis and possibly limit the validity of
the inferences drawn?
This comment suggests a highly restrictive approach that is similar
to biological/social modeling problems. The pattern recognition approach
used in this project views the chemical, toxicity, and biological variables
as descriptors of the sediment stations. These data for an individual
station were collected simultaneously during the Commencement Bay study
(i.e., chemical and toxicity data were from the same sediment homogenate,
and benthic data were from replicate grab samples collected at the same
location and time as the chemistry/toxicity samples). While it certainly
can be appropriate to analyze these data sets separately, it is also appropriate
to analyze the combined data in an exploratory mode to determine if inter-
pretable results can be derived. Canonical correlation is a subset of
path modeling that was recommended in this report for potential future
analyses.
[see Appendix D for discussion relevant to comment #13]
14. Are there "generally accepted" rules regarding the number of samples
required for given numbers of variables? If so, were they followed
in the factor analysis presented in the report? If not, why and how
will this affect the results?
A general rule of having three to four times as many samples as variables
is important before applying many analytical techniques (e.g., especially regres-
sion analysis). This rule is not applicable when factor analysis is being used
as a Karhunen-Loeve transformation for information compression (i.e., to reduce
the number of dimensions that represent the data set; see Watanabe 1973).
The value of this application of factor analysis was in identifying key
variables from a large list of variables that were most useful in describing
trends within the data set.
TASK 3 REPORT: EVALUATION OF APPROACHES FOR THE DEVELOPMENT OF SEDIMENT
QUALITY VALUES FOR PUGET SOUND
15. Different approaches or sediment quality values may be needed for
application in different situations (i.e., sediment quality values
for dredged material may need to be different than sediment quality
values for problem identification. Discuss the different potential
uses of sediment quality values and whether different sets of values
could be appropriate for different uses.
Potential uses of sediment quality values were addressed in the Task 4
and 5a report (p. 48-55; Recommended Uses of Sediment Quality Values).
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Different sets of values could be appropriate for different uses but likely
1^™^,^ Project-specific interpretation. A comment given at a joint
KbUDA/PSEP sediment criteria workshop on Jaunary 8, 1986 highlighted the
need to provide sediment quality values for more than one approach to enable
program managers some latitude in determining appropriate values for individual
programs based on administrative and technical factors. In response to
this comment, sediment quality values were summarized in the draft report
for three approaches (and several indicators for the empirical approaches).
Also percentile data for the distribution of chemical values in approximately
200 Puget Sound sediment samples were summarized for comparison with the
range of sediment quality values (see Table 10 in Section 6.3 of the final
report).
Decisions concerning the appropriate use of different sediment quality
values (e.g., use specific values, a combination of values, or values modified
by some "safety factor") are policy decisions of management agencies, and
are outside of the scope of this report.
16. What criteria/rationale was used for not including other methods (i.e.,
bioassay approach, reference area approach, etc.) in the discussion
and evaluation of approaches to setting recommended sediment quality
values? Concern was expressed that the bioassay approach (spiking
sediments in the lab with known concentrations of contaminants and
measuring toxicity) wasn't given fair consideration. The reviewers
claim that while the bioassay approach has the benefit of the ability
to allow identification and experimental control of physical, chemical,
and biological variables, the AET approach cannot distinguish patterns
of natural variability from those indicating pollution impacts. Given
its merits, why was the bioassay approach eliminated from further
consideration?
The reasons for excluding the water quality and bioassay approaches
from the discussion and evaluation section are noted in Section 3.0 of
the final report (and p. 34 of the Task 3 draft report). The reference
area approach was not excluded from the discussion and evaluation section
(see Section 3.1.1 of the final report).
The treatment of the spiked bioassay approach requires further discussion.
An important (albeit not clearly stated) criterion for detailed evaluation of
approaches in the Task 3 report was that they be able to provide chemical-
specific sediment quality values for testing at the present time. The
spiked bioassay approach is a powerful and systematic way to establish
dose-response relationships for benthic organisms, but it has thus far been used
to generate data for very few chemicals and for a few different organisms. A
considerable amount of time and effort would be required to generate sediment
quality values for a wide range of chemicals (as can be addressed by the
AET and SLC approaches using Puget Sound data) and for a wide range of
organisms (to account for possible effects on more sensitive organisms).
The spiked bioassay approach, unlike other approaches considered, can quantita-
tively assess additive, synergistic, and antagonistic effects, but this
would be a formidable task considering all possible combinations of contaminants
and their relative concentrations.
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The possible role of spiked bioassays in establishing and verifying
sediment quality values was discussed in the Task 4 and 5a report ("Prioritiza-
tion of Laboratory Cause-Effect Studies" section, p. 53). Spiked bioassays
are distinguished from (and treated separately from) field bioassays in
the final report. This modification should alleviate confusion regarding
the treatment of bioassays in this project.
Finally, the AET approach attempts to distinguish patterns of natural
variability from those indicating pollution impacts by statistically comparing
sample responses to reference benthic and bioassay samples that have similar
grain size distributions and are collected at similar water depths. This
statistical comparison reduces the potential for habitat-related factors
to confound the results or mask apparent relationships (see response to
comment #8).
17. Why is the Screening Level Concentration (toxicity endpoint) approach
limited to nonpolar organic contaminants?
The developers of the screening level concentration (toxicity endpoint)
approach have suggested that it be used for screening nonpolar organic
compounds. The restriction to this class of compounds is related to organic
carbon normalization theory, which assumes that interstitial water is the
primary biological uptake route for sedimentary contaminants and that
sedimentary organic matter is the predominant factor controlling compound
distribution in sediment-interstitial water systems (e.g., Battelle 1986).
Because of this assumption, organic carbon normalization is not appropriate
for metals and polar organic compounds (see pp. 13-14 of Task 3 draft).
In the present evaluation of SLC values, dry-weight normalization
was tested along with organic carbon normalization and demonstrated consistently
better "efficiency" in correctly identifying only stations with biological
impacts for the 3 chemicals evaluated. Similar results were observed in
the AET accuracy evaluation based on a larger number of chemicals. Dry-
weight normalization enables one to establish SSLC (species screening level
concentrations) for a wide range of compounds, including metals and polar
organic compounds. Thus, if dry-weight normalization is considered appropriate
(e.g., if the mass loading of a contaminant in sediment is considered to
be a more important influence on toxicity than the attenuating effects
of organic carbon), then the SLC approach need not be limited to nonpolar
organic compounds. The accuracy of SLC for additional chemicals must be
evaluated before a final recommendation can be made on this issue.
[see Section 2.6.3 and comment #10 for discussion relevant to comment #17]
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18. A reviewer commented that an incorrect statement was made that samples
in which organisms were not identified were or should be included
in calculations of screening level concentrations (toxicity endpoint
values). According to a representative of Battelle, the firm developing
the method, the stations should not be included. Were such samples
included in the Tetra Tech analyses, and if so, do the sediment quality
values change if these stations are removed?
Samples in which organisms of a given species were not present were
not included in the calculations of screening level concentrations (see
Section 5.4.2 of the final report). The term "presence/absence" in the
Task 3 draft referred to the initial data selection process used in the
approach (i.e., chemical data at stations would be included if a given
species of interest were present or disregarded if the species were absent).
The term "presence/absence" was used to highlight the contrast between
the toxicity endpoint approach, which uses benthic infaunal data but does
not rely on absolute abundance, and other approaches that rely on assessments
based on absolute infaunal abundances (e.g., AET).
19. Several readers expressed concern that the report's claim that the
AET approach does not require normalization to organic carbon content
goes against all that has been learned from experimental sediment
bioassays. What hypothesis can be forwarded to explain the weaker
showing of organic carbon normalization relative to dry weight? Could
this be an artifact of the particular database used?
Simply stated, organic carbon normalization theory assumes that inter-
stitial water in the primary source of nonpolar organic contaminants to
biota, and that, under equilibrium conditions, the distribution of nonpolar
contaminants between sedimentary organic matter and water (Koc) is constant
(and predictable). Organic carbon tends to act as a sink for nonpolar
contaminants (i.e., organic carbon content and sediment toxicity should
be inversely related). Hence, as sediment organic carbon content increases,
toxicity "threshold" values expressed per gram of bulk sediment should
decrease. If contaminant concentrations are normalized to organic carbon
content, threshold values should be constant for that contaminant in all
sediments.
Dry-weight normalization simply assumes that mass loading of a contaminant
in sediment is a predominant factor influencing toxicity to benthic organisms
(although organic carbon interactions may be a secondary factor). The
(empirical) AET approach does not favor one of these mechanistic explanations
over the other, but can operate whether one, a combination of the two,
or alternative assumptions are appropriate. The results from this approach
suggest that further research is required to confirm the underlying mechanism,
but that dry-weight normalization is the most accurate of the three normaliza-
tions tested.
For contaminated sediments in the environment, organic carbon normalization
could be less predictive than dry-weight normalization if sediment/interstitial
water systems are not at equilibrium (a key assumption of the organic carbon
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normalization model), if all sediment organic matter does not have uniform
affinity for hydrophobia pollutants, or if interstitial water is not the
predominant route of contaminant uptake. For example, it is quite plausible
that the equilibrium assumption could be violated in the environment by
kinetic aspects of sorption/desorption processes. Equilibrium requires
fairly rapid transfer of a contaminant between various phases in a system.
Studies of sorption/desorption kinetics have demonstrated that equilibration
of a nonpolar organic compound between sediment and aqueous phases could
take days to months, or longer (Karickhoff and Morris 1985a,b). In part,
slow rates of equilibration could be caused by entrainment or trapping
of contaminants within refractory (stable) organic matter (e.g., humic
substances or fecal pellets) (Freeman and Cheung 1981; Karickhoff and Morris
1985a). Prahl and Carpenter (1983) observed that PAH were disproportionately
concentrated in certain fractions of refractory sedimentary organic matter
(e.g., charcoal fragments and vascular plant detritus, such as lignin).
This disproportionality indicates that PAH may not have been at equilibrium
in the sediment phase or, alternatively, that different kinds of organic
matter may have different affinities for PAH. This latter possibility
is supported by studies of dissolved and solid humic materials and their
associations with hydrophobic organic pollutants (Carter and Suffet 1985;
Diachenko 1981).
If sediment-water equilibrium is not often attained in the environment,
or various types of sediment organic matter have differing affinities for
hydrophobic pollutants, or interstitial water is not the primary route
of exposure for organisms, then the relationship between sediment organic
carbon content and bioavailable portions of nonpolar organic compound loadings
in sediment may not be consistent in environmental samples. Yet a consistent,
quantitative relationship is the basis for organic carbon normalization
theory.
To our knowledge, only two studies in the open literature address
the relationship between sediment bioassay results and sediment organic
carbon content [Adams et al. (1984) and Swartz et al. (1986)]. The study
by Adams et al. (1984) argues for organic carbon normalization of nonpolar
organic compounds. Adams et al. (1984) conducted a series of bioassays
in which freshwater midges (Chi ronomus tentans) were exposed to water,
sediments (with various levels of organic carbon content), and food contaminated
with Kepone (a relatively nonpolar ketone insecticide). No-effect concentra-
tions based on total sediment Kepone concentrations increased in proportion
to total organic carbon content of sediments, whereas no-effect levels
based on interstitial water Kepone concentrations were fairly constant
regardless of sediment concentration. The authors suggested that no-effect
concentrations should be based on sediment organic carbon content, not
on bulk sediment weight.
One possible reason for the success of organic carbon normalization
in the Adams et al. (1984) study is that sediments were spiked in the laboratory
with a carrier solvent containing Kepone. It is plausible that such spiking
procedures will promote homogeneous distribution of target compounds in
sediments and will preclude some factors that could impede equilibrium
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in the environment (e.g., incorporation into fecal pellets and other refractory
organic matter).
Swartz et al . (1986), in a study involving amphipod bioassays, found
that slight enhancement of total volatile solids content of sediment (resulting
from addition of small amounts of sewage sludge or fine particles enriched
in organic matter) reduced the toxicity of cadmium spiked into sediment.
These results do not provide a strong argument for organic carbon normalization
because the experiment did not distinguish between the effects of organic
matter "binding" of cadmium vs. the effects of fine particle "binding"
of cadmium. For example, it is possible that addition of fine iron oxide
particles (not enriched with organic matter) could have reduced toxicity
as well. In general, the applicability of organic carbon normalization
to metals and polar, ionizable organic compounds is limited because of
the variety of environmental factors other than TOC (e.g., pH, redox potential,
presence of Fe/Mn oxides and hydroxides) that could strongly influence
the sedimentary associations of these chemicals (see Task 3 draft, pp. 13-14).
It is unlikely that the database used for evaluation of AET influenced
the success of organic carbon versus dry weight AET (see response to comment
#10). The amphipod bioassay and benthic infaunal stations were compiled
from numerous studies and study areas. The oyster larvae and Microtox
bioassay samples were taken from the Commencement Bay study only. However,
evidence of the better predictive success of dry-weight AET relative to
organic carbon AET does not consist solely of oyster larvae and Microtox
bioassay AET (although they support the trends observed for amphipod bioassay
and benthic AET).
[This discussion for comment #19 is also presented in Section 8.6]
20. Concern was expressed that none of the approaches considered may adequately
address interactive effects. How would chemical interactiveness affect
uncertainty associated with sediment quality values developed using
different approaches? Discussion should include factors that could
overestimate or underestimate possible adverse effects and the general
likelihood/frequency/probability of these effects occurring.
The frequency that interactive effects occur in environmental settings
cannot be confidently determined using existing data. Additive, synergistic
(i.e., supra-additive), and antagonistic effects of contaminants are not
wel'l understood but can be expected to have variable effects on the uncertainty
of sediment quality values generated by different approaches. If interactive
effects occurred on a major scale and the effects were not accounted for
by one of the approaches, the ability of the approach to correctly identify
problem sediments should be reduced. For all approaches based on field
data, collection of representative samples over a wide concentration range
and s'ediment type should help in addressing these concerns.
The only systematic approach to identifying and quantifying interactive
effects is the spiked laboratory bioassay (see paragraph 2, p. 23, Task 3
draft). This approach allows for control of contaminant mixtures, type of
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test organism, and various other test conditions. It should be noted that
while such tests are feasible, they would require considerable effort (i.e.,
years or decades) to be applicable to the numerous possible contaminant
mixtures in the environment. Interim measures are needed to address the
expressed need by agencies for application of sediment quality values as
a regulatory or investigative tool.
The equilibrium partitioning sediment-water approach and the related
Water Quality Criteria approach are based on toxicological data for chemicals
tested individually. Thus, the approaches are not designed to address
interactive effects of contaminants (a safety factor is built into the
water quality criteria used in the approaches, but this factor is not based
upon knowledge of interactive effects of contaminants). To the extent
that interactive effects occur in the environment and to the extent that
they alter the absolute threshold concentrations of individual contaminants,
these effects will increase the uncertainty of equilibrium partitioning values.
The uncertainty of the screening level concentration (SLC) approach
is potentially increased by the existence of interactive effects; the increase
in uncertainty will be less pronounced when large data sets collected from
diverse areas are used to generate sediment quality values with this approach.
Additivity and synergism can produce a comparatively low SSLC (species
screening level concentration) for a given chemical by causing species
absence at concentrations that would not eliminate a species in the absence
of these interactive effects. This would reduce the pool of "non-impacted"
stations used to generate an SSLC. If a large database is used such that
chemicals occur over a wide range of concentrations at stations where additivity
and synergism are not operative, then the SSLC will be not be biased by
these effects. Antagonism could potentially increase sediment quality
values set by the SLC approach by allowing a species to survive in a sample
at a concentration (of a given chemical) that would normally eliminate
the species. With a large database and the 90-percent safety factor in
SSLC values, such cases would probably have little effect.
Similar to the SLC approach, the uncertainty of the AET approach is
increased by the possibility of interactive effects; the increase in uncertainty
will be less pronounced when large data sets collected from diverse areas
are used to generate AET. Additivity and synergism can produce a comparatively
low AET for a given chemical by causing impacts at concentrations that
would not cause impacts in the absence of these interactive effects. This
would effectively reduce the pool of nonimpacted stations used to generate
AET. This effect is reduced if a large database is used such that chemicals
occur over a wide range of concentrations at stations where additivity
and synergism are not operative. Anatagonism will produce comparatively
high AET if the AET is established at a station where antagonism occurs.
A large database could not rectify this elevation of AET because the station
at which antagonism occurred would tend to be the non-impacted station
with the highest concentration.
[see Sections 2.6.4, 2.7.4 for discussion relevant to comment #20]
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21. Concern was expressed that the Apparent Effects Threshold (AET) approach
is limited to the number of species for which testing information
is available. National water quality criteria are based on eight
families of organisms, while sediment quality values developed using
the AET approach may not necessarily account for effects of some contami-
nants on other aquatic animals. Can AET really tell us anything other
than how one species responds to a given test? What confidence do
we have that sediment quality values based on AET developed using
one species will be appropriate (and protective) for use in making
decisions that could affect other species?
AET were generated for four biological indicators in this project
[i.e., amphipod bioassays, oyster larvae bioassays, Microtox bioassays,
and benthic infaunal abundances (at the major taxon rather than species
level)]. Other AET would have been generated if synoptic data had been
available because the AET approach is not inherently limited to any specific
indicators. The AET approach itself is simply a procedure for classifying
and ranking synoptic biological and chemical data and could be applied
to biological effects data (e.g., bioassays) for as many diverse species
as were used to develop U.S. EPA water quality criteria. Also, AET derived
from benthic infaunal abundance data take the responses of a variety of
benthic species into account. Benthic AET at the major taxon level (as
presented in this report) do not provide the "resolution" that a species-level
benthic AET could provide. Development of species-level AET was beyond
the scope of this work, but is recommended in the final report for future
work.
Contaminant concentrations below the AET developed for the four indicators
used in this project could potentially be harmful to untested aquatic species
(and in some cases to the species tested). The same could be true for
U.S. EPA water quality criteria, as toxicological data for all aquatic
species have not been incorporated into these criteria. Because the species
(and life stages) of bioassay organisms used in the present project are
thought to be relatively sensitive, there is reason to believe that sediment
quality values based on AET from these bioassays are representative of
a wide range of organisms. Greater confidence would result from application
of the potential effects threshold (i.e., the concentration of a contaminant
below which no statistically significant effects were observed in any sample)
as a sediment quality value, but this concentration is often below reference
conditions and is not recommended (see Section 2.7.2 discussion of the
AET approach). Considering the limited availability of field toxicological
data for various species, the best approaches to ensuring protection of
a wide range of species with AET are as follows:
• Develop AET based on species-specific benthic infaunal abundance
data (and as an environmentally protective measure, use
the most apparently sensitive of these could be used for
decision-making)
• Rely on bioassay data for species known to be sensitive
to contaminants.
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Development of AET for a wide range of species should help to better define
the sensitivity of individual species. This analysis could ultimately
enable AET to be used in a modified SLC approach, in which sediment quality
values are set to protect some percentage of species as indicated by the
lowest AET for a range of species. In such an approach, the AET would
not by themselves be considered "protective".
22. Concern was expressed that selection of study sites can greatly affect
AET because thresholds may depend on each specific mix of contaminants.
What evidence/confidence do we have that AET generated using one database
can adequately and consistently identify known or suspected impacted
stations from another database?
An additional accuracy analysis has been included in the final report
to address two issues: the applicability of AET generated from one study
area to another, and the potential bias that could result from using the
same data for establishing and then evaluating AET. AET (dry-weight normalized)
generated from the 56 Commencement Bay Remedial Investigation samples were
evaluated for accuracy with the remaining data in the compiled Puget Sound
database (134 samples, excluding Commencement Bay samples). The analysis
was carried out as before:
• The chemical database (from the 134 non-Commencement Bay
samples) was subdivided into groups of stations tested for
the same biological indicators (either amphipod bioassay
or benthic infaunal analysis; Microtox and oyster larvae
bioassays were not performed for these samples)
• The stations of each group were classified as "impacted"
(and "severely impacted") or "non-impacted" (i.e., without
significant effects relative to reference conditions)
• Using only Commencement Bay data, AET were generated for
all appropriate chemicals and were used to predict problem
stations from independent chemical concentration data for
the non-Commencement Bay stations (the predicted problem
stations were non-Commencement Bay stations with one or
more chemicals exceeding the Commencement Bay AET, i.e.,
those stations predicted to have biological effects)
• Measurements of accuracy ("sensitivity" and "efficiency,"
as defined in the final report) were calculated for each
subgroup of stations as:
sensitivity _ predicted problem stations with impacts
(impacted) ~ all stations with impacts
^severel'v ^ = predicted problem stations with severe impacts
impacted) a11 stations with severe impacts
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efficiency =
predicted problem stations with impacts
all potential problem stations
The results are tabulated below:
Amphipod
Benthic
Sensitivity
("Impacted")
72 (18/25)
90 ( 9/10)
Sensitivity
("Severely Impacted")
100 (8/8)
100 (4/4)
Efficiency
37 (18/49)
31 ( 9/29)
Thus, AET based on the chemical mixtures represented by a range of
stations in one study area (Commencement Bay) appear to be fairly successful
at predicting biological impacts in diverse areas of Puget Sound. Furthermore,
these accuracy results support (and even slightly exceed) results obtained
by generating and evaluating AET with the same database (see Table 10 in
the Task 4 and 5a draft). However, the efficiency of Commencement Bay
AET for both biological indicators (31 and 37 percent) is a more reliable
estimate of true performance than the 100-percent efficiency reported in
Table 10 (as footnoted in Table 10, the 100-percent values were a result
of the way in which AET are generated). Efficiency may be better if two
larger, independent databases are used in such analyses.
23. Concern was expressed that the AET approach optimistically dismisses
the possibility of effects being caused by non-quantified (covarying)
chemicals. Several reviewers were of the opinion that a range of
concentrations must be tested empirically in the lab on a compound-
by-compound basis before criteria can be established. Is it possible
that the AET approach really only has utility in establishing for
later lab testing possible ranges of contamination that induce biological
effects, and not for setting recommended sediment quality values?
Unmeasured, covarying chemicals would not be expected to substantially
decrease the ability of AET to predict biologically impacted stations (excluding
interactive effects; see response #8). If an unmeasured chemical (or group
of chemicals) varies consistently in the environment with a measured chemical
(e q concentrations of certain alkylated PAH often correlate well with
those" of their unalkylated priority pollutant counterparts), then the AET
established for the measured contaminant will (indirectly) apply to, or
result in management of, the unmeasured contaminant. In such cases, a
measured contaminant would be used as an "indicator" for an unmeasured
contaminant (or group of unmeasured contaminants). Because all potential
contaminant^ cannot be measured routinely, management schemes must rely
to some extent on "indicator" chemicals.
If an unmeasured chemical (or group of chemicals) covaries with a
measured chemical in some cases but not in others (e.g., if a certain industrial
recess releases an unusual mixture of contaminants) the effect should
be discerned if a sufficiently large data set is used to establish AET.
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Use of a large data set comprising samples from a variety of areas with
wide-ranging chemical concentrations would decrease the likelihood that
an unrealistically low AET would be set. Because AET are set by the highest
concentrations in samples wi thout observed biological impacts, AET will
not be affected by less contaminated samples in which unmeasured contaminants
cause impacts.
If an unmeasured toxic chemical does not co-occur with any measured
chemicals, it is likely that the AET approach will not predict impacts
at stations where the chemical is inducing toxic effects. This was one
of the sources of uncertainty addressed by the accuracy (sensitivity) evalua-
tion. In that evaluation, AET proved fairly successful at predicting impacted
stations (e.g., 54-94 percent accuracy in predicting impacted stations
depending on the biological indicator, and 92-100 percent accurate in predicting
"severely" impacted stations based on the same indicators). Note that,
like the AET approach, the spiked bioassay approach cannot be expected
to predict impacts in environmental samples in which unmeasured toxic chemicals
do not co-occur with measured chemicals. For this reason, chemical-specific
sediment quality values are recommended as one tool that can be used with
other tools (e.g., direct biological testing) for the management of sediments.
Nonetheless, the use of laboratory (e.g., spiked bioassay) studies
for confirming or "fine-tuning" AET values is desirable and will better
define the uncertainty of AET. This recommendation is discussed in the
"Prioritization of Laboratory Cause-Effect Studies" section in the Task 4
and 5a draft report.
24. What was the rationale for using the total number of individuals in
a class or phylum as opposed to finer levels of classification as
the measure of community disturbance?
Higher level taxa were used to set AET values for two major reasons.
First, because the AET approach is based on pair-wise statistical comparisons
with reference conditions, the benthic taxa must either be abundant enough
or have a low enough variance to allow major depressions to be discriminated
statistically. If these criteria are not met, it may be very difficult
to discriminate a depression and, in some cases, complete absence of a
taxon may not be indicated statistically as a significant impact. Therefore,
use of taxa that are either rare or highly variable may not result in a
useful indicator of environmental impact.
In developing the AET approach for the Commencement Bay Superfund
Study, it was found that almost all species, except the four or five most
abundant ones, were either too rare or too variable to be used as sensitive
indicators of impacts. By contrast, higher level taxa such as total taxa,
Polychaeta, Mollusca, and Crustacea were found to be variable, but abundant
enough to statistically discriminate major depressions in numbers of indivi-
duals. Echinodermata, a fifth higher taxon, was found to be both rare
and variable, so that depressions rarely could be discriminated. It therefore
was decided that total taxa, Polychaeta, Mollusca, and Crustacea were the
best available taxa for pair-wise statistical comparisons with reference
conditions. An alternate strategy might have been to use the four to five
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most abundant species, but it was decided that an index based on a greater
number of species considered as an aggregate might be more representative.
It should be noted, however, that the four to five most abundant species
dominated most of the higher taxa, and therefore exerted considerable influence
as to whether or not a depression was discriminated.
The second major reason for using higher taxa was that comparisons
with bioassay results (i.e., amphipod mortality and oyster larvae abnormality)
as part of the Commencement Bay Superfund Study showed that impacted or
non-impacted designations made by benthic and bioassay indicators agreed
at 67-79 percent of the 48 stations evaluated. This level of agreement
is significant (P<0.05, binomial test), and suggests that benthic comparisons
based on higher taxa were as sensitive in the Commencement Bay study as
the bioassays in identifying problem sediments, although different organisms
may differ widely in their sensitivity to individual chemicals present
as a complex mixture of chemicals in contaminated sediments. This independent
corroboration of the use of higher level taxa contributed to its acceptance
for setting benthic AET in Commencement Bay.
TASK 4 AND 5a REPORT: APPLICATION OF SELECTED SEDIMENT QUALITY VALUE APPROACHES
TO PUGET SOUND DATA
25. Concern was expressed that reliance on acute responses may generate
sediment quality values that are not protective of human health or
of chronic impacts on aquatic organisms, such as demersal fishes.
In order to be protective, one reviewer was of the opinion that sediment
quality values should be generated based on sediment concentrations
necessary to maintain contaminant levels in edible seafoods below
proposed tissue criterion.
Reliance on acute responses (i.e., acute toxicity bioassays) may indeed
generate sediment quality values that are not protective of human health
or against chronic health impacts to aquatic organisms (e.g., demersal
fishes). Some of the sediment quality values generated in this project
incorporated chronic effects data [e.g., equilibrium partitioning values
based on chronic water quality criteria, or AET based on benthic infaunal
abundances (the latter analyses would incorporate chronic toxicity, for
example, if samples were not taken during or shortly after a large contaminant
influx)]. However, these criteria do not directly address human health
or health of benthic biota and demersal fishes.
If human health (with regard to seafood consumption) or health of
demersal fishes are primary management objectives, both the equilibrium
partitioning or AET approaches could be oriented toward those objectives.
The sediment-biota equilibrium partitioning approach (discussed in Task
3) would be applicable for nonpolar organic compounds in shellfish if appro-
priate tissue criteria and bioaccumulation factors were available, and
if various assumptions were not violated (see Task 3). The approach would
involve greater uncertainty for bioaccumulation in demersal fishes than
for shellfish because of the more complex equilibrium relationships and
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bioaccumulation factors that apply to fish-sediment systems relative to
systems consisting of relatively immobile benthic infauna and sediments.
The AET approach could also be used to focus on human health and chronic
effects in fish. AET could be based on shellfish bioaccumulation ("impacted"
stations would be defined by shellfish tissue concentrations above an estab-
lished human dietary criterion), fish bioaccumulation ("impacted" areas
or groups of stations within a trawl area would be defined by fish tissue
concentrations above an established human dietary criterion), or fish histo-
pathology ("impacted" areas or groups of stations within a trawl area would
be defined by the frequency of certain histopathological conditions in
fish). The mobility of fish relative to shellfish or other benthic organisms
would preclude synoptic collection of chemical and biological data, thus
increasing the uncertainty of AET developed for mobile species. A fish
trawl (unlike a benthic infaunal sample or a sediment sample used for bioassays)
cannot be related to a single sediment chemistry sample. Instead, chemical
concentrations for multiple sediment samples in a trawl area must be averaged,
which will incorporate environmental variability of contamination into AET.
AET could be developed based on results of chronic laboratory tests.
Any sediment quality value could be modified (e.g., using a safety factor)
in an attempt to protect against chronic effects. Such sediment quality
values could be used in conjunction with direct bioassessments in a two-part
decision-making approach.
26. Concern was expressed regarding an apparent apples/oranges comparison
of sediment quality values generated by different approaches. Reviewers
were concerned that numbers were not directly comparable because sediment
quality values were generated by different approaches using different
databases and types of calculations and normalizations. Are these
concerns warranted? If the comparisons included in the report are
appropriate, discuss why.
Because the generation of sediment quality values by different approaches
(inherently involving different kinds of data and calculations) was one
of the major objectives of this project, it is assumed that this comment
refers to comparisons made in the accuracy section. The accuracy measurements
in Table 10 (based on comparison of potential problem stations and truly
impacted stations) were calculated identically for the equilibrium partitioning
and AET approaches. Direct comparisons of the predictive success (accuracy)
of both approaches were considered appropriate as a simulation of their
relative performance when applied in Puget Sound. The evaluation process
consisted of the following steps (pp. 25-26):
• "The chemical database was subdivided into groups of stations
that were tested for the same biological effects indicators"
(this was necessary because all stations were not tested
for all indicators)
• "The stations of each group were classified as 'impacted'
or 'nonimpacted' based on the appropriate statistical cri-
teria. .."
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• "For each approach, sediment quality values for all applicable
chemicals... were compared with the corresponding chemical
data for each station."
t Sediment quality values normalized to a given variable (e.g.,
to organic carbon content, as for equilibrium partitioning
values) were compared to chemical data from the Puget Sound
database that were normalized to the same variable. "When
one or more chemicals exceeded the appropriate sediment
quality values at a given station, that station was considered
to be indicated as a potential problem station." (In essence,
potential problem stations are predicted to have biological
impacts.)
Accuracy of the toxicity endpoint approach was evaluated less thoroughly
than for the equilibrium partitioning and AET approaches for reasons explained
in the first paragraph of the draft section entitled "Preliminary Evaluation
of the Toxicity Endpoint Approach": "The evaluation of the toxicity endpoint
approach could not be conducted as thoroughly as that of the equilibrium
partitioning and AET approaches because PNEL [SLC] values were not developed
for all appropriate chemicals and did not incorporate data from a large number
of stations." These accuracy results were presented in a different section
than "Evaluation of Equilibrium Partitioning and AET Sediment Quality Values"
to preclude direct comparisons of the more thorough (equilibrium partitioning
and AET) and less thorough (toxicity endpoint) accuracy evaluations.
It was considered unreasonable to calculate the accuracy of toxicity
endpoint values in predicting problem stations because the values were
generated for only three chemicals (or chemical groups): naphthalene,
high molecular weight PAH (HPAH), and mercury. These three chemicals (or
chemical groups) could not be expected to account for all biological impacts
in Puget Sound that could be associated with other chemicals. Therefore,
the following measure of efficiency was used:
. . = predicted problem stations (based on chemical x) with impacts
efficiency - a11 predicted problem stations (based on chemical x)
where:
chemical x = naphthalene, HPAH, or mercury.
This measure of efficiency is described in Section 7.1 and Figure 13 of
the final report.
27 Comment on the appropriateness of quantitatively comparing the sediment
quality values (Table 3 in the draft report) based on different normaliza-
tion factors if assumptions regarding these factors are required for
comparison (i.e., assuming 1-percent organic carbon content).
Equilibrium partitioning values were presented on a dry-weight basis
in Table 3 (Table 6 of the final report) because this is the form of data
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typically reported in environmental studies and is familiar to most readers.
The equilibrium partitioning dry weight values were not used in the accuracy
evaluation of the equilibrium partitioning approach; however, they were
considered useful for giving readers an idea of the magnitude of the values
relative to other sediment quality values (e.g., Fourmile Rock Interim
Sediment Criteria), which are reported as dry-weight concentrations.
As footnoted in Table 3, a 1-percent organic carbon content is assumed
for equilibrium partitioning values, but "to adjust the value for a different
organic carbon content, multiply by the percent organic carbon." Thus,
the dry-weight equilibrium partitioning values can be easily adjusted for
any organic carbon content. Because the mean and median organic carbon
content of the compiled Puget Sound data set are 2.0 and 1.3 respectively,
it might be most representative to increase equilibrium partitioning values
by these factors.
Use of any of the organic carbon values in the Puget Sound database
would not invalidate the general observations made about relative magnitudes
of AET and equilibrium partitioning values in the Task 4 and 5a draft.
These observations are further supported by Table 4, which is an unqualified
comparison of equilibrium partitioning and AET values based on organic
carbon normalization.
28. Criteria should be presented to show how the 66 chemicals were selected.
Were the data screened, and if so, how? Basis for selection of normal-
ization parameters should also be presented.
Because AET can be established for any chemical, there was no selection
scheme based on chemical type used to pare down the chemicals in the compiled
Puget Sound database. However, as noted in the draft Task 4 and 5a report
(p. 9), "the frequency of occurrence and range of detected concentrations
of chemicals limit their appropriateness for establishing AET. Chemicals
seldom detected in the Puget Sound data set (e.g., hexachloroethane, heptachlor)
were not used because they did not cover a wide range of concentrations."
Beryllium and chromium are initially discussed and then dismissed in the
final report (Section 5.3.2) because their range of concentrations do not
exceed those found in nine different Puget Sound reference areas [as summarized
in Tetra Tech (1985a)].
The normalization variables were discussed in the draft Task 3 report
(Ancillary Sediment Variables; see Appendix G of the final report) and
are commonly used by environmental scientists. Organic carbon normalization
also enabled a direct comparison of AET and equilibrium partitioning sediment
quality values (Table 4 of the draft Task 4a and 5 report; Table 7 of the
final report).
29. To what extent do AET address other compounds not identified or measured
in sediment samples?
See discussion for comment #23. Aside from expanding the chemical
database to include unusual compounds that do not co-occur with measured
chemicals, direct biological testing is the only means to provide additional
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information beyond what can be provided by sediment quality values based
on AET or other available approaches.
30. Discuss the appropriateness (or lack thereof) of comparing AET generated
by using liquid (i.e., oyster larvae, Microtox) and non-liquid tests
(i.e., benthic community, amphipod bioassay).
A broadly based toxicity index that is based on multiple indicators
and encompasses a wider range of sediment toxicity than would be evident
from a single testing procedure is desirable for the development of sediment
quality values. The Apparent Effects Threshold (AET) is an example of
such an index because it identifies, for each data set and kind of bioassay,
the sediment concentration above which significant toxic responses were
always observed. Thus, the range of AET values for the different bioassay
(and benthic infaunal) indicators provides an index of the range of biological
variability (e.g., differences in organism sensitivity and route of exposure)
normally encountered in multiple species toxicity testing.
Comparability of the Microtox, oyster embryo, and amphipod bioassys used
to characterize toxicity of Commencement Bay sediments is evaluated by Williams
et al. (1986). Correlation analyses indicated a high level of agreement among
the three bioassays (Kendall's coefficient of concordance = 0.64, P<0.001).
Pair-wise comparisons using Pearson's correlation also indicated a high
level of agreement:
• Oyster embryo vs. amphipod (R = 0.86, P<0.001)
• Oyster embryo vs. Microtox (R = 0.62, P<0.001)
• Amphipod vs. Microtox (R = 0.48, P<0.001).
The magnitude of individual correlations suggests considerable variability
among the three bioassays, which may be partially attributable to differences
in exposure routes inherent in the experimental design of each bioassay.
An additional source of variability is interspecific differences in sensitivity
to the kinds of contaminants in the various sediment samples.
Differences among the bioassays in duration of exposure (i.e., 15 min
vs. 48 h vs. 10 day), and exposure medium (i.e., saline extract vs. sediment
slurry vs. whole sediments) may affect comparability of results. Most impor-
tantly, differences in sediment manipulation and exposure medium may affect the
relative proportions of polar, nonpolar, and sediment-bound contaminants in
each experimental system. It is not surprising that the Microtox and amphipod
bioassay results showed the lowest level of agreement, although the agreement
was still significant. These two bioassays are at opposite ends of the aqueous-
whole sediment exposure spectrum. Nevertheless, the three bioassays showed a
significant (P<0.05) level of concordance that indicates a robustness to with-
stand much of the variability in bioassay sensitivity, sediment heterogeneity,
and experimental exposure bias. Sediment bioassays results showed significant
agreement (67-79 percent; P<0.05) with the presence or absence of benthic
infaunal depressions in Commencement Bay (Tetra Tech 1985a).
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31. What do we know about the relative sensitivity of AET species compared
to screening level concentration species? Discuss the extent to which
species sensitivity is or is not addressed by the AET approach.
By the design of the screening level concentration (SLC) approach,
the most sensitive species of those evaluated are species that establish
the critical concentrations of chemical contaminants. The AET approach,
by contrast, can establish values for any single species, regardless of
its sensitivity. As currently applied, the AET approach applied to benthic
infauna data is based on higher level taxa. As such, current benthic AET
values probably are high estimates (i.e., not protective of all sensitive
species) of critical concentrations of chemical contaminants because less
sensitive species may be included in the analyses. However, to generate
lower (i.e., presumably more protective) estimates, the AET approach can
be applied to sensitive species alone, (see additional discussion for conroent
#25).
3Z. Provide scientific reasoning for selection of 80-percent depression
as indication of sensitivity for use in the Screening Level Concentra-
tion (toxicity endpoint) approach. Is this truly a sensitive indicator?
Use of an 80-percent depression is as arbitrary as using P=0.05 as
the critical level of statistical significance. There is no scientific
basis for either. Instead, each should be set by a consensus of knowledgeable
parties. A critical level of 80 percent was used as a first-cut test level
for the screening level concentration (SLC) approach because 33 of 37 stations
exhibiting statistically significant (P<0.05) depressions in higher-level
benthic taxa in the Commencement Bay Superfund Study also exhibited a >80-
percent depression in abundance relative to reference conditions. If the
SLC approach is to be evaluated further in the future, it is highly desirable
to determine how different (higher and lower) critical depression levels
influence the results. Only then can a more informed decision be made
as to what critical depression level provides results that are "adequately"
sensitive. Based on the limited comparison made in this project (see Section
6.2.1 of the final report), SLC values based on absence/presence exceeded
those based on an 80 percent depression criterion by 12-56 percent. Hence,
SLC based on the 80 percent depression criterion are potentially more sensitive
indicators of contamination.
33. Circularity - Concern was expressed that use of the same data to define
and then evaluate the accuracy of AET and toxicity endpoint-generated
sediment quality values will bias the results.
The reason for using the entire Puget Sound database to generate AET
(Tables 3-5 of the Task 4 and 5a draft report; Tables 6-8 of the final
report) was that the reliability of AET is expected to be greater when
larger databases are used. This in turn necessitated that accuracy of
the AET was assessed with the same database from which AET were generated.
This issue is also addressed in response #22. As discussed in that
response, the accuracy of AET established from one database (Commencement
Bay/Carr Inlet) and evaluated with an independent database supported (and
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even slightly exceeded) the results presented in Table 10 of the draft
report (Table 13 of the final report, in which AET were evaluated with
the same data that were used to generate the AET). The results of the
two accuracy evaluations (tabulated below) indicate that the original accuracy
analysis was not biased. However, the efficiency of AET can only be realis-
tically evaluated with independent databases.
Sensitivity Sensitivity
AET Tested ("Impacted") ("Severely Impacted") Efficiency
Amphipod (DW)a 54 92 100b
Benthic (DW)a 82 92 100b
Amphipod (DW)C 72 100 37
Benthic (DW)C 90 100 31
a From Table 13 of the final report (Table 10 of the draft Task 4 report),
AET generated and evaluated with the same data set.
b As noted in Table 13, efficiency is 100 percent by definition.
c AET generated with data from one data set (Commencement Bay, 56 samples)
and evaluated with data from several independent studies (Puget Sound data
in the compiled database, excluding Commencement Bay; 134 samples).
There is no reason to suspect circularity in the preliminary accuracy
evaluation of toxicity endpoint values because these values were generated
with a subset of the Commencement Bay database but were evaluated with
the entire compiled Puget Sound database.
34. There is more to uncertainty than technical variance. The weight
of evidence supporting certain numbers or the number of as sumptions/ steps
removed from the observation directly affects the "confidence" in
the use of the numbers. How does the report deal with this aspect
of uncertainty in evaluating the sediment quality values developed
from the different approaches?
Uncertainty was evaluated in this project with estimations of accuracy
(success at predicting biological impacts) and precision (an approximation
of "technical variance").
The accuracy evaluation was considered the best way to evaluate the
overall ability of each approach to predict biological impacts. The accuracy
analysis could not quantify various elements of uncertainty in each approach,
but instead provided an estimate of how the combined uncertainties of an
approach would affect its ultimate predictive success. This estimate was
considered particularly useful because numerous factors that affected the
uncertainty of the AET and equilibrium partitioning approaches were not
quantifiable, including factors that may have resulted in partially offsetting
effects.
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The precision analysis was an attempt to quantify the expected variability
of sediment quality values given the particular constraints in the design
and use of an approach. The equilibrium partitioning approach, which is
theoretically based, requires a number of estimations and assumptions (e.g.,
estimation of Koc values from Kow values, assumption of thermodynamic equili-
brium) to derive a sediment quality value. Quantifiable and unquantifiable
factors relating to the uncertainty of equilibrium partitioning values
were discussed in "Estimated Minimum Confidence Limits for Equilibrium
Partitioning Values." The precision of equilibrium partitioning values
could only be estimated for those chemicals with established chronic water
quality criteria; the uncertainty associated with estimated water quality
criteria was not possible to quantify.
For AET values, the effect of "weight of evidence" was not addressed
directly in the Task 4 and 5a draft, but was incorporated in the approach
used in "Estimated Confidence Intervals for AET Values" (see Section 7.2.2
of the final report). Unquestionably, there is less uncertainty for an
AET based on many observations than for an AET based on few observations.
(That is the reason that larger databases with wide-ranging chemical concentra-
tions are required for generating reliable AET.) Confidence limits for
AET were defined as the concentration range from two or three (non-impacted)
stations below the AET to one station above the AET (based on statistical
classification arguments). The number of stations used to establish an
AET (i.e., weight of evidence) would be expected to have a marked effect
on these confidence limits, because small data sets would tend to have
less continuous distributions of chemical concentrations than large data
sets. That is, small data sets would tend to have larger concentration
gaps between stations (and correspondingly wider confidence limits) than
larger data sets. Discussion of this concept has been reinforced in the
final report (Section 7.2.2).
35. Concern was expressed that Microtox data were derived from stored
sediments. Is this correct? If so, how might storage have affected
the resulting AET? If storage is not a problem, couldn't other data
sets which were excluded have been included in the database?
Sediments used for Microtox bioassays in the Commencement Bay Remedial
Investigation were stored for less than 3 wk at 4° C in test tubes that
were flushed with nitrogen and then sealed. Under these inert atmospheric
conditions, the storage time is not expected to have a significant effect
on the results. With respect to chemical changes, U.S. EPA Contract Laboratory
Program guidelines allow 2 wk storage of refrigerated sediments without
any controls on the overlying atmosphere, and 40 day storage of refrigerated
sediment extracts. Up to a 4 wk storage period (under nitrogen) has been
recommended by the Evaluation Procedures Work Group of the Puget Sound
Dredged Disposal Analyis program (PSDDA) for Microtox bioassays; a 2 wk
period is recommended by the Puget Sound Estuary Program (PSEP; Tetra Tech
1986d). Synoptic bioassay data were excluded from the Puget Sound database
for this project if they were frozen because of concern by some investigators
that freezing may alter the toxicity of sediments (Task 1 draft). However,
samples that were stored at 4° C (with or without nitrogen) were not excluded.
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36. Does the recommendation that detection limits be set at 1/2 AET represent
any method difficulties given current protocols? Which AET did the
recommendation refer to (amphipod, oyster larvae, microtox, or benthic
community)?
The Puget Sound Estuary Program draft protocol for organic compounds
recommends detection limits of 1-50 ug/kg (dry weight) for analysis of
semi-volatile organic compounds in sediments by gas chromatography/mass
spectroscopy (GC/MS) (Tetra Tech 1986b). These detection limits were agreed
upon at a workshop of regional experts. Recommended detection limits for gas
chromatography/ electron capture detection (GC/ECD) analysis are 0.1-5 ug/kg
for pesticides and 5-20 ug/kg for PCBs. Comparison of these values with
AET in Table 3 of the final report reveals that detection limits of 1/2
AET levels (e.g., for the lowest AET, usually Microtox) are reasonable.
The most serious problems would probably be presented by p,p'-DDT, 2-methyl-
phenol , 2,4-dimethylphenol, N-nitrosodiphenyl amine, and benzyl alcohol.
Detection limits of 1/2 lowest AET for chlorinated benzenes (1,2-dichloro-
and 1,2,4-trichlorobenzene) could also be difficult to attain, but are
technically feasible with existing procedures.
Required detection limits of 1/2 AET for volatile organic compounds
and metals/metalloids should not present analytical problems.
37. What scientific reasoning can be provided for using the "Criterion
Maximum Concentration" value as the final chronic value instead of
the "Criterion Continuous Concentration (CCC)?" Wouldn't the longest
term CCC be more appropriate for setting a final chronic value?
The procedure that was used in estimating water quality criteria for
the equilibrium partitioning application was discussed on pages 7-8 (and
noted in Table 1, footnote 2) of the draft Task 4 and 5a report (see Section
5.2.3 and Table 4, footnote 2 of the final report). Criterion Maximum
Concentrations were not used as final chronic values instead of Criterion
Continuous Concentrations), criteria were selected based on preference
and availability. Chronic criteria (now referred to as Criterion Continuous
Concentrations by U.S. EPA) were preferred and used when available. In
the absence of U.S. EPA determined chronic criteria, chronic criteria were
estimated from the lowest concentration observed to induce chronic toxicity
in saltwater organisms [based on data in U.S. EPA (1980)]. Acute criteria
(Criterion Maximum Concentrations) or estimations of acute criteria were
used only if chronic criteria or chronic data were unavailable. Final
Chronic Values could not be calculated from acute criteria (or from estimated
acute criteria) because Final AcuterChronic Ratios were unavailable.
Although estimates of water quality criteria add unquantifiable uncertainty
to equilibrium partitioning sediment quality values, they were necessary
to evaluate the method with the.widest possible range of nonpolar organic
pollutants. If the equilibrium partitioning application was limited to
chemical s'with established chronic water quality criteria, only p,p'-DDT,
PCBs chlordane, dieldrin, and heptachlor could have been used. These
chemicals alone could not be expected to be useful for predicting biologically
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impacted stations in Puget Sound. The predictive success (expressed as
"efficiency") of equilibrium partitioning sediment quality values for individual
chemicals with established chronic water quality criteria was addressed
in the Task 4 and 5a draft (p. 43) and in more detail in the final report
(see Section 7.1.1).
38. Discuss/identify evidence of analyses on the underlying distribution
of the data, and the basis for the transformations used throughout
the report. Parametric statistics implicitly require a normal distribution
to be valid. According to several reviewers, if this distribution
analysis was not performed prior to the additional statistical analysis,
the results could be invalid.
The primary test used to evaluate bioassay and benthic impacts was
the t-test, a test that is mathematically equivalent to the single-classifi-
cation ANOVA based on two groups (Sokal and Rohlf 1981). Although one
assumption of ANOVA is that the data are distributed normally, the consequences
of non-normality are not too serious and only very skewed distributions
have a marked effect on test results (Snedecor and Cochran 1967; Zar 1974;
Sokal and Rohlf 1981).
For the bioassay results, there was no reason to expect distributions
to be markedly skewed, as the five replicate values for each test were
generated from subsamples of a homogeneous composite under carefully controlled
conditions. These test conditions suggest that the values of all five
replicates should be very similar and that the random error encountered
among replicates should be relatively small. Transformation of the bioassay
results therefore were not considered necessary.
For the benthic results, there was considerable reason to believe
that the abundance data were strongly skewed, as that pattern is typical
of benthic infaunal assemblages (Gray 1981). Accordingly, abundances of
infauna were 1 ogio~transformed before statistical analyses were conducted
(see Section 5.3.4).
39. Will the utility of the equilibrium partitioning and/or Screening
Concentration Level (toxicity endpoint) approaches increase with a
growing Puget Sound database?
As a general rule, approaches based on field data (e.g., the toxicity
endpoint approach) are expected to generate more reliable sediment quality
values when based on large data sets (e.g., data sets with wide-ranging
chemical concentrations including different contamination sources, and including
data for a variety of organisms). Hence, addition of more species-specific
data (with synoptic chemistry data) to the Puget Sound database will enable
the generation of more reliable SLC values than were generated in the limited
application in this project (see Section 8.8 of the final report).
Sediment quality values for the equilibrium partitioning approach are
not based on field data. Hence, expansion of the Puget Sound database would
not affect current values. However, new field data can be used to further
evaluate the ability of the approach to predict biological impacts. The
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establishment of more chronic saltwater water quality criteria by U.S. EPA
may enhance the "precision" of equilibrium partitioning sediment quality values
for chemicals for which only estimated criteria could be determined. Thus,
an increase in the U.S. EPA toxicological database should increase the utility
of the equilibrium partitioning approach for nonpolar organic compounds.
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